Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.
Speaker 1: On this week's podcast, what Can I Say? Savita Subremanian
Speaker 1: formerly of Merrill Lynch, they got bought by Bank America.
Speaker 1: She's been with them for twenty three years. Her current
Speaker 1: title is Head of Equity and Quantitative Strategies. Savita is
Speaker 1: one of these women in the world of finance who
Speaker 1: is a powerhouse. Her quant work is wildly respected on
Speaker 1: the street. She's a regular on the Institutional investor All Star.
Speaker 1: I think, like the past eleven years. She manages literally
Speaker 1: hundreds of models and helps create just an endless amount
Speaker 1: of research and content. Her work is super high quality
Speaker 1: and is relied on by a lot of institutional as
Speaker 1: well as main street investors. I found the conversation really fascinating.
Speaker 1: She is one of the few people who combine quantitative
Speaker 1: investing with behavioral finance, not a common one two punch,
Speaker 1: and she's fantastic at it. I found the conversation to
Speaker 1: be absolutely intriguing, in a whole lot of fun, and
Speaker 1: I think you will also with no further ado, my
Speaker 1: discussion with Bank of America's Savita Supermanian. Thank you so much, Supermanian.
Speaker 1: I think I'm getting your name, Supramani.
Speaker 2: I've heard all sorts of things.
Speaker 1: I try not to butcher people's names. But let's talk
Speaker 1: a little bit about your background. So BA in mathematics
Speaker 1: and philosophy from Berkeley, an NBA from Columbia. I'm kind
Speaker 1: of intrigued by the idea of philosophy and math. What
Speaker 1: was the career plan?
Speaker 3: Yeah, well there was no career plan really, so at
Speaker 3: Berkeley I ended up changing my major a few times.
Speaker 2: From what well, I started out as.
Speaker 3: An electrical engineering computer science major, uh huh. And then
Speaker 3: I realized that there are basically no girls in any
Speaker 3: of those classes.
Speaker 1: Well back then, maybe not more today now, Yeah, which.
Speaker 2: Is a huge relief.
Speaker 3: But I also realized that I love to write, I
Speaker 3: love to read, and I kind of wanted to have
Speaker 3: some sort of a liberal arts aspect in my career.
Speaker 3: I took a class called existentialism in film and Literature.
Speaker 3: It's like one of these Berkeley classes, right, you know,
Speaker 3: this like completely pointless once you graduate, But it was.
Speaker 1: It's pointless one year I took an existential class in college. Yeah,
Speaker 1: I got a great mark on the midterm and the
Speaker 1: final was a paper which I never handed in. The
Speaker 1: professor asked me why, and I said, what does it matter?
Speaker 1: And he's like, you know, I feel compelled to give
Speaker 1: you a great Yeah. I wish that was a joke,
Speaker 1: but it's actually it's actually true.
Speaker 2: So I wasn't that smart. I did all the work.
Speaker 1: I read a quote from you way back when you
Speaker 1: said your parents were pushing you to be either an
Speaker 1: engineer or a doctor. Is this true? I mean, it's
Speaker 1: such a cliche Indian parents, Jewish parents go to school
Speaker 1: become a doctor.
Speaker 2: Well, I mean there's a reason it's a cliche. It's
Speaker 2: pretty much the norm.
Speaker 3: I mean, it happened to like me and everybody I
Speaker 3: know who's a you know, child of an immigrant from India,
Speaker 3: So it's kind of I mean, I think it was.
Speaker 3: You know, it was the seventies. It was unclear how
Speaker 3: anybody was going to make their living.
Speaker 2: My parents were.
Speaker 3: Both in high tech. My dad was an engineer, my
Speaker 3: mom was a software person. So really, yeah, they were
Speaker 3: both steeped.
Speaker 2: In technology in Silicon Valley.
Speaker 3: In Silicon Valley, they were you know, early early days
Speaker 3: in Mountain View, before it was you know, googleized routed. Yeah, exactly,
Speaker 3: before there was traffic, but it was it was I
Speaker 3: think that my parents, you know, they came here for
Speaker 3: to have a better life, to make some money, you know,
Speaker 3: not you know, to to basically live the American dream.
Speaker 3: And I think that the only legitimate careers were really
Speaker 3: in the sciences or you know, kind of practical applications.
Speaker 3: Today they've completely accepted me for who I am, as
Speaker 3: the dark you know, dark art of finance person.
Speaker 1: But but back the.
Speaker 2: Exactly.
Speaker 1: See for Jewish parents, if you go to law school,
Speaker 1: they'll put up with them.
Speaker 2: It's like the old school is just barely accepted.
Speaker 1: Right, It's all right, well, well will allow it. It's
Speaker 1: three years, will allow. But really, medical school is our
Speaker 1: first choice exactly.
Speaker 2: Yes, you know the drill.
Speaker 3: So I was a rebel and and I mean the
Speaker 3: reason I did mathematics and philosophy was that I have
Speaker 3: a very short attention span. So I found myself getting
Speaker 3: kind of bored with my mouth problem sets, and then
Speaker 3: I could shift to philosophy and then go back and forth.
Speaker 2: So it was actually pretty ideal for me.
Speaker 1: So how do you end up at Scudder Kemper in
Speaker 1: both New York and San Francisco In the nineteen nineties.
Speaker 1: At that point, I know you will talk about your
Speaker 1: internship a little later, but at that point, are you like,
Speaker 1: I think this is the career I want to be in.
Speaker 3: No, I had no idea when I graduated what I
Speaker 3: wanted to do. In fact, I was convinced that I
Speaker 3: wanted to be a professor in philosophy. And I took
Speaker 3: the gre and all those tests, and I applied, and
Speaker 3: I was going to get a PhD in philosophy, and
Speaker 3: I did all the work. But I realized I had
Speaker 3: to support myself while I was waiting to hear back.
Speaker 2: So I got a job in finance. I moved to
Speaker 2: New York because I'd always wanted.
Speaker 3: To be in New York. New York was my destination.
Speaker 3: And I got a job at Scudder doing something really random.
Speaker 2: I think it was.
Speaker 3: I think I was working as a technical writer on
Speaker 3: their software application. But I was just kind of bouncing
Speaker 3: around and looking for, you know, place where I could
Speaker 3: earn a study living and bide my time before I
Speaker 3: went to grad school. And then I started to realize
Speaker 3: that philosophers of professors of philosophy end up having to
Speaker 3: live in really random places in the country, and.
Speaker 1: Wherever they get a job.
Speaker 3: Wherever they get a job there, you know, they don't
Speaker 3: make a lot of cash.
Speaker 2: And meanwhile I.
Speaker 3: Was doing you know, I was working at this financial
Speaker 3: services company, and I was really interested in what they
Speaker 3: were doing. It was kind of like philosophy meets mathematics,
Speaker 3: because finance, to me is sort of a fuzzy science
Speaker 3: with no answers.
Speaker 2: Very logical.
Speaker 3: So it's got this math angle where you know, it's
Speaker 3: all numbers, but then there's this behavioral angle and psychological
Speaker 3: angle where you know, it's kind of a fun problem
Speaker 3: to tackle. So I realized I could make a lot
Speaker 3: more money working in finance and being a philosophy professor,
Speaker 3: and I basically kind of stayed the course.
Speaker 1: Today's episode of Barry confirming his priors is brought to
Speaker 1: you by so that very much is you know, one
Speaker 1: of the reasons I was looking forward to this conversation
Speaker 1: is how much everything you write is just right in
Speaker 1: my sweet spot. You can pull that out. But let's
Speaker 1: I want to talk about the internship. So let's let's
Speaker 1: let's go over there. So I mentioned you were an
Speaker 1: intern in college, and this is kind of fascinating. You
Speaker 1: interned for a Merrill Lynch quant team, which fast forward
Speaker 1: twenty plus years later, that's now the team that you
Speaker 1: lead at Bank America Merrill Lynch now known as b
Speaker 1: of A Right exactly.
Speaker 3: So that was actually my internship during business school. So
Speaker 3: after working at Scudder, I realized I didn't really have
Speaker 3: the foundations for financials. I didn't understand, you know, kind
Speaker 3: of how to parson income statement. And so I went
Speaker 3: to business school. I decided to go to business school
Speaker 3: and get that formal education. And then in the year
Speaker 3: the year in between your one and two of business school,
Speaker 3: I did my internship with Merrill Lynch with a gentleman
Speaker 3: named Rich Bernstein.
Speaker 2: And yes, you know him.
Speaker 1: I know Rich.
Speaker 3: And that was the beginning of, you know, a wonderful career.
Speaker 3: But it's it's sort of strange. I don't know whether
Speaker 3: to feel proud or depressed about this. But I am
Speaker 3: the only person I know from business school. I graduated
Speaker 3: Columbia two thousand and two, and I'm the only person
Speaker 3: I know who stayed in the same.
Speaker 2: Job for the last twenty three years.
Speaker 1: So you shouldn't be depressed about that. You should think
Speaker 1: about you should be grateful for Oh, I found what
Speaker 1: I wanted to do right out of school. It's true,
Speaker 1: and I've been honing that craft for twenty three years.
Speaker 4: That that is, that's half full.
Speaker 1: A lot of people, especially in finance, kind of flit
Speaker 1: from flower to flower until they find the right nectar. Yes,
Speaker 1: that works for them, and it's kinda Look, it's not
Speaker 1: just me. I've seen a bunch of people. They start
Speaker 1: out as brokers, they eventually get a CFP and they
Speaker 1: go to the advisory side, or people start out with
Speaker 1: the CFA and they decide, you know, I would rather
Speaker 1: manage the portfolio than tell I'd rather be a PM
Speaker 1: than advise the PM. And so people kind of have to,
Speaker 1: you know, that journey. You were fortunate that so not
Speaker 1: only did Scudder lead you to business school, but business
Speaker 1: school led you to the job that you've had for
Speaker 1: the rest of your life.
Speaker 3: Rich to quant strategy, now equity. It's just been a
Speaker 3: dream come true.
Speaker 1: So you had mentioned the behavioral side of finance. Not
Speaker 1: a lot of quants marry behavioral finance to the mathematical side.
Speaker 1: Tell us how this sort of mixture, which I love,
Speaker 1: it works so well for me. I started on a
Speaker 1: trading desk. I kind of stop into behavioral finance in
Speaker 1: the mid nineties, right before all the cool kids were
Speaker 1: doing it, and it suddenly like, oh, all of this
Speaker 1: stuff that seems sort of random, Now at least there's
Speaker 1: an explanation for the randomness, and it kind of makes
Speaker 1: sense why people do the things they do. We're you know,
Speaker 1: we're just not wired for.
Speaker 2: This, right, right, right? Right?
Speaker 1: No.
Speaker 2: I think that that's.
Speaker 3: The part of it that I find the most interesting
Speaker 3: is the idea that you know, a stock price doesn't
Speaker 3: really have a you know, the fair value of an
Speaker 3: investment instrument is somewhat arbitrary, right, And then it's you know,
Speaker 3: it's supplied demand, it's perception.
Speaker 2: Perception is reality for many of these companies. So, I mean,
Speaker 2: I think the.
Speaker 3: Day that I realized that behavioral finance deserves a very
Speaker 3: prominent place in the arsenal of models.
Speaker 2: That we all use was when I got it.
Speaker 3: I got the job as equity strategists, and I realized
Speaker 3: that probably the most important number that I publish is
Speaker 3: our year end target. It's kind of a silly number,
Speaker 3: but people are going to think you're smart or dumb
Speaker 3: based on that number. And so I said, Okay, let's
Speaker 3: use all these quant models that I've been building for
Speaker 3: the last ten plus years, and after testing all of them,
Speaker 3: it turned out that there was one model that was
Speaker 3: better than everything else predicting the next twelve months of
Speaker 3: S and P returns, and that was a behavioral model.
Speaker 1: Really, how do you measure behavior in a quantitative model
Speaker 1: for equities?
Speaker 2: It's a very cool model.
Speaker 3: And I actually was lucky enough to inherit it from
Speaker 3: my former boss, Rich, who I think inherited it from
Speaker 3: his former boss. So it's been around at Merrill for
Speaker 3: you know, since the eighties.
Speaker 1: Who was who was Rich's former boss?
Speaker 2: I can't remember. We'll have to get him on and
Speaker 2: ask him.
Speaker 1: I've had him on. Yeah, I'm sure he's told me,
Speaker 1: but he may.
Speaker 3: Yeah, we'll we'll look it up in the annals. But
Speaker 3: you know, it's been around for it predates Rich Bernstein's
Speaker 3: so so basically this model is just a simple straight
Speaker 3: average of all the Wall Street strategists recommended allocations to
Speaker 3: stocks in a balanced portfolio. So if you go to
Speaker 3: your broker and he or she tells you you should
Speaker 3: put you know, sixty percent in stocks, or you should
Speaker 3: put forty percent in stocks. We take all those numbers
Speaker 3: from the different houses and we average them together. We've
Speaker 3: been doing this every month since you nineteen eighty, and
Speaker 3: it turns out to be the best contrary indicator.
Speaker 1: Oh really, I thought you were going to go with, Oh,
Speaker 1: it's a very wisdom of crowds and whatever it averages
Speaker 1: out run.
Speaker 2: The opera to the opposite.
Speaker 1: Yeah, no kidding.
Speaker 3: That was the punchline of indicator, and I thought that
Speaker 3: was so fascinating. But then when you peel back the onion,
Speaker 3: you realize there's a reason for it. It's because you know,
Speaker 3: when everybody's looking at all this data and it all
Speaker 3: seems terrible, chances are that information's priced into the market
Speaker 3: and it's going to surprise in the opposite direction.
Speaker 1: I want to say to go back to Rich Bernstein's boss.
Speaker 1: Was it Bob Farrell or was Bob Farrell two bosses before? Gosh,
Speaker 1: I don't kind of remember him as late eighties, early nineties.
Speaker 3: Yeah, Bob Ferrell was I never seventies eighty like way
Speaker 3: before my time?
Speaker 1: You ever have?
Speaker 2: Oh yeah, yeah, yeah.
Speaker 1: I met him at a market technicians association all night.
Speaker 1: Then I interviewed him for one of their events. But
Speaker 1: Bob Farrell's ten investing rules. Yes, that was gospel, and
Speaker 1: to this day is still like, you're hard pressedifying another
Speaker 1: ten rules that are as insightful and astute and still relevant.
Speaker 2: Completely.
Speaker 1: It's he always been spectacular.
Speaker 3: Yeah, he was onto something and he probably he created this,
Speaker 3: this framework. I don't recall, but I mean I still
Speaker 3: have financial advisor sending me these Bob Ferrell quotes.
Speaker 2: And like, bring it, this is great. He was. He
Speaker 2: was a legend, right.
Speaker 1: I want to say that might have been one of
Speaker 1: his quotes. I could quickly find it, which was something like,
Speaker 1: if everybody's talking about it, it's already reflected in the price. No,
Speaker 1: there's no surprise there exactly when all the experts and
Speaker 1: forecasts agree, something else is going to happen. That's right,
Speaker 1: rule number nine from Bob. So you're you're definitely channeling
Speaker 1: a little Farrell. Ye. So, given this, how do you
Speaker 1: draw a price target or a market forecast from here's
Speaker 1: the average of all the Wall Street strategists. Let's say
Speaker 1: it's plus eight percent. Yeah, what do you do with that?
Speaker 1: On average? Well, you about plus eight nine percent on
Speaker 1: the S and B we Yeah.
Speaker 2: So here's the thing.
Speaker 3: I mean, if you think about just how much this
Speaker 3: number changes over time. So it's been you know, back
Speaker 3: in two thousand and one, strategists were telling you to
Speaker 3: put about seventy percent of your money in stocks. But
Speaker 3: then you know, just in I think it was twenty twelve,
Speaker 3: coming out of the financial crisis, you know, after after
Speaker 3: one round of QI, Europe was in a you know,
Speaker 3: a recession.
Speaker 4: Everybody was Brexit, Grexit, it was all happen.
Speaker 3: Everything was all happening. The US just got downgraded. And
Speaker 3: that was when that indicator plummeted to forty three percent.
Speaker 2: Wow, which was exactly the right time you wanted to
Speaker 2: buy equities. I remember printed money.
Speaker 1: Since twenty ten, eleven, twenty twelve, there was so much
Speaker 1: skepticism yeah about equity markets. And my pushback to people
Speaker 1: was always, show me another time when down fifty seven
Speaker 1: percent wasn't a spectacular entry right into US equities, right?
Speaker 1: And the answer is always twenty nine and thirty two? Okay,
Speaker 1: is this like thirty two? Is this remotely like twenty nine? Right?
Speaker 1: I mean you already had the dot com implosion. If
Speaker 1: you want to say that down eighty one percent was
Speaker 1: your twenty nine, fine, But that was you know, seven
Speaker 1: eight years ago and here we are down fifty seve.
Speaker 2: Here we are again.
Speaker 3: I know, I know, it was an interesting time, and
Speaker 3: that's right when I got the job as strategists. So
Speaker 3: it was really interesting because I was looking at this model,
Speaker 3: which was my holy grail.
Speaker 2: Right out of everything.
Speaker 3: We'd back tested, this had the best predictive power over
Speaker 3: the next twelve months highest R squared and it was
Speaker 3: telling us to back up the truck on equities. It
Speaker 3: was as low as it had ever been since the
Speaker 3: nineteen eighties.
Speaker 1: Wow.
Speaker 3: And I remember, you know, thinking, oh my gosh, is
Speaker 3: this a data error, and I like, triple quadruple check
Speaker 3: the data. But it was, you know, really a prescient
Speaker 3: signal that a lot of bad news was really priced
Speaker 3: into the market and it was more likely to move higher.
Speaker 3: And you know, since then, it hasn't dropped to forty
Speaker 3: three percent, but it's been pretty low. I mean, I
Speaker 3: think we've been in this market environment since the GFC
Speaker 3: where global financial crisis, where folks have just been worried,
Speaker 3: and the most recent event that we anchor our memories
Speaker 3: to is this horrible credit crisis that derailed the banking sector,
Speaker 3: that crushed the consumer, and now we're just assuming that's
Speaker 3: going to repeat over and over again.
Speaker 1: That's the post GFC PTSD.
Speaker 4: Exactly.
Speaker 1: What was your experience during the first quarter of twenty
Speaker 1: twenty during the pandemic, SMP down thirty four percent. Yeah,
Speaker 1: neatly within the quarter. I noticed some people kind of panic,
Speaker 1: then here comes and other people like, no, Dow, I'm
Speaker 1: thirty four percent. I'm a buyer.
Speaker 2: But yeah, I think that it was.
Speaker 3: It was one of those moments where I think I
Speaker 3: went on TV at some point and they said, you know,
Speaker 3: do you buy here or is.
Speaker 2: There more to go?
Speaker 1: And I yes, and yes.
Speaker 2: I said, you buy here. You pick your stocks, but
Speaker 2: you buy here.
Speaker 3: There are gonna be a lot of really high quality
Speaker 3: companies that have been crushed by fear and loathing and
Speaker 3: you know, just heading for the hills. And this is
Speaker 3: an opportunity that we're probably going to look back on
Speaker 3: and want to buy what I wish we'd bought these companies.
Speaker 5: You know.
Speaker 1: Unfortunately, sometimes people in media or elsewhere they talk about
Speaker 1: catching the bottom and rather than being the bottom tick,
Speaker 1: you could look at that big sweeping parabola and say,
Speaker 1: I don't need to be at the bottom. I just
Speaker 1: want to buy as we're getting close and buy as
Speaker 1: we're moving away from it, and so that two years
Speaker 1: from now, my average cost is just far below where
Speaker 1: the markets so exactly, you don't have to nail the bottom.
Speaker 2: No, and you never will nail the bottom.
Speaker 1: Yeah, someone is going to get lucky, someone's going to
Speaker 1: get that bottom tick. But ninety nine percent of people
Speaker 1: or not right right, So rather than try and pick
Speaker 1: that yeah, hey, down x percent, at down twenty five percent,
Speaker 1: I'm a buyer at down thirty percent, I'm a buyer
Speaker 1: and I don't have enough dry powder that I can
Speaker 1: keep buying down forty percent, down fifty percent completely at
Speaker 1: a certain point when everybody's terrified. It's a spectacular it.
Speaker 3: Is, it's a spectacular buying opportunity. I mean, there's one
Speaker 3: thing that I've looked at that seems to be a
Speaker 3: good leading indicator of, you know, when you want to
Speaker 3: start stepping in, which is I mean momentum.
Speaker 1: Right.
Speaker 2: There's a reason that there are.
Speaker 3: So many momentum investors because the market usually figures out
Speaker 3: whether things are kind of getting worse or getting better.
Speaker 3: And one of the models that we've used to determine
Speaker 3: whether something is actually cheap and attractive or cheap, and
Speaker 3: a falling knife is a falling knife is looking at
Speaker 3: earnings revisions coupled with price momentum.
Speaker 2: And what we've found is that.
Speaker 3: When stocks are going lower but analysts haven't taken down
Speaker 3: their earnings, so it looks cheap, but it's only because
Speaker 3: the cell side is late to react, that's when you
Speaker 3: don't want to buy it.
Speaker 1: Uh huh, you want to. So if there's downside momentum
Speaker 1: and you've had a whole bunch of hey, we're changing
Speaker 1: our earnings estimate, we're changing our price targets, right, that
Speaker 1: means it should be mostly priced.
Speaker 3: Then exactly, So you want to buy a falling You
Speaker 3: want to buy a value stock when its price decline
Speaker 3: is starting to slow down, but estimate revisions are still
Speaker 3: deeply negative. So you're in this environment where everybody hates
Speaker 3: risk and they're downgrading, downgrading, downgrading, but the market's telling you, Okay,
Speaker 3: things are actually not as bad.
Speaker 1: Huh. Really interesting. So let's talk a little bit about
Speaker 1: a day in the life of a big bank's chief
Speaker 1: quant tell us, how do you spend your time, what
Speaker 1: are you doing during the day, and what do you, you know,
Speaker 1: what keeps you curious, what keeps you wondering about what
Speaker 1: comes next?
Speaker 2: Yeah, so my day.
Speaker 3: Is never the same, and I'm sure it's like this
Speaker 3: for you. I mean, most people have kind of things
Speaker 3: thrown out them that are, you know, out of the ordinary.
Speaker 2: And I can't say that, you know.
Speaker 3: I walk into the office and I sit down at
Speaker 3: my desk, and I start chugging away at the computer,
Speaker 3: even though that's what I secretly want to do.
Speaker 1: That's what work from home is for. Yes, stay at home,
Speaker 1: keep your face in the computer. You're good. Once you
Speaker 1: get into the office.
Speaker 2: It's that's game over.
Speaker 3: But no, but I think that where I get my
Speaker 3: best ideas is from talking to super smart people like you,
Speaker 3: like our financial advisors, like our hedge fund clients, our
Speaker 3: long only investor clients, pensions. So everyone out there who's
Speaker 3: been a professional investor for a while has some edge
Speaker 3: that is, you know, otherwise they would have been fired
Speaker 3: or left the industry. But I've found that people's edges
Speaker 3: are different from one another. So I feel like every
Speaker 3: time I talk to somebody new, there's an angle.
Speaker 2: That I haven't thought about. And then what I like
Speaker 2: to do is try to recreate.
Speaker 3: That framework in a model, a replicable model, and then
Speaker 3: test it to see whether it's something worth throwing into.
Speaker 2: The mix or not.
Speaker 3: And you know a lot of my work is just
Speaker 3: looking at does does this you know this this indicator
Speaker 3: like a PE ratio? Right, we all talk about pe
Speaker 3: ratiows and how you want to be you want to
Speaker 3: bilo pe stocks and you know, sell expensive stocks. But
Speaker 3: turns out the PE race shows sometimes predict performance and
Speaker 3: sometimes they don't.
Speaker 1: You can it's kind of worthless if you can tell
Speaker 1: is this is this a good moments where I MPE?
Speaker 1: Or is this a bad moment? Yes?
Speaker 2: Is this a good value stock or is it a
Speaker 2: value trapped?
Speaker 3: So those are some of the things that we test
Speaker 3: and then you know, from talking to clients, we get
Speaker 3: ideas around should you have a regime indicator? Should you
Speaker 3: think about what regime the market is in to train
Speaker 3: your framework on what types of attributes to look for?
Speaker 3: What attributes right now are scarce versus abundant? And where
Speaker 3: will investors pay up for a scarcity in the current environment.
Speaker 3: So you know, a lot of these are really born
Speaker 3: from behavioral finance and thinking about how people you know,
Speaker 3: look for opportunities, whether they're going to be a bargain
Speaker 3: hunter or whether they're going to be risk averse and
Speaker 3: look for unassailable growth. But it's interesting because I think
Speaker 3: that my best ideas to this day have come from
Speaker 3: talking to our really smart clients out there on the field.
Speaker 1: So you guys run literally dozens of quant models. I
Speaker 1: get your research. I get a handful of research specific
Speaker 1: people at I still think of as Merrill Lynch, but
Speaker 1: me too, but I notice. So we'll talk about the
Speaker 1: content you guys put out, which is enormous, and we'll
Speaker 1: talk about the models. Let's start with the model since
Speaker 1: you mentioned it. So you talked about the consensus of
Speaker 1: strategists and how that is often I'm assuming not always,
Speaker 1: but frequently a contrary indicator.
Speaker 3: Yes, it's often, I mean really, it works the best
Speaker 3: at extremes, so if you're in some kind of neutral territory,
Speaker 3: it's not as informative.
Speaker 1: But if true for all sentiment measures.
Speaker 3: For any sentiment measure, exactly, So there are times when
Speaker 3: you really really really want to pay attention to it,
Speaker 3: and then there are other times where it gives you
Speaker 3: a little bit more of a muddled signal.
Speaker 1: So that one stands out as prescient. What what else
Speaker 1: do you think adds a whole lot value and helps
Speaker 1: you navigate what's going on?
Speaker 3: So I think when when you look at I mean
Speaker 3: one of the things that we've started looking at is
Speaker 3: just like kind of non financial data, so you know,
Speaker 3: not fundamental data like w And.
Speaker 1: You're making a face as you say that, so I
Speaker 1: could tell you're like, you're like, is the jury still
Speaker 1: out on that? Or how are you playing with non
Speaker 1: financial data?
Speaker 2: Look, I think that some of it is really useful.
Speaker 2: A lot of it is just garbage.
Speaker 1: When you say garbage, is it is it not accurately
Speaker 1: depicting that subsector of the world, or is it just
Speaker 1: a noisy series with not a lot of signal in it.
Speaker 3: I mean a lot of it is just noise or
Speaker 3: or corporate management trying to gain the system. I'll give
Speaker 3: you an example, So let's talk about earning surprise.
Speaker 1: Right.
Speaker 3: Earning surprise is something that should work. Right if a
Speaker 3: company beats everybody's expectations on earnings, it should drive monstrous performance,
Speaker 3: especially if.
Speaker 2: It's a big beat.
Speaker 3: But what we've all realized over the last you know,
Speaker 3: twenty years since reg FD in two thousand and one
Speaker 3: is that management gains their numbers and then they beat
Speaker 3: these made up numbers systematically, and that surprise factor no
Speaker 3: longer seems to be as effective as before we had
Speaker 3: this sort of massaging of consensus SYSTEMA.
Speaker 1: The day before we recorded this, you put out a
Speaker 1: research report strong quarter earnings per share up six percent
Speaker 1: year of a year with better guidance. And here's the
Speaker 1: really amazing part. With eighty three percent of the S
Speaker 1: and P five hundred reporting earnings, sales are roughly in line,
Speaker 1: and the stats were seventy two percent of these companies
Speaker 1: being on earnings. So if three quarters are beating on earnings,
Speaker 1: what's the.
Speaker 2: Value cares exactly?
Speaker 3: Maybe we pay attention to missus because those guys really
Speaker 3: screwed up and couldn't beat their made up numbers. So,
Speaker 3: you know, I think that there are different factors that
Speaker 3: tend to you know, at some point work and then
Speaker 3: everybody figures out that they work, and then they start
Speaker 3: getting gamed. I mean, quants have basically made markets that
Speaker 3: much more efficient by or maybe inefficient.
Speaker 2: I'm not sure what the right way to do well.
Speaker 1: I think I agree with you. I think quants have
Speaker 1: made generally speaking, big money relying on data that's consistent. Yeah,
Speaker 1: you know what starts to happen is the inefficiencies get
Speaker 1: arbitraged out.
Speaker 2: Right, and the inefficiencies go away.
Speaker 1: So some people have blamed quants on why value has underperformed,
Speaker 1: why small caps aren't doing what the small cap factor
Speaker 1: is supposed to be.
Speaker 2: I don't buy into that.
Speaker 1: I'm right. I think the jury is still out on
Speaker 1: that accusation.
Speaker 2: Yeah.
Speaker 1: That said, there are a lot of models out there
Speaker 1: that aren't particularly great. Let me ask you what quant
Speaker 1: models do people seem to really be enamored with that
Speaker 1: you think aren't really worth it. You mentioned pe and
Speaker 1: fair value. Those aren't particularly useful to them.
Speaker 3: Snapshot multiples are not useful, right, I think price to
Speaker 3: normalized earnings is useful. But you know, the other data
Speaker 3: set that I just wonder about is flows.
Speaker 1: Because they're always on such a giant lag, like they
Speaker 1: were outflows throughout twenty three from mutual funds. Right, And
Speaker 1: if you're saying, well, I guess if you're going the
Speaker 1: other way, if you're saying it's a sentiment indicator. But
Speaker 1: that's not how people talk. People talk about, Oh, we
Speaker 1: have all these giant inflows into markets, right O.
Speaker 3: Way cares that was yesterday, right, I mean, why does
Speaker 3: that tell us anything about the future.
Speaker 1: You got me, give me another model you think is
Speaker 1: overrated that people rely on.
Speaker 3: So I think another model that's overrated is just pure momentum,
Speaker 3: because I think momentum works when it's yes, exactly, so
Speaker 3: it's when it works well when it's accompanied by a
Speaker 3: fundamental reason. But the idea that you can predict price
Speaker 3: using price, to me, just seems to flaunt some kind
Speaker 3: of basic financial understanding.
Speaker 1: Isn't that the entire undergirding of trend following.
Speaker 2: Yeah, so trend following.
Speaker 3: I mean I worry because I think we've been in
Speaker 3: a market where trend following has worked remarkably well for
Speaker 3: at least you.
Speaker 1: Know, a decade, certainly for commodities and for currencies, yeah, exactly,
Speaker 1: maybe less so for equities or fixed income.
Speaker 2: I mean even inequity is.
Speaker 3: One of the best performing quantitative factors has been momentum.
Speaker 2: For a really, really, really.
Speaker 3: Long time, and one of the worst performing factors has
Speaker 3: been valuation. So we're now in an environment where all
Speaker 3: the forty five year old portfolio managers out there have
Speaker 3: been have worked their entire careers in these momentum fueled markets,
Speaker 3: and they've been trained to believe that valuation doesn't matter.
Speaker 3: And I think that's wrong, because valuation does matter. You know,
Speaker 3: it matters over a longer time period than maybe just
Speaker 3: the next day or two.
Speaker 1: Valuation matters.
Speaker 4: Eventually, it matters.
Speaker 3: And in fact, one of the most powerful market timing
Speaker 3: models not over the next year, but over the next
Speaker 3: ten years is looking at just a price to normalized
Speaker 3: earnings ratio for the S and P five hundred that
Speaker 3: has explained eighty percent of ten year returns. That's a
Speaker 3: super high ursc.
Speaker 1: How do you think of cape?
Speaker 2: Yeah, so this is.
Speaker 3: A cyclically adjusted PE ratio, and I think that that's
Speaker 3: exactly what you want to pay attention to when you're
Speaker 3: thinking about the long term. Unfortunately, nobody has the luxury
Speaker 3: of picking stocks for a ten year period anymore, except
Speaker 3: for in you know, our personal accounts. But professional money
Speaker 3: managers have basically been trained to believe that price predicts price,
Speaker 3: and that has worked for a really long time. But
Speaker 3: I feel like there aren't any value investors left out there.
Speaker 2: Do you ever worry about that?
Speaker 1: So I have a vivid recollection of reading Adam Smith's
Speaker 1: The Money Game and not really understanding the discussion he
Speaker 1: had when I first read this, you know, thirty years ago,
Speaker 1: that there's a fund manager and all this fund manager
Speaker 1: does is hire young, twenty something fund managers. And he
Speaker 1: describes it as they're smart enough and not battle scard
Speaker 1: enough to buy the stuff that terrifies me. And so
Speaker 1: I will ride these managers until they blow up, and
Speaker 1: then I'll fire them and replace them with the next.
Speaker 1: Like it's a chapter in the Money Game. And when
Speaker 1: I was younger, I didn't get it. But exactly what
Speaker 1: you said about if you're forty five, yes, and per
Speaker 1: you know, up until last year, the current generation of
Speaker 1: bond managers never seen a rising rate environment exactly. So
Speaker 1: what ends up happening is you have to bring in
Speaker 1: these young people who don't come with the baggage and memory. Yes,
Speaker 1: so they'll do things that you you're terrified of, and
Speaker 1: then eventually the conveyor belt replaces them. But I didn't
Speaker 1: understand that when I first read it, I don't know,
Speaker 1: twenty five years ago. Now I kind of get it
Speaker 1: for exactly the reason you described.
Speaker 2: It's brilliant. Yeah, yeah, yeah, that makes sense.
Speaker 1: And that book is just absolutely a you know, a
Speaker 1: Wall Street classic.
Speaker 3: Yeah, and maybe that means that we should only have
Speaker 3: the tales of the distribution, like the really old investors
Speaker 3: and the really young investors take out.
Speaker 1: So it's a bar belt take out everybody. You and
Speaker 1: I were out. They got to be older than me
Speaker 1: or or younger than you, and that's that's the range.
Speaker 1: So thank you for getting us losing the job, right,
Speaker 1: But there is something to be said. So sometimes that
Speaker 1: works out and sometime that is disastrous. Yes. So on Twitter,
Speaker 1: I've been having this ongoing DM conversation with the guy
Speaker 1: he's still anonymous behind TikTok Investors, And what he does
Speaker 1: is he goes to TikTok and he finds the most absurd,
Speaker 1: ridiculous investment or money advice on TikTok. And it's that
Speaker 1: exact thing. It's twenty something with no experience, the one,
Speaker 1: the one he said this morning is this guy who's
Speaker 1: twenty something. He says, So I figured out how I
Speaker 1: never have to pay taxes again. I make all my
Speaker 1: money in bitcoin. I got a bitcoin credit card, I
Speaker 1: go to the supermarket, I do this, I do that.
Speaker 1: It's all tax free, Like, who's gonna tell me I
Speaker 1: can't do that? And then the voiceover is the irs, Yes,
Speaker 1: they tracked all of those. Everybody, right, you're gonna get
Speaker 1: a ten ninety nine from wherever your bitcoin exchanges that
Speaker 1: goes to the I R S. What do you think
Speaker 1: they like they woke up yesterday? I mean, come on.
Speaker 1: So the problem with people who don't have the battle scars, Yes,
Speaker 1: the problem with those of us with battle scars are
Speaker 1: sometimes we're a little risk averse. The problem with people
Speaker 1: with no battle scars or they have no sense of hey,
Speaker 1: there's a whole lot of risk in here, in not
Speaker 1: paying your taxes, or in day trading from home or
Speaker 1: whatever some of the.
Speaker 2: Meme stocks and whatnot. Yeah, no, you're right.
Speaker 3: So you need that sort of institutional knowledge, that domain
Speaker 3: knowledge from the super old investor, and then you need
Speaker 3: this like whole cadre of young investors that are kind
Speaker 3: of moronic but also are willing to step in and take.
Speaker 2: A lot of risk.
Speaker 1: So what you're saying, it takes all kinds.
Speaker 2: It takes all kinds. It takes.
Speaker 1: So when I started out on the desk, one of
Speaker 1: my favorite, my head trader, had all these great lines
Speaker 1: that I should have written down. I only remember some
Speaker 1: of them. But I used to ask a question, why
Speaker 1: is this person saying this? This is so obviously wrong
Speaker 1: and money losing. And he's like, Hey, someone's got to
Speaker 1: be on the other side of the trade. Otherwise, who
Speaker 1: are you going to buy from? Right? I guess that's true.
Speaker 1: It's two sides to make them.
Speaker 3: That's the fascinating thing about markets, isn't it. There's always
Speaker 3: somebody that's willing to sell at a certain price, and
Speaker 3: there's always willing there's somebody that's willing to buy.
Speaker 1: So speaking of selling, let's talk about something that dates
Speaker 1: back decades, the cell side indicator. I remember it in
Speaker 1: the early days with the Maryland cell side Indicator. Now
Speaker 1: it's the Bank of America. What is the cell side indicator?
Speaker 1: How does it work?
Speaker 2: This is the model I was telling you.
Speaker 1: About, the consensus.
Speaker 3: Using Wall Street to do the opposite and make lots
Speaker 3: of money.
Speaker 2: Mm hmm. That's exactly what it is.
Speaker 1: And you had nothing to do with its creation. You
Speaker 1: inherited it. I have you tweaked it all since you've
Speaker 1: had it.
Speaker 3: I've looked at it to see whether you know it
Speaker 3: makes sense to use differently or legs whether there's information
Speaker 3: content in the actual distribution of strategists numbers.
Speaker 2: But I think it's just kind of it's a simple.
Speaker 3: Tool that just works because of the fact that, you
Speaker 3: know what we were talking about, just the fact that sentiment,
Speaker 3: when everybody thinks one thing, the market's going to do
Speaker 3: the opposite of whatever they're expecting.
Speaker 1: Has the change in institutional sales and trading, and just
Speaker 1: the way the cell side has morphed over the past
Speaker 1: few decades. A lot of the cell side has moved
Speaker 1: to the buy side. Yeah, a lot of big big
Speaker 1: funds have their own analysts now that they used to
Speaker 1: rely on on the street for right, Does that change
Speaker 1: this at all?
Speaker 2: No, It's interesting.
Speaker 3: This is one model that has still kind of retained
Speaker 3: its efficacy. In fact, it's become more effective since the
Speaker 3: global financial crisis if you just look at its track
Speaker 3: record of predicting positive or negative returns. So it's kind
Speaker 3: of interesting to see that just this old, kind of
Speaker 3: hoary chestnut of a model still works exactly the same
Speaker 3: way it always did and kind of sussing out group
Speaker 3: think hurting and basically doing the opposite. So that's why
Speaker 3: It's one of my favorites.
Speaker 1: So you guys have a huge institutional and sort of
Speaker 1: mom and pop main street client base. What sort of
Speaker 1: analysis do you do with your own data? You mentioned
Speaker 1: flows Connor are so laggy. Is there anything you see,
Speaker 1: especially on the behavioral side from like Herb Greenberg used
Speaker 1: to talk about his email hate meter, like if he
Speaker 1: said something and he got like a ton of I'm
Speaker 1: going to be right, Yeah, I'm onto something here. If
Speaker 1: everybody hates yes, I use.
Speaker 3: That as an informal gauge of you know what, if
Speaker 3: we're getting a lot of pushback on a call, I feel,
Speaker 3: you know, stressed out because everybody's yelling at me, But
Speaker 3: I also feel better about our call. But look, I
Speaker 3: think there are lots of tools you can use. So
Speaker 3: one tool that I really like is looking at positioning
Speaker 3: of the byside because what we've found is, especially today,
Speaker 3: there's a lot of group thing, there's a lot of
Speaker 3: career risk driving investment decisions.
Speaker 1: When you say especially today, hasn't that always been true?
Speaker 2: I don't know.
Speaker 3: I mean one of the things that I've been looking
Speaker 3: at is just active share, the average active fund, and
Speaker 3: it's gotten very like the average active fund has gotten
Speaker 3: closer and closer to the benchmark over the last five years.
Speaker 1: Bill Miller says active management is being destroyed by closet indexers. Yes,
Speaker 1: and that's the guy who beat thesm P five hundred
Speaker 1: and fifteen years in a row, right up until the
Speaker 1: financial crisis.
Speaker 3: Yeah, And I think that is there empirically, that's borne
Speaker 3: out by what we're seeing in our data. But what's
Speaker 3: really interesting is if you have a list of companies,
Speaker 3: one of the things we do every month, then it's
Speaker 3: just a laborious, horrible process. I used to do it,
Speaker 3: and now I'm fortunate to have one of my teammates
Speaker 3: do it. But you just basically scrape all the thirteen
Speaker 3: f's out there, come up with what everybody loves and
Speaker 3: what everybody hates. And it's kind of like the cell
Speaker 3: side indicator. If you've got a stock that is massively overweight,
Speaker 3: everybody owns.
Speaker 2: It in the professional community, there's probably not.
Speaker 4: That much upside it was left to buy, exactly.
Speaker 2: So I think that positioning data is important.
Speaker 3: I love looking at like a new tool that we've
Speaker 3: been using more is kind of natural language processing applied
Speaker 3: to research or transcripts or you know. I'll give you
Speaker 3: one example. So we came up with this analyst tone
Speaker 3: metric tone Tony. So we look at our own research
Speaker 3: and we track whether analysts within a sector are getting
Speaker 3: more positive or negative by virtue of just their their language,
Speaker 3: not their ratings or their.
Speaker 1: You're counting how many great quarter guys or we're.
Speaker 3: Well, yeah, essentially we're looking at we're using these like dictionary,
Speaker 3: these lexicon models to suss out how increasingly positive or
Speaker 3: negative analysts are getting on certain companies, certain sectors, certain themes.
Speaker 3: And it turns out to be a very good leading
Speaker 3: indicator for analysts changing their ratings, for stock performance, for
Speaker 3: earnings revisions. So there is something to be said for
Speaker 3: NLP or you know, kind of these more big data
Speaker 3: tools that are actually tracking broader signals over a long
Speaker 3: period of time.
Speaker 1: So that's a very specific application of AI to research.
Speaker 1: How do you see AI coming into your space, into
Speaker 1: the quants or behavior space. Everybody says it's going to
Speaker 1: have a giant impact. When do you see that happening?
Speaker 1: If not already, I.
Speaker 2: Mean I think it's already happened.
Speaker 3: If you think about just like industries have just gone away, right,
Speaker 3: you can.
Speaker 2: I mean, look, I think it's going to replace some
Speaker 2: of us.
Speaker 3: It's going to replace a lot of these processes that
Speaker 3: we do that are really really boring and laborious. But
Speaker 3: I think at some level you still need to have
Speaker 3: that domain knowledge and that level of expertise that trains
Speaker 3: the models.
Speaker 2: I mean, essentially, I think.
Speaker 3: That we could just create a pocket analyst at this point.
Speaker 3: You could create an analyst that, you know, basically puts
Speaker 3: together the rough limbs of a you know, an earnings report,
Speaker 3: a report on earnings or report on you know, a
Speaker 3: specific event, and then you have the analyst himself or
Speaker 3: herself read it and make sure it makes sense and
Speaker 3: you know, tweak.
Speaker 2: It, et cetera. But there's a lot of that route.
Speaker 3: Activity that can be replaced by AI. Whether AI can
Speaker 3: invest better than a human being, I doubt it, because
Speaker 3: you know, I think that at some level you need
Speaker 3: that domain experience, you need that behavioral angle. You need
Speaker 3: to analyze what's different this time, because there always is
Speaker 3: something different this time. I think that that's the other
Speaker 3: thing I've learned in finance is that you can never
Speaker 3: just apply the last crisis playbook to the current environment.
Speaker 3: And that's something that I think it's hard to train
Speaker 3: a bot or a process on how to actually sort
Speaker 3: of determine what you need to factor in this time
Speaker 3: that is different from all of the historical data.
Speaker 1: They may not repeat, but they rhyme as the old
Speaker 1: joke and very very true.
Speaker 3: Yeah exactly. But there's always something that nobody's paying attention
Speaker 3: to that's going to blow everything up. And that's what
Speaker 3: you know, we need the human beings to fly around
Speaker 3: and look into the whites of the eyes of company
Speaker 3: management and you know, kind of figure out what's really
Speaker 3: going on behind the data. And I think it's like
Speaker 3: there's an example of this if you think about, you know,
Speaker 3: even that NLP process that I talked about, where you're
Speaker 3: looking for positive and negative sentiment. So one of the
Speaker 3: things that happened over the last you know, ten years,
Speaker 3: is that management realized that quants are scraping their transcripts
Speaker 3: on conference calls for positive and negative words. And then
Speaker 3: there was a way to game it. You could just
Speaker 3: inject more positive words or you know, take out all
Speaker 3: the negative words. You could you could basically edit your
Speaker 3: script so that it would look like, you know, you
Speaker 3: were you were saying all the right things for a
Speaker 3: quant model. So those are the types of things that
Speaker 3: I think, you know, AI is never going to figure out,
Speaker 3: you know, when that's already in the market, when folks
Speaker 3: are gaming the system, versus when it's an actual, accurate signal.
Speaker 1: Huh, that's incredible. So let's talk a little bit about
Speaker 1: some things that are going on. I saw a quote
Speaker 1: of yours that I really liked. The idea that the
Speaker 1: market is too expensive should be debunked. Explain why.
Speaker 3: Yeah, So I think that there is this tendency of
Speaker 3: quants myself included, to look at a time series and say, okay,
Speaker 3: if the pe of the S and P five hundred
Speaker 3: right now is twenty one times, and it has mostly
Speaker 3: been below fifteen times, and whenever it's been twenty one
Speaker 3: times in the past, it's gone down. Those types of
Speaker 3: analyzes I think are just deeply flawed, especially in light
Speaker 3: of the fact that the market itself is not one
Speaker 3: kind of monolith that's always the same. It's a changing animal.
Speaker 3: And if you look at the S and P today,
Speaker 3: fifty percent of it is asset light innovation oriented healthcare
Speaker 3: and tech, whereas in nineteen eighty seventy percent of it
Speaker 3: was manufacturing asset intensive, et cetera.
Speaker 1: So let me ask you a question about that asset
Speaker 1: light side. People Michael mobisonas one, have made the argument
Speaker 1: that intangibles, intellectual property, patents, algorithms, et cetera, are deserving
Speaker 1: of a higher multiple, that they don't require a massive
Speaker 1: investment in factories, and they're not capital intensive, right, they're
Speaker 1: not manpower intensive, they don't need a ton of labor.
Speaker 1: Shouldn't they be awarded a higher multiple than you know,
Speaker 1: a steel factory, right.
Speaker 2: Right, right, right.
Speaker 3: So that's the idea is that the margins are more stable,
Speaker 3: they're less reliant on risky labor, which you know, people
Speaker 3: can go on strike or sue companies, whereas processes can't. Yeah,
Speaker 3: so I think there's there's a validity to that point.
Speaker 3: I mean, when I look at the S and P today,
Speaker 3: it's you know, it's not only is it a different
Speaker 3: animal in terms of its sector mix, but it's also
Speaker 3: less levered. Everybody took advantage of super low interest rates
Speaker 3: to turn out their debt, and you know kind of
Speaker 3: so fixed rate obligations are day rigueur for the JUST
Speaker 3: and P company versus floating rate obligations. A few years,
Speaker 3: you know, prior to the crisis. I think that also
Speaker 3: when you look at the labor intensity of the S
Speaker 3: and P five hundred, it's become much more labor light.
Speaker 3: And oh, by the way, AI is going to give
Speaker 3: us the opportunity over the next ten years to become
Speaker 3: even more labor light. I think the whole bulk caase
Speaker 3: around AI right now is not buying the chip makers,
Speaker 3: it's buying the index because the index is about to
Speaker 3: become that much higher quality.
Speaker 1: You know, let me see if I understand that, because
Speaker 1: it's really fascinating. Everybody's so focused with Nvidia and now
Speaker 1: Intel has kept quite a bit and a few other
Speaker 1: chip makers. But really what you're saying is, look at
Speaker 1: who has a giant or outsized set of labor costs.
Speaker 1: Either they're going to be able to reduce their headcount
Speaker 1: or their existing headcount is going to become so much
Speaker 1: more productive working with AI that we're not recognizing.
Speaker 3: You know, those describing that that that premium to all
Speaker 3: the clunky services companies out there. Like this is why
Speaker 3: I'm bullish on large cap banks.
Speaker 4: One of the reasons is which are cheap now relatively speaking.
Speaker 3: Which are still in that value cohort and they are
Speaker 3: also one of the few sectors that's become more labor
Speaker 3: intensive since the financial crisis. Why because these regulated banks
Speaker 3: had to hire all these legal and compliance and expert
Speaker 3: folks to make sure we weren't doing anything bad. Right,
Speaker 3: So today, think about all those processes, those are much
Speaker 3: easier to replace with an automated AI, like you know,
Speaker 3: bought whatever you want to call it, than than than
Speaker 3: any period of time in the past. Generative AI is new,
Speaker 3: it's a new thing. It's it's a game changer for
Speaker 3: many industries. Call centers have gone away. I mean entire
Speaker 3: industries have gone away overnight because of the advent of
Speaker 3: generative AI. And that's where I think it's really bullish,
Speaker 3: is in the ability to replace a lot of these
Speaker 3: rote activities that people right now are being paid to do.
Speaker 3: So one of the things that I've seen in my
Speaker 3: quant work is that if you look at any sector
Speaker 3: of the market, in any peer group, and you look
Speaker 3: at the labor intensive companies and the companies that are
Speaker 3: labor light, the companies that are labor light almost always
Speaker 3: outperform their labor intensive peers. So we are sitting right
Speaker 3: now at a point in time where over the next
Speaker 3: five to ten years or I don't know how long
Speaker 3: it takes, the S and P five hundred has this
Speaker 3: opportunity and this new tool to become even labor lighter
Speaker 3: than it is today.
Speaker 2: That is hugely bullish.
Speaker 1: Huh, really really interesting. So this leads me to what
Speaker 1: you've said not too long ago. There's a lot more
Speaker 1: to the S and P five hundred than the Semis
Speaker 1: and the megacap tech. Is this is AI what's driving? Hey,
Speaker 1: you got to look past past Nvidia and pass the
Speaker 1: magnificent seven to who are going to be the beneficiaries
Speaker 1: of all this new technology?
Speaker 2: Yeah, I think that's right.
Speaker 3: I think it's not just new economy chip purveyors, but
Speaker 3: it's also the companies that buy the.
Speaker 2: Chips and become better.
Speaker 3: But I also think there's something going on right now
Speaker 3: that we should be really excited about, which is that
Speaker 3: interest rates are no longer at zero. They're at five percent.
Speaker 3: So the Fed has done a lot of work for us.
Speaker 3: Companies are behaving much more rationally today than they have
Speaker 3: in the past. They're thinking about how to become more efficient.
Speaker 3: This is something they haven't thought about for a really
Speaker 3: long time because they had all these easy ways to
Speaker 3: make money. If I'm a corporate, if I'm a CFO
Speaker 3: and I'm not going to make my earnings numbers next quarter,
Speaker 3: I could have borrowed cash for free and bought back
Speaker 3: enough shares to beat that number. So there were lots
Speaker 3: of low quality ways of making money since the global
Speaker 3: financial crisis. But now we're back to a more normal
Speaker 3: hurdle rate. Five percent interest rates is not super high.
Speaker 3: I think it's manageable, right, and companies are making all
Speaker 3: the right moves. If you look at even these growth
Speaker 3: companies like Meta or Alphabet are now initiating dividends. They
Speaker 3: realize that part of their mantra needs to be cash
Speaker 3: returning and capital discipline.
Speaker 2: As well as growth. So, you know, I think that.
Speaker 3: We're at a point where the reasons to be optimistic
Speaker 3: on stocks are that much more than when we were
Speaker 3: at zero interest rates pre pandemic.
Speaker 2: I mean, think about it.
Speaker 3: The market has absorbed so much bad news over the
Speaker 3: last few years.
Speaker 1: You not too long ago someone asked you about markets
Speaker 1: climb a wall of worry, like it's a bad thing. Yeah,
Speaker 1: isn't that a good thing? Isn't that people are stressed
Speaker 1: out about things that the market's already sussed out.
Speaker 3: Right exactly, I think that's right. And I think, you know,
Speaker 3: even when you think about where we were in twenty
Speaker 3: twenty one, at the end of twenty twenty one, I
Speaker 3: felt really nervous about stocks because for the first time,
Speaker 3: we were forecasting.
Speaker 2: Negative real rates.
Speaker 3: Uh huh, which is really, you know, kind of a.
Speaker 1: Problematic to say the least.
Speaker 3: It's irrational negative real rates. That's an irrational Let me.
Speaker 1: Borrow some money from you, and I need a quarterly
Speaker 1: check from.
Speaker 4: You, exactly.
Speaker 3: I mean, it doesn't make any sense. We were forecasting
Speaker 3: something that didn't make any sense. You know, every economist
Speaker 3: out there was forecasting negative real rates, and that just
Speaker 3: felt like something had gone wrong. Nobody was expecting two
Speaker 3: wars to break out. Nobody was expecting the Fed to
Speaker 3: hike interest rates from zero to five in a very
Speaker 3: short period of time. By the end of twenty twenty one,
Speaker 3: our sell side indicator was at the most bullish levels
Speaker 3: we'd seen since.
Speaker 4: Really the global financial crisis.
Speaker 3: Yep, nobody thought anything was going to go wrong, and
Speaker 3: then whamo, you saw a bear market.
Speaker 1: So today, and by the way, a bear market in
Speaker 1: both stocks and bond and bond it's something that you
Speaker 1: don't see every forty years was the last time we
Speaker 1: saw that exactly So there the cell side indicator really
Speaker 1: worked exactly as planned. So let's talk about where we
Speaker 1: are in the current cycle. I know you like to
Speaker 1: discuss there are different phases of the of the both
Speaker 1: the market and the economic cycle. Where are we in
Speaker 1: this cycle and what does that mean for the next
Speaker 1: couple of years.
Speaker 3: Yeah, I mean, so this is one area where I'm
Speaker 3: going to say this time it is different. I'm going
Speaker 3: to see those dreaded words because I think that, you know,
Speaker 3: where we are today is not necessarily as clear cut
Speaker 3: in terms of late cycle, early cycle, you know, recession,
Speaker 3: no recession. I think we you know, I think we've
Speaker 3: had areas of strength and areas weakness over the last
Speaker 3: few years. I mean, we had a global pandemic, a
Speaker 3: complete shutdown of global economic activity, and then you had
Speaker 3: certain pockets of the economy become oversubscribed and other parts
Speaker 3: of the economy become undersubscribed. And there's there's been that
Speaker 3: shakeout ever since. So I still think we're in this
Speaker 3: environment where goods versus services, we're working out that demand.
Speaker 3: We've seen inventory tightness and inventory laxity, so we've seen
Speaker 3: a lot of like kind of cross currents that would
Speaker 3: problematize just calling this a normal FED hiking cycle.
Speaker 2: I do think that the.
Speaker 3: Other factor that has shifted demonstrably and deserves more airtime
Speaker 3: is the idea that you know, if you look at
Speaker 3: the areas of risk today across the spectrum, corporates and
Speaker 3: consumers were just given a bunch of money from the
Speaker 3: Fed and the government. The areas of risk and indebtedness
Speaker 3: are sitting in the on the government balance sheet, right,
Speaker 3: not necessarily on corporate or consumer balance sheets.
Speaker 1: Right. Everybody refinanced except Uncle Sam exactly.
Speaker 3: Uncle Sam took the whole pile of it, and it's
Speaker 3: sitting right there on our balance.
Speaker 1: And I recall seeing a number of senators and congressmen,
Speaker 1: and they should chisel this on their tombstones. You know,
Speaker 1: if we refinance at lower rates, it'll just encourage more spending.
Speaker 1: It's like, no, they're going to spend more no matter
Speaker 1: what the rates are. You might as well get a
Speaker 1: better rate, you know. It was just one of those
Speaker 1: like dumb things that politicians say that you know, as
Speaker 1: soon as you hear, it's not true, and now we're
Speaker 1: stuck with a lot of debt and we didn't even
Speaker 1: get a benefit of a decade of low rates.
Speaker 2: Right right.
Speaker 3: I mean, I think this debt sitting on government balance,
Speaker 3: she's something to worry about. I mean, I think the
Speaker 3: other aspect to worry about is not publicly traded equities,
Speaker 3: which are marked to market on every change in every
Speaker 3: macro now, tick by tick, tick by tick on a
Speaker 3: millisecond basis. But if you look at private credit, private equity,
Speaker 3: commercial real estate, we already know it's it's you know,
Speaker 3: it's problematic residential real estate. We haven't seen a lot
Speaker 3: of turnover in residential real estate because nobody wants to
Speaker 3: walk away from older. Yeah, so I think those are
Speaker 3: the areas where we should be more worried. But if
Speaker 3: you're looking at a stock, it's pricing in the current
Speaker 3: environment of rates inflation, like kind of everything that's going
Speaker 3: on right now is in a publicly traded equity vehicle.
Speaker 1: Not too long ago, we were having a conversation about,
Speaker 1: you know, so everything going on in the college campuses. Now.
Speaker 1: We were talking about the various endowments and how they
Speaker 1: performed and somehow in twenty twenty two, when when stocks
Speaker 1: were down about twenty percent and bonds were down about
Speaker 1: fifteen percent, these endowments, some of which are twenty thirty
Speaker 1: forty percent alternatives like private equity and private credit, they
Speaker 1: did just fine. It's it's great when you get to
Speaker 1: mark to make believe. You know, you could just put
Speaker 1: what should we mark this? I don't know what do
Speaker 1: you want it to be?
Speaker 2: Right?
Speaker 1: Right, Let's put it flat for the year flat in
Speaker 1: this environment looks great. I wish I could get away
Speaker 1: with that. I actually have to report real performance, not
Speaker 1: made up stuff exactly. And I've heard consultants pitch it.
Speaker 1: You know, in a down year, you have like two
Speaker 1: years to change your mark on that, and by the
Speaker 1: time you change your market it's probably recovered. Yeah.
Speaker 3: I mean, I think this is an area that could
Speaker 3: be ripe for regulation. I just don't know how the
Speaker 3: regulators will figure out how to regulate it, and I'm
Speaker 3: sure that that will create this sort of whack a
Speaker 3: mole type of environment.
Speaker 1: Well, if you remember back during the financial crisis, when
Speaker 1: everybody had to mark to market, even things held to
Speaker 1: maturity that were underwater, they had a market to market,
Speaker 1: and that was one of the changes that came about. Okay,
Speaker 1: if this doesn't have any payments due and you're it's
Speaker 1: in your hold to maturity account, you don't have to
Speaker 1: mark to market, which allows a lot of junk to
Speaker 1: kind of get swept under the rug absolutely, and that
Speaker 1: becomes you know, that becomes a feature, not a buck.
Speaker 3: And here's the really worrisome thing. So if you think
Speaker 3: about just private equity, the amount of capital raised since
Speaker 3: twenty seventeen is basically it doubled the size of the
Speaker 3: private equity market. Think about how we were we were
Speaker 3: geared in twenty seventeen, twenty eighteen, nineteen twenty. We weren't
Speaker 3: thinking about five percent interest rates.
Speaker 4: Right, it was we were low for longer.
Speaker 3: This suflation is going to stay low, disinflationary pressures, disruption,
Speaker 3: blah blah blah. That was the mantra during that entire
Speaker 3: stretch of time where where a ton of money was
Speaker 3: raised in these long duration growth themes that were priced
Speaker 3: for an environment of zero rates forever.
Speaker 1: Right, you're getting nothing on bars, but hey, look I
Speaker 1: can get you five or six percent in private equity.
Speaker 1: The only rub is it's locked up for seven years exactly.
Speaker 1: So once you had the pandemic, which changed everything, you
Speaker 1: had the biggest fiscal stimulus since World War Two and
Speaker 1: the first CARES Act, to say nothing of CARES Act two,
Speaker 1: those two undred President Trump and Karsact three under President Biden.
Speaker 1: The fiscal you mentioned regime change earlier, yep, the previous
Speaker 1: regime was all monetary in the twenty tens. In the
Speaker 1: twenty twenties, it's mostly fiscal fiscal.
Speaker 3: It's inflationary, it's protectionist. I mean, everything going on right now,
Speaker 3: deglobalization and physical stimulus, these are inflationary trends. So I
Speaker 3: think that the idea that inflation and rates are going
Speaker 3: to remain low is you know, it's problematic. And you know,
Speaker 3: I mean even this year, look what happened. The Fed
Speaker 3: was supposed to cut like what was it four times?
Speaker 1: That's well. We were also supposed to get a recession
Speaker 1: and that was I know. Ye today he's right and
Speaker 1: none of them happen. That's that is your cell side
Speaker 1: indicator and action. All the consensus things or acession in
Speaker 1: twenty two or a recession in twenty three, the federal
Speaker 1: start cutting in twenty three. Now we're going to push
Speaker 1: it out to twenty four. None of that has proven
Speaker 1: to be true.
Speaker 3: Yeah, yeah, yeah, I mean I think that where we
Speaker 3: are today is actually a reasonably healthy point for equities.
Speaker 3: But the areas that I worry about are that is
Speaker 3: that bottomless pit of you know, unmarked assets that have
Speaker 3: doubled or quadrupled in size in asset allocation. I mean,
Speaker 3: think about the average teacher or firefighter's pension plan. It's
Speaker 3: thirty percent ill liquid today versus wow, five percent you know,
Speaker 3: back in the two thousand. So you know, stuff has changed,
Speaker 3: and that's where I worry. But I don't worry as
Speaker 3: much about you know, big cap companies that everybody is
Speaker 3: tracking and watching and monitoring.
Speaker 1: So I want to get to my favorite questions that
Speaker 1: we ask all of our guests. But before I do that,
Speaker 1: I just have to throw a curve ball at you.
Speaker 1: So you had mentioned your predecessor, Rich Bernstein, who had
Speaker 1: been with Meryl for a long time before he went
Speaker 1: out and launched Rich Bernstein Associates, Responsanate Advisors Advice RBA.
Speaker 1: Right when he left Meryl, he was roasted, and you
Speaker 1: famously read about ten bullet.
Speaker 3: Points ten things I've learned from Rich. In my ten
Speaker 3: years working.
Speaker 1: For they were hilarious, perhaps my favorite. A midlife crisis
Speaker 1: on Wall Street doesn't have to involve a ferrarian hair plugs,
Speaker 1: a Mini Cooper, and a leather rubber metal man bracelet
Speaker 1: will do just fine. Tell us a little bit about
Speaker 1: your Rich's exit roast.
Speaker 3: Oh goodness, it was terrible because I went first and
Speaker 3: I said ten really mean things about Rich and then
Speaker 3: everybody that did the speech after me said really nice
Speaker 3: things about.
Speaker 1: But that's what a roast is supposed to build.
Speaker 3: Well, I was like, this is not a good roast.
Speaker 3: You guys need to get into the trenches and say
Speaker 3: some mean things. But I was the really mean one
Speaker 3: and everybody else was reliving.
Speaker 1: So if they were going to do a roast of you,
Speaker 1: what would the worst thing they say about you on
Speaker 1: the way?
Speaker 2: Oh gosh, there's so many things they could say.
Speaker 1: Well, what's the nice thing we would say about you?
Speaker 1: Let me rephrase that. What would you be most proud
Speaker 1: of someone saying about you?
Speaker 2: Well, that's a good question. I think I would be
Speaker 2: happy if somebody said about me that I was.
Speaker 3: I helped them in their career. I mean, I think
Speaker 3: that's what we're all here for. But I think the
Speaker 3: terrible things that people could say about me were that I,
Speaker 3: you know, chronically forget my ID, like four out of
Speaker 3: five days a week. I don't bring my ID to
Speaker 3: the office, and I have to get the security guard
Speaker 3: to look me up in the system.
Speaker 1: They're sofisted. This is absolutely true story one day. So
Speaker 1: sometimes I take this off when we're recording. On the
Speaker 1: other side of that studio is where Mike sits, some
Speaker 1: guy named Mike Bloomberg, and he must have taken his
Speaker 1: off and gone up to get coffee or something up there,
Speaker 1: and on the way back, the guard says, sorry, I
Speaker 1: can't let you down without a U A tag and
Speaker 1: to his credit and this is a good display of leadership,
Speaker 1: turn around, went down to the basement, got a temporary
Speaker 1: came back and everybody saw it. If Mike did it, well,
Speaker 1: then how could we not. That's right, That's that's pretty
Speaker 1: hig So what happens when you show up without your
Speaker 1: you know, your badge?
Speaker 3: Well, the sad thing is that all the security guards
Speaker 3: they know me because I'm vivoting.
Speaker 1: Don't you have to swipe in?
Speaker 3: Well, they give me a bat like a temporary idea
Speaker 3: and then I go upstairs. But but yeah, there are
Speaker 3: a lot of things that that I could be roasted on.
Speaker 3: I always walk the wrong direction out of a door.
Speaker 3: I always go the opposite direction of where I'm supposed.
Speaker 2: To be going.
Speaker 1: No, you don't have good internal gyroscope.
Speaker 3: Good, Yeah, my compass is completely destroyed. But yeah, there
Speaker 3: are a lot of there's a lot of raw material
Speaker 3: to roast me on.
Speaker 2: I mean, it was well, I.
Speaker 1: Hope I get invited to that. That sounds like that'll
Speaker 1: be fun. So let's jump to our favorite questions that
Speaker 1: we ask all our guests, starting with what have you
Speaker 1: been streaming these days? What are you watching? Oh? Well,
Speaker 1: just watching listening to whatever?
Speaker 2: Was I just finished.
Speaker 3: Watching The Gilded Age, which I thought was really fascinating.
Speaker 1: It's a Gilded Age.
Speaker 3: It's on HBO Max, and it's about like old New York,
Speaker 3: like basically, you know, the Upper east Side, in the
Speaker 3: in the in the railroad baron.
Speaker 1: Was that really the Gilded Era?
Speaker 3: I suppose that's what they call it. I mean it
Speaker 3: seemed pretty interesting. It was kind of fun if you
Speaker 3: live in New York to watch that.
Speaker 2: I rewatched Breaking Bad.
Speaker 1: Because we were just talking about I saw the first season
Speaker 1: and kind of tapped out afterwards.
Speaker 3: I know, No, I mean, I I hate to say this,
Speaker 3: but I really feel like you need to give it
Speaker 3: another season.
Speaker 1: I mean, during the during the pandemic, we were you know,
Speaker 1: you're stuck at home. We went through a bunch of
Speaker 1: things like mad Men. I had never watched a single
Speaker 1: episode of that without when that was on TV, and
Speaker 1: we blew right through it. So the competition for things
Speaker 1: that were like when someone says you got to give
Speaker 1: it a couple of seasons, I'm like, it turns out
Speaker 1: I don't have to. But I understand. I understand the point. Yeah,
Speaker 1: we talked about Game of Thrones. Yeah, are you a fan?
Speaker 2: No, can't get into it.
Speaker 1: I watched the and I know a million people say
Speaker 1: it's the greatest show. You're sci fi fantasy guy, you
Speaker 1: should love it. Like, first of all, I can't keep
Speaker 1: up with all the names. My brain is oatmeal, right,
Speaker 1: It's like, wait, I need a I need a notepad,
Speaker 1: like this is who of Visigoth? Of what? Like? I
Speaker 1: just I like, I think.
Speaker 3: I fell asleep like three times trying to watch the
Speaker 3: first episode.
Speaker 1: The first the first couple of episodes are very slow,
Speaker 1: and then the other you know, so the first season
Speaker 1: of White White Lotus was great.
Speaker 2: Oh yeah, I loved White Loadus.
Speaker 1: But we're watching the second season and everybody is just
Speaker 1: a They're not Succession bad, which is another show that
Speaker 1: everybody says it's great and why do I want to
Speaker 1: spend my time with these people? But like I want
Speaker 1: to be entertained and come away with like right, yes,
Speaker 1: not like wow, those people are jerks. Thank goodness. I
Speaker 1: don't work with anyone like them. It's just like so
Speaker 1: what else? So so if you watch The Gilded Age, yes,
Speaker 1: did you see the Crown? Oh?
Speaker 2: I love the Crown Love.
Speaker 1: Every episode was a joy, yeah, just visually a feast.
Speaker 3: For the It was my twelve year old son watched that,
Speaker 3: which really which was kind of.
Speaker 2: Cool because I didn't realize he.
Speaker 1: Was, well, how did he How did he find it?
Speaker 2: I don't know.
Speaker 3: He just wandered into the room while I was watching it,
Speaker 3: and then he sat down and then he was engrossed,
Speaker 3: and we're watching this series together about the Queen of England.
Speaker 1: It was really fascinating. It was it was I know
Speaker 1: it's sort of semi fictional.
Speaker 2: But semi I.
Speaker 1: Found myself asking questions and googling things. O me too,
Speaker 1: Did that happen? Really? It was amazing. Give me one
Speaker 1: other thing you're watching that you thought was fun.
Speaker 3: Okay, let's see Breaking Bad, the Crown.
Speaker 2: Gosh, I'm coming up blank.
Speaker 1: You know the the problem with Breaking Bad? There was
Speaker 1: a show I don't remember what I watched called Fauda
Speaker 1: about Israeli counterintelligence agents that are infiltrating various terrorist groups.
Speaker 1: And it's so stressful that if you watch this show
Speaker 1: after eight o'clock at night, you're not going to sleep
Speaker 1: till midnight. And like you, I'm an early riser. I
Speaker 1: can't like be on the edge of my seat wondering
Speaker 1: who's gonna, you know, be found out and been murdered
Speaker 1: by the.
Speaker 2: Okay, I just remembered a show that gave me like
Speaker 2: PTSD twenty four.
Speaker 1: Have you ever watched though, Oh sure, oh oh, with
Speaker 1: a clock ticking down the whole time?
Speaker 3: It was like, but I binge watched that because you
Speaker 3: can't not watch an entire season if your calendar alive.
Speaker 1: Once you get once you get into one episode.
Speaker 2: You're just gonna put it was so stressful.
Speaker 3: I think that might have taken years off of my life.
Speaker 1: We just finished The Gentleman, which is also kind of
Speaker 1: stressful and you so I always save some comedy show
Speaker 1: as sort of like a palate cleanser. Now it's Brooklyn
Speaker 1: ninety nine, but before that, it was, oh god, FANTASTICA
Speaker 1: ted Lasso was like regular. The other show that's we've
Speaker 1: been watching on HBO that we loved is Hacks Is.
Speaker 1: Season three just dropped and it's so great.
Speaker 2: Yeah.
Speaker 1: So it's a woman comedian in Vegas who is slightly
Speaker 1: past her sell by date and her pushback against the
Speaker 1: men that run the casinos and the writer who wants
Speaker 1: her to become younger and hipper in her material, kind
Speaker 1: of a tell all thing, and it's just really fascinating.
Speaker 4: Look at that.
Speaker 1: So season one and two were both great. It's not
Speaker 1: quite as cringey as Curb, but there are moments where
Speaker 1: you like, don't don't do that, don't do that. Oh
Speaker 1: you know, you just see it coming. It's just don't
Speaker 1: tweet that. That's just going to bite you in the behind.
Speaker 1: Don't don't, But you get sucked into it and you're
Speaker 1: rooting for the character. So that's a perfect example of
Speaker 1: fascinating characters who are flawed but likable like you want them.
Speaker 2: You want them to see that, right exactly.
Speaker 1: Maybe I'm too old school Hollywood, but I don't really
Speaker 1: want to watch people who I can but you.
Speaker 3: Hate I know, right, right exactly, you don't need to
Speaker 3: go home to people.
Speaker 1: That are turn That's right to someone say something that like,
Speaker 1: I think at the slap that guy you mentioned, Rich Bernstein,
Speaker 1: tell us about your mentors who helped guide your career.
Speaker 3: Oh, Rich, definitely, like just one of the key people
Speaker 3: that you know really made me who I am today.
Speaker 3: I mean, I have to say my mother is like,
Speaker 3: really who I imprinted on the software coder?
Speaker 2: My mom was a coder. Yep.
Speaker 3: She came here from India when she was just twenty
Speaker 3: years old. She had an arranged marriage they're now divorced,
Speaker 3: one of the worst arranged marriages of all time.
Speaker 2: But she was you know, she had a lot of guts.
Speaker 3: She wore a sorry to work every day, really, but
Speaker 3: somehow ascended the corporate ladder at Digital Equipment Corporation and
Speaker 3: became a manager. Even though people were like, you need
Speaker 3: to stop wearing the sari, she kept wearing it.
Speaker 2: She was true to herself.
Speaker 3: So I kind of look at her as a role
Speaker 3: model of how to just get starf I've done, you know,
Speaker 3: fade the haters and you know, do something good for
Speaker 3: the world, create some value.
Speaker 1: Really really interesting. Let's talk about books. I mentioned Adam
Speaker 1: Smith's Money Game. What are some of your favorites? What
Speaker 1: are you reading right now? Oh?
Speaker 2: Right now, I'm actually reading.
Speaker 3: Well, I'm rereading an Agatha Christie novel that I love.
Speaker 3: Which one the Murder on the Orient Express. Sure, I
Speaker 3: know I'm obsessed with.
Speaker 1: You know, there's been I think three or four movie
Speaker 1: film versions. I don't mean like subsequent.
Speaker 2: But they're all terrible. Have you seen them?
Speaker 1: So? Not like the early ones are kind of talky
Speaker 1: and slow, but they're kind of interesting character studies.
Speaker 5: And oh yeah yeah, it's they're truer to the book
Speaker 5: then you know, it's not supposed to be a James
Speaker 5: Bond novel, right, but some some of them try and
Speaker 5: turn them into almost actions.
Speaker 3: Yeah, yeah, yeah, yeah, yeah. My favorite book of all
Speaker 3: time is a book called Confederacy of Dunces.
Speaker 1: Sure did you read that long time ago?
Speaker 2: I love that book.
Speaker 1: I the author is, it's.
Speaker 2: John Kennedy to o'tool, and.
Speaker 1: Then I did not read it thinking of a different book.
Speaker 2: Okay, so I'll get you a copy. It's it's a
Speaker 2: good one.
Speaker 3: Hold on, I'm also reading this book by Peter Atia
Speaker 3: on how to live huh well, not necessarily long, but
Speaker 3: how to remain healthy and thriving. I mean, I find
Speaker 3: that health is becoming a bigger part of my serious,
Speaker 3: you know, concern set these days as I get older.
Speaker 3: I mean, I turned fifty a year ago, and I'm
Speaker 3: starting to think about, you know, I want to see
Speaker 3: my grandkids, So how do I keep this thing going
Speaker 3: and be happy and healthy.
Speaker 1: It's not just about longevity, but of quality of life.
Speaker 3: Exactly, and that's what that's what Peter a Tea is
Speaker 3: really focused on. So I thought that was an interesting one.
Speaker 3: But yeah, there's so many things to read. I don't
Speaker 3: read a lot of non.
Speaker 4: Fiction, especially oh really, I don't read a lot that.
Speaker 2: Has to do with financial markets.
Speaker 1: As I've gotten older, I find myself reading more and
Speaker 1: more nonfiction.
Speaker 2: Really.
Speaker 1: And when I was younger, you know, a big sci
Speaker 1: fi fan.
Speaker 2: Yeah, just like Dick. That was my favorite.
Speaker 1: So my people don't realize Minority Report, Blade Runner Total
Speaker 1: recall these are all and then the the I think
Speaker 1: it was the Amazon series, Uh, that takes place when
Speaker 1: it's a it's an alternative history where Japan and Germany
Speaker 1: when World War Two.
Speaker 2: It's Amazon series.
Speaker 1: That's an Amazon series based on a Philip K. Dick book,
Speaker 1: which of course escapes my my recollection. I read that
Speaker 1: One Man in the High Tower was the film Dick book, right,
Speaker 1: and that became an now Amazon series. I can't believe
Speaker 1: I pulled that that title out of that.
Speaker 2: That was really good. I kind of forgot.
Speaker 3: The nice thing about getting older is that you can
Speaker 3: reread and it's fresh.
Speaker 1: Right, Three Stigmata of Palmer Eldridge, you bick Like, I
Speaker 1: remember those books being super dense and super you know, heady, Yeah,
Speaker 1: and rereading them now it's like, oh, okay, I have
Speaker 1: a different context. Yeah. What sort of advice would you
Speaker 1: give a recent college grad interested in the career in
Speaker 1: either finance, quantitative analysis, or investing.
Speaker 3: Well, I mean the first piece of advice isn't specific
Speaker 3: to finance, but it's just, you know, don't be a jerk.
Speaker 1: Okay.
Speaker 3: I think there are so many people out there who
Speaker 3: are trying to prove that they know more than the
Speaker 3: next guy that you know, they stopped listening. They're just
Speaker 3: like you know, trying to seem smart. And I think
Speaker 3: that's your number one enemy in.
Speaker 1: What drives that? Is that a modern thing with social
Speaker 1: media or is that always throughout your career.
Speaker 3: I don't think an issue, just like insecure people that
Speaker 3: needs to prove themselves. And what I've found is, you know,
Speaker 3: if the way you treat people that are working for
Speaker 3: you says a lot about you. And the problem is
Speaker 3: if you're mean to the people that work for you,
Speaker 3: someday they might become your boss. So I think that's
Speaker 3: another piece of advice I would give.
Speaker 1: This has nothing to do with you being an intern
Speaker 1: at the Merril Kuan shop and eventually leading that job.
Speaker 3: No, no, no, I've not personally experienced that too many
Speaker 3: times in my life, but I've heard about it many times,
Speaker 3: and I think that's just bad practice when it comes
Speaker 3: to finance and investing. I think the idea of being
Speaker 3: flexible in thought, always checking your own by bases. I mean,
Speaker 3: this is where the philosophy comes in. So Friedrich Nietzsche'
Speaker 3: is this has this theory of constantly overcoming and that's
Speaker 3: the idea that you should always critically examine your assumptions
Speaker 3: and make sure that you're not making a mistake.
Speaker 4: Life is struggle, Yes, I mean life is struggle.
Speaker 3: That's also a Nietzschean great right, But I think the
Speaker 3: idea of just always kind of checking yourself and seeing
Speaker 3: whether you're assuming things that aren't necessarily true.
Speaker 1: And our final question, what do you know about the
Speaker 1: world of investing today? You wish you knew when you
Speaker 1: were getting started in the early two thousands.
Speaker 3: Look, I wish i'd started investing earlier. I was always
Speaker 3: too risk averse, and then once I started to get
Speaker 3: some kahones, I was, you know, ten years into my career,
Speaker 3: I wish I'd just socked away more money.
Speaker 2: And you know, kind of.
Speaker 3: The riskiest, most volatile asset classes, because that's where when
Speaker 3: you're young, you can really take a punt.
Speaker 1: For the risk and if you have a setback, so what, yeah,
Speaker 1: overcome it.
Speaker 3: There's time, and volatility get gets easier with time. I
Speaker 3: think the other kind of metric that I wish I'd
Speaker 3: known about is and this is specific to the S
Speaker 3: and P five hundred, but the interesting thing is, if
Speaker 3: you own the SMP for.
Speaker 2: A day, you have about a fifty to fifty chance
Speaker 2: of making money or losing.
Speaker 4: Money, but meaning the next day, the next day.
Speaker 3: So you know, your probability of making money by buying
Speaker 3: and selling the S and P over a one day
Speaker 3: period is about a coin flip, a little bit better
Speaker 3: than a coin flip. But if you have a buy and.
Speaker 2: Hold over a ten year period, your probability.
Speaker 3: Of losing money is demnimous. It's like less than five percent.
Speaker 3: So that's the idea of just extending your holding period,
Speaker 3: set it, and forget it. I think those are some
Speaker 3: of the tricks that I try to impress upon individual investors.
Speaker 3: Is you know, the day that you want to see well,
Speaker 3: because the market just went down a lot, is probably
Speaker 3: the worst day to sell. Because the best days for
Speaker 3: the S and P typically follow the worst day.
Speaker 1: They cluster together, huh.
Speaker 2: So it's just, you know, get rid of emotion when
Speaker 2: it comes to investing.
Speaker 1: Savita, thank you for being so generous with your time.
Speaker 1: This was really fascinating. We have been speaking with Savita Subhimanian.
Speaker 1: She is the head of US Equity and Quantitative Strategy
Speaker 1: for a Bank of America. If you enjoy this conversation.
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