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Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: On this week's podcast, what Can I Say? Savita Subremanian

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Speaker 1: formerly of Merrill Lynch, they got bought by Bank America.

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Speaker 1: She's been with them for twenty three years. Her current

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Speaker 1: title is Head of Equity and Quantitative Strategies. Savita is

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Speaker 1: one of these women in the world of finance who

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Speaker 1: is a powerhouse. Her quant work is wildly respected on

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Speaker 1: the street. She's a regular on the Institutional investor All Star.

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Speaker 1: I think, like the past eleven years. She manages literally

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Speaker 1: hundreds of models and helps create just an endless amount

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Speaker 1: of research and content. Her work is super high quality

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Speaker 1: and is relied on by a lot of institutional as

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Speaker 1: well as main street investors. I found the conversation really fascinating.

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Speaker 1: She is one of the few people who combine quantitative

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Speaker 1: investing with behavioral finance, not a common one two punch,

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Speaker 1: and she's fantastic at it. I found the conversation to

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Speaker 1: be absolutely intriguing, in a whole lot of fun, and

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Speaker 1: I think you will also with no further ado, my

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Speaker 1: discussion with Bank of America's Savita Supermanian. Thank you so much, Supermanian.

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Speaker 1: I think I'm getting your name, Supramani.

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Speaker 2: I've heard all sorts of things.

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Speaker 1: I try not to butcher people's names. But let's talk

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Speaker 1: a little bit about your background. So BA in mathematics

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Speaker 1: and philosophy from Berkeley, an NBA from Columbia. I'm kind

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Speaker 1: of intrigued by the idea of philosophy and math. What

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Speaker 1: was the career plan?

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Speaker 3: Yeah, well there was no career plan really, so at

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Speaker 3: Berkeley I ended up changing my major a few times.

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Speaker 2: From what well, I started out as.

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Speaker 3: An electrical engineering computer science major, uh huh. And then

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Speaker 3: I realized that there are basically no girls in any

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Speaker 3: of those classes.

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Speaker 1: Well back then, maybe not more today now, Yeah, which.

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Speaker 2: Is a huge relief.

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Speaker 3: But I also realized that I love to write, I

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Speaker 3: love to read, and I kind of wanted to have

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Speaker 3: some sort of a liberal arts aspect in my career.

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Speaker 3: I took a class called existentialism in film and Literature.

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Speaker 3: It's like one of these Berkeley classes, right, you know,

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Speaker 3: this like completely pointless once you graduate, But it was.

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Speaker 1: It's pointless one year I took an existential class in college. Yeah,

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Speaker 1: I got a great mark on the midterm and the

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Speaker 1: final was a paper which I never handed in. The

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Speaker 1: professor asked me why, and I said, what does it matter?

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Speaker 1: And he's like, you know, I feel compelled to give

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Speaker 1: you a great Yeah. I wish that was a joke,

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Speaker 1: but it's actually it's actually true.

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Speaker 2: So I wasn't that smart. I did all the work.

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Speaker 1: I read a quote from you way back when you

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Speaker 1: said your parents were pushing you to be either an

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Speaker 1: engineer or a doctor. Is this true? I mean, it's

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Speaker 1: such a cliche Indian parents, Jewish parents go to school

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Speaker 1: become a doctor.

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Speaker 2: Well, I mean there's a reason it's a cliche. It's

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Speaker 2: pretty much the norm.

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Speaker 3: I mean, it happened to like me and everybody I

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Speaker 3: know who's a you know, child of an immigrant from India,

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Speaker 3: So it's kind of I mean, I think it was.

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Speaker 3: You know, it was the seventies. It was unclear how

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Speaker 3: anybody was going to make their living.

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Speaker 2: My parents were.

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Speaker 3: Both in high tech. My dad was an engineer, my

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Speaker 3: mom was a software person. So really, yeah, they were

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Speaker 3: both steeped.

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Speaker 2: In technology in Silicon Valley.

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Speaker 3: In Silicon Valley, they were you know, early early days

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Speaker 3: in Mountain View, before it was you know, googleized routed. Yeah, exactly,

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Speaker 3: before there was traffic, but it was it was I

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Speaker 3: think that my parents, you know, they came here for

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Speaker 3: to have a better life, to make some money, you know,

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Speaker 3: not you know, to to basically live the American dream.

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Speaker 3: And I think that the only legitimate careers were really

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Speaker 3: in the sciences or you know, kind of practical applications.

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Speaker 3: Today they've completely accepted me for who I am, as

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Speaker 3: the dark you know, dark art of finance person.

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Speaker 1: But but back the.

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Speaker 2: Exactly.

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Speaker 1: See for Jewish parents, if you go to law school,

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Speaker 1: they'll put up with them.

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Speaker 2: It's like the old school is just barely accepted.

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Speaker 1: Right, It's all right, well, well will allow it. It's

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Speaker 1: three years, will allow. But really, medical school is our

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Speaker 1: first choice exactly.

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Speaker 2: Yes, you know the drill.

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Speaker 3: So I was a rebel and and I mean the

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Speaker 3: reason I did mathematics and philosophy was that I have

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Speaker 3: a very short attention span. So I found myself getting

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Speaker 3: kind of bored with my mouth problem sets, and then

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Speaker 3: I could shift to philosophy and then go back and forth.

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Speaker 2: So it was actually pretty ideal for me.

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Speaker 1: So how do you end up at Scudder Kemper in

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Speaker 1: both New York and San Francisco In the nineteen nineties.

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Speaker 1: At that point, I know you will talk about your

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Speaker 1: internship a little later, but at that point, are you like,

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Speaker 1: I think this is the career I want to be in.

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Speaker 3: No, I had no idea when I graduated what I

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Speaker 3: wanted to do. In fact, I was convinced that I

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Speaker 3: wanted to be a professor in philosophy. And I took

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Speaker 3: the gre and all those tests, and I applied, and

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Speaker 3: I was going to get a PhD in philosophy, and

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Speaker 3: I did all the work. But I realized I had

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Speaker 3: to support myself while I was waiting to hear back.

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Speaker 2: So I got a job in finance. I moved to

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Speaker 2: New York because I'd always wanted.

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Speaker 3: To be in New York. New York was my destination.

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Speaker 3: And I got a job at Scudder doing something really random.

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Speaker 2: I think it was.

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Speaker 3: I think I was working as a technical writer on

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Speaker 3: their software application. But I was just kind of bouncing

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Speaker 3: around and looking for, you know, place where I could

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Speaker 3: earn a study living and bide my time before I

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Speaker 3: went to grad school. And then I started to realize

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Speaker 3: that philosophers of professors of philosophy end up having to

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Speaker 3: live in really random places in the country, and.

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Speaker 1: Wherever they get a job.

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Speaker 3: Wherever they get a job there, you know, they don't

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Speaker 3: make a lot of cash.

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Speaker 2: And meanwhile I.

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Speaker 3: Was doing you know, I was working at this financial

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Speaker 3: services company, and I was really interested in what they

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Speaker 3: were doing. It was kind of like philosophy meets mathematics,

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Speaker 3: because finance, to me is sort of a fuzzy science

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Speaker 3: with no answers.

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Speaker 2: Very logical.

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Speaker 3: So it's got this math angle where you know, it's

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Speaker 3: all numbers, but then there's this behavioral angle and psychological

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Speaker 3: angle where you know, it's kind of a fun problem

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Speaker 3: to tackle. So I realized I could make a lot

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Speaker 3: more money working in finance and being a philosophy professor,

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Speaker 3: and I basically kind of stayed the course.

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Speaker 1: Today's episode of Barry confirming his priors is brought to

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Speaker 1: you by so that very much is you know, one

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Speaker 1: of the reasons I was looking forward to this conversation

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Speaker 1: is how much everything you write is just right in

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Speaker 1: my sweet spot. You can pull that out. But let's

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Speaker 1: I want to talk about the internship. So let's let's

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Speaker 1: let's go over there. So I mentioned you were an

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Speaker 1: intern in college, and this is kind of fascinating. You

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Speaker 1: interned for a Merrill Lynch quant team, which fast forward

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Speaker 1: twenty plus years later, that's now the team that you

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Speaker 1: lead at Bank America Merrill Lynch now known as b

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Speaker 1: of A Right exactly.

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Speaker 3: So that was actually my internship during business school. So

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Speaker 3: after working at Scudder, I realized I didn't really have

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Speaker 3: the foundations for financials. I didn't understand, you know, kind

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Speaker 3: of how to parson income statement. And so I went

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Speaker 3: to business school. I decided to go to business school

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Speaker 3: and get that formal education. And then in the year

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Speaker 3: the year in between your one and two of business school,

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Speaker 3: I did my internship with Merrill Lynch with a gentleman

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Speaker 3: named Rich Bernstein.

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Speaker 2: And yes, you know him.

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Speaker 1: I know Rich.

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Speaker 3: And that was the beginning of, you know, a wonderful career.

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Speaker 3: But it's it's sort of strange. I don't know whether

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Speaker 3: to feel proud or depressed about this. But I am

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Speaker 3: the only person I know from business school. I graduated

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Speaker 3: Columbia two thousand and two, and I'm the only person

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Speaker 3: I know who stayed in the same.

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Speaker 2: Job for the last twenty three years.

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Speaker 1: So you shouldn't be depressed about that. You should think

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Speaker 1: about you should be grateful for Oh, I found what

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Speaker 1: I wanted to do right out of school. It's true,

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Speaker 1: and I've been honing that craft for twenty three years.

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Speaker 4: That that is, that's half full.

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Speaker 1: A lot of people, especially in finance, kind of flit

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Speaker 1: from flower to flower until they find the right nectar. Yes,

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Speaker 1: that works for them, and it's kinda Look, it's not

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Speaker 1: just me. I've seen a bunch of people. They start

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Speaker 1: out as brokers, they eventually get a CFP and they

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Speaker 1: go to the advisory side, or people start out with

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Speaker 1: the CFA and they decide, you know, I would rather

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Speaker 1: manage the portfolio than tell I'd rather be a PM

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Speaker 1: than advise the PM. And so people kind of have to,

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Speaker 1: you know, that journey. You were fortunate that so not

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Speaker 1: only did Scudder lead you to business school, but business

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Speaker 1: school led you to the job that you've had for

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Speaker 1: the rest of your life.

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Speaker 3: Rich to quant strategy, now equity. It's just been a

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Speaker 3: dream come true.

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Speaker 1: So you had mentioned the behavioral side of finance. Not

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Speaker 1: a lot of quants marry behavioral finance to the mathematical side.

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Speaker 1: Tell us how this sort of mixture, which I love,

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Speaker 1: it works so well for me. I started on a

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Speaker 1: trading desk. I kind of stop into behavioral finance in

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Speaker 1: the mid nineties, right before all the cool kids were

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Speaker 1: doing it, and it suddenly like, oh, all of this

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Speaker 1: stuff that seems sort of random, Now at least there's

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Speaker 1: an explanation for the randomness, and it kind of makes

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Speaker 1: sense why people do the things they do. We're you know,

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Speaker 1: we're just not wired for.

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Speaker 2: This, right, right, right? Right?

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Speaker 1: No.

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Speaker 2: I think that that's.

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Speaker 3: The part of it that I find the most interesting

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Speaker 3: is the idea that you know, a stock price doesn't

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Speaker 3: really have a you know, the fair value of an

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Speaker 3: investment instrument is somewhat arbitrary, right, And then it's you know,

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Speaker 3: it's supplied demand, it's perception.

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Speaker 2: Perception is reality for many of these companies. So, I mean,

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Speaker 2: I think the.

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Speaker 3: Day that I realized that behavioral finance deserves a very

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Speaker 3: prominent place in the arsenal of models.

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Speaker 2: That we all use was when I got it.

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Speaker 3: I got the job as equity strategists, and I realized

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Speaker 3: that probably the most important number that I publish is

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Speaker 3: our year end target. It's kind of a silly number,

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Speaker 3: but people are going to think you're smart or dumb

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Speaker 3: based on that number. And so I said, Okay, let's

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Speaker 3: use all these quant models that I've been building for

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Speaker 3: the last ten plus years, and after testing all of them,

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Speaker 3: it turned out that there was one model that was

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Speaker 3: better than everything else predicting the next twelve months of

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Speaker 3: S and P returns, and that was a behavioral model.

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Speaker 1: Really, how do you measure behavior in a quantitative model

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Speaker 1: for equities?

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Speaker 2: It's a very cool model.

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Speaker 3: And I actually was lucky enough to inherit it from

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Speaker 3: my former boss, Rich, who I think inherited it from

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Speaker 3: his former boss. So it's been around at Merrill for

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Speaker 3: you know, since the eighties.

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Speaker 1: Who was who was Rich's former boss?

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Speaker 2: I can't remember. We'll have to get him on and

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Speaker 2: ask him.

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Speaker 1: I've had him on. Yeah, I'm sure he's told me,

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Speaker 1: but he may.

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Speaker 3: Yeah, we'll we'll look it up in the annals. But

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Speaker 3: you know, it's been around for it predates Rich Bernstein's

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Speaker 3: so so basically this model is just a simple straight

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Speaker 3: average of all the Wall Street strategists recommended allocations to

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Speaker 3: stocks in a balanced portfolio. So if you go to

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Speaker 3: your broker and he or she tells you you should

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Speaker 3: put you know, sixty percent in stocks, or you should

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Speaker 3: put forty percent in stocks. We take all those numbers

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Speaker 3: from the different houses and we average them together. We've

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Speaker 3: been doing this every month since you nineteen eighty, and

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Speaker 3: it turns out to be the best contrary indicator.

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Speaker 1: Oh really, I thought you were going to go with, Oh,

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Speaker 1: it's a very wisdom of crowds and whatever it averages

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Speaker 1: out run.

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Speaker 2: The opera to the opposite.

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Speaker 1: Yeah, no kidding.

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Speaker 3: That was the punchline of indicator, and I thought that

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Speaker 3: was so fascinating. But then when you peel back the onion,

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Speaker 3: you realize there's a reason for it. It's because you know,

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Speaker 3: when everybody's looking at all this data and it all

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Speaker 3: seems terrible, chances are that information's priced into the market

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Speaker 3: and it's going to surprise in the opposite direction.

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Speaker 1: I want to say to go back to Rich Bernstein's boss.

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Speaker 1: Was it Bob Farrell or was Bob Farrell two bosses before? Gosh,

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Speaker 1: I don't kind of remember him as late eighties, early nineties.

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Speaker 3: Yeah, Bob Ferrell was I never seventies eighty like way

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Speaker 3: before my time?

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Speaker 1: You ever have?

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Speaker 2: Oh yeah, yeah, yeah.

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Speaker 1: I met him at a market technicians association all night.

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Speaker 1: Then I interviewed him for one of their events. But

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Speaker 1: Bob Farrell's ten investing rules. Yes, that was gospel, and

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Speaker 1: to this day is still like, you're hard pressedifying another

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Speaker 1: ten rules that are as insightful and astute and still relevant.

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Speaker 2: Completely.

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Speaker 1: It's he always been spectacular.

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Speaker 3: Yeah, he was onto something and he probably he created this,

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Speaker 3: this framework. I don't recall, but I mean I still

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Speaker 3: have financial advisor sending me these Bob Ferrell quotes.

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Speaker 2: And like, bring it, this is great. He was. He

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Speaker 2: was a legend, right.

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Speaker 1: I want to say that might have been one of

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Speaker 1: his quotes. I could quickly find it, which was something like,

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Speaker 1: if everybody's talking about it, it's already reflected in the price. No,

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Speaker 1: there's no surprise there exactly when all the experts and

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Speaker 1: forecasts agree, something else is going to happen. That's right,

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Speaker 1: rule number nine from Bob. So you're you're definitely channeling

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Speaker 1: a little Farrell. Ye. So, given this, how do you

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Speaker 1: draw a price target or a market forecast from here's

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Speaker 1: the average of all the Wall Street strategists. Let's say

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Speaker 1: it's plus eight percent. Yeah, what do you do with that?

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Speaker 1: On average? Well, you about plus eight nine percent on

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Speaker 1: the S and B we Yeah.

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Speaker 2: So here's the thing.

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Speaker 3: I mean, if you think about just how much this

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Speaker 3: number changes over time. So it's been you know, back

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Speaker 3: in two thousand and one, strategists were telling you to

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Speaker 3: put about seventy percent of your money in stocks. But

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Speaker 3: then you know, just in I think it was twenty twelve,

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Speaker 3: coming out of the financial crisis, you know, after after

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Speaker 3: one round of QI, Europe was in a you know,

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Speaker 3: a recession.

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Speaker 4: Everybody was Brexit, Grexit, it was all happen.

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Speaker 3: Everything was all happening. The US just got downgraded. And

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Speaker 3: that was when that indicator plummeted to forty three percent.

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Speaker 2: Wow, which was exactly the right time you wanted to

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Speaker 2: buy equities. I remember printed money.

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Speaker 1: Since twenty ten, eleven, twenty twelve, there was so much

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Speaker 1: skepticism yeah about equity markets. And my pushback to people

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Speaker 1: was always, show me another time when down fifty seven

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Speaker 1: percent wasn't a spectacular entry right into US equities, right?

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Speaker 1: And the answer is always twenty nine and thirty two? Okay,

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Speaker 1: is this like thirty two? Is this remotely like twenty nine? Right?

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Speaker 1: I mean you already had the dot com implosion. If

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Speaker 1: you want to say that down eighty one percent was

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Speaker 1: your twenty nine, fine, But that was you know, seven

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Speaker 1: eight years ago and here we are down fifty seve.

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Speaker 2: Here we are again.

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Speaker 3: I know, I know, it was an interesting time, and

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Speaker 3: that's right when I got the job as strategists. So

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Speaker 3: it was really interesting because I was looking at this model,

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Speaker 3: which was my holy grail.

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Speaker 2: Right out of everything.

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Speaker 3: We'd back tested, this had the best predictive power over

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Speaker 3: the next twelve months highest R squared and it was

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Speaker 3: telling us to back up the truck on equities. It

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Speaker 3: was as low as it had ever been since the

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Speaker 3: nineteen eighties.

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Speaker 1: Wow.

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Speaker 3: And I remember, you know, thinking, oh my gosh, is

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Speaker 3: this a data error, and I like, triple quadruple check

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Speaker 3: the data. But it was, you know, really a prescient

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Speaker 3: signal that a lot of bad news was really priced

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Speaker 3: into the market and it was more likely to move higher.

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Speaker 3: And you know, since then, it hasn't dropped to forty

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Speaker 3: three percent, but it's been pretty low. I mean, I

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Speaker 3: think we've been in this market environment since the GFC

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Speaker 3: where global financial crisis, where folks have just been worried,

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Speaker 3: and the most recent event that we anchor our memories

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Speaker 3: to is this horrible credit crisis that derailed the banking sector,

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Speaker 3: that crushed the consumer, and now we're just assuming that's

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Speaker 3: going to repeat over and over again.

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Speaker 1: That's the post GFC PTSD.

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Speaker 4: Exactly.

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Speaker 1: What was your experience during the first quarter of twenty

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Speaker 1: twenty during the pandemic, SMP down thirty four percent. Yeah,

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Speaker 1: neatly within the quarter. I noticed some people kind of panic,

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Speaker 1: then here comes and other people like, no, Dow, I'm

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Speaker 1: thirty four percent. I'm a buyer.

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Speaker 2: But yeah, I think that it was.

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Speaker 3: It was one of those moments where I think I

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Speaker 3: went on TV at some point and they said, you know,

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Speaker 3: do you buy here or is.

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Speaker 2: There more to go?

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Speaker 1: And I yes, and yes.

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Speaker 2: I said, you buy here. You pick your stocks, but

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Speaker 2: you buy here.

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Speaker 3: There are gonna be a lot of really high quality

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Speaker 3: companies that have been crushed by fear and loathing and

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Speaker 3: you know, just heading for the hills. And this is

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Speaker 3: an opportunity that we're probably going to look back on

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Speaker 3: and want to buy what I wish we'd bought these companies.

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Speaker 5: You know.

350
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Speaker 1: Unfortunately, sometimes people in media or elsewhere they talk about

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Speaker 1: catching the bottom and rather than being the bottom tick,

352
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Speaker 1: you could look at that big sweeping parabola and say,

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Speaker 1: I don't need to be at the bottom. I just

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Speaker 1: want to buy as we're getting close and buy as

355
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Speaker 1: we're moving away from it, and so that two years

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Speaker 1: from now, my average cost is just far below where

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Speaker 1: the markets so exactly, you don't have to nail the bottom.

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Speaker 2: No, and you never will nail the bottom.

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Speaker 1: Yeah, someone is going to get lucky, someone's going to

360
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Speaker 1: get that bottom tick. But ninety nine percent of people

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Speaker 1: or not right right, So rather than try and pick

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Speaker 1: that yeah, hey, down x percent, at down twenty five percent,

363
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Speaker 1: I'm a buyer at down thirty percent, I'm a buyer

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Speaker 1: and I don't have enough dry powder that I can

365
00:19:19,240 --> 00:19:22,719
Speaker 1: keep buying down forty percent, down fifty percent completely at

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Speaker 1: a certain point when everybody's terrified. It's a spectacular it.

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Speaker 3: Is, it's a spectacular buying opportunity. I mean, there's one

368
00:19:29,680 --> 00:19:33,159
Speaker 3: thing that I've looked at that seems to be a

369
00:19:33,200 --> 00:19:36,199
Speaker 3: good leading indicator of, you know, when you want to

370
00:19:36,520 --> 00:19:40,400
Speaker 3: start stepping in, which is I mean momentum.

371
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Speaker 1: Right.

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Speaker 2: There's a reason that there are.

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Speaker 3: So many momentum investors because the market usually figures out

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Speaker 3: whether things are kind of getting worse or getting better.

375
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Speaker 3: And one of the models that we've used to determine

376
00:19:56,840 --> 00:20:01,280
Speaker 3: whether something is actually cheap and attractive or cheap, and

377
00:20:01,320 --> 00:20:07,360
Speaker 3: a falling knife is a falling knife is looking at

378
00:20:07,440 --> 00:20:10,560
Speaker 3: earnings revisions coupled with price momentum.

379
00:20:11,280 --> 00:20:12,720
Speaker 2: And what we've found is that.

380
00:20:12,680 --> 00:20:17,880
Speaker 3: When stocks are going lower but analysts haven't taken down

381
00:20:17,920 --> 00:20:21,200
Speaker 3: their earnings, so it looks cheap, but it's only because

382
00:20:21,600 --> 00:20:24,600
Speaker 3: the cell side is late to react, that's when you

383
00:20:24,720 --> 00:20:25,520
Speaker 3: don't want to buy it.

384
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Speaker 1: Uh huh, you want to. So if there's downside momentum

385
00:20:28,800 --> 00:20:31,200
Speaker 1: and you've had a whole bunch of hey, we're changing

386
00:20:31,200 --> 00:20:34,119
Speaker 1: our earnings estimate, we're changing our price targets, right, that

387
00:20:34,520 --> 00:20:36,800
Speaker 1: means it should be mostly priced.

388
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Speaker 3: Then exactly, So you want to buy a falling You

389
00:20:39,280 --> 00:20:42,720
Speaker 3: want to buy a value stock when its price decline

390
00:20:42,800 --> 00:20:46,560
Speaker 3: is starting to slow down, but estimate revisions are still

391
00:20:46,640 --> 00:20:50,760
Speaker 3: deeply negative. So you're in this environment where everybody hates

392
00:20:50,920 --> 00:20:55,240
Speaker 3: risk and they're downgrading, downgrading, downgrading, but the market's telling you, Okay,

393
00:20:55,240 --> 00:20:56,920
Speaker 3: things are actually not as bad.

394
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Speaker 1: Huh. Really interesting. So let's talk a little bit about

395
00:21:01,160 --> 00:21:04,040
Speaker 1: a day in the life of a big bank's chief

396
00:21:04,119 --> 00:21:07,159
Speaker 1: quant tell us, how do you spend your time, what

397
00:21:07,160 --> 00:21:09,959
Speaker 1: are you doing during the day, and what do you, you know,

398
00:21:10,000 --> 00:21:13,800
Speaker 1: what keeps you curious, what keeps you wondering about what

399
00:21:13,920 --> 00:21:14,520
Speaker 1: comes next?

400
00:21:14,680 --> 00:21:15,920
Speaker 2: Yeah, so my day.

401
00:21:15,760 --> 00:21:18,080
Speaker 3: Is never the same, and I'm sure it's like this

402
00:21:18,240 --> 00:21:22,080
Speaker 3: for you. I mean, most people have kind of things

403
00:21:22,080 --> 00:21:25,679
Speaker 3: thrown out them that are, you know, out of the ordinary.

404
00:21:25,840 --> 00:21:28,240
Speaker 2: And I can't say that, you know.

405
00:21:28,320 --> 00:21:30,560
Speaker 3: I walk into the office and I sit down at

406
00:21:30,600 --> 00:21:32,600
Speaker 3: my desk, and I start chugging away at the computer,

407
00:21:32,680 --> 00:21:34,640
Speaker 3: even though that's what I secretly want to do.

408
00:21:35,880 --> 00:21:38,360
Speaker 1: That's what work from home is for. Yes, stay at home,

409
00:21:38,480 --> 00:21:41,040
Speaker 1: keep your face in the computer. You're good. Once you

410
00:21:41,040 --> 00:21:42,720
Speaker 1: get into the office.

411
00:21:42,359 --> 00:21:44,160
Speaker 2: It's that's game over.

412
00:21:44,680 --> 00:21:47,600
Speaker 3: But no, but I think that where I get my

413
00:21:47,720 --> 00:21:51,200
Speaker 3: best ideas is from talking to super smart people like you,

414
00:21:51,400 --> 00:21:54,760
Speaker 3: like our financial advisors, like our hedge fund clients, our

415
00:21:54,840 --> 00:22:00,280
Speaker 3: long only investor clients, pensions. So everyone out there who's

416
00:22:00,320 --> 00:22:05,160
Speaker 3: been a professional investor for a while has some edge

417
00:22:05,560 --> 00:22:08,640
Speaker 3: that is, you know, otherwise they would have been fired

418
00:22:08,720 --> 00:22:12,320
Speaker 3: or left the industry. But I've found that people's edges

419
00:22:12,359 --> 00:22:14,920
Speaker 3: are different from one another. So I feel like every

420
00:22:14,920 --> 00:22:19,399
Speaker 3: time I talk to somebody new, there's an angle.

421
00:22:19,080 --> 00:22:21,119
Speaker 2: That I haven't thought about. And then what I like

422
00:22:21,240 --> 00:22:24,920
Speaker 2: to do is try to recreate.

423
00:22:24,680 --> 00:22:29,000
Speaker 3: That framework in a model, a replicable model, and then

424
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Speaker 3: test it to see whether it's something worth throwing into.

425
00:22:32,920 --> 00:22:33,960
Speaker 2: The mix or not.

426
00:22:34,400 --> 00:22:36,520
Speaker 3: And you know a lot of my work is just

427
00:22:36,600 --> 00:22:41,720
Speaker 3: looking at does does this you know this this indicator

428
00:22:41,760 --> 00:22:44,200
Speaker 3: like a PE ratio? Right, we all talk about pe

429
00:22:44,320 --> 00:22:46,639
Speaker 3: ratiows and how you want to be you want to

430
00:22:46,640 --> 00:22:50,639
Speaker 3: bilo pe stocks and you know, sell expensive stocks. But

431
00:22:50,880 --> 00:22:54,480
Speaker 3: turns out the PE race shows sometimes predict performance and

432
00:22:54,560 --> 00:22:55,560
Speaker 3: sometimes they don't.

433
00:22:55,640 --> 00:22:58,639
Speaker 1: You can it's kind of worthless if you can tell

434
00:22:58,840 --> 00:23:01,240
Speaker 1: is this is this a good moments where I MPE?

435
00:23:01,680 --> 00:23:03,680
Speaker 1: Or is this a bad moment? Yes?

436
00:23:03,840 --> 00:23:05,920
Speaker 2: Is this a good value stock or is it a

437
00:23:06,000 --> 00:23:06,720
Speaker 2: value trapped?

438
00:23:06,720 --> 00:23:09,119
Speaker 3: So those are some of the things that we test

439
00:23:09,280 --> 00:23:11,920
Speaker 3: and then you know, from talking to clients, we get

440
00:23:11,960 --> 00:23:15,919
Speaker 3: ideas around should you have a regime indicator? Should you

441
00:23:15,960 --> 00:23:19,119
Speaker 3: think about what regime the market is in to train

442
00:23:19,280 --> 00:23:23,040
Speaker 3: your framework on what types of attributes to look for?

443
00:23:24,400 --> 00:23:30,840
Speaker 3: What attributes right now are scarce versus abundant? And where

444
00:23:30,880 --> 00:23:36,000
Speaker 3: will investors pay up for a scarcity in the current environment.

445
00:23:36,119 --> 00:23:38,639
Speaker 3: So you know, a lot of these are really born

446
00:23:38,760 --> 00:23:42,399
Speaker 3: from behavioral finance and thinking about how people you know,

447
00:23:42,480 --> 00:23:44,760
Speaker 3: look for opportunities, whether they're going to be a bargain

448
00:23:44,800 --> 00:23:47,119
Speaker 3: hunter or whether they're going to be risk averse and

449
00:23:47,200 --> 00:23:51,680
Speaker 3: look for unassailable growth. But it's interesting because I think

450
00:23:51,720 --> 00:23:54,840
Speaker 3: that my best ideas to this day have come from

451
00:23:54,880 --> 00:23:58,760
Speaker 3: talking to our really smart clients out there on the field.

452
00:23:59,160 --> 00:24:04,040
Speaker 1: So you guys run literally dozens of quant models. I

453
00:24:04,720 --> 00:24:08,440
Speaker 1: get your research. I get a handful of research specific

454
00:24:08,480 --> 00:24:13,080
Speaker 1: people at I still think of as Merrill Lynch, but

455
00:24:13,520 --> 00:24:17,320
Speaker 1: me too, but I notice. So we'll talk about the

456
00:24:17,359 --> 00:24:20,360
Speaker 1: content you guys put out, which is enormous, and we'll

457
00:24:20,359 --> 00:24:22,960
Speaker 1: talk about the models. Let's start with the model since

458
00:24:23,000 --> 00:24:27,159
Speaker 1: you mentioned it. So you talked about the consensus of

459
00:24:27,240 --> 00:24:31,920
Speaker 1: strategists and how that is often I'm assuming not always,

460
00:24:32,400 --> 00:24:34,800
Speaker 1: but frequently a contrary indicator.

461
00:24:35,160 --> 00:24:37,879
Speaker 3: Yes, it's often, I mean really, it works the best

462
00:24:37,920 --> 00:24:41,679
Speaker 3: at extremes, so if you're in some kind of neutral territory,

463
00:24:41,760 --> 00:24:43,280
Speaker 3: it's not as informative.

464
00:24:43,320 --> 00:24:45,320
Speaker 1: But if true for all sentiment measures.

465
00:24:45,000 --> 00:24:47,640
Speaker 3: For any sentiment measure, exactly, So there are times when

466
00:24:47,720 --> 00:24:50,080
Speaker 3: you really really really want to pay attention to it,

467
00:24:50,119 --> 00:24:51,600
Speaker 3: and then there are other times where it gives you

468
00:24:51,640 --> 00:24:53,680
Speaker 3: a little bit more of a muddled signal.

469
00:24:54,400 --> 00:24:57,919
Speaker 1: So that one stands out as prescient. What what else

470
00:24:58,480 --> 00:25:01,760
Speaker 1: do you think adds a whole lot value and helps

471
00:25:01,760 --> 00:25:03,040
Speaker 1: you navigate what's going on?

472
00:25:04,119 --> 00:25:07,720
Speaker 3: So I think when when you look at I mean

473
00:25:07,840 --> 00:25:09,800
Speaker 3: one of the things that we've started looking at is

474
00:25:09,880 --> 00:25:14,040
Speaker 3: just like kind of non financial data, so you know,

475
00:25:14,119 --> 00:25:15,679
Speaker 3: not fundamental data like w And.

476
00:25:15,720 --> 00:25:18,520
Speaker 1: You're making a face as you say that, so I

477
00:25:18,560 --> 00:25:21,880
Speaker 1: could tell you're like, you're like, is the jury still

478
00:25:21,880 --> 00:25:23,800
Speaker 1: out on that? Or how are you playing with non

479
00:25:23,880 --> 00:25:24,840
Speaker 1: financial data?

480
00:25:24,840 --> 00:25:28,679
Speaker 2: Look, I think that some of it is really useful.

481
00:25:30,440 --> 00:25:32,280
Speaker 2: A lot of it is just garbage.

482
00:25:33,160 --> 00:25:36,359
Speaker 1: When you say garbage, is it is it not accurately

483
00:25:36,400 --> 00:25:39,560
Speaker 1: depicting that subsector of the world, or is it just

484
00:25:40,000 --> 00:25:42,399
Speaker 1: a noisy series with not a lot of signal in it.

485
00:25:42,880 --> 00:25:45,119
Speaker 3: I mean a lot of it is just noise or

486
00:25:45,520 --> 00:25:49,239
Speaker 3: or corporate management trying to gain the system. I'll give

487
00:25:49,240 --> 00:25:52,320
Speaker 3: you an example, So let's talk about earning surprise.

488
00:25:52,720 --> 00:25:52,880
Speaker 1: Right.

489
00:25:52,920 --> 00:25:56,119
Speaker 3: Earning surprise is something that should work. Right if a

490
00:25:56,160 --> 00:26:01,919
Speaker 3: company beats everybody's expectations on earnings, it should drive monstrous performance,

491
00:26:02,000 --> 00:26:02,640
Speaker 3: especially if.

492
00:26:02,560 --> 00:26:03,320
Speaker 2: It's a big beat.

493
00:26:03,920 --> 00:26:07,199
Speaker 3: But what we've all realized over the last you know,

494
00:26:07,840 --> 00:26:10,360
Speaker 3: twenty years since reg FD in two thousand and one

495
00:26:11,160 --> 00:26:15,200
Speaker 3: is that management gains their numbers and then they beat

496
00:26:15,359 --> 00:26:20,880
Speaker 3: these made up numbers systematically, and that surprise factor no

497
00:26:20,920 --> 00:26:23,800
Speaker 3: longer seems to be as effective as before we had

498
00:26:23,840 --> 00:26:26,400
Speaker 3: this sort of massaging of consensus SYSTEMA.

499
00:26:26,480 --> 00:26:29,439
Speaker 1: The day before we recorded this, you put out a

500
00:26:29,480 --> 00:26:34,479
Speaker 1: research report strong quarter earnings per share up six percent

501
00:26:34,600 --> 00:26:37,959
Speaker 1: year of a year with better guidance. And here's the

502
00:26:38,160 --> 00:26:42,240
Speaker 1: really amazing part. With eighty three percent of the S

503
00:26:42,280 --> 00:26:46,399
Speaker 1: and P five hundred reporting earnings, sales are roughly in line,

504
00:26:46,600 --> 00:26:50,439
Speaker 1: and the stats were seventy two percent of these companies

505
00:26:50,720 --> 00:26:54,720
Speaker 1: being on earnings. So if three quarters are beating on earnings,

506
00:26:54,960 --> 00:26:55,359
Speaker 1: what's the.

507
00:26:55,400 --> 00:26:57,320
Speaker 2: Value cares exactly?

508
00:26:57,400 --> 00:27:00,560
Speaker 3: Maybe we pay attention to missus because those guys really

509
00:27:00,600 --> 00:27:04,600
Speaker 3: screwed up and couldn't beat their made up numbers. So,

510
00:27:05,000 --> 00:27:07,880
Speaker 3: you know, I think that there are different factors that

511
00:27:08,000 --> 00:27:11,560
Speaker 3: tend to you know, at some point work and then

512
00:27:11,600 --> 00:27:13,880
Speaker 3: everybody figures out that they work, and then they start

513
00:27:13,880 --> 00:27:17,520
Speaker 3: getting gamed. I mean, quants have basically made markets that

514
00:27:17,640 --> 00:27:20,119
Speaker 3: much more efficient by or maybe inefficient.

515
00:27:20,240 --> 00:27:21,639
Speaker 2: I'm not sure what the right way to do well.

516
00:27:21,640 --> 00:27:23,320
Speaker 1: I think I agree with you. I think quants have

517
00:27:23,400 --> 00:27:30,480
Speaker 1: made generally speaking, big money relying on data that's consistent. Yeah,

518
00:27:30,520 --> 00:27:33,800
Speaker 1: you know what starts to happen is the inefficiencies get

519
00:27:33,920 --> 00:27:35,000
Speaker 1: arbitraged out.

520
00:27:34,960 --> 00:27:37,639
Speaker 2: Right, and the inefficiencies go away.

521
00:27:37,760 --> 00:27:43,840
Speaker 1: So some people have blamed quants on why value has underperformed,

522
00:27:43,880 --> 00:27:46,680
Speaker 1: why small caps aren't doing what the small cap factor

523
00:27:46,760 --> 00:27:47,320
Speaker 1: is supposed to be.

524
00:27:47,600 --> 00:27:49,320
Speaker 2: I don't buy into that.

525
00:27:49,560 --> 00:27:51,600
Speaker 1: I'm right. I think the jury is still out on

526
00:27:51,680 --> 00:27:52,720
Speaker 1: that accusation.

527
00:27:53,080 --> 00:27:53,399
Speaker 2: Yeah.

528
00:27:53,480 --> 00:27:55,920
Speaker 1: That said, there are a lot of models out there

529
00:27:56,000 --> 00:27:59,960
Speaker 1: that aren't particularly great. Let me ask you what quant

530
00:28:00,119 --> 00:28:03,679
Speaker 1: models do people seem to really be enamored with that

531
00:28:03,760 --> 00:28:06,880
Speaker 1: you think aren't really worth it. You mentioned pe and

532
00:28:06,920 --> 00:28:11,040
Speaker 1: fair value. Those aren't particularly useful to them.

533
00:28:11,200 --> 00:28:15,240
Speaker 3: Snapshot multiples are not useful, right, I think price to

534
00:28:15,280 --> 00:28:18,720
Speaker 3: normalized earnings is useful. But you know, the other data

535
00:28:18,760 --> 00:28:22,240
Speaker 3: set that I just wonder about is flows.

536
00:28:21,840 --> 00:28:25,000
Speaker 1: Because they're always on such a giant lag, like they

537
00:28:25,040 --> 00:28:28,879
Speaker 1: were outflows throughout twenty three from mutual funds. Right, And

538
00:28:28,920 --> 00:28:31,760
Speaker 1: if you're saying, well, I guess if you're going the

539
00:28:31,800 --> 00:28:35,240
Speaker 1: other way, if you're saying it's a sentiment indicator. But

540
00:28:35,600 --> 00:28:37,960
Speaker 1: that's not how people talk. People talk about, Oh, we

541
00:28:38,000 --> 00:28:41,200
Speaker 1: have all these giant inflows into markets, right O.

542
00:28:41,240 --> 00:28:44,080
Speaker 3: Way cares that was yesterday, right, I mean, why does

543
00:28:44,120 --> 00:28:45,840
Speaker 3: that tell us anything about the future.

544
00:28:46,200 --> 00:28:49,560
Speaker 1: You got me, give me another model you think is

545
00:28:49,720 --> 00:28:51,320
Speaker 1: overrated that people rely on.

546
00:28:52,160 --> 00:28:57,080
Speaker 3: So I think another model that's overrated is just pure momentum,

547
00:28:57,680 --> 00:29:02,960
Speaker 3: because I think momentum works when it's yes, exactly, so

548
00:29:03,040 --> 00:29:06,560
Speaker 3: it's when it works well when it's accompanied by a

549
00:29:06,600 --> 00:29:11,840
Speaker 3: fundamental reason. But the idea that you can predict price

550
00:29:12,080 --> 00:29:15,960
Speaker 3: using price, to me, just seems to flaunt some kind

551
00:29:15,960 --> 00:29:18,040
Speaker 3: of basic financial understanding.

552
00:29:18,320 --> 00:29:22,280
Speaker 1: Isn't that the entire undergirding of trend following.

553
00:29:22,880 --> 00:29:24,160
Speaker 2: Yeah, so trend following.

554
00:29:24,400 --> 00:29:26,880
Speaker 3: I mean I worry because I think we've been in

555
00:29:26,920 --> 00:29:30,800
Speaker 3: a market where trend following has worked remarkably well for

556
00:29:30,840 --> 00:29:31,560
Speaker 3: at least you.

557
00:29:31,520 --> 00:29:35,880
Speaker 1: Know, a decade, certainly for commodities and for currencies, yeah, exactly,

558
00:29:36,000 --> 00:29:38,280
Speaker 1: maybe less so for equities or fixed income.

559
00:29:38,440 --> 00:29:39,560
Speaker 2: I mean even inequity is.

560
00:29:39,600 --> 00:29:43,520
Speaker 3: One of the best performing quantitative factors has been momentum.

561
00:29:43,080 --> 00:29:45,040
Speaker 2: For a really, really, really.

562
00:29:44,800 --> 00:29:47,040
Speaker 3: Long time, and one of the worst performing factors has

563
00:29:47,080 --> 00:29:50,040
Speaker 3: been valuation. So we're now in an environment where all

564
00:29:50,040 --> 00:29:53,760
Speaker 3: the forty five year old portfolio managers out there have

565
00:29:53,800 --> 00:29:58,360
Speaker 3: been have worked their entire careers in these momentum fueled markets,

566
00:29:58,440 --> 00:30:01,520
Speaker 3: and they've been trained to believe that valuation doesn't matter.

567
00:30:01,880 --> 00:30:05,840
Speaker 3: And I think that's wrong, because valuation does matter. You know,

568
00:30:05,880 --> 00:30:08,680
Speaker 3: it matters over a longer time period than maybe just

569
00:30:08,760 --> 00:30:09,960
Speaker 3: the next day or two.

570
00:30:10,200 --> 00:30:11,200
Speaker 1: Valuation matters.

571
00:30:11,320 --> 00:30:12,920
Speaker 4: Eventually, it matters.

572
00:30:12,960 --> 00:30:15,320
Speaker 3: And in fact, one of the most powerful market timing

573
00:30:15,360 --> 00:30:17,880
Speaker 3: models not over the next year, but over the next

574
00:30:17,920 --> 00:30:21,720
Speaker 3: ten years is looking at just a price to normalized

575
00:30:21,720 --> 00:30:24,520
Speaker 3: earnings ratio for the S and P five hundred that

576
00:30:24,560 --> 00:30:28,320
Speaker 3: has explained eighty percent of ten year returns. That's a

577
00:30:28,480 --> 00:30:29,600
Speaker 3: super high ursc.

578
00:30:29,720 --> 00:30:31,000
Speaker 1: How do you think of cape?

579
00:30:31,760 --> 00:30:32,680
Speaker 2: Yeah, so this is.

580
00:30:32,680 --> 00:30:36,360
Speaker 3: A cyclically adjusted PE ratio, and I think that that's

581
00:30:36,400 --> 00:30:38,960
Speaker 3: exactly what you want to pay attention to when you're

582
00:30:39,000 --> 00:30:42,240
Speaker 3: thinking about the long term. Unfortunately, nobody has the luxury

583
00:30:42,280 --> 00:30:44,760
Speaker 3: of picking stocks for a ten year period anymore, except

584
00:30:44,760 --> 00:30:48,640
Speaker 3: for in you know, our personal accounts. But professional money

585
00:30:48,640 --> 00:30:52,479
Speaker 3: managers have basically been trained to believe that price predicts price,

586
00:30:52,920 --> 00:30:55,720
Speaker 3: and that has worked for a really long time. But

587
00:30:55,840 --> 00:30:59,160
Speaker 3: I feel like there aren't any value investors left out there.

588
00:31:00,000 --> 00:31:01,400
Speaker 2: Do you ever worry about that?

589
00:31:01,920 --> 00:31:06,440
Speaker 1: So I have a vivid recollection of reading Adam Smith's

590
00:31:06,840 --> 00:31:11,320
Speaker 1: The Money Game and not really understanding the discussion he

591
00:31:11,440 --> 00:31:14,440
Speaker 1: had when I first read this, you know, thirty years ago,

592
00:31:15,040 --> 00:31:18,000
Speaker 1: that there's a fund manager and all this fund manager

593
00:31:18,080 --> 00:31:23,040
Speaker 1: does is hire young, twenty something fund managers. And he

594
00:31:23,200 --> 00:31:27,960
Speaker 1: describes it as they're smart enough and not battle scard

595
00:31:28,120 --> 00:31:31,600
Speaker 1: enough to buy the stuff that terrifies me. And so

596
00:31:31,840 --> 00:31:35,760
Speaker 1: I will ride these managers until they blow up, and

597
00:31:35,800 --> 00:31:38,080
Speaker 1: then I'll fire them and replace them with the next.

598
00:31:38,240 --> 00:31:40,760
Speaker 1: Like it's a chapter in the Money Game. And when

599
00:31:40,760 --> 00:31:43,000
Speaker 1: I was younger, I didn't get it. But exactly what

600
00:31:43,040 --> 00:31:47,680
Speaker 1: you said about if you're forty five, yes, and per

601
00:31:47,800 --> 00:31:51,320
Speaker 1: you know, up until last year, the current generation of

602
00:31:51,520 --> 00:31:55,760
Speaker 1: bond managers never seen a rising rate environment exactly. So

603
00:31:55,840 --> 00:31:58,520
Speaker 1: what ends up happening is you have to bring in

604
00:31:58,560 --> 00:32:03,960
Speaker 1: these young people who don't come with the baggage and memory. Yes,

605
00:32:04,080 --> 00:32:07,560
Speaker 1: so they'll do things that you you're terrified of, and

606
00:32:07,600 --> 00:32:10,960
Speaker 1: then eventually the conveyor belt replaces them. But I didn't

607
00:32:11,040 --> 00:32:13,800
Speaker 1: understand that when I first read it, I don't know,

608
00:32:14,160 --> 00:32:17,080
Speaker 1: twenty five years ago. Now I kind of get it

609
00:32:17,440 --> 00:32:19,520
Speaker 1: for exactly the reason you described.

610
00:32:19,560 --> 00:32:22,280
Speaker 2: It's brilliant. Yeah, yeah, yeah, that makes sense.

611
00:32:22,120 --> 00:32:25,520
Speaker 1: And that book is just absolutely a you know, a

612
00:32:25,600 --> 00:32:26,520
Speaker 1: Wall Street classic.

613
00:32:26,840 --> 00:32:29,640
Speaker 3: Yeah, and maybe that means that we should only have

614
00:32:29,800 --> 00:32:34,080
Speaker 3: the tales of the distribution, like the really old investors

615
00:32:34,120 --> 00:32:36,480
Speaker 3: and the really young investors take out.

616
00:32:36,560 --> 00:32:40,960
Speaker 1: So it's a bar belt take out everybody. You and

617
00:32:41,000 --> 00:32:42,920
Speaker 1: I were out. They got to be older than me

618
00:32:43,200 --> 00:32:46,600
Speaker 1: or or younger than you, and that's that's the range.

619
00:32:47,720 --> 00:32:51,200
Speaker 1: So thank you for getting us losing the job, right,

620
00:32:52,440 --> 00:32:57,360
Speaker 1: But there is something to be said. So sometimes that

621
00:32:57,480 --> 00:33:04,440
Speaker 1: works out and sometime that is disastrous. Yes. So on Twitter,

622
00:33:04,520 --> 00:33:09,320
Speaker 1: I've been having this ongoing DM conversation with the guy

623
00:33:09,400 --> 00:33:14,080
Speaker 1: he's still anonymous behind TikTok Investors, And what he does

624
00:33:14,560 --> 00:33:18,720
Speaker 1: is he goes to TikTok and he finds the most absurd,

625
00:33:19,000 --> 00:33:23,800
Speaker 1: ridiculous investment or money advice on TikTok. And it's that

626
00:33:24,000 --> 00:33:27,840
Speaker 1: exact thing. It's twenty something with no experience, the one,

627
00:33:28,160 --> 00:33:31,960
Speaker 1: the one he said this morning is this guy who's

628
00:33:32,000 --> 00:33:35,120
Speaker 1: twenty something. He says, So I figured out how I

629
00:33:35,320 --> 00:33:38,400
Speaker 1: never have to pay taxes again. I make all my

630
00:33:38,480 --> 00:33:41,800
Speaker 1: money in bitcoin. I got a bitcoin credit card, I

631
00:33:41,880 --> 00:33:44,680
Speaker 1: go to the supermarket, I do this, I do that.

632
00:33:44,960 --> 00:33:48,920
Speaker 1: It's all tax free, Like, who's gonna tell me I

633
00:33:48,960 --> 00:33:53,840
Speaker 1: can't do that? And then the voiceover is the irs, Yes,

634
00:33:53,920 --> 00:33:56,920
Speaker 1: they tracked all of those. Everybody, right, you're gonna get

635
00:33:56,920 --> 00:34:00,880
Speaker 1: a ten ninety nine from wherever your bitcoin exchanges that

636
00:34:00,920 --> 00:34:03,640
Speaker 1: goes to the I R S. What do you think

637
00:34:03,400 --> 00:34:06,920
Speaker 1: they like they woke up yesterday? I mean, come on.

638
00:34:07,400 --> 00:34:12,160
Speaker 1: So the problem with people who don't have the battle scars, Yes,

639
00:34:12,200 --> 00:34:14,600
Speaker 1: the problem with those of us with battle scars are

640
00:34:14,640 --> 00:34:17,200
Speaker 1: sometimes we're a little risk averse. The problem with people

641
00:34:17,239 --> 00:34:20,120
Speaker 1: with no battle scars or they have no sense of hey,

642
00:34:20,120 --> 00:34:22,439
Speaker 1: there's a whole lot of risk in here, in not

643
00:34:22,480 --> 00:34:26,600
Speaker 1: paying your taxes, or in day trading from home or

644
00:34:27,120 --> 00:34:29,760
Speaker 1: whatever some of the.

645
00:34:29,320 --> 00:34:31,680
Speaker 2: Meme stocks and whatnot. Yeah, no, you're right.

646
00:34:31,760 --> 00:34:35,640
Speaker 3: So you need that sort of institutional knowledge, that domain

647
00:34:35,719 --> 00:34:39,120
Speaker 3: knowledge from the super old investor, and then you need

648
00:34:39,200 --> 00:34:42,919
Speaker 3: this like whole cadre of young investors that are kind

649
00:34:42,920 --> 00:34:46,160
Speaker 3: of moronic but also are willing to step in and take.

650
00:34:46,000 --> 00:34:46,719
Speaker 2: A lot of risk.

651
00:34:47,160 --> 00:34:50,080
Speaker 1: So what you're saying, it takes all kinds.

652
00:34:50,280 --> 00:34:51,439
Speaker 2: It takes all kinds. It takes.

653
00:34:52,760 --> 00:34:54,680
Speaker 1: So when I started out on the desk, one of

654
00:34:54,719 --> 00:34:58,240
Speaker 1: my favorite, my head trader, had all these great lines

655
00:34:58,320 --> 00:35:01,839
Speaker 1: that I should have written down. I only remember some

656
00:35:01,880 --> 00:35:05,480
Speaker 1: of them. But I used to ask a question, why

657
00:35:05,560 --> 00:35:08,520
Speaker 1: is this person saying this? This is so obviously wrong

658
00:35:08,640 --> 00:35:11,360
Speaker 1: and money losing. And he's like, Hey, someone's got to

659
00:35:11,400 --> 00:35:13,080
Speaker 1: be on the other side of the trade. Otherwise, who

660
00:35:13,080 --> 00:35:15,399
Speaker 1: are you going to buy from? Right? I guess that's true.

661
00:35:15,440 --> 00:35:16,880
Speaker 1: It's two sides to make them.

662
00:35:17,120 --> 00:35:20,280
Speaker 3: That's the fascinating thing about markets, isn't it. There's always

663
00:35:20,320 --> 00:35:23,160
Speaker 3: somebody that's willing to sell at a certain price, and

664
00:35:23,239 --> 00:35:25,759
Speaker 3: there's always willing there's somebody that's willing to buy.

665
00:35:25,880 --> 00:35:29,200
Speaker 1: So speaking of selling, let's talk about something that dates

666
00:35:29,239 --> 00:35:33,200
Speaker 1: back decades, the cell side indicator. I remember it in

667
00:35:33,239 --> 00:35:35,719
Speaker 1: the early days with the Maryland cell side Indicator. Now

668
00:35:35,719 --> 00:35:38,880
Speaker 1: it's the Bank of America. What is the cell side indicator?

669
00:35:38,960 --> 00:35:39,840
Speaker 1: How does it work?

670
00:35:40,000 --> 00:35:41,759
Speaker 2: This is the model I was telling you.

671
00:35:41,680 --> 00:35:43,760
Speaker 1: About, the consensus.

672
00:35:43,360 --> 00:35:47,160
Speaker 3: Using Wall Street to do the opposite and make lots

673
00:35:47,200 --> 00:35:47,600
Speaker 3: of money.

674
00:35:47,840 --> 00:35:49,799
Speaker 2: Mm hmm. That's exactly what it is.

675
00:35:49,880 --> 00:35:52,640
Speaker 1: And you had nothing to do with its creation. You

676
00:35:52,760 --> 00:35:55,480
Speaker 1: inherited it. I have you tweaked it all since you've

677
00:35:55,520 --> 00:35:55,839
Speaker 1: had it.

678
00:35:56,040 --> 00:35:58,319
Speaker 3: I've looked at it to see whether you know it

679
00:35:58,400 --> 00:36:01,800
Speaker 3: makes sense to use differently or legs whether there's information

680
00:36:01,960 --> 00:36:07,480
Speaker 3: content in the actual distribution of strategists numbers.

681
00:36:07,520 --> 00:36:11,080
Speaker 2: But I think it's just kind of it's a simple.

682
00:36:11,200 --> 00:36:16,799
Speaker 3: Tool that just works because of the fact that, you

683
00:36:16,800 --> 00:36:19,560
Speaker 3: know what we were talking about, just the fact that sentiment,

684
00:36:19,760 --> 00:36:23,200
Speaker 3: when everybody thinks one thing, the market's going to do

685
00:36:23,280 --> 00:36:25,200
Speaker 3: the opposite of whatever they're expecting.

686
00:36:25,320 --> 00:36:29,759
Speaker 1: Has the change in institutional sales and trading, and just

687
00:36:29,800 --> 00:36:32,080
Speaker 1: the way the cell side has morphed over the past

688
00:36:32,120 --> 00:36:35,040
Speaker 1: few decades. A lot of the cell side has moved

689
00:36:35,480 --> 00:36:37,880
Speaker 1: to the buy side. Yeah, a lot of big big

690
00:36:37,920 --> 00:36:41,120
Speaker 1: funds have their own analysts now that they used to

691
00:36:41,120 --> 00:36:43,840
Speaker 1: rely on on the street for right, Does that change

692
00:36:43,840 --> 00:36:44,319
Speaker 1: this at all?

693
00:36:44,719 --> 00:36:45,640
Speaker 2: No, It's interesting.

694
00:36:45,680 --> 00:36:48,520
Speaker 3: This is one model that has still kind of retained

695
00:36:48,520 --> 00:36:52,200
Speaker 3: its efficacy. In fact, it's become more effective since the

696
00:36:52,239 --> 00:36:55,359
Speaker 3: global financial crisis if you just look at its track

697
00:36:55,440 --> 00:36:59,719
Speaker 3: record of predicting positive or negative returns. So it's kind

698
00:36:59,719 --> 00:37:02,160
Speaker 3: of interesting to see that just this old, kind of

699
00:37:02,200 --> 00:37:06,359
Speaker 3: hoary chestnut of a model still works exactly the same

700
00:37:06,360 --> 00:37:10,360
Speaker 3: way it always did and kind of sussing out group

701
00:37:10,440 --> 00:37:16,359
Speaker 3: think hurting and basically doing the opposite. So that's why

702
00:37:16,360 --> 00:37:17,440
Speaker 3: It's one of my favorites.

703
00:37:17,800 --> 00:37:24,000
Speaker 1: So you guys have a huge institutional and sort of

704
00:37:24,000 --> 00:37:28,759
Speaker 1: mom and pop main street client base. What sort of

705
00:37:29,440 --> 00:37:32,840
Speaker 1: analysis do you do with your own data? You mentioned

706
00:37:32,840 --> 00:37:36,120
Speaker 1: flows Connor are so laggy. Is there anything you see,

707
00:37:36,560 --> 00:37:40,879
Speaker 1: especially on the behavioral side from like Herb Greenberg used

708
00:37:40,880 --> 00:37:44,120
Speaker 1: to talk about his email hate meter, like if he

709
00:37:44,200 --> 00:37:47,920
Speaker 1: said something and he got like a ton of I'm

710
00:37:47,960 --> 00:37:50,239
Speaker 1: going to be right, Yeah, I'm onto something here. If

711
00:37:50,280 --> 00:37:52,160
Speaker 1: everybody hates yes, I use.

712
00:37:52,120 --> 00:37:55,120
Speaker 3: That as an informal gauge of you know what, if

713
00:37:55,120 --> 00:37:57,280
Speaker 3: we're getting a lot of pushback on a call, I feel,

714
00:37:57,600 --> 00:37:59,960
Speaker 3: you know, stressed out because everybody's yelling at me, But

715
00:38:00,120 --> 00:38:03,480
Speaker 3: I also feel better about our call. But look, I

716
00:38:03,520 --> 00:38:06,680
Speaker 3: think there are lots of tools you can use. So

717
00:38:06,960 --> 00:38:10,360
Speaker 3: one tool that I really like is looking at positioning

718
00:38:10,520 --> 00:38:14,480
Speaker 3: of the byside because what we've found is, especially today,

719
00:38:14,560 --> 00:38:16,879
Speaker 3: there's a lot of group thing, there's a lot of

720
00:38:17,000 --> 00:38:19,480
Speaker 3: career risk driving investment decisions.

721
00:38:19,480 --> 00:38:22,440
Speaker 1: When you say especially today, hasn't that always been true?

722
00:38:22,560 --> 00:38:23,120
Speaker 2: I don't know.

723
00:38:23,480 --> 00:38:25,120
Speaker 3: I mean one of the things that I've been looking

724
00:38:25,120 --> 00:38:30,000
Speaker 3: at is just active share, the average active fund, and

725
00:38:30,040 --> 00:38:34,640
Speaker 3: it's gotten very like the average active fund has gotten

726
00:38:34,680 --> 00:38:37,759
Speaker 3: closer and closer to the benchmark over the last five years.

727
00:38:37,760 --> 00:38:43,399
Speaker 1: Bill Miller says active management is being destroyed by closet indexers. Yes,

728
00:38:43,480 --> 00:38:45,720
Speaker 1: and that's the guy who beat thesm P five hundred

729
00:38:45,719 --> 00:38:48,840
Speaker 1: and fifteen years in a row, right up until the

730
00:38:48,880 --> 00:38:49,920
Speaker 1: financial crisis.

731
00:38:50,000 --> 00:38:53,040
Speaker 3: Yeah, And I think that is there empirically, that's borne

732
00:38:53,040 --> 00:38:55,359
Speaker 3: out by what we're seeing in our data. But what's

733
00:38:55,440 --> 00:38:58,720
Speaker 3: really interesting is if you have a list of companies,

734
00:38:58,719 --> 00:39:00,359
Speaker 3: one of the things we do every month, then it's

735
00:39:00,400 --> 00:39:04,239
Speaker 3: just a laborious, horrible process. I used to do it,

736
00:39:04,280 --> 00:39:06,440
Speaker 3: and now I'm fortunate to have one of my teammates

737
00:39:06,480 --> 00:39:09,719
Speaker 3: do it. But you just basically scrape all the thirteen

738
00:39:09,840 --> 00:39:12,759
Speaker 3: f's out there, come up with what everybody loves and

739
00:39:12,800 --> 00:39:14,719
Speaker 3: what everybody hates. And it's kind of like the cell

740
00:39:14,719 --> 00:39:18,920
Speaker 3: side indicator. If you've got a stock that is massively overweight,

741
00:39:19,080 --> 00:39:20,080
Speaker 3: everybody owns.

742
00:39:19,880 --> 00:39:22,960
Speaker 2: It in the professional community, there's probably not.

743
00:39:22,920 --> 00:39:25,520
Speaker 4: That much upside it was left to buy, exactly.

744
00:39:25,600 --> 00:39:27,640
Speaker 2: So I think that positioning data is important.

745
00:39:28,600 --> 00:39:31,399
Speaker 3: I love looking at like a new tool that we've

746
00:39:31,400 --> 00:39:36,600
Speaker 3: been using more is kind of natural language processing applied

747
00:39:36,680 --> 00:39:41,400
Speaker 3: to research or transcripts or you know. I'll give you

748
00:39:41,400 --> 00:39:44,200
Speaker 3: one example. So we came up with this analyst tone

749
00:39:44,320 --> 00:39:49,400
Speaker 3: metric tone Tony. So we look at our own research

750
00:39:49,920 --> 00:39:53,880
Speaker 3: and we track whether analysts within a sector are getting

751
00:39:53,920 --> 00:39:58,759
Speaker 3: more positive or negative by virtue of just their their language,

752
00:39:58,840 --> 00:40:00,120
Speaker 3: not their ratings or their.

753
00:40:00,080 --> 00:40:04,000
Speaker 1: You're counting how many great quarter guys or we're.

754
00:40:03,719 --> 00:40:07,320
Speaker 3: Well, yeah, essentially we're looking at we're using these like dictionary,

755
00:40:07,400 --> 00:40:12,440
Speaker 3: these lexicon models to suss out how increasingly positive or

756
00:40:12,440 --> 00:40:16,719
Speaker 3: negative analysts are getting on certain companies, certain sectors, certain themes.

757
00:40:17,080 --> 00:40:19,120
Speaker 3: And it turns out to be a very good leading

758
00:40:19,160 --> 00:40:25,560
Speaker 3: indicator for analysts changing their ratings, for stock performance, for

759
00:40:25,600 --> 00:40:28,799
Speaker 3: earnings revisions. So there is something to be said for

760
00:40:29,040 --> 00:40:31,920
Speaker 3: NLP or you know, kind of these more big data

761
00:40:31,960 --> 00:40:36,839
Speaker 3: tools that are actually tracking broader signals over a long

762
00:40:36,920 --> 00:40:37,640
Speaker 3: period of time.

763
00:40:37,840 --> 00:40:42,120
Speaker 1: So that's a very specific application of AI to research.

764
00:40:42,719 --> 00:40:46,120
Speaker 1: How do you see AI coming into your space, into

765
00:40:46,120 --> 00:40:50,759
Speaker 1: the quants or behavior space. Everybody says it's going to

766
00:40:50,760 --> 00:40:54,320
Speaker 1: have a giant impact. When do you see that happening?

767
00:40:54,440 --> 00:40:55,759
Speaker 1: If not already, I.

768
00:40:55,760 --> 00:40:57,160
Speaker 2: Mean I think it's already happened.

769
00:40:57,200 --> 00:41:02,439
Speaker 3: If you think about just like industries have just gone away, right,

770
00:41:03,000 --> 00:41:05,399
Speaker 3: you can.

771
00:41:05,160 --> 00:41:07,120
Speaker 2: I mean, look, I think it's going to replace some

772
00:41:07,280 --> 00:41:07,760
Speaker 2: of us.

773
00:41:07,960 --> 00:41:10,560
Speaker 3: It's going to replace a lot of these processes that

774
00:41:10,600 --> 00:41:16,520
Speaker 3: we do that are really really boring and laborious. But

775
00:41:16,680 --> 00:41:18,520
Speaker 3: I think at some level you still need to have

776
00:41:18,560 --> 00:41:22,680
Speaker 3: that domain knowledge and that level of expertise that trains

777
00:41:22,680 --> 00:41:23,280
Speaker 3: the models.

778
00:41:24,360 --> 00:41:26,040
Speaker 2: I mean, essentially, I think.

779
00:41:25,840 --> 00:41:29,440
Speaker 3: That we could just create a pocket analyst at this point.

780
00:41:29,480 --> 00:41:33,600
Speaker 3: You could create an analyst that, you know, basically puts

781
00:41:33,600 --> 00:41:38,280
Speaker 3: together the rough limbs of a you know, an earnings report,

782
00:41:38,480 --> 00:41:41,560
Speaker 3: a report on earnings or report on you know, a

783
00:41:41,600 --> 00:41:46,200
Speaker 3: specific event, and then you have the analyst himself or

784
00:41:46,280 --> 00:41:48,680
Speaker 3: herself read it and make sure it makes sense and

785
00:41:48,719 --> 00:41:49,719
Speaker 3: you know, tweak.

786
00:41:49,440 --> 00:41:52,480
Speaker 2: It, et cetera. But there's a lot of that route.

787
00:41:52,320 --> 00:41:56,080
Speaker 3: Activity that can be replaced by AI. Whether AI can

788
00:41:56,080 --> 00:41:59,160
Speaker 3: invest better than a human being, I doubt it, because

789
00:41:59,239 --> 00:42:01,439
Speaker 3: you know, I think that at some level you need

790
00:42:01,440 --> 00:42:04,640
Speaker 3: that domain experience, you need that behavioral angle. You need

791
00:42:04,680 --> 00:42:07,960
Speaker 3: to analyze what's different this time, because there always is

792
00:42:08,120 --> 00:42:11,359
Speaker 3: something different this time. I think that that's the other

793
00:42:11,400 --> 00:42:14,200
Speaker 3: thing I've learned in finance is that you can never

794
00:42:14,400 --> 00:42:19,799
Speaker 3: just apply the last crisis playbook to the current environment.

795
00:42:20,760 --> 00:42:23,680
Speaker 3: And that's something that I think it's hard to train

796
00:42:23,880 --> 00:42:28,640
Speaker 3: a bot or a process on how to actually sort

797
00:42:28,680 --> 00:42:32,120
Speaker 3: of determine what you need to factor in this time

798
00:42:32,160 --> 00:42:34,680
Speaker 3: that is different from all of the historical data.

799
00:42:34,960 --> 00:42:37,319
Speaker 1: They may not repeat, but they rhyme as the old

800
00:42:37,400 --> 00:42:38,920
Speaker 1: joke and very very true.

801
00:42:39,160 --> 00:42:43,120
Speaker 3: Yeah exactly. But there's always something that nobody's paying attention

802
00:42:43,239 --> 00:42:46,600
Speaker 3: to that's going to blow everything up. And that's what

803
00:42:47,000 --> 00:42:50,720
Speaker 3: you know, we need the human beings to fly around

804
00:42:51,000 --> 00:42:53,320
Speaker 3: and look into the whites of the eyes of company

805
00:42:53,360 --> 00:42:57,160
Speaker 3: management and you know, kind of figure out what's really

806
00:42:57,239 --> 00:43:00,680
Speaker 3: going on behind the data. And I think it's like

807
00:43:00,719 --> 00:43:02,920
Speaker 3: there's an example of this if you think about, you know,

808
00:43:03,040 --> 00:43:06,359
Speaker 3: even that NLP process that I talked about, where you're

809
00:43:06,400 --> 00:43:09,319
Speaker 3: looking for positive and negative sentiment. So one of the

810
00:43:09,320 --> 00:43:12,360
Speaker 3: things that happened over the last you know, ten years,

811
00:43:12,560 --> 00:43:19,120
Speaker 3: is that management realized that quants are scraping their transcripts

812
00:43:19,120 --> 00:43:23,560
Speaker 3: on conference calls for positive and negative words. And then

813
00:43:23,600 --> 00:43:25,759
Speaker 3: there was a way to game it. You could just

814
00:43:26,040 --> 00:43:29,239
Speaker 3: inject more positive words or you know, take out all

815
00:43:29,280 --> 00:43:32,080
Speaker 3: the negative words. You could you could basically edit your

816
00:43:32,120 --> 00:43:34,839
Speaker 3: script so that it would look like, you know, you

817
00:43:34,880 --> 00:43:37,640
Speaker 3: were you were saying all the right things for a

818
00:43:37,719 --> 00:43:40,120
Speaker 3: quant model. So those are the types of things that

819
00:43:40,200 --> 00:43:43,120
Speaker 3: I think, you know, AI is never going to figure out,

820
00:43:43,480 --> 00:43:47,000
Speaker 3: you know, when that's already in the market, when folks

821
00:43:47,040 --> 00:43:51,320
Speaker 3: are gaming the system, versus when it's an actual, accurate signal.

822
00:43:51,440 --> 00:43:55,440
Speaker 1: Huh, that's incredible. So let's talk a little bit about

823
00:43:56,680 --> 00:43:58,880
Speaker 1: some things that are going on. I saw a quote

824
00:43:58,880 --> 00:44:02,960
Speaker 1: of yours that I really liked. The idea that the

825
00:44:03,000 --> 00:44:07,000
Speaker 1: market is too expensive should be debunked. Explain why.

826
00:44:07,920 --> 00:44:11,480
Speaker 3: Yeah, So I think that there is this tendency of

827
00:44:11,800 --> 00:44:16,719
Speaker 3: quants myself included, to look at a time series and say, okay,

828
00:44:17,120 --> 00:44:19,200
Speaker 3: if the pe of the S and P five hundred

829
00:44:19,280 --> 00:44:24,000
Speaker 3: right now is twenty one times, and it has mostly

830
00:44:24,080 --> 00:44:27,680
Speaker 3: been below fifteen times, and whenever it's been twenty one

831
00:44:27,719 --> 00:44:31,239
Speaker 3: times in the past, it's gone down. Those types of

832
00:44:31,239 --> 00:44:35,200
Speaker 3: analyzes I think are just deeply flawed, especially in light

833
00:44:35,239 --> 00:44:38,960
Speaker 3: of the fact that the market itself is not one

834
00:44:39,320 --> 00:44:43,320
Speaker 3: kind of monolith that's always the same. It's a changing animal.

835
00:44:43,520 --> 00:44:45,120
Speaker 3: And if you look at the S and P today,

836
00:44:45,440 --> 00:44:48,840
Speaker 3: fifty percent of it is asset light innovation oriented healthcare

837
00:44:48,840 --> 00:44:51,799
Speaker 3: and tech, whereas in nineteen eighty seventy percent of it

838
00:44:51,880 --> 00:44:54,600
Speaker 3: was manufacturing asset intensive, et cetera.

839
00:44:55,280 --> 00:44:57,240
Speaker 1: So let me ask you a question about that asset

840
00:44:57,400 --> 00:45:02,560
Speaker 1: light side. People Michael mobisonas one, have made the argument

841
00:45:02,640 --> 00:45:10,120
Speaker 1: that intangibles, intellectual property, patents, algorithms, et cetera, are deserving

842
00:45:10,200 --> 00:45:13,080
Speaker 1: of a higher multiple, that they don't require a massive

843
00:45:13,120 --> 00:45:17,600
Speaker 1: investment in factories, and they're not capital intensive, right, they're

844
00:45:17,600 --> 00:45:19,960
Speaker 1: not manpower intensive, they don't need a ton of labor.

845
00:45:20,280 --> 00:45:23,400
Speaker 1: Shouldn't they be awarded a higher multiple than you know,

846
00:45:23,440 --> 00:45:25,160
Speaker 1: a steel factory, right.

847
00:45:25,120 --> 00:45:25,799
Speaker 2: Right, right, right.

848
00:45:25,880 --> 00:45:28,600
Speaker 3: So that's the idea is that the margins are more stable,

849
00:45:28,719 --> 00:45:32,080
Speaker 3: they're less reliant on risky labor, which you know, people

850
00:45:32,080 --> 00:45:36,359
Speaker 3: can go on strike or sue companies, whereas processes can't. Yeah,

851
00:45:36,360 --> 00:45:38,760
Speaker 3: so I think there's there's a validity to that point.

852
00:45:38,840 --> 00:45:40,880
Speaker 3: I mean, when I look at the S and P today,

853
00:45:41,880 --> 00:45:44,040
Speaker 3: it's you know, it's not only is it a different

854
00:45:44,080 --> 00:45:47,000
Speaker 3: animal in terms of its sector mix, but it's also

855
00:45:47,680 --> 00:45:52,040
Speaker 3: less levered. Everybody took advantage of super low interest rates

856
00:45:52,080 --> 00:45:54,759
Speaker 3: to turn out their debt, and you know kind of

857
00:45:55,120 --> 00:46:00,360
Speaker 3: so fixed rate obligations are day rigueur for the JUST

858
00:46:00,400 --> 00:46:03,680
Speaker 3: and P company versus floating rate obligations. A few years,

859
00:46:03,800 --> 00:46:07,080
Speaker 3: you know, prior to the crisis. I think that also

860
00:46:07,440 --> 00:46:10,319
Speaker 3: when you look at the labor intensity of the S

861
00:46:10,360 --> 00:46:13,279
Speaker 3: and P five hundred, it's become much more labor light.

862
00:46:13,680 --> 00:46:16,319
Speaker 3: And oh, by the way, AI is going to give

863
00:46:16,400 --> 00:46:19,800
Speaker 3: us the opportunity over the next ten years to become

864
00:46:19,960 --> 00:46:24,040
Speaker 3: even more labor light. I think the whole bulk caase

865
00:46:24,080 --> 00:46:28,160
Speaker 3: around AI right now is not buying the chip makers,

866
00:46:28,640 --> 00:46:31,600
Speaker 3: it's buying the index because the index is about to

867
00:46:31,640 --> 00:46:34,280
Speaker 3: become that much higher quality.

868
00:46:34,719 --> 00:46:38,279
Speaker 1: You know, let me see if I understand that, because

869
00:46:38,320 --> 00:46:41,719
Speaker 1: it's really fascinating. Everybody's so focused with Nvidia and now

870
00:46:41,760 --> 00:46:44,680
Speaker 1: Intel has kept quite a bit and a few other

871
00:46:44,760 --> 00:46:48,080
Speaker 1: chip makers. But really what you're saying is, look at

872
00:46:48,120 --> 00:46:52,520
Speaker 1: who has a giant or outsized set of labor costs.

873
00:46:53,640 --> 00:46:55,800
Speaker 1: Either they're going to be able to reduce their headcount

874
00:46:56,080 --> 00:46:58,799
Speaker 1: or their existing headcount is going to become so much

875
00:46:58,840 --> 00:47:04,480
Speaker 1: more productive working with AI that we're not recognizing.

876
00:47:04,000 --> 00:47:09,040
Speaker 3: You know, those describing that that that premium to all

877
00:47:09,160 --> 00:47:12,399
Speaker 3: the clunky services companies out there. Like this is why

878
00:47:12,440 --> 00:47:14,320
Speaker 3: I'm bullish on large cap banks.

879
00:47:14,480 --> 00:47:17,680
Speaker 4: One of the reasons is which are cheap now relatively speaking.

880
00:47:17,400 --> 00:47:20,200
Speaker 3: Which are still in that value cohort and they are

881
00:47:20,280 --> 00:47:24,440
Speaker 3: also one of the few sectors that's become more labor

882
00:47:24,480 --> 00:47:29,880
Speaker 3: intensive since the financial crisis. Why because these regulated banks

883
00:47:29,880 --> 00:47:33,200
Speaker 3: had to hire all these legal and compliance and expert

884
00:47:33,280 --> 00:47:36,600
Speaker 3: folks to make sure we weren't doing anything bad. Right,

885
00:47:37,280 --> 00:47:41,680
Speaker 3: So today, think about all those processes, those are much

886
00:47:42,040 --> 00:47:47,000
Speaker 3: easier to replace with an automated AI, like you know,

887
00:47:47,200 --> 00:47:51,879
Speaker 3: bought whatever you want to call it, than than than

888
00:47:52,040 --> 00:47:55,479
Speaker 3: any period of time in the past. Generative AI is new,

889
00:47:55,719 --> 00:47:59,000
Speaker 3: it's a new thing. It's it's a game changer for

890
00:47:59,120 --> 00:48:02,560
Speaker 3: many industries. Call centers have gone away. I mean entire

891
00:48:02,680 --> 00:48:06,239
Speaker 3: industries have gone away overnight because of the advent of

892
00:48:06,320 --> 00:48:09,120
Speaker 3: generative AI. And that's where I think it's really bullish,

893
00:48:09,600 --> 00:48:13,359
Speaker 3: is in the ability to replace a lot of these

894
00:48:13,600 --> 00:48:18,319
Speaker 3: rote activities that people right now are being paid to do.

895
00:48:18,560 --> 00:48:19,800
Speaker 3: So one of the things that I've seen in my

896
00:48:19,880 --> 00:48:23,000
Speaker 3: quant work is that if you look at any sector

897
00:48:23,120 --> 00:48:25,839
Speaker 3: of the market, in any peer group, and you look

898
00:48:25,840 --> 00:48:29,000
Speaker 3: at the labor intensive companies and the companies that are

899
00:48:29,080 --> 00:48:33,280
Speaker 3: labor light, the companies that are labor light almost always

900
00:48:33,360 --> 00:48:37,480
Speaker 3: outperform their labor intensive peers. So we are sitting right

901
00:48:37,560 --> 00:48:40,120
Speaker 3: now at a point in time where over the next

902
00:48:40,160 --> 00:48:41,759
Speaker 3: five to ten years or I don't know how long

903
00:48:41,800 --> 00:48:45,120
Speaker 3: it takes, the S and P five hundred has this

904
00:48:45,239 --> 00:48:50,160
Speaker 3: opportunity and this new tool to become even labor lighter

905
00:48:50,200 --> 00:48:51,120
Speaker 3: than it is today.

906
00:48:51,560 --> 00:48:53,640
Speaker 2: That is hugely bullish.

907
00:48:53,960 --> 00:48:59,200
Speaker 1: Huh, really really interesting. So this leads me to what

908
00:48:59,239 --> 00:49:02,239
Speaker 1: you've said not too long ago. There's a lot more

909
00:49:02,280 --> 00:49:04,560
Speaker 1: to the S and P five hundred than the Semis

910
00:49:04,920 --> 00:49:09,399
Speaker 1: and the megacap tech. Is this is AI what's driving? Hey,

911
00:49:09,400 --> 00:49:12,840
Speaker 1: you got to look past past Nvidia and pass the

912
00:49:12,920 --> 00:49:16,480
Speaker 1: magnificent seven to who are going to be the beneficiaries

913
00:49:16,480 --> 00:49:17,680
Speaker 1: of all this new technology?

914
00:49:17,760 --> 00:49:18,759
Speaker 2: Yeah, I think that's right.

915
00:49:18,800 --> 00:49:21,640
Speaker 3: I think it's not just new economy chip purveyors, but

916
00:49:21,680 --> 00:49:23,239
Speaker 3: it's also the companies that buy the.

917
00:49:23,200 --> 00:49:24,440
Speaker 2: Chips and become better.

918
00:49:25,120 --> 00:49:27,600
Speaker 3: But I also think there's something going on right now

919
00:49:27,640 --> 00:49:30,120
Speaker 3: that we should be really excited about, which is that

920
00:49:30,160 --> 00:49:33,200
Speaker 3: interest rates are no longer at zero. They're at five percent.

921
00:49:33,320 --> 00:49:35,279
Speaker 3: So the Fed has done a lot of work for us.

922
00:49:35,719 --> 00:49:38,480
Speaker 3: Companies are behaving much more rationally today than they have

923
00:49:38,600 --> 00:49:42,200
Speaker 3: in the past. They're thinking about how to become more efficient.

924
00:49:42,320 --> 00:49:44,080
Speaker 3: This is something they haven't thought about for a really

925
00:49:44,120 --> 00:49:46,719
Speaker 3: long time because they had all these easy ways to

926
00:49:46,760 --> 00:49:50,000
Speaker 3: make money. If I'm a corporate, if I'm a CFO

927
00:49:50,120 --> 00:49:52,840
Speaker 3: and I'm not going to make my earnings numbers next quarter,

928
00:49:53,400 --> 00:49:56,839
Speaker 3: I could have borrowed cash for free and bought back

929
00:49:56,920 --> 00:50:00,279
Speaker 3: enough shares to beat that number. So there were lots

930
00:50:00,280 --> 00:50:02,880
Speaker 3: of low quality ways of making money since the global

931
00:50:02,880 --> 00:50:05,960
Speaker 3: financial crisis. But now we're back to a more normal

932
00:50:06,080 --> 00:50:09,520
Speaker 3: hurdle rate. Five percent interest rates is not super high.

933
00:50:09,640 --> 00:50:12,960
Speaker 3: I think it's manageable, right, and companies are making all

934
00:50:13,000 --> 00:50:15,240
Speaker 3: the right moves. If you look at even these growth

935
00:50:15,280 --> 00:50:20,600
Speaker 3: companies like Meta or Alphabet are now initiating dividends. They

936
00:50:20,640 --> 00:50:23,920
Speaker 3: realize that part of their mantra needs to be cash

937
00:50:23,960 --> 00:50:26,120
Speaker 3: returning and capital discipline.

938
00:50:25,680 --> 00:50:28,120
Speaker 2: As well as growth. So, you know, I think that.

939
00:50:28,080 --> 00:50:31,320
Speaker 3: We're at a point where the reasons to be optimistic

940
00:50:31,760 --> 00:50:36,600
Speaker 3: on stocks are that much more than when we were

941
00:50:36,680 --> 00:50:39,319
Speaker 3: at zero interest rates pre pandemic.

942
00:50:39,840 --> 00:50:40,600
Speaker 2: I mean, think about it.

943
00:50:40,600 --> 00:50:43,520
Speaker 3: The market has absorbed so much bad news over the

944
00:50:43,600 --> 00:50:44,560
Speaker 3: last few years.

945
00:50:44,960 --> 00:50:49,480
Speaker 1: You not too long ago someone asked you about markets

946
00:50:49,480 --> 00:50:53,600
Speaker 1: climb a wall of worry, like it's a bad thing. Yeah,

947
00:50:53,840 --> 00:50:56,160
Speaker 1: isn't that a good thing? Isn't that people are stressed

948
00:50:56,160 --> 00:50:59,400
Speaker 1: out about things that the market's already sussed out.

949
00:50:59,480 --> 00:51:02,080
Speaker 3: Right exactly, I think that's right. And I think, you know,

950
00:51:02,280 --> 00:51:05,480
Speaker 3: even when you think about where we were in twenty

951
00:51:05,600 --> 00:51:07,680
Speaker 3: twenty one, at the end of twenty twenty one, I

952
00:51:07,760 --> 00:51:11,680
Speaker 3: felt really nervous about stocks because for the first time,

953
00:51:12,440 --> 00:51:14,840
Speaker 3: we were forecasting.

954
00:51:14,080 --> 00:51:15,320
Speaker 2: Negative real rates.

955
00:51:15,600 --> 00:51:21,800
Speaker 3: Uh huh, which is really, you know, kind of a.

956
00:51:20,320 --> 00:51:22,080
Speaker 1: Problematic to say the least.

957
00:51:21,800 --> 00:51:26,480
Speaker 3: It's irrational negative real rates. That's an irrational Let me.

958
00:51:26,480 --> 00:51:28,920
Speaker 1: Borrow some money from you, and I need a quarterly

959
00:51:29,000 --> 00:51:29,520
Speaker 1: check from.

960
00:51:29,400 --> 00:51:30,279
Speaker 4: You, exactly.

961
00:51:30,520 --> 00:51:33,839
Speaker 3: I mean, it doesn't make any sense. We were forecasting

962
00:51:33,880 --> 00:51:37,600
Speaker 3: something that didn't make any sense. You know, every economist

963
00:51:37,680 --> 00:51:42,200
Speaker 3: out there was forecasting negative real rates, and that just

964
00:51:42,400 --> 00:51:45,879
Speaker 3: felt like something had gone wrong. Nobody was expecting two

965
00:51:45,920 --> 00:51:48,360
Speaker 3: wars to break out. Nobody was expecting the Fed to

966
00:51:48,440 --> 00:51:51,239
Speaker 3: hike interest rates from zero to five in a very

967
00:51:51,239 --> 00:51:54,920
Speaker 3: short period of time. By the end of twenty twenty one,

968
00:51:54,960 --> 00:51:57,560
Speaker 3: our sell side indicator was at the most bullish levels

969
00:51:57,600 --> 00:51:58,760
Speaker 3: we'd seen since.

970
00:51:58,560 --> 00:51:59,960
Speaker 4: Really the global financial crisis.

971
00:52:00,120 --> 00:52:02,440
Speaker 3: Yep, nobody thought anything was going to go wrong, and

972
00:52:02,480 --> 00:52:04,200
Speaker 3: then whamo, you saw a bear market.

973
00:52:04,600 --> 00:52:06,719
Speaker 1: So today, and by the way, a bear market in

974
00:52:06,719 --> 00:52:09,359
Speaker 1: both stocks and bond and bond it's something that you

975
00:52:09,400 --> 00:52:12,279
Speaker 1: don't see every forty years was the last time we

976
00:52:12,360 --> 00:52:15,680
Speaker 1: saw that exactly So there the cell side indicator really

977
00:52:15,719 --> 00:52:20,359
Speaker 1: worked exactly as planned. So let's talk about where we

978
00:52:20,440 --> 00:52:24,520
Speaker 1: are in the current cycle. I know you like to

979
00:52:24,560 --> 00:52:28,320
Speaker 1: discuss there are different phases of the of the both

980
00:52:28,320 --> 00:52:31,640
Speaker 1: the market and the economic cycle. Where are we in

981
00:52:31,680 --> 00:52:34,160
Speaker 1: this cycle and what does that mean for the next

982
00:52:34,160 --> 00:52:34,840
Speaker 1: couple of years.

983
00:52:35,160 --> 00:52:37,680
Speaker 3: Yeah, I mean, so this is one area where I'm

984
00:52:37,719 --> 00:52:41,919
Speaker 3: going to say this time it is different. I'm going

985
00:52:42,000 --> 00:52:45,560
Speaker 3: to see those dreaded words because I think that, you know,

986
00:52:45,719 --> 00:52:51,040
Speaker 3: where we are today is not necessarily as clear cut

987
00:52:51,320 --> 00:52:54,839
Speaker 3: in terms of late cycle, early cycle, you know, recession,

988
00:52:54,960 --> 00:52:57,520
Speaker 3: no recession. I think we you know, I think we've

989
00:52:57,560 --> 00:53:01,200
Speaker 3: had areas of strength and areas weakness over the last

990
00:53:01,200 --> 00:53:04,600
Speaker 3: few years. I mean, we had a global pandemic, a

991
00:53:04,719 --> 00:53:10,399
Speaker 3: complete shutdown of global economic activity, and then you had

992
00:53:10,600 --> 00:53:14,600
Speaker 3: certain pockets of the economy become oversubscribed and other parts

993
00:53:14,600 --> 00:53:17,680
Speaker 3: of the economy become undersubscribed. And there's there's been that

994
00:53:17,840 --> 00:53:20,440
Speaker 3: shakeout ever since. So I still think we're in this

995
00:53:20,560 --> 00:53:23,800
Speaker 3: environment where goods versus services, we're working out that demand.

996
00:53:24,640 --> 00:53:28,919
Speaker 3: We've seen inventory tightness and inventory laxity, so we've seen

997
00:53:28,960 --> 00:53:31,520
Speaker 3: a lot of like kind of cross currents that would

998
00:53:31,560 --> 00:53:35,680
Speaker 3: problematize just calling this a normal FED hiking cycle.

999
00:53:36,880 --> 00:53:37,839
Speaker 2: I do think that the.

1000
00:53:37,760 --> 00:53:43,080
Speaker 3: Other factor that has shifted demonstrably and deserves more airtime

1001
00:53:43,640 --> 00:53:45,799
Speaker 3: is the idea that you know, if you look at

1002
00:53:45,840 --> 00:53:49,799
Speaker 3: the areas of risk today across the spectrum, corporates and

1003
00:53:49,880 --> 00:53:53,399
Speaker 3: consumers were just given a bunch of money from the

1004
00:53:53,440 --> 00:53:57,520
Speaker 3: Fed and the government. The areas of risk and indebtedness

1005
00:53:57,600 --> 00:54:02,560
Speaker 3: are sitting in the on the government balance sheet, right,

1006
00:54:03,000 --> 00:54:05,759
Speaker 3: not necessarily on corporate or consumer balance sheets.

1007
00:54:05,800 --> 00:54:09,200
Speaker 1: Right. Everybody refinanced except Uncle Sam exactly.

1008
00:54:09,400 --> 00:54:14,279
Speaker 3: Uncle Sam took the whole pile of it, and it's

1009
00:54:14,320 --> 00:54:16,640
Speaker 3: sitting right there on our balance.

1010
00:54:16,560 --> 00:54:19,719
Speaker 1: And I recall seeing a number of senators and congressmen,

1011
00:54:20,120 --> 00:54:23,239
Speaker 1: and they should chisel this on their tombstones. You know,

1012
00:54:23,320 --> 00:54:26,920
Speaker 1: if we refinance at lower rates, it'll just encourage more spending.

1013
00:54:27,360 --> 00:54:29,359
Speaker 1: It's like, no, they're going to spend more no matter

1014
00:54:29,360 --> 00:54:31,239
Speaker 1: what the rates are. You might as well get a

1015
00:54:31,239 --> 00:54:34,080
Speaker 1: better rate, you know. It was just one of those

1016
00:54:34,239 --> 00:54:38,000
Speaker 1: like dumb things that politicians say that you know, as

1017
00:54:38,040 --> 00:54:40,479
Speaker 1: soon as you hear, it's not true, and now we're

1018
00:54:40,520 --> 00:54:43,359
Speaker 1: stuck with a lot of debt and we didn't even

1019
00:54:43,400 --> 00:54:46,040
Speaker 1: get a benefit of a decade of low rates.

1020
00:54:46,120 --> 00:54:47,040
Speaker 2: Right right.

1021
00:54:47,080 --> 00:54:49,880
Speaker 3: I mean, I think this debt sitting on government balance,

1022
00:54:49,920 --> 00:54:52,440
Speaker 3: she's something to worry about. I mean, I think the

1023
00:54:52,560 --> 00:54:56,480
Speaker 3: other aspect to worry about is not publicly traded equities,

1024
00:54:56,480 --> 00:54:59,400
Speaker 3: which are marked to market on every change in every

1025
00:54:59,440 --> 00:55:02,000
Speaker 3: macro now, tick by tick, tick by tick on a

1026
00:55:02,000 --> 00:55:06,080
Speaker 3: millisecond basis. But if you look at private credit, private equity,

1027
00:55:06,320 --> 00:55:09,160
Speaker 3: commercial real estate, we already know it's it's you know,

1028
00:55:09,160 --> 00:55:12,239
Speaker 3: it's problematic residential real estate. We haven't seen a lot

1029
00:55:12,239 --> 00:55:15,480
Speaker 3: of turnover in residential real estate because nobody wants to

1030
00:55:15,520 --> 00:55:18,560
Speaker 3: walk away from older. Yeah, so I think those are

1031
00:55:18,560 --> 00:55:20,480
Speaker 3: the areas where we should be more worried. But if

1032
00:55:20,520 --> 00:55:23,360
Speaker 3: you're looking at a stock, it's pricing in the current

1033
00:55:24,000 --> 00:55:27,480
Speaker 3: environment of rates inflation, like kind of everything that's going

1034
00:55:27,520 --> 00:55:31,560
Speaker 3: on right now is in a publicly traded equity vehicle.

1035
00:55:31,920 --> 00:55:35,240
Speaker 1: Not too long ago, we were having a conversation about,

1036
00:55:35,480 --> 00:55:37,920
Speaker 1: you know, so everything going on in the college campuses. Now.

1037
00:55:38,280 --> 00:55:41,040
Speaker 1: We were talking about the various endowments and how they

1038
00:55:41,120 --> 00:55:46,240
Speaker 1: performed and somehow in twenty twenty two, when when stocks

1039
00:55:46,239 --> 00:55:49,680
Speaker 1: were down about twenty percent and bonds were down about

1040
00:55:49,719 --> 00:55:53,600
Speaker 1: fifteen percent, these endowments, some of which are twenty thirty

1041
00:55:53,680 --> 00:55:57,960
Speaker 1: forty percent alternatives like private equity and private credit, they

1042
00:55:58,000 --> 00:56:01,080
Speaker 1: did just fine. It's it's great when you get to

1043
00:56:01,239 --> 00:56:03,640
Speaker 1: mark to make believe. You know, you could just put

1044
00:56:03,880 --> 00:56:06,279
Speaker 1: what should we mark this? I don't know what do

1045
00:56:06,280 --> 00:56:07,000
Speaker 1: you want it to be?

1046
00:56:07,440 --> 00:56:07,600
Speaker 2: Right?

1047
00:56:07,680 --> 00:56:10,080
Speaker 1: Right, Let's put it flat for the year flat in

1048
00:56:10,120 --> 00:56:13,399
Speaker 1: this environment looks great. I wish I could get away

1049
00:56:13,400 --> 00:56:17,840
Speaker 1: with that. I actually have to report real performance, not

1050
00:56:18,040 --> 00:56:21,719
Speaker 1: made up stuff exactly. And I've heard consultants pitch it.

1051
00:56:21,840 --> 00:56:25,440
Speaker 1: You know, in a down year, you have like two

1052
00:56:25,520 --> 00:56:27,719
Speaker 1: years to change your mark on that, and by the

1053
00:56:27,760 --> 00:56:30,200
Speaker 1: time you change your market it's probably recovered. Yeah.

1054
00:56:30,400 --> 00:56:32,440
Speaker 3: I mean, I think this is an area that could

1055
00:56:32,440 --> 00:56:36,160
Speaker 3: be ripe for regulation. I just don't know how the

1056
00:56:36,200 --> 00:56:39,080
Speaker 3: regulators will figure out how to regulate it, and I'm

1057
00:56:39,120 --> 00:56:41,520
Speaker 3: sure that that will create this sort of whack a

1058
00:56:41,560 --> 00:56:42,920
Speaker 3: mole type of environment.

1059
00:56:43,000 --> 00:56:46,160
Speaker 1: Well, if you remember back during the financial crisis, when

1060
00:56:46,280 --> 00:56:51,239
Speaker 1: everybody had to mark to market, even things held to

1061
00:56:51,320 --> 00:56:54,440
Speaker 1: maturity that were underwater, they had a market to market,

1062
00:56:54,560 --> 00:56:57,480
Speaker 1: and that was one of the changes that came about. Okay,

1063
00:56:57,520 --> 00:57:00,839
Speaker 1: if this doesn't have any payments due and you're it's

1064
00:57:00,880 --> 00:57:04,400
Speaker 1: in your hold to maturity account, you don't have to

1065
00:57:04,400 --> 00:57:07,279
Speaker 1: mark to market, which allows a lot of junk to

1066
00:57:07,360 --> 00:57:10,839
Speaker 1: kind of get swept under the rug absolutely, and that

1067
00:57:10,880 --> 00:57:13,440
Speaker 1: becomes you know, that becomes a feature, not a buck.

1068
00:57:13,760 --> 00:57:16,479
Speaker 3: And here's the really worrisome thing. So if you think

1069
00:57:16,520 --> 00:57:21,080
Speaker 3: about just private equity, the amount of capital raised since

1070
00:57:21,200 --> 00:57:26,760
Speaker 3: twenty seventeen is basically it doubled the size of the

1071
00:57:26,760 --> 00:57:30,360
Speaker 3: private equity market. Think about how we were we were

1072
00:57:30,920 --> 00:57:35,360
Speaker 3: geared in twenty seventeen, twenty eighteen, nineteen twenty. We weren't

1073
00:57:35,400 --> 00:57:38,000
Speaker 3: thinking about five percent interest rates.

1074
00:57:37,800 --> 00:57:39,440
Speaker 4: Right, it was we were low for longer.

1075
00:57:39,480 --> 00:57:44,240
Speaker 3: This suflation is going to stay low, disinflationary pressures, disruption,

1076
00:57:44,400 --> 00:57:47,960
Speaker 3: blah blah blah. That was the mantra during that entire

1077
00:57:48,000 --> 00:57:50,880
Speaker 3: stretch of time where where a ton of money was

1078
00:57:50,960 --> 00:57:56,520
Speaker 3: raised in these long duration growth themes that were priced

1079
00:57:56,560 --> 00:57:58,640
Speaker 3: for an environment of zero rates forever.

1080
00:57:58,800 --> 00:58:00,880
Speaker 1: Right, you're getting nothing on bars, but hey, look I

1081
00:58:00,880 --> 00:58:02,920
Speaker 1: can get you five or six percent in private equity.

1082
00:58:03,160 --> 00:58:06,040
Speaker 1: The only rub is it's locked up for seven years exactly.

1083
00:58:06,240 --> 00:58:11,040
Speaker 1: So once you had the pandemic, which changed everything, you

1084
00:58:11,160 --> 00:58:13,880
Speaker 1: had the biggest fiscal stimulus since World War Two and

1085
00:58:14,000 --> 00:58:16,880
Speaker 1: the first CARES Act, to say nothing of CARES Act two,

1086
00:58:17,680 --> 00:58:21,320
Speaker 1: those two undred President Trump and Karsact three under President Biden.

1087
00:58:21,880 --> 00:58:25,880
Speaker 1: The fiscal you mentioned regime change earlier, yep, the previous

1088
00:58:25,920 --> 00:58:29,040
Speaker 1: regime was all monetary in the twenty tens. In the

1089
00:58:29,080 --> 00:58:31,880
Speaker 1: twenty twenties, it's mostly fiscal fiscal.

1090
00:58:32,200 --> 00:58:36,919
Speaker 3: It's inflationary, it's protectionist. I mean, everything going on right now,

1091
00:58:37,000 --> 00:58:41,600
Speaker 3: deglobalization and physical stimulus, these are inflationary trends. So I

1092
00:58:41,640 --> 00:58:44,080
Speaker 3: think that the idea that inflation and rates are going

1093
00:58:44,120 --> 00:58:49,360
Speaker 3: to remain low is you know, it's problematic. And you know,

1094
00:58:49,720 --> 00:58:51,840
Speaker 3: I mean even this year, look what happened. The Fed

1095
00:58:51,960 --> 00:58:54,960
Speaker 3: was supposed to cut like what was it four times?

1096
00:58:55,320 --> 00:58:57,280
Speaker 1: That's well. We were also supposed to get a recession

1097
00:58:57,480 --> 00:59:00,800
Speaker 1: and that was I know. Ye today he's right and

1098
00:59:00,960 --> 00:59:03,320
Speaker 1: none of them happen. That's that is your cell side

1099
00:59:03,360 --> 00:59:06,720
Speaker 1: indicator and action. All the consensus things or acession in

1100
00:59:06,760 --> 00:59:09,120
Speaker 1: twenty two or a recession in twenty three, the federal

1101
00:59:09,160 --> 00:59:11,040
Speaker 1: start cutting in twenty three. Now we're going to push

1102
00:59:11,040 --> 00:59:13,880
Speaker 1: it out to twenty four. None of that has proven

1103
00:59:13,880 --> 00:59:14,360
Speaker 1: to be true.

1104
00:59:14,600 --> 00:59:17,320
Speaker 3: Yeah, yeah, yeah, I mean I think that where we

1105
00:59:17,400 --> 00:59:20,400
Speaker 3: are today is actually a reasonably healthy point for equities.

1106
00:59:21,400 --> 00:59:23,400
Speaker 3: But the areas that I worry about are that is

1107
00:59:23,440 --> 00:59:27,520
Speaker 3: that bottomless pit of you know, unmarked assets that have

1108
00:59:27,720 --> 00:59:31,240
Speaker 3: doubled or quadrupled in size in asset allocation. I mean,

1109
00:59:31,280 --> 00:59:35,200
Speaker 3: think about the average teacher or firefighter's pension plan. It's

1110
00:59:35,280 --> 00:59:39,600
Speaker 3: thirty percent ill liquid today versus wow, five percent you know,

1111
00:59:40,280 --> 00:59:43,240
Speaker 3: back in the two thousand. So you know, stuff has changed,

1112
00:59:43,320 --> 00:59:45,400
Speaker 3: and that's where I worry. But I don't worry as

1113
00:59:45,480 --> 00:59:49,240
Speaker 3: much about you know, big cap companies that everybody is

1114
00:59:49,320 --> 00:59:51,600
Speaker 3: tracking and watching and monitoring.

1115
00:59:51,920 --> 00:59:53,960
Speaker 1: So I want to get to my favorite questions that

1116
00:59:54,000 --> 00:59:56,360
Speaker 1: we ask all of our guests. But before I do that,

1117
00:59:56,880 --> 01:00:00,200
Speaker 1: I just have to throw a curve ball at you.

1118
01:00:00,560 --> 01:00:04,080
Speaker 1: So you had mentioned your predecessor, Rich Bernstein, who had

1119
01:00:04,080 --> 01:00:06,240
Speaker 1: been with Meryl for a long time before he went

1120
01:00:06,280 --> 01:00:12,480
Speaker 1: out and launched Rich Bernstein Associates, Responsanate Advisors Advice RBA.

1121
01:00:12,680 --> 01:00:19,880
Speaker 1: Right when he left Meryl, he was roasted, and you

1122
01:00:20,000 --> 01:00:24,439
Speaker 1: famously read about ten bullet.

1123
01:00:24,080 --> 01:00:26,840
Speaker 3: Points ten things I've learned from Rich. In my ten

1124
01:00:26,920 --> 01:00:27,959
Speaker 3: years working.

1125
01:00:27,640 --> 01:00:33,440
Speaker 1: For they were hilarious, perhaps my favorite. A midlife crisis

1126
01:00:33,440 --> 01:00:37,200
Speaker 1: on Wall Street doesn't have to involve a ferrarian hair plugs,

1127
01:00:37,680 --> 01:00:41,560
Speaker 1: a Mini Cooper, and a leather rubber metal man bracelet

1128
01:00:41,960 --> 01:00:44,680
Speaker 1: will do just fine. Tell us a little bit about

1129
01:00:44,720 --> 01:00:46,640
Speaker 1: your Rich's exit roast.

1130
01:00:46,760 --> 01:00:50,880
Speaker 3: Oh goodness, it was terrible because I went first and

1131
01:00:50,960 --> 01:00:54,640
Speaker 3: I said ten really mean things about Rich and then

1132
01:00:54,800 --> 01:00:59,080
Speaker 3: everybody that did the speech after me said really nice

1133
01:00:59,120 --> 01:01:00,120
Speaker 3: things about.

1134
01:01:00,720 --> 01:01:02,959
Speaker 1: But that's what a roast is supposed to build.

1135
01:01:03,080 --> 01:01:06,040
Speaker 3: Well, I was like, this is not a good roast.

1136
01:01:06,120 --> 01:01:08,640
Speaker 3: You guys need to get into the trenches and say

1137
01:01:08,640 --> 01:01:11,200
Speaker 3: some mean things. But I was the really mean one

1138
01:01:11,200 --> 01:01:12,560
Speaker 3: and everybody else was reliving.

1139
01:01:12,840 --> 01:01:15,160
Speaker 1: So if they were going to do a roast of you,

1140
01:01:15,640 --> 01:01:17,680
Speaker 1: what would the worst thing they say about you on

1141
01:01:17,720 --> 01:01:17,960
Speaker 1: the way?

1142
01:01:18,080 --> 01:01:21,040
Speaker 2: Oh gosh, there's so many things they could say.

1143
01:01:22,400 --> 01:01:24,600
Speaker 1: Well, what's the nice thing we would say about you?

1144
01:01:25,280 --> 01:01:28,600
Speaker 1: Let me rephrase that. What would you be most proud

1145
01:01:28,800 --> 01:01:30,160
Speaker 1: of someone saying about you?

1146
01:01:32,120 --> 01:01:34,640
Speaker 2: Well, that's a good question. I think I would be

1147
01:01:34,720 --> 01:01:39,000
Speaker 2: happy if somebody said about me that I was.

1148
01:01:40,400 --> 01:01:42,880
Speaker 3: I helped them in their career. I mean, I think

1149
01:01:42,880 --> 01:01:44,840
Speaker 3: that's what we're all here for. But I think the

1150
01:01:44,960 --> 01:01:47,520
Speaker 3: terrible things that people could say about me were that I,

1151
01:01:47,560 --> 01:01:50,600
Speaker 3: you know, chronically forget my ID, like four out of

1152
01:01:50,640 --> 01:01:52,800
Speaker 3: five days a week. I don't bring my ID to

1153
01:01:52,920 --> 01:01:54,959
Speaker 3: the office, and I have to get the security guard

1154
01:01:55,000 --> 01:01:56,400
Speaker 3: to look me up in the system.

1155
01:01:56,440 --> 01:02:01,360
Speaker 1: They're sofisted. This is absolutely true story one day. So

1156
01:02:01,800 --> 01:02:04,440
Speaker 1: sometimes I take this off when we're recording. On the

1157
01:02:04,480 --> 01:02:06,840
Speaker 1: other side of that studio is where Mike sits, some

1158
01:02:06,840 --> 01:02:10,000
Speaker 1: guy named Mike Bloomberg, and he must have taken his

1159
01:02:10,200 --> 01:02:13,360
Speaker 1: off and gone up to get coffee or something up there,

1160
01:02:13,640 --> 01:02:16,600
Speaker 1: and on the way back, the guard says, sorry, I

1161
01:02:16,600 --> 01:02:20,080
Speaker 1: can't let you down without a U A tag and

1162
01:02:21,040 --> 01:02:24,960
Speaker 1: to his credit and this is a good display of leadership,

1163
01:02:24,960 --> 01:02:27,640
Speaker 1: turn around, went down to the basement, got a temporary

1164
01:02:28,280 --> 01:02:31,160
Speaker 1: came back and everybody saw it. If Mike did it, well,

1165
01:02:31,160 --> 01:02:33,600
Speaker 1: then how could we not. That's right, That's that's pretty

1166
01:02:33,640 --> 01:02:35,960
Speaker 1: hig So what happens when you show up without your

1167
01:02:36,520 --> 01:02:37,560
Speaker 1: you know, your badge?

1168
01:02:37,560 --> 01:02:39,760
Speaker 3: Well, the sad thing is that all the security guards

1169
01:02:39,800 --> 01:02:41,760
Speaker 3: they know me because I'm vivoting.

1170
01:02:41,960 --> 01:02:43,280
Speaker 1: Don't you have to swipe in?

1171
01:02:43,960 --> 01:02:46,640
Speaker 3: Well, they give me a bat like a temporary idea

1172
01:02:46,640 --> 01:02:49,280
Speaker 3: and then I go upstairs. But but yeah, there are

1173
01:02:49,320 --> 01:02:52,640
Speaker 3: a lot of things that that I could be roasted on.

1174
01:02:52,720 --> 01:02:55,960
Speaker 3: I always walk the wrong direction out of a door.

1175
01:02:56,160 --> 01:02:58,920
Speaker 3: I always go the opposite direction of where I'm supposed.

1176
01:02:58,560 --> 01:02:59,040
Speaker 2: To be going.

1177
01:02:59,320 --> 01:03:01,600
Speaker 1: No, you don't have good internal gyroscope.

1178
01:03:01,880 --> 01:03:07,280
Speaker 3: Good, Yeah, my compass is completely destroyed. But yeah, there

1179
01:03:07,280 --> 01:03:09,280
Speaker 3: are a lot of there's a lot of raw material

1180
01:03:09,400 --> 01:03:10,160
Speaker 3: to roast me on.

1181
01:03:10,520 --> 01:03:11,320
Speaker 2: I mean, it was well, I.

1182
01:03:11,320 --> 01:03:13,480
Speaker 1: Hope I get invited to that. That sounds like that'll

1183
01:03:13,480 --> 01:03:16,080
Speaker 1: be fun. So let's jump to our favorite questions that

1184
01:03:16,120 --> 01:03:19,400
Speaker 1: we ask all our guests, starting with what have you

1185
01:03:19,480 --> 01:03:22,280
Speaker 1: been streaming these days? What are you watching? Oh? Well,

1186
01:03:22,520 --> 01:03:24,040
Speaker 1: just watching listening to whatever?

1187
01:03:24,160 --> 01:03:25,200
Speaker 2: Was I just finished.

1188
01:03:24,880 --> 01:03:27,680
Speaker 3: Watching The Gilded Age, which I thought was really fascinating.

1189
01:03:27,680 --> 01:03:29,040
Speaker 1: It's a Gilded Age.

1190
01:03:29,080 --> 01:03:32,240
Speaker 3: It's on HBO Max, and it's about like old New York,

1191
01:03:32,440 --> 01:03:36,200
Speaker 3: like basically, you know, the Upper east Side, in the

1192
01:03:36,040 --> 01:03:39,000
Speaker 3: in the in the railroad baron.

1193
01:03:41,000 --> 01:03:42,439
Speaker 1: Was that really the Gilded Era?

1194
01:03:43,000 --> 01:03:45,160
Speaker 3: I suppose that's what they call it. I mean it

1195
01:03:45,280 --> 01:03:47,920
Speaker 3: seemed pretty interesting. It was kind of fun if you

1196
01:03:47,960 --> 01:03:49,440
Speaker 3: live in New York to watch that.

1197
01:03:51,560 --> 01:03:53,520
Speaker 2: I rewatched Breaking Bad.

1198
01:03:53,320 --> 01:03:56,480
Speaker 1: Because we were just talking about I saw the first season

1199
01:03:56,560 --> 01:03:58,360
Speaker 1: and kind of tapped out afterwards.

1200
01:03:58,360 --> 01:04:01,200
Speaker 3: I know, No, I mean, I I hate to say this,

1201
01:04:01,280 --> 01:04:03,080
Speaker 3: but I really feel like you need to give it

1202
01:04:03,120 --> 01:04:03,960
Speaker 3: another season.

1203
01:04:04,400 --> 01:04:08,040
Speaker 1: I mean, during the during the pandemic, we were you know,

1204
01:04:08,080 --> 01:04:10,000
Speaker 1: you're stuck at home. We went through a bunch of

1205
01:04:10,040 --> 01:04:13,200
Speaker 1: things like mad Men. I had never watched a single

1206
01:04:13,240 --> 01:04:16,200
Speaker 1: episode of that without when that was on TV, and

1207
01:04:16,240 --> 01:04:19,480
Speaker 1: we blew right through it. So the competition for things

1208
01:04:19,520 --> 01:04:22,360
Speaker 1: that were like when someone says you got to give

1209
01:04:22,360 --> 01:04:24,840
Speaker 1: it a couple of seasons, I'm like, it turns out

1210
01:04:24,880 --> 01:04:30,040
Speaker 1: I don't have to. But I understand. I understand the point. Yeah,

1211
01:04:30,360 --> 01:04:33,120
Speaker 1: we talked about Game of Thrones. Yeah, are you a fan?

1212
01:04:33,360 --> 01:04:34,880
Speaker 2: No, can't get into it.

1213
01:04:35,160 --> 01:04:38,120
Speaker 1: I watched the and I know a million people say

1214
01:04:38,160 --> 01:04:41,800
Speaker 1: it's the greatest show. You're sci fi fantasy guy, you

1215
01:04:41,800 --> 01:04:44,440
Speaker 1: should love it. Like, first of all, I can't keep

1216
01:04:44,520 --> 01:04:47,640
Speaker 1: up with all the names. My brain is oatmeal, right,

1217
01:04:47,680 --> 01:04:49,800
Speaker 1: It's like, wait, I need a I need a notepad,

1218
01:04:50,280 --> 01:04:53,320
Speaker 1: like this is who of Visigoth? Of what? Like? I

1219
01:04:53,440 --> 01:04:55,320
Speaker 1: just I like, I think.

1220
01:04:55,200 --> 01:04:58,280
Speaker 3: I fell asleep like three times trying to watch the

1221
01:04:58,320 --> 01:05:00,320
Speaker 3: first episode.

1222
01:05:00,040 --> 01:05:03,800
Speaker 1: The first the first couple of episodes are very slow,

1223
01:05:04,400 --> 01:05:07,160
Speaker 1: and then the other you know, so the first season

1224
01:05:07,200 --> 01:05:08,840
Speaker 1: of White White Lotus was great.

1225
01:05:09,240 --> 01:05:10,800
Speaker 2: Oh yeah, I loved White Loadus.

1226
01:05:10,840 --> 01:05:13,720
Speaker 1: But we're watching the second season and everybody is just

1227
01:05:13,800 --> 01:05:17,720
Speaker 1: a They're not Succession bad, which is another show that

1228
01:05:18,040 --> 01:05:20,080
Speaker 1: everybody says it's great and why do I want to

1229
01:05:20,120 --> 01:05:22,919
Speaker 1: spend my time with these people? But like I want

1230
01:05:22,920 --> 01:05:27,600
Speaker 1: to be entertained and come away with like right, yes,

1231
01:05:27,760 --> 01:05:31,200
Speaker 1: not like wow, those people are jerks. Thank goodness. I

1232
01:05:31,200 --> 01:05:34,720
Speaker 1: don't work with anyone like them. It's just like so

1233
01:05:34,800 --> 01:05:39,120
Speaker 1: what else? So so if you watch The Gilded Age, yes,

1234
01:05:39,320 --> 01:05:41,080
Speaker 1: did you see the Crown? Oh?

1235
01:05:41,120 --> 01:05:42,160
Speaker 2: I love the Crown Love.

1236
01:05:42,960 --> 01:05:47,400
Speaker 1: Every episode was a joy, yeah, just visually a feast.

1237
01:05:47,040 --> 01:05:49,440
Speaker 3: For the It was my twelve year old son watched that,

1238
01:05:49,480 --> 01:05:51,439
Speaker 3: which really which was kind of.

1239
01:05:51,360 --> 01:05:53,120
Speaker 2: Cool because I didn't realize he.

1240
01:05:53,080 --> 01:05:55,240
Speaker 1: Was, well, how did he How did he find it?

1241
01:05:55,320 --> 01:05:55,680
Speaker 2: I don't know.

1242
01:05:55,760 --> 01:05:58,240
Speaker 3: He just wandered into the room while I was watching it,

1243
01:05:58,320 --> 01:06:01,480
Speaker 3: and then he sat down and then he was engrossed,

1244
01:06:01,480 --> 01:06:04,760
Speaker 3: and we're watching this series together about the Queen of England.

1245
01:06:05,160 --> 01:06:09,000
Speaker 1: It was really fascinating. It was it was I know

1246
01:06:09,080 --> 01:06:12,640
Speaker 1: it's sort of semi fictional.

1247
01:06:12,240 --> 01:06:14,960
Speaker 2: But semi I.

1248
01:06:14,360 --> 01:06:17,640
Speaker 1: Found myself asking questions and googling things. O me too,

1249
01:06:17,760 --> 01:06:20,680
Speaker 1: Did that happen? Really? It was amazing. Give me one

1250
01:06:20,720 --> 01:06:22,720
Speaker 1: other thing you're watching that you thought was fun.

1251
01:06:23,080 --> 01:06:26,919
Speaker 3: Okay, let's see Breaking Bad, the Crown.

1252
01:06:27,640 --> 01:06:29,120
Speaker 2: Gosh, I'm coming up blank.

1253
01:06:29,920 --> 01:06:32,520
Speaker 1: You know the the problem with Breaking Bad? There was

1254
01:06:32,560 --> 01:06:36,040
Speaker 1: a show I don't remember what I watched called Fauda

1255
01:06:36,280 --> 01:06:44,080
Speaker 1: about Israeli counterintelligence agents that are infiltrating various terrorist groups.

1256
01:06:44,640 --> 01:06:48,320
Speaker 1: And it's so stressful that if you watch this show

1257
01:06:48,480 --> 01:06:50,840
Speaker 1: after eight o'clock at night, you're not going to sleep

1258
01:06:50,880 --> 01:06:54,440
Speaker 1: till midnight. And like you, I'm an early riser. I

1259
01:06:54,880 --> 01:06:58,560
Speaker 1: can't like be on the edge of my seat wondering

1260
01:06:58,640 --> 01:07:01,480
Speaker 1: who's gonna, you know, be found out and been murdered

1261
01:07:01,520 --> 01:07:01,880
Speaker 1: by the.

1262
01:07:02,240 --> 01:07:05,040
Speaker 2: Okay, I just remembered a show that gave me like

1263
01:07:05,400 --> 01:07:07,880
Speaker 2: PTSD twenty four.

1264
01:07:08,000 --> 01:07:11,160
Speaker 1: Have you ever watched though, Oh sure, oh oh, with

1265
01:07:11,280 --> 01:07:13,040
Speaker 1: a clock ticking down the whole time?

1266
01:07:13,800 --> 01:07:17,000
Speaker 3: It was like, but I binge watched that because you

1267
01:07:17,160 --> 01:07:21,320
Speaker 3: can't not watch an entire season if your calendar alive.

1268
01:07:21,200 --> 01:07:23,720
Speaker 1: Once you get once you get into one episode.

1269
01:07:23,400 --> 01:07:25,280
Speaker 2: You're just gonna put it was so stressful.

1270
01:07:25,320 --> 01:07:28,360
Speaker 3: I think that might have taken years off of my life.

1271
01:07:28,920 --> 01:07:33,440
Speaker 1: We just finished The Gentleman, which is also kind of

1272
01:07:33,480 --> 01:07:37,720
Speaker 1: stressful and you so I always save some comedy show

1273
01:07:37,720 --> 01:07:40,640
Speaker 1: as sort of like a palate cleanser. Now it's Brooklyn

1274
01:07:40,720 --> 01:07:45,080
Speaker 1: ninety nine, but before that, it was, oh god, FANTASTICA

1275
01:07:45,480 --> 01:07:49,080
Speaker 1: ted Lasso was like regular. The other show that's we've

1276
01:07:49,080 --> 01:07:52,520
Speaker 1: been watching on HBO that we loved is Hacks Is.

1277
01:07:52,600 --> 01:07:56,560
Speaker 1: Season three just dropped and it's so great.

1278
01:07:56,800 --> 01:07:57,320
Speaker 2: Yeah.

1279
01:07:57,360 --> 01:08:01,680
Speaker 1: So it's a woman comedian in Vegas who is slightly

1280
01:08:01,800 --> 01:08:06,320
Speaker 1: past her sell by date and her pushback against the

1281
01:08:06,400 --> 01:08:09,760
Speaker 1: men that run the casinos and the writer who wants

1282
01:08:09,760 --> 01:08:13,000
Speaker 1: her to become younger and hipper in her material, kind

1283
01:08:13,000 --> 01:08:17,280
Speaker 1: of a tell all thing, and it's just really fascinating.

1284
01:08:16,920 --> 01:08:18,320
Speaker 4: Look at that.

1285
01:08:19,160 --> 01:08:23,960
Speaker 1: So season one and two were both great. It's not

1286
01:08:24,080 --> 01:08:27,360
Speaker 1: quite as cringey as Curb, but there are moments where

1287
01:08:27,360 --> 01:08:30,320
Speaker 1: you like, don't don't do that, don't do that. Oh

1288
01:08:30,360 --> 01:08:33,200
Speaker 1: you know, you just see it coming. It's just don't

1289
01:08:33,200 --> 01:08:35,639
Speaker 1: tweet that. That's just going to bite you in the behind.

1290
01:08:35,720 --> 01:08:39,360
Speaker 1: Don't don't, But you get sucked into it and you're

1291
01:08:39,400 --> 01:08:41,920
Speaker 1: rooting for the character. So that's a perfect example of

1292
01:08:42,600 --> 01:08:47,760
Speaker 1: fascinating characters who are flawed but likable like you want them.

1293
01:08:47,880 --> 01:08:50,240
Speaker 2: You want them to see that, right exactly.

1294
01:08:50,600 --> 01:08:54,120
Speaker 1: Maybe I'm too old school Hollywood, but I don't really

1295
01:08:54,120 --> 01:08:56,080
Speaker 1: want to watch people who I can but you.

1296
01:08:56,080 --> 01:09:01,800
Speaker 3: Hate I know, right, right exactly, you don't need to

1297
01:09:01,840 --> 01:09:03,240
Speaker 3: go home to people.

1298
01:09:02,960 --> 01:09:06,800
Speaker 1: That are turn That's right to someone say something that like,

1299
01:09:06,960 --> 01:09:10,920
Speaker 1: I think at the slap that guy you mentioned, Rich Bernstein,

1300
01:09:11,040 --> 01:09:14,599
Speaker 1: tell us about your mentors who helped guide your career.

1301
01:09:14,880 --> 01:09:18,200
Speaker 3: Oh, Rich, definitely, like just one of the key people

1302
01:09:18,240 --> 01:09:20,479
Speaker 3: that you know really made me who I am today.

1303
01:09:20,840 --> 01:09:23,160
Speaker 3: I mean, I have to say my mother is like,

1304
01:09:23,880 --> 01:09:26,839
Speaker 3: really who I imprinted on the software coder?

1305
01:09:27,240 --> 01:09:29,040
Speaker 2: My mom was a coder. Yep.

1306
01:09:29,160 --> 01:09:32,400
Speaker 3: She came here from India when she was just twenty

1307
01:09:32,479 --> 01:09:35,320
Speaker 3: years old. She had an arranged marriage they're now divorced,

1308
01:09:35,640 --> 01:09:37,800
Speaker 3: one of the worst arranged marriages of all time.

1309
01:09:38,400 --> 01:09:41,120
Speaker 2: But she was you know, she had a lot of guts.

1310
01:09:41,200 --> 01:09:44,559
Speaker 3: She wore a sorry to work every day, really, but

1311
01:09:44,720 --> 01:09:48,400
Speaker 3: somehow ascended the corporate ladder at Digital Equipment Corporation and

1312
01:09:48,439 --> 01:09:51,400
Speaker 3: became a manager. Even though people were like, you need

1313
01:09:51,439 --> 01:09:53,880
Speaker 3: to stop wearing the sari, she kept wearing it.

1314
01:09:53,960 --> 01:09:55,240
Speaker 2: She was true to herself.

1315
01:09:56,320 --> 01:09:58,439
Speaker 3: So I kind of look at her as a role

1316
01:09:58,479 --> 01:10:01,479
Speaker 3: model of how to just get starf I've done, you know,

1317
01:10:01,640 --> 01:10:05,240
Speaker 3: fade the haters and you know, do something good for

1318
01:10:05,320 --> 01:10:07,080
Speaker 3: the world, create some value.

1319
01:10:07,760 --> 01:10:11,559
Speaker 1: Really really interesting. Let's talk about books. I mentioned Adam

1320
01:10:11,600 --> 01:10:13,840
Speaker 1: Smith's Money Game. What are some of your favorites? What

1321
01:10:13,840 --> 01:10:15,880
Speaker 1: are you reading right now? Oh?

1322
01:10:15,960 --> 01:10:17,439
Speaker 2: Right now, I'm actually reading.

1323
01:10:17,520 --> 01:10:20,439
Speaker 3: Well, I'm rereading an Agatha Christie novel that I love.

1324
01:10:20,600 --> 01:10:23,400
Speaker 3: Which one the Murder on the Orient Express. Sure, I

1325
01:10:23,439 --> 01:10:24,760
Speaker 3: know I'm obsessed with.

1326
01:10:26,040 --> 01:10:29,280
Speaker 1: You know, there's been I think three or four movie

1327
01:10:29,320 --> 01:10:32,120
Speaker 1: film versions. I don't mean like subsequent.

1328
01:10:31,560 --> 01:10:34,720
Speaker 2: But they're all terrible. Have you seen them?

1329
01:10:34,960 --> 01:10:38,040
Speaker 1: So? Not like the early ones are kind of talky

1330
01:10:38,400 --> 01:10:43,559
Speaker 1: and slow, but they're kind of interesting character studies.

1331
01:10:43,160 --> 01:10:47,200
Speaker 5: And oh yeah yeah, it's they're truer to the book

1332
01:10:47,600 --> 01:10:49,920
Speaker 5: then you know, it's not supposed to be a James

1333
01:10:49,960 --> 01:10:53,000
Speaker 5: Bond novel, right, but some some of them try and

1334
01:10:53,040 --> 01:10:54,920
Speaker 5: turn them into almost actions.

1335
01:10:55,000 --> 01:10:58,840
Speaker 3: Yeah, yeah, yeah, yeah, yeah. My favorite book of all

1336
01:10:58,920 --> 01:11:02,040
Speaker 3: time is a book called Confederacy of Dunces.

1337
01:11:02,400 --> 01:11:04,920
Speaker 1: Sure did you read that long time ago?

1338
01:11:05,080 --> 01:11:06,080
Speaker 2: I love that book.

1339
01:11:06,120 --> 01:11:08,240
Speaker 1: I the author is, it's.

1340
01:11:08,200 --> 01:11:11,160
Speaker 2: John Kennedy to o'tool, and.

1341
01:11:11,479 --> 01:11:13,920
Speaker 1: Then I did not read it thinking of a different book.

1342
01:11:14,320 --> 01:11:16,439
Speaker 2: Okay, so I'll get you a copy. It's it's a

1343
01:11:16,439 --> 01:11:16,960
Speaker 2: good one.

1344
01:11:17,040 --> 01:11:22,480
Speaker 3: Hold on, I'm also reading this book by Peter Atia

1345
01:11:23,000 --> 01:11:26,880
Speaker 3: on how to live huh well, not necessarily long, but

1346
01:11:26,960 --> 01:11:31,840
Speaker 3: how to remain healthy and thriving. I mean, I find

1347
01:11:31,880 --> 01:11:37,400
Speaker 3: that health is becoming a bigger part of my serious,

1348
01:11:37,439 --> 01:11:41,280
Speaker 3: you know, concern set these days as I get older.

1349
01:11:41,360 --> 01:11:44,320
Speaker 3: I mean, I turned fifty a year ago, and I'm

1350
01:11:44,360 --> 01:11:46,639
Speaker 3: starting to think about, you know, I want to see

1351
01:11:46,640 --> 01:11:49,639
Speaker 3: my grandkids, So how do I keep this thing going

1352
01:11:49,720 --> 01:11:50,880
Speaker 3: and be happy and healthy.

1353
01:11:51,160 --> 01:11:54,640
Speaker 1: It's not just about longevity, but of quality of life.

1354
01:11:54,439 --> 01:11:56,720
Speaker 3: Exactly, and that's what that's what Peter a Tea is

1355
01:11:56,720 --> 01:11:59,160
Speaker 3: really focused on. So I thought that was an interesting one.

1356
01:12:00,560 --> 01:12:02,360
Speaker 3: But yeah, there's so many things to read. I don't

1357
01:12:02,400 --> 01:12:03,479
Speaker 3: read a lot of non.

1358
01:12:03,400 --> 01:12:07,400
Speaker 4: Fiction, especially oh really, I don't read a lot that.

1359
01:12:07,439 --> 01:12:09,320
Speaker 2: Has to do with financial markets.

1360
01:12:09,800 --> 01:12:12,880
Speaker 1: As I've gotten older, I find myself reading more and

1361
01:12:13,040 --> 01:12:14,120
Speaker 1: more nonfiction.

1362
01:12:14,479 --> 01:12:14,799
Speaker 2: Really.

1363
01:12:14,880 --> 01:12:17,400
Speaker 1: And when I was younger, you know, a big sci

1364
01:12:17,520 --> 01:12:18,000
Speaker 1: fi fan.

1365
01:12:18,400 --> 01:12:21,760
Speaker 2: Yeah, just like Dick. That was my favorite.

1366
01:12:22,439 --> 01:12:31,920
Speaker 1: So my people don't realize Minority Report, Blade Runner Total

1367
01:12:31,960 --> 01:12:35,559
Speaker 1: recall these are all and then the the I think

1368
01:12:35,600 --> 01:12:41,080
Speaker 1: it was the Amazon series, Uh, that takes place when

1369
01:12:43,040 --> 01:12:46,240
Speaker 1: it's a it's an alternative history where Japan and Germany

1370
01:12:46,280 --> 01:12:47,639
Speaker 1: when World War Two.

1371
01:12:48,680 --> 01:12:50,000
Speaker 2: It's Amazon series.

1372
01:12:50,040 --> 01:12:53,479
Speaker 1: That's an Amazon series based on a Philip K. Dick book,

1373
01:12:53,760 --> 01:12:59,120
Speaker 1: which of course escapes my my recollection. I read that

1374
01:12:59,200 --> 01:13:04,120
Speaker 1: One Man in the High Tower was the film Dick book, right,

1375
01:13:04,160 --> 01:13:07,840
Speaker 1: and that became an now Amazon series. I can't believe

1376
01:13:07,880 --> 01:13:09,679
Speaker 1: I pulled that that title out of that.

1377
01:13:09,680 --> 01:13:11,759
Speaker 2: That was really good. I kind of forgot.

1378
01:13:12,040 --> 01:13:14,160
Speaker 3: The nice thing about getting older is that you can

1379
01:13:14,200 --> 01:13:15,559
Speaker 3: reread and it's fresh.

1380
01:13:16,640 --> 01:13:22,000
Speaker 1: Right, Three Stigmata of Palmer Eldridge, you bick Like, I

1381
01:13:22,080 --> 01:13:27,960
Speaker 1: remember those books being super dense and super you know, heady, Yeah,

1382
01:13:28,120 --> 01:13:30,559
Speaker 1: and rereading them now it's like, oh, okay, I have

1383
01:13:30,600 --> 01:13:34,120
Speaker 1: a different context. Yeah. What sort of advice would you

1384
01:13:34,120 --> 01:13:37,320
Speaker 1: give a recent college grad interested in the career in

1385
01:13:37,400 --> 01:13:41,120
Speaker 1: either finance, quantitative analysis, or investing.

1386
01:13:43,120 --> 01:13:45,519
Speaker 3: Well, I mean the first piece of advice isn't specific

1387
01:13:45,520 --> 01:13:49,280
Speaker 3: to finance, but it's just, you know, don't be a jerk.

1388
01:13:50,560 --> 01:13:50,920
Speaker 1: Okay.

1389
01:13:51,800 --> 01:13:53,599
Speaker 3: I think there are so many people out there who

1390
01:13:53,640 --> 01:13:56,160
Speaker 3: are trying to prove that they know more than the

1391
01:13:56,160 --> 01:14:00,880
Speaker 3: next guy that you know, they stopped listening. They're just

1392
01:14:01,040 --> 01:14:03,160
Speaker 3: like you know, trying to seem smart. And I think

1393
01:14:03,200 --> 01:14:06,120
Speaker 3: that's your number one enemy in.

1394
01:14:06,520 --> 01:14:09,479
Speaker 1: What drives that? Is that a modern thing with social

1395
01:14:09,520 --> 01:14:11,880
Speaker 1: media or is that always throughout your career.

1396
01:14:12,120 --> 01:14:15,800
Speaker 3: I don't think an issue, just like insecure people that

1397
01:14:16,280 --> 01:14:20,120
Speaker 3: needs to prove themselves. And what I've found is, you know,

1398
01:14:20,240 --> 01:14:24,400
Speaker 3: if the way you treat people that are working for

1399
01:14:24,439 --> 01:14:28,000
Speaker 3: you says a lot about you. And the problem is

1400
01:14:28,240 --> 01:14:30,400
Speaker 3: if you're mean to the people that work for you,

1401
01:14:30,520 --> 01:14:33,639
Speaker 3: someday they might become your boss. So I think that's

1402
01:14:33,680 --> 01:14:35,400
Speaker 3: another piece of advice I would give.

1403
01:14:35,520 --> 01:14:37,679
Speaker 1: This has nothing to do with you being an intern

1404
01:14:38,640 --> 01:14:41,360
Speaker 1: at the Merril Kuan shop and eventually leading that job.

1405
01:14:41,560 --> 01:14:44,800
Speaker 3: No, no, no, I've not personally experienced that too many

1406
01:14:44,840 --> 01:14:47,920
Speaker 3: times in my life, but I've heard about it many times,

1407
01:14:47,960 --> 01:14:51,719
Speaker 3: and I think that's just bad practice when it comes

1408
01:14:51,800 --> 01:14:56,120
Speaker 3: to finance and investing. I think the idea of being

1409
01:14:56,240 --> 01:15:01,000
Speaker 3: flexible in thought, always checking your own by bases. I mean,

1410
01:15:01,439 --> 01:15:04,160
Speaker 3: this is where the philosophy comes in. So Friedrich Nietzsche'

1411
01:15:04,160 --> 01:15:07,880
Speaker 3: is this has this theory of constantly overcoming and that's

1412
01:15:07,880 --> 01:15:11,480
Speaker 3: the idea that you should always critically examine your assumptions

1413
01:15:11,520 --> 01:15:15,200
Speaker 3: and make sure that you're not making a mistake.

1414
01:15:15,520 --> 01:15:18,320
Speaker 4: Life is struggle, Yes, I mean life is struggle.

1415
01:15:18,360 --> 01:15:21,600
Speaker 3: That's also a Nietzschean great right, But I think the

1416
01:15:21,600 --> 01:15:24,840
Speaker 3: idea of just always kind of checking yourself and seeing

1417
01:15:24,840 --> 01:15:27,640
Speaker 3: whether you're assuming things that aren't necessarily true.

1418
01:15:28,120 --> 01:15:30,600
Speaker 1: And our final question, what do you know about the

1419
01:15:30,640 --> 01:15:33,920
Speaker 1: world of investing today? You wish you knew when you

1420
01:15:33,920 --> 01:15:36,840
Speaker 1: were getting started in the early two thousands.

1421
01:15:37,080 --> 01:15:40,360
Speaker 3: Look, I wish i'd started investing earlier. I was always

1422
01:15:40,400 --> 01:15:43,400
Speaker 3: too risk averse, and then once I started to get

1423
01:15:43,439 --> 01:15:48,160
Speaker 3: some kahones, I was, you know, ten years into my career,

1424
01:15:49,280 --> 01:15:51,240
Speaker 3: I wish I'd just socked away more money.

1425
01:15:51,280 --> 01:15:52,639
Speaker 2: And you know, kind of.

1426
01:15:52,840 --> 01:15:56,360
Speaker 3: The riskiest, most volatile asset classes, because that's where when

1427
01:15:56,400 --> 01:15:59,320
Speaker 3: you're young, you can really take a punt.

1428
01:16:00,120 --> 01:16:02,920
Speaker 1: For the risk and if you have a setback, so what, yeah,

1429
01:16:03,040 --> 01:16:03,559
Speaker 1: overcome it.

1430
01:16:03,800 --> 01:16:09,439
Speaker 3: There's time, and volatility get gets easier with time. I

1431
01:16:09,479 --> 01:16:13,120
Speaker 3: think the other kind of metric that I wish I'd

1432
01:16:13,120 --> 01:16:16,479
Speaker 3: known about is and this is specific to the S

1433
01:16:16,520 --> 01:16:20,080
Speaker 3: and P five hundred, but the interesting thing is, if

1434
01:16:20,080 --> 01:16:22,200
Speaker 3: you own the SMP for.

1435
01:16:22,160 --> 01:16:25,080
Speaker 2: A day, you have about a fifty to fifty chance

1436
01:16:25,120 --> 01:16:26,680
Speaker 2: of making money or losing.

1437
01:16:26,400 --> 01:16:29,639
Speaker 4: Money, but meaning the next day, the next day.

1438
01:16:29,520 --> 01:16:33,479
Speaker 3: So you know, your probability of making money by buying

1439
01:16:33,520 --> 01:16:35,479
Speaker 3: and selling the S and P over a one day

1440
01:16:35,479 --> 01:16:37,840
Speaker 3: period is about a coin flip, a little bit better

1441
01:16:37,840 --> 01:16:40,160
Speaker 3: than a coin flip. But if you have a buy and.

1442
01:16:40,160 --> 01:16:44,000
Speaker 2: Hold over a ten year period, your probability.

1443
01:16:43,280 --> 01:16:48,160
Speaker 3: Of losing money is demnimous. It's like less than five percent.

1444
01:16:48,880 --> 01:16:51,759
Speaker 3: So that's the idea of just extending your holding period,

1445
01:16:52,280 --> 01:16:54,519
Speaker 3: set it, and forget it. I think those are some

1446
01:16:54,600 --> 01:16:58,280
Speaker 3: of the tricks that I try to impress upon individual investors.

1447
01:16:58,360 --> 01:17:00,240
Speaker 3: Is you know, the day that you want to see well,

1448
01:17:00,520 --> 01:17:03,280
Speaker 3: because the market just went down a lot, is probably

1449
01:17:03,320 --> 01:17:06,760
Speaker 3: the worst day to sell. Because the best days for

1450
01:17:06,840 --> 01:17:09,760
Speaker 3: the S and P typically follow the worst day.

1451
01:17:09,680 --> 01:17:11,600
Speaker 1: They cluster together, huh.

1452
01:17:11,360 --> 01:17:14,080
Speaker 2: So it's just, you know, get rid of emotion when

1453
01:17:14,080 --> 01:17:15,040
Speaker 2: it comes to investing.

1454
01:17:15,520 --> 01:17:18,360
Speaker 1: Savita, thank you for being so generous with your time.

1455
01:17:18,400 --> 01:17:23,760
Speaker 1: This was really fascinating. We have been speaking with Savita Subhimanian.

1456
01:17:24,080 --> 01:17:27,680
Speaker 1: She is the head of US Equity and Quantitative Strategy

1457
01:17:28,040 --> 01:17:31,560
Speaker 1: for a Bank of America. If you enjoy this conversation.

1458
01:17:31,800 --> 01:17:34,960
Speaker 1: Check out any of the five hundred we've had over

1459
01:17:35,000 --> 01:17:40,280
Speaker 1: the past ten years. You can find those at iTunes, Spotify, YouTube,

1460
01:17:40,680 --> 01:17:45,760
Speaker 1: wherever you find your favorite podcast. Speaking of podcasts, check

1461
01:17:45,760 --> 01:17:49,760
Speaker 1: out my new podcast At the Money, short conversations with

1462
01:17:49,920 --> 01:17:53,840
Speaker 1: experts about your money, earning it, spending it, and most

1463
01:17:53,880 --> 01:17:57,439
Speaker 1: of all, investing it. Find that wherever you find your

1464
01:17:57,479 --> 01:18:00,840
Speaker 1: favorite podcasts, or here in the Master and Business feed.

1465
01:18:01,479 --> 01:18:03,240
Speaker 1: I would be remiss if I did not thank the

1466
01:18:03,240 --> 01:18:08,120
Speaker 1: crackstaff that helps put these conversations together each week. Sarah

1467
01:18:08,160 --> 01:18:12,679
Speaker 1: Livesey is my audio engineer. Attiko Vaalbroun is my project manager.

1468
01:18:13,200 --> 01:18:17,000
Speaker 1: Anna Luke is my producer. Sage Bauman is the head

1469
01:18:17,000 --> 01:18:21,679
Speaker 1: of podcasts here at Bloomberg. Sean Russo is my head

1470
01:18:21,680 --> 01:18:26,639
Speaker 1: of research. I'm Barry Britons. You've been listening to Masters

1471
01:18:26,720 --> 01:18:38,400
Speaker 1: in Business on Bloomberg Radio.