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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is a Masters

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Speaker 1: in Business with Barry Ritholts on Bloomberg Radio this week.

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Speaker 2: Really an extra extra special guest. Lisa Shalitt, chief Investment

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Speaker 2: Officer at Morgan Stanley, has had a number of fascinating

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Speaker 2: roles in Wall Street, which is kind of amusing considering

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Speaker 2: she had no interest in working on Wall Street and

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Speaker 2: yet she was CEO and chairman at Sanford Bernstein. She

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Speaker 2: was CIO at Merrill Lynch Asset Management, and now CIO

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Speaker 2: at both Morgan Stanley Wealth Management and runs their asset

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Speaker 2: on location models and their outsourced chief investment Officer model,

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Speaker 2: So she's seen this industry from all sides. Not only

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Speaker 2: is CEO running operations running a substantial firm, but as

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Speaker 2: CIO for Morgan Stanley is over six trillion dollars, she

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Speaker 2: is directly responsible for one hundred billion dollars. There are

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Speaker 2: a few people in this industry who understand what it's

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Speaker 2: like to work with institutions, work with families, work with

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Speaker 2: individuals as well as work with advisors and brokers the

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Speaker 2: way Lisa does. She absolutely has a unique background and

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Speaker 2: a unique perch on wealth management and what's going on

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Speaker 2: in the world. I found this conversation to be absolutely fascinating,

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Speaker 2: and I think you will also, with no further ado,

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Speaker 2: my conversation with Morgan Stanley's Lisa Shallion.

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Speaker 3: Thank you. It's great to be here, Baik.

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Speaker 2: Great to have you. I've really been looking forward to

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Speaker 2: this conversation. You have an absolutely bonkers CV. We'll get

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Speaker 2: into that in a little bit. I'm just old better

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Speaker 2: than the alternative, I like to say it, right, But

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Speaker 2: let's start with your background in your career Applied mathematics

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Speaker 2: and economics from Brown and then a Harvard MBA. That

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Speaker 2: sounds like you were on a career path to a

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Speaker 2: Wall Street quant from early on. Tell us what what

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Speaker 2: the career plans were?

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Speaker 3: Not at all? Right? I in college, I was a

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Speaker 3: drivetime disc jockey. I you know, abhord the idea of

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Speaker 3: working on Wall Street. Uh, and so you know, coming

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Speaker 3: out of school, once I realized that journalists and folks

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Speaker 3: in radio don't make much money in the long run.

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Speaker 3: No offense, h this is my side hustle to anyone

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Speaker 3: around here. You know, I thought I was going to

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Speaker 3: take the high road and be a management consultant. So

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Speaker 3: that's what I did for the first job.

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Speaker 2: So what changed your mind to say, all right, let

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Speaker 2: me let me go see what these finance bros On

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Speaker 2: Wall Street are all about.

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Speaker 3: Yeah. So, you know, I did the consulting thing both

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Speaker 3: before and after business school, and you know, fundamentally, I

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Speaker 3: was never home. I was traveling and on an airplane

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Speaker 3: all the time. I was literally arriving back home Saturday mornings,

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Speaker 3: leaving Sunday nights. You know, I was starting to hit

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Speaker 3: that you know those magic numbers in the thirties when

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Speaker 3: women are like, if I don't get it done now,

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Speaker 3: it's now or never. So I took the plunge. I quit.

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Speaker 3: I did not have a job, and I said, Okay,

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Speaker 3: I'm going to go out there and see what's going on.

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Speaker 3: I knew that I wanted to work with clients. That

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Speaker 3: was one of the pieces of the consulting gig that

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Speaker 3: appealed to me. I wanted to work with super smart

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Speaker 3: people also, something I had loved in that career, and

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Speaker 3: I really just you know, wanted to be somewhere where

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Speaker 3: I was constantly learning and growing. Right, and I'm a

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Speaker 3: New Yorker, So I was coming home most of the

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Speaker 3: search people at that time, you know, said to me,

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Speaker 3: the only place ago if you want to do that,

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Speaker 3: is Wall Street. I kind of balked, and they said,

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Speaker 3: but there's just this one place. There's this one place,

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Speaker 3: and the one place for those on Wall Street in

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Speaker 3: the mid nineties that was very special, was very independent,

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Speaker 3: was Sanford Bernstein. I walked in the door and I

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Speaker 3: literally fell in love. I can honestly tell you from

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Speaker 3: the minute I walked in the door, I knew I

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Speaker 3: was home, and I always thought I would die there.

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Speaker 3: But obviously, you know, life is long and stuff happens.

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Speaker 3: But it was a wonderful, wonderful It was the seminal

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Speaker 3: chapter in my career.

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Speaker 2: I'm trying to remember. Did they get rolled up with

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Speaker 2: Pimcohen from Alliance? Is that right? So Alliance burning?

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Speaker 3: So Sanford ce Bernstein was independent. When founder mister Bernstein passed,

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Speaker 3: we needed to settle his estate and a decision was

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Speaker 3: made to merge with Alliance Capital, which was a growth

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Speaker 3: shop at the time. We thought it would be synergistic

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Speaker 3: because the asset management business of Sanford Bernstein, as everyone

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Speaker 3: I think knows, was a deep value shop, right, and

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Speaker 3: so that merger happened. I want to say, somewhere in

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Speaker 3: the in the early two thousands, we became Alliance Spernstein,

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Speaker 3: and you know then, you know, we kind of wrote

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Speaker 3: it to till the Great Financial Crisis, and our deep

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Speaker 3: value exposure to financials kind of helped unwind us quite

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Speaker 3: a bit. And I think, you know, Alliance Spernstein really

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Speaker 3: spun for quite a long time. It took, you know,

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Speaker 3: a long long time to get out of that mess.

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Speaker 3: I left because I got tired of firing all my friends.

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Speaker 2: That's tough, Yeah, because you were not just in the

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Speaker 2: investing side, you were chair and CEO, chief executive officer. Yes,

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Speaker 2: that's got to be a very difficult experience right in

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Speaker 2: the teeth of the financial price it.

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Speaker 3: Was got awful. And really, you know, the trauma was

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Speaker 3: when lu Sanders, who at the time had been the

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Speaker 3: storied CEO of the firm, he had been my personal rabbi,

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Speaker 3: when he was asked to step down, and you know,

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Speaker 3: therein began I think the unraveling and a little bit

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Speaker 3: of the the loss of that you know, cultural juice

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Speaker 3: that had kind of historically made that firm special.

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Speaker 2: So you leave Sanford Bernstein then, which had really become

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Speaker 2: Alliance Bernstein end up at Merrill Lynch where eventually your

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Speaker 2: same role chief investment officer for Bank America Merrill Lynch

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Speaker 2: Wealth Management. First, was there still remnants of Mother Merrill

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Speaker 2: when you joined post merger?

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Speaker 3: There were certainly remnants, So you know, just to reframe,

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Speaker 3: you know, folks who are Wall Street historians will understand

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Speaker 3: this chapter. One of the reasons I went to meryll

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Speaker 3: Is I was recruited by one of my best friends,

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Speaker 3: who is Sally Crotchek.

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Speaker 2: Oh.

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Speaker 3: Really, Sally and I grew up at Sanford Bernstein together

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Speaker 3: as baby analysts, and at that time she was running

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Speaker 3: you know, the Merrill Lynch brokerage business for b of A,

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Speaker 3: and she hired me to come in and be the

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Speaker 3: chief investment officer at Wealth Management. If you remember, during

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Speaker 3: this period of time was right after the financial crisis,

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Speaker 3: the worst of it. It was twenty ten, twenty eleven,

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Speaker 3: and you know, she had kind of gone to bat

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Speaker 3: very controversially asking the bank to protect clients on some

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Speaker 3: of the products that had gone bad, and that didn't

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Speaker 3: go so well for her, and within four months of

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Speaker 3: my arrival she actually heard that she was fired on TV.

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Speaker 3: We were together in her office, and there was literally

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Speaker 3: a cry on on the bus atom of the screen

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Speaker 3: that says, you know, Crawcheck to leave Bank of America Marylynch.

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Speaker 2: Well, that was sweet of them to do it that way.

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Speaker 2: You know, I have a vivid recollection from the people

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Speaker 2: we were talking, Yeah, Franco and Dave Roseen Kergan. I

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Speaker 2: know a lot of Rich Barnstein and all these people

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Speaker 2: I know from the two thousands Yes era Merrill Lynch.

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Speaker 2: And one of the fascinating things about Sally Kratchek was

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Speaker 2: her defense of the Merrill Lynch brands Yes post merger,

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Speaker 2: and she really helped turn around a malaise just a

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Speaker 2: lack of office morale amongst here. You have this storied

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Speaker 2: name that was picked up on the cheap during the

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Speaker 2: financial crisis and was wildly underperforming as an organization. And

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Speaker 2: full credit to her for really saving Merri Lynch as

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Speaker 2: a name and turning tens of thousands of people's jobs around.

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Speaker 2: She really did you's work there, didn't she?

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Speaker 3: Yes?

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Speaker 2: Absolutely, So you become chief investment officer for Bank America

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Speaker 2: Merrill Lynch Wealth Management. What did you take away from that?

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Speaker 2: You've had this role in several organizations, what was really

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Speaker 2: unique and special about Bank America Marill Lynch.

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Speaker 3: Yeah. So what you know, when I was running the

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Speaker 3: wealth management business, you know, reflecting on my experience with

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Speaker 3: Sanford Bernstein, Sanford Bernstein was what we call a closed shop, right,

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Speaker 3: all the clients were getting proprietary Sanford Bernstein asset management product.

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Speaker 3: And when I arrived at Merrill Lynch, it was really

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Speaker 3: my first exposure to really entrepreneurial, extremely talented and aggressive

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Speaker 3: financial advisors who were operating with what we in the

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Speaker 3: industry call an open architecture platform right where they could

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Speaker 3: you know, kind of place best of brief product with

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Speaker 3: their clients. And so that opened a whole new world

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Speaker 3: for me in thinking about asset allocation and thinking about advice,

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Speaker 3: and thinking about active and passive constructions together, thinking about alternatives,

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Speaker 3: and so you know, what made Merrill extraordinarily special were

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Speaker 3: the financial advisors who were just spectacular to your point,

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Speaker 3: the Thundering Herd.

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Speaker 2: Yep, yep, remember those those ads from like the sixties

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Speaker 2: and sevenss on TV. They were absolutely unique. So culturally

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Speaker 2: I have to think Sanford Bernstein and Merrill Lynch were

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Speaker 2: both very different. What did you bring from those two

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Speaker 2: organizations to your work at Morgan Stanley, either philosophically or cultural.

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Speaker 3: Yeah. So I think from from my time at San

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Speaker 3: for Bernstein, I like to think I brought, you know,

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Speaker 3: kind of my love of original research, my love of

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Speaker 3: you know, that independent streak, that desire to really you know,

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Speaker 3: call out conflict of interest and say no, this is

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Speaker 3: you know, this is what the numbers really tell you.

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Speaker 3: I like to think I brought that. I think, you know,

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Speaker 3: from Merrill it was really that appreciation of how do

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Speaker 3: you work through financial advisor so and you know, as

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Speaker 3: a chief investment officer, how do you earn the trust

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Speaker 3: of financial advisors to have influence? Right, because they're what

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Speaker 3: stand between you and the client. And so, you know,

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Speaker 3: I think I think I started that process in my

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Speaker 3: career at Meryl. I think in many ways I still

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Speaker 3: wake up every day and I think I've got more

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Speaker 3: to learn in terms of how to be a better

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Speaker 3: partner to financial advisors today at Morgan Stanley.

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Speaker 2: And what It's kind of interesting given the open architecture

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Speaker 2: at Merrill and the proprietary work at Alliance Earnstein, Morgan

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Speaker 2: Stanley's a little bit of both. You have consilient research

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Speaker 2: and a number of people running their own funds that

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Speaker 2: are specific to Morgan Stanley as well as the open architecture.

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Speaker 2: How do you look at the combination of both closed

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Speaker 2: and open together.

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Speaker 3: Yeah, well, look, I think it does a lot of things. First,

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Speaker 3: it avails me of some of the best colleagues on

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Speaker 3: the planet, right, so I'm surrounded not only by folks

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Speaker 3: in the wealth management business, but obviously I'm attached to

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Speaker 3: one of the best equity in trading franchises globally. And

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Speaker 3: then to your point, you know, connected to pms that

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Speaker 3: you know are walking the floors with me. But look,

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Speaker 3: you know, I want to be really clear when I

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Speaker 3: think about my clients. We're arm's length, so proprietary product

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Speaker 3: might be appropriate for them if they're open to it. If,

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Speaker 3: on the other hand, they say conflicts of interest matter

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Speaker 3: a lot to me, I want everything to totally transparent.

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Speaker 3: We have those options as well. So you know, I

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Speaker 3: think about it as as you know, we we work

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Speaker 3: with clients. We do what clients are in their best interest.

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Speaker 3: And I know it sounds a little bit like an advertisement,

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Speaker 3: but I really believe that.

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Speaker 2: Well, the next question, the obvious question is who are

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Speaker 2: the clients? Are they institutions, are they households? Are they

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Speaker 2: a little bit of both?

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Speaker 3: Yeah. So, as you may know, Barry, you know, over

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Speaker 3: the last you know, really decade since since Gorman acquired

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Speaker 3: Smith Barney, we've been expanding our footprint in terms of

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Speaker 3: the client segments that we are focused on serving really exponentially.

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Speaker 3: So while you might once upon a time have thought about,

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Speaker 3: you know, the Morgan Stanley Financial Advisors as as you know,

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Speaker 3: serving that ultra high net worth you know, core client,

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Speaker 3: you know, now we're you know, serving folks in the

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Speaker 3: mass market through e trade, We're serving family offices, We're

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Speaker 3: serve institutions. We've done acquisitions in the stock plan businesses

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Speaker 3: and the retirement businesses. You noted in my bio that

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Speaker 3: I run help run one of our ocio businesses, our

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Speaker 3: outsource where we're working with foundations and endowments and family offices.

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Speaker 3: So now we're everywhere, and we're serving every type of

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Speaker 3: wealth client internationally, domestic, self directed through a brokerage account,

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Speaker 3: all the way through complete discretionary.

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Speaker 2: I recall back in the day Morgan Stanley as well,

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Speaker 2: they're kind of a Goldman Sachs want to bee and

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Speaker 2: that is no longer the case, it's the best of Goldman,

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Speaker 2: the best of Merril and on. This is really inside

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Speaker 2: baseball stuff. So I apologize to listeners, but on the

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Speaker 2: league tables to say who's number one and underwriting, who's

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Speaker 2: number one? In attracting new wealth management, who's number one

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Speaker 2: in self directed? Like, you guys are competitive across the board,

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Speaker 2: and it's not like the old days where Goldman has

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Speaker 2: a good year and they you know, take the top

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Speaker 2: spot everywhere. That doesn't seem to happen anymore. It seems

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Speaker 2: like the industry has become so competitive you want to

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Speaker 2: be in the top five or top ten. But the

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Speaker 2: days of you know, taking number one with a bullet

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Speaker 2: across all these different areas, they really seem to have faded.

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Speaker 3: Yeah, they have. I mean, I think that ours is

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Speaker 3: a business in almost every segment that requires a lot

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Speaker 3: of scale, and as you know, developing scale very often

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Speaker 3: means investing aggressively in tech, investing aggressively in talent, and

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Speaker 3: you've got to pick your spots right. And so you know,

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Speaker 3: to your point, I think every you know segment today

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Speaker 3: is a little bit of a gunfight. I like to

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Speaker 3: think that you know, in core wealth management, Morgan Stanley,

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Speaker 3: and you know where we've come, you know, first under

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Speaker 3: James Gorman and now hopefully under Ted Picks. Leadership is

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Speaker 3: really you know, differentiating us and allowing us to pull

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Speaker 3: away from the pack at least in wealth management.

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Speaker 2: And you mentioned the investment in technology and people and

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Speaker 2: the ability to scale at your size, and there's only

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Speaker 2: you know, a dozen or two companies that can make

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Speaker 2: this claim that flywheel begins to become very self reinforcing,

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Speaker 2: and you have the ability to just continue to add

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Speaker 2: divisions to fill in. Oh we're a little soft here.

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Speaker 2: Let's let's bulk this up a little bit and put

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Speaker 2: a little muscle on it because we have the ability

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Speaker 2: to offer these services to all our clients. What's it

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Speaker 2: been like watching the how long have you there?

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Speaker 3: You're there almost thirteen?

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Speaker 2: They're over at dteen. Yeah, so and from twenty twelve

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Speaker 2: to twenty twenty five, that's a huge run. A lot

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Speaker 2: of big financial players, Vanguard, black Rock going down the

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Speaker 2: list have really added some heft, So is Morgan Stanley.

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Speaker 2: What's been like watching that over the past decade plus.

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Speaker 3: Yeah, it's been extraordinarily exciting for us. Obviously, you always

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Speaker 3: want to be working in a growth business, and so

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Speaker 3: you know, we've been in a situation where we're hiring people,

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Speaker 3: which is always exciting. We're going after new types of clients,

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Speaker 3: new problems, new situations, which keeps you on your toes

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Speaker 3: and keeps you growing, and you know, really completely new

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Speaker 3: business segments. I mean, I can't tell you how to

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Speaker 3: your point that flywheel between moving up market into institutions

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Speaker 3: feeds your self directed business. I mean, let me just

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Speaker 3: give you an example. Let's assume that we are administering

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Speaker 3: a stock plan for a large corporate client. Now we're

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Speaker 3: going in and we're saying to that corporate client. Instead

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Speaker 3: of you know, having a financial advisor going to the

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Speaker 3: country club on Saturday acquiring a client, mono we mono

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Speaker 3: one at a time, we're now walking into a c

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Speaker 3: suite and saying to that CFO or that chief talent officer, Hey,

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Speaker 3: can we provide all of your employees with a financial

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Speaker 3: wellness program? Can we give every single one of your

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Speaker 3: employees a free financial plan? Can we give every single

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Speaker 3: one of your employees a account or advice you know,

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Speaker 3: to their first you know, purchase in a five twenty

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Speaker 3: nine account. Things like that where suddenly you're acquiring clients

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Speaker 3: at scale.

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Speaker 2: Huh, really really interesting. So let's talk a little bit

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Speaker 2: about Morgan Stanley. We mentioned you a previously at Alliance

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Speaker 2: Bernstein and then you were at Bank America Merrill Lynch.

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Speaker 2: What led you to make the jump to Morgan Stanley.

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Speaker 3: So I had when, as I noted, I'd gone to

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Speaker 3: work at Meryl very much to partner with my very

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Speaker 3: good friend Sally Kratchek, and after she had left, I

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Speaker 3: made the decision that without her there, I kind of

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Speaker 3: felt among the you know, the thundering herd without a rabbi,

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Speaker 3: if you will, And I left it. At that point,

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Speaker 3: I really thought I was going to do my own thing.

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Speaker 3: I thought I was going to do something entrepreneurial. I

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Speaker 3: thought I might join an RIA or form my own

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Speaker 3: RIA at that point, and I just I got a

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Speaker 3: call from Greg Fleming. Greg Fleming was one of the

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Speaker 3: co presidents at Morgan Stanley at the time, and he said, look,

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Speaker 3: you know, I have a lot of contacts over there

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Speaker 3: at Merrill Lynch. The financial advisors really love you, you know,

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Speaker 3: come on in and meet our team. And so I

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Speaker 3: I did, and you know, I had a very similar

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Speaker 3: feeling to that feeling I had when I first went

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Speaker 3: into Bernstein of you know, these are just great people

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Speaker 3: and I would enjoy working with the people. And you know,

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Speaker 3: before I knew it, there I was, you know, sitting

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Speaker 3: next to Mike Wilson, who I know, you know, Mike

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Speaker 3: was taking a stint of rotation through wealth management, and

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Speaker 3: you know I joined I joined him to build the

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Speaker 3: team and and really you know, create the platform that

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Speaker 3: we have today. When when Morgan Stanley and Smith Barney

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Speaker 3: were merging, there was really no centralized CIO office. It

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Speaker 3: was the only place that that that talent was coming

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Speaker 3: from was from Smith Barney, from the Smith Barney side,

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Speaker 3: And so we wanted to recraft a more Morgan Stanley

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Speaker 3: integrated firm offering, and so I joined Mike Wilson to

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Speaker 3: help build that.

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Speaker 2: So, so let's talk a little bit about what goes

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Speaker 2: into managing one hundred plus billion dollars in assets. How

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Speaker 2: do you develop that, how do you think about asset allocation?

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Speaker 2: And how do you think about the end clients? Given

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Speaker 2: how broad your audience and clients are, how do you

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Speaker 2: create a set of options that checks all the boxes

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Speaker 2: that you know you need to check? To do this

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Speaker 2: right but also gives a broad variety of clients what

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Speaker 2: they're looking for.

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Speaker 3: Yeah, so Barry for US asset allocation. All asset allocations

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Speaker 3: starts with financial planning, and all financial planning starts with

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Speaker 3: the client. But you can't do a financial plan without

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Speaker 3: having what we call capital market assumptions. You know, what

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Speaker 3: do we think every asset class is going to do

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Speaker 3: over the next three, five, ten, twenty years. Our customization

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Speaker 3: of asset allocation really starts with financial planning. That is

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Speaker 3: the lynch pin, and we fundamentally believe that you've got

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Speaker 3: to understand a client's cash flow, that the client has

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Speaker 3: to understand their own cash flows. You know. One of

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Speaker 3: the things that I know, you know, having worked with

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Speaker 3: a lot of clients, is very often clients don't know

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Speaker 3: themselves right the the good old fashion, Hey, I'm kind

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Speaker 3: of aggressive, I'm kind of conservative. Those are such non

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Speaker 3: normative terms. You never know, are we talking about politics,

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Speaker 3: are we talking.

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Speaker 2: About you know, how about whatever the market did the

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Speaker 2: past six months?

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Speaker 3: And so so, working through the behavioral pieces, the getting

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Speaker 3: to know your client, the working through a plan with them,

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Speaker 3: really getting into what are their hopes, wishes, dreams, you know,

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Speaker 3: what does money mean to them? Why have they accumulated it?

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Speaker 3: How have they accumulated it? H What do they hope

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Speaker 3: their legacy will be? Does it have to do with

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Speaker 3: a charity, you know, a cause, a family member or

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Speaker 3: members and build a plan from them?

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Speaker 2: Huh? Really really quite interesting. So, since you've joined Morgan Stanley,

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Speaker 2: and I'm going to assume this isn't a coincidence, their

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Speaker 2: focus has increasingly been on the wealth management side of

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Speaker 2: the business, which was a big change to the nineteen

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Speaker 2: nineties and the two thousands, tell us a little bit

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Speaker 2: about why and how this focus shifted and what your role.

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Speaker 3: Is in that. Sure. So look, I think you know

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Speaker 3: this is I think history is going to be extraordinarily

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Speaker 3: kind to James Gorman. I think James I feel so

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Speaker 3: extraordinarily lucky to have served in the firm while he

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Speaker 3: was the CEO. I think, you know, strategically, you know,

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Speaker 3: back during the financial crisis, he developed a vision and

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Speaker 3: that vision was I believe that the wealth management business

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Speaker 3: is a growth oriented business. I believe it needs scale,

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Speaker 3: and I believe that when combined with the more cyclical

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Speaker 3: markets based businesses or the banking based businesses, can add

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Speaker 3: ballast and create shareholder value. And I think that he

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Speaker 3: embraced that vision, and that vision had kind of three

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Speaker 3: chapters to it. The first was, you know, let's buy

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Speaker 3: Smith Barney and get physical scale, right, just the physical

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Speaker 3: scale of a large number of advisors. Let's invest aggressively

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Speaker 3: in technology to support those advisors. I think the second

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Speaker 3: part of that growth was to say, let's transform how

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Speaker 3: we serve our clients and the client segments that we serve.

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Speaker 3: And they started to explore these other acquisitions. First the

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Speaker 3: acquisitions of these stock plan businesses, which are essentially tech businesses,

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Speaker 3: tech platform businesses, but would allow us to go from

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Speaker 3: acquiring clients one at a time to in groups. And

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Speaker 3: then you know, the last piece of the strategy was

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Speaker 3: really you know, let's let's go after E Trade and

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Speaker 3: Eat Advance and acquire those and then we'll have the

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Speaker 3: machinery so that you can, you know, acquire clients at

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Speaker 3: the early stages of their life cycle, allow them to

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Speaker 3: be self directed, and ultimately graduate to advice, so that

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Speaker 3: your financial advisors actually constantly have a source of new clients,

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Speaker 3: of new wealth clients that they don't have to be

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Speaker 3: at the country club every single weekend.

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Speaker 2: So what you're describing is you're starting with clients that

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Speaker 2: have no minimum and they're self directed to d trade.

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Speaker 2: I don't mean this in a negative way. They sort

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Speaker 2: of move up or graduate to a little more full service.

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Speaker 2: They want a financial plan, they want some advice, they

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Speaker 2: want to think about whether it's saving for home or college,

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Speaker 2: or or or retirement. And then the next step up

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Speaker 2: seems to be full on wealth management, where you're dealing

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Speaker 2: with philanthropy, generational wealth transfer, a lot of bells and whistles,

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Speaker 2: including a state planning tax. You guys offer the full

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Speaker 2: suite of.

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Speaker 3: Services, absolutely, and I think one of the things that

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Speaker 3: a lot of folks don't know about us is we're

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Speaker 3: the eight hundred pound gorilla in actually offering alternatives to

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Speaker 3: private wealth clients. You know, we are larger than some

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Speaker 3: of our well known competitors by a factor, and so

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Speaker 3: what that means is we're now in a position we're

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Speaker 3: literally about eighty percent of the alternatives that I would

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Speaker 3: show you as a client are either you know, first look,

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Speaker 3: meaning we're getting the first look or or best price

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Speaker 3: by a lot.

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Speaker 2: So it's funny because you mentioned Gorman taking over from

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Speaker 2: his predecessor. Yeah, John macko Mac, who I've had on

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Speaker 2: the show, who was just delightful. But the Mac era

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Speaker 2: of Morgan Stanley seemed to have more successfully navigated the

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Speaker 2: financial crisis than many of their competitors, And part of

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Speaker 2: me can't help but feel that coming out of the

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Speaker 2: crisis in better shape than so many others really allowed

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Speaker 2: more instantly to blow up over the next fifty when

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Speaker 2: when everybody else had blown up during the financial crisis

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Speaker 2: in the bad way, they really bulked up in the

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Speaker 2: good way. Following that is is that a fair assessment?

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Speaker 3: That that is a fair assessment, Barry, I think I

434
00:27:39,240 --> 00:27:42,400
Speaker 3: look at it in a very particular way. A host

435
00:27:42,520 --> 00:27:47,679
Speaker 3: of our competitors were forced quote unquote into the arms

436
00:27:47,680 --> 00:27:51,200
Speaker 3: of the big banks, right, So the the b of

437
00:27:51,280 --> 00:27:59,280
Speaker 3: a Meryll situation, right, Jp, Morgan exactly you had you had,

438
00:27:59,440 --> 00:28:03,200
Speaker 3: you know, city had to make choices around Smith Barney.

439
00:28:03,240 --> 00:28:07,199
Speaker 3: It was very, very hard what what Mac and James

440
00:28:07,240 --> 00:28:11,120
Speaker 3: Gorman did to rescue Morgan Stanley. And literally they talk

441
00:28:11,200 --> 00:28:15,119
Speaker 3: about it as an overnight rescue where half the employees

442
00:28:15,160 --> 00:28:18,480
Speaker 3: were packing the boxes just like everybody else, uh, and

443
00:28:18,560 --> 00:28:22,120
Speaker 3: the other half were were on the phone with colleagues

444
00:28:22,160 --> 00:28:25,439
Speaker 3: in Japan. And as you may recall, what saved Morgan

445
00:28:25,520 --> 00:28:30,960
Speaker 3: Stanley was a huge equity infusion from MUFG, from Mitsubishi

446
00:28:31,320 --> 00:28:35,840
Speaker 3: Financial Group. And what was wonderful about that is, not

447
00:28:35,920 --> 00:28:39,800
Speaker 3: only was it premised on a fantastic, you know, partnership,

448
00:28:40,080 --> 00:28:44,440
Speaker 3: but it was an arms length partnership that allowed the

449
00:28:44,480 --> 00:28:48,840
Speaker 3: business to be rescued but not devoured. And I think

450
00:28:48,920 --> 00:28:53,520
Speaker 3: that for some of our competitors who were suddenly during

451
00:28:53,560 --> 00:28:58,720
Speaker 3: the Great Financial Crisis inside you know, systemically important banks,

452
00:28:59,360 --> 00:29:04,160
Speaker 3: their knees right just by sheer dint of size got

453
00:29:04,200 --> 00:29:07,840
Speaker 3: squashed a little bit because the bank obviously had, you know,

454
00:29:07,960 --> 00:29:11,640
Speaker 3: the CEOs of City, the CEO of Chase, the CEO

455
00:29:12,240 --> 00:29:15,000
Speaker 3: at Wells, the CEO at b of A. You know,

456
00:29:15,080 --> 00:29:18,080
Speaker 3: they're sitting there with the Fed and the SEC every

457
00:29:18,120 --> 00:29:20,640
Speaker 3: five minutes. Now I'm not saying Morgan Stanley wasn't at

458
00:29:20,680 --> 00:29:24,320
Speaker 3: those meetings, but the stakes were different because we weren't

459
00:29:24,600 --> 00:29:28,400
Speaker 3: a commercial bank with a balance sheet the size that

460
00:29:28,440 --> 00:29:29,000
Speaker 3: those guys have.

461
00:29:29,240 --> 00:29:33,600
Speaker 2: But even more importantly is you're at Alliance Bernstein. Bernstein

462
00:29:33,640 --> 00:29:36,400
Speaker 2: gives up control in the merger. You're at Meryl, Meryl

463
00:29:36,440 --> 00:29:39,360
Speaker 2: gives up control in the merger, third times a charm

464
00:29:39,400 --> 00:29:42,840
Speaker 2: when you end up at Morgan Stanley. Mitsubishi had a

465
00:29:42,840 --> 00:29:47,320
Speaker 2: substantial steak, but they didn't take a controlling steak, and

466
00:29:47,480 --> 00:29:51,560
Speaker 2: the local US based management were able to continue making

467
00:29:51,600 --> 00:29:55,560
Speaker 2: the choices they made. I have to think that was

468
00:29:55,760 --> 00:30:00,160
Speaker 2: just a giant home run investment for our MUFG. That

469
00:30:00,240 --> 00:30:02,320
Speaker 2: has to be just a giant winner for them.

470
00:30:03,600 --> 00:30:06,080
Speaker 3: And I And you know, I think if again, if

471
00:30:06,120 --> 00:30:08,920
Speaker 3: you go back and look at it, you know where

472
00:30:08,960 --> 00:30:12,880
Speaker 3: where are the Morgan Stanley stock bottomed and where we

473
00:30:12,960 --> 00:30:16,200
Speaker 3: are today? Like I said, I think the history books

474
00:30:16,240 --> 00:30:18,719
Speaker 3: are going to be quite kind to mister Gorman.

475
00:30:19,240 --> 00:30:22,120
Speaker 2: And you know, you you talked about some of the

476
00:30:22,160 --> 00:30:26,400
Speaker 2: acquisitions Smith Barney, Eating Vance. I'm trying to remember the

477
00:30:26,560 --> 00:30:29,480
Speaker 2: direct indexer you bought. I didn't know if it came

478
00:30:29,680 --> 00:30:32,760
Speaker 2: through Eaton Vance. Yes, what was that? Parametric?

479
00:30:33,000 --> 00:30:38,560
Speaker 3: Yes? Yes, so yeah, so fantastic, Uh Memory Barry, because

480
00:30:39,120 --> 00:30:45,040
Speaker 3: that has been transformational as you know, indexing, tax management, Uh,

481
00:30:45,240 --> 00:30:49,160
Speaker 3: direct indexing or the ability to customize are you know,

482
00:30:49,320 --> 00:30:52,120
Speaker 3: all demands and and it's a tech it's a very

483
00:30:52,240 --> 00:30:56,000
Speaker 3: tech heavy business. So Parametric was buried inside of Eating Vance.

484
00:30:56,080 --> 00:31:00,040
Speaker 3: It is you know definitely diamonds in the rough that

485
00:30:59,920 --> 00:31:05,000
Speaker 3: we got and now is a key capability offering within

486
00:31:05,040 --> 00:31:05,960
Speaker 3: the suite of products.

487
00:31:06,000 --> 00:31:09,440
Speaker 2: Huh. Really fascinating. So let's talk a little bit about

488
00:31:09,440 --> 00:31:12,360
Speaker 2: what's going on these days, and I want to start

489
00:31:12,440 --> 00:31:15,640
Speaker 2: with a quote of yours that I really like. We're

490
00:31:15,720 --> 00:31:19,400
Speaker 2: all long term investors until the market goes down, and

491
00:31:19,440 --> 00:31:22,640
Speaker 2: we're recording this in the midst of a pretty healthy

492
00:31:22,960 --> 00:31:29,680
Speaker 2: sell off in February and March, especially now that the

493
00:31:29,760 --> 00:31:35,160
Speaker 2: new North American tariffs seem to be taking place. Tell us,

494
00:31:35,200 --> 00:31:38,120
Speaker 2: what why do we give up our long term perspectives

495
00:31:38,120 --> 00:31:40,200
Speaker 2: once the market starts heading south.

496
00:31:40,760 --> 00:31:44,680
Speaker 3: So there's the emotions and then there's the math, right,

497
00:31:44,720 --> 00:31:47,440
Speaker 3: So what I always say is that you know what

498
00:31:47,800 --> 00:31:51,520
Speaker 3: the Nobel Prize winners and behavioral economics will tell you

499
00:31:52,280 --> 00:31:57,480
Speaker 3: is that emotionally, losses hurt four to five times more

500
00:31:57,560 --> 00:32:05,880
Speaker 3: than gains satisfy. And that's actually intuitively appropriate because typically

501
00:32:06,160 --> 00:32:10,280
Speaker 3: our wealth, we feel it has taken blood, sweat and

502
00:32:10,360 --> 00:32:15,320
Speaker 3: tears to acquire or accumulate. And when we experience a loss, right,

503
00:32:15,320 --> 00:32:19,440
Speaker 3: a fifty percent loss can happen right in a very

504
00:32:19,480 --> 00:32:23,240
Speaker 3: short period of time, But to round trip and recover

505
00:32:23,640 --> 00:32:26,520
Speaker 3: our high water mark. We've got to be up one

506
00:32:26,600 --> 00:32:30,320
Speaker 3: hundred percent, right, which may take us twice the three

507
00:32:30,360 --> 00:32:34,880
Speaker 3: times as long. And so the math is asymmetric. The

508
00:32:34,920 --> 00:32:39,840
Speaker 3: emotions are asymmetric, and fear, as we know, just the

509
00:32:39,880 --> 00:32:42,960
Speaker 3: same way. When things are running hard and you feel

510
00:32:42,960 --> 00:32:46,200
Speaker 3: like you've got the pomo and the missing out, it's greed.

511
00:32:46,760 --> 00:32:49,280
Speaker 3: When you know there's a lot of red on the screen,

512
00:32:49,520 --> 00:32:53,400
Speaker 3: people are you know, your stomach's you know, totally seizing up,

513
00:32:53,480 --> 00:32:56,240
Speaker 3: and it's about fear. I don't want to experience loss.

514
00:32:56,360 --> 00:32:58,760
Speaker 3: I don't want to have to make a decision of

515
00:32:58,800 --> 00:32:59,720
Speaker 3: what do I do here?

516
00:33:00,080 --> 00:33:03,160
Speaker 2: Yeah, the asymmetries are really fascinating. I am not a

517
00:33:03,160 --> 00:33:06,720
Speaker 2: fan of Vegas or casinos, but I go there as

518
00:33:06,720 --> 00:33:10,320
Speaker 2: a sociologist and I always find it amusing that right

519
00:33:10,360 --> 00:33:14,160
Speaker 2: off the casino floor is a big, beautiful jewelry store

520
00:33:14,240 --> 00:33:19,160
Speaker 2: filled with lots of expensive watches, and because those gains,

521
00:33:19,920 --> 00:33:24,160
Speaker 2: it's house money. It's ephemeral, but losses are an existential thread.

522
00:33:24,200 --> 00:33:26,959
Speaker 2: It currently feels like the world is coming to an end.

523
00:33:27,000 --> 00:33:31,120
Speaker 2: Exact for get down fifty percent. We're recording this five six,

524
00:33:31,240 --> 00:33:34,080
Speaker 2: seven percent off the highs and people are talking like

525
00:33:34,160 --> 00:33:36,680
Speaker 2: it's the end of the world. Let's talk about another

526
00:33:36,680 --> 00:33:39,560
Speaker 2: one of your quotes, that kind of quote my eye,

527
00:33:39,680 --> 00:33:45,040
Speaker 2: which was discussing the Great normalization? What is the Great normalization?

528
00:33:45,600 --> 00:33:50,200
Speaker 3: So, you know, we've been trying to remind clients how

529
00:33:50,280 --> 00:33:56,040
Speaker 3: extraordinary in financial history the past fifteen years have been.

530
00:33:56,240 --> 00:34:00,440
Speaker 3: Since the Great Financial Crisis, We've had an unprecedented level

531
00:34:00,920 --> 00:34:06,560
Speaker 3: of Federal Reserve involvement. We've had markets that have been

532
00:34:06,760 --> 00:34:10,560
Speaker 3: buttressed by the Federal Reserve balance sheet, that have been

533
00:34:10,719 --> 00:34:15,799
Speaker 3: buttressed by a disproportioned amount of time having financial repression

534
00:34:15,920 --> 00:34:20,680
Speaker 3: or low rates, rates being held down. We've had gone

535
00:34:20,760 --> 00:34:27,160
Speaker 3: through the COVID crisis, which stimulated unprecedented fiscal stimulus. As

536
00:34:27,200 --> 00:34:33,520
Speaker 3: a share of GDP and performance, what clients have actually

537
00:34:33,800 --> 00:34:37,400
Speaker 3: experienced If you go back to March of two thousand

538
00:34:37,440 --> 00:34:40,680
Speaker 3: and nine, right, and you and I remember March of

539
00:34:40,680 --> 00:34:44,759
Speaker 3: two thousand and nine, the bottom we were probably looking

540
00:34:44,840 --> 00:34:47,040
Speaker 3: at an S and P five hundred that was trading

541
00:34:47,040 --> 00:34:49,000
Speaker 3: in the mid six hundred.

542
00:34:48,800 --> 00:34:51,399
Speaker 2: Sixty six six. I remember the devil's bottom.

543
00:34:51,120 --> 00:34:54,200
Speaker 3: The devil's bottom, and look at where we are now.

544
00:34:54,280 --> 00:34:57,680
Speaker 3: Even though we're off, we're still up. During that time

545
00:34:57,880 --> 00:35:01,560
Speaker 3: nine x nine x over fifteen years. So I tell

546
00:35:01,600 --> 00:35:04,600
Speaker 3: people what, let's put this in perspective, What that kind

547
00:35:04,600 --> 00:35:09,120
Speaker 3: of mathematically translates to is we've for fifteen years, we've

548
00:35:09,200 --> 00:35:14,000
Speaker 3: compounded at about fifteen percent per year. So that's two

549
00:35:14,160 --> 00:35:18,200
Speaker 3: times normal for a business cycle. Let's call it a

550
00:35:18,440 --> 00:35:21,640
Speaker 3: you know, where we had two very short recessions, two

551
00:35:21,840 --> 00:35:26,080
Speaker 3: back to back very long business cycles. Not normal. What

552
00:35:26,280 --> 00:35:30,800
Speaker 3: was also not normal is during that time the degree

553
00:35:30,840 --> 00:35:35,600
Speaker 3: to which US exceptionalism and the US outperformed the rest

554
00:35:35,640 --> 00:35:39,040
Speaker 3: of the world. I mean, we were outperforming every year,

555
00:35:39,160 --> 00:35:42,360
Speaker 3: year in year out by six hundred and seven hundred

556
00:35:42,360 --> 00:35:45,480
Speaker 3: basis points per year. And so when we you know,

557
00:35:45,640 --> 00:35:48,719
Speaker 3: kind of came into January of twenty twenty five, we

558
00:35:48,719 --> 00:35:51,040
Speaker 3: were starting to talk to folks about, look at where

559
00:35:51,040 --> 00:35:56,320
Speaker 3: the dollar is versus virtually every other currency, super strong.

560
00:35:56,840 --> 00:36:01,000
Speaker 3: Look at the share of US equities versus the rest

561
00:36:01,000 --> 00:36:04,400
Speaker 3: of the world. We're ten percent of the world's population,

562
00:36:04,800 --> 00:36:08,440
Speaker 3: we're twenty five percent of the world's GDP, we're thirty

563
00:36:08,440 --> 00:36:12,560
Speaker 3: three percent of global corporate profits, but we were sixty

564
00:36:12,840 --> 00:36:18,960
Speaker 3: seven percent of all stock market gap just extreme. And

565
00:36:19,040 --> 00:36:22,960
Speaker 3: so what we were starting to talk to clients about is, look,

566
00:36:23,320 --> 00:36:26,480
Speaker 3: this is an extraordinary amount of large s, and a

567
00:36:26,560 --> 00:36:30,640
Speaker 3: lot of it has come from FED accommodation from stimulus.

568
00:36:31,080 --> 00:36:33,640
Speaker 3: Now we're on the other side of that. We have

569
00:36:33,840 --> 00:36:38,520
Speaker 3: a very robust economy. We've relevered the economy, if you will.

570
00:36:38,560 --> 00:36:43,040
Speaker 3: Where the leverage of the private sector, the household sector,

571
00:36:43,080 --> 00:36:45,560
Speaker 3: the corporate sector that got us into the Great financial

572
00:36:45,640 --> 00:36:49,359
Speaker 3: crisis that's been healed. Right. We have households that can

573
00:36:49,400 --> 00:36:52,760
Speaker 3: still carry for the most part, their interest burdens.

574
00:36:52,560 --> 00:36:53,960
Speaker 2: Very very low historically.

575
00:36:54,080 --> 00:36:54,279
Speaker 3: Right.

576
00:36:54,400 --> 00:36:57,799
Speaker 2: It's not the total debt, it's the debt relative to

577
00:36:57,840 --> 00:37:00,319
Speaker 2: discretionary come exactly exactly.

578
00:37:00,480 --> 00:37:05,680
Speaker 3: Corporations that still have an extraordinarily relative low locked in

579
00:37:05,920 --> 00:37:11,000
Speaker 3: cost to capital. And what's become relevered is the federal

580
00:37:11,040 --> 00:37:14,359
Speaker 3: balance sheet and the government balance sheet. And now here

581
00:37:14,400 --> 00:37:18,040
Speaker 3: we are in every couple of decades, we have to

582
00:37:18,080 --> 00:37:22,360
Speaker 3: go through these periods where there's heat in the economy,

583
00:37:22,360 --> 00:37:26,200
Speaker 3: and inflation is one manifestation of the heat. Real growth

584
00:37:26,239 --> 00:37:29,880
Speaker 3: and investment is another manifestation of the heat. But the

585
00:37:29,920 --> 00:37:33,319
Speaker 3: other manifestation is you probably have overdone it on the

586
00:37:33,360 --> 00:37:35,839
Speaker 3: stimulus and you've got to pull it back and there's

587
00:37:35,880 --> 00:37:38,400
Speaker 3: going to be some pain. So when we talk about normalization.

588
00:37:38,520 --> 00:37:41,280
Speaker 3: We say, look, we're not going back to two percent

589
00:37:41,360 --> 00:37:46,080
Speaker 3: interest rates, normal cost of capital in an economy like

590
00:37:46,120 --> 00:37:50,960
Speaker 3: Americas that has real fundamental growth of two percent and

591
00:37:51,120 --> 00:37:54,879
Speaker 3: real inflation or experienced inflation of two and a half

592
00:37:54,920 --> 00:37:57,480
Speaker 3: to three, which is what we've had for the last

593
00:37:57,520 --> 00:38:02,240
Speaker 3: eighty years, right, not two percent target that the Fed says. Right.

594
00:38:02,760 --> 00:38:06,200
Speaker 3: What that tells you is that long term rates used

595
00:38:06,239 --> 00:38:10,200
Speaker 3: to be normal at five to six percent, right, that's

596
00:38:10,239 --> 00:38:14,640
Speaker 3: not crazy, right, and yet the market continued to sell

597
00:38:14,640 --> 00:38:17,279
Speaker 3: it at twenty two times forward multiple. So what we've

598
00:38:17,280 --> 00:38:19,560
Speaker 3: been saying is part of the great normalization is over

599
00:38:19,560 --> 00:38:24,200
Speaker 3: the next couple of years, we think long rates start

600
00:38:24,280 --> 00:38:27,200
Speaker 3: to move towards five to six percent like they were

601
00:38:27,239 --> 00:38:30,640
Speaker 3: in the aughts, in the two thousands and in the nineties, right,

602
00:38:31,120 --> 00:38:35,239
Speaker 3: and multiples start mean reverting a little bit to seventeen.

603
00:38:35,440 --> 00:38:39,040
Speaker 3: And that's the great normalization. Your earnings actually start growing

604
00:38:39,080 --> 00:38:40,279
Speaker 3: into those multiples.

605
00:38:40,600 --> 00:38:43,480
Speaker 2: You mentioned the two percent target of the Federal Reserve.

606
00:38:43,560 --> 00:38:47,520
Speaker 2: Did you work with Roger Ferguson when he was at Meryl. No,

607
00:38:47,600 --> 00:38:51,239
Speaker 2: I did not, But he eventually became vice chairman of

608
00:38:51,280 --> 00:38:55,320
Speaker 2: the Federal Reserve. Yes, and put out this delightful research

609
00:38:55,400 --> 00:39:01,600
Speaker 2: piece that said the two percent inflation target comes from

610
00:39:01,640 --> 00:39:04,920
Speaker 2: a New Zealand television show in the nineteen eighties and

611
00:39:04,960 --> 00:39:07,560
Speaker 2: it has nothing whatsoever to do with the modern economy.

612
00:39:08,080 --> 00:39:11,680
Speaker 2: I'm to this day delighted by that. And I don't

613
00:39:11,760 --> 00:39:15,080
Speaker 2: understand why the Federal Reserve continues to be so locked

614
00:39:15,080 --> 00:39:17,800
Speaker 2: in on two percent, which we had in the twenty

615
00:39:17,840 --> 00:39:21,560
Speaker 2: tens when yes, deflation was at risk. Correct Now that

616
00:39:21,600 --> 00:39:25,280
Speaker 2: we've moved from a monetary regime to a physical regime,

617
00:39:26,080 --> 00:39:29,080
Speaker 2: three percent seems to make more sense and we're there,

618
00:39:29,200 --> 00:39:31,600
Speaker 2: We're there. I don't know why they're stuck on that.

619
00:39:32,440 --> 00:39:36,280
Speaker 2: I think they're just afraid of making mistake Again. Part

620
00:39:36,320 --> 00:39:40,000
Speaker 2: of the normalization that, hey, the Fed's a little behind

621
00:39:40,040 --> 00:39:43,200
Speaker 2: the curve with what's going on in the rest of

622
00:39:43,239 --> 00:39:43,800
Speaker 2: the economy.

623
00:39:43,960 --> 00:39:46,759
Speaker 3: No, exactly, And I think one of the things that

624
00:39:47,520 --> 00:39:51,359
Speaker 3: has the market having to adjust is this idea of

625
00:39:51,400 --> 00:39:55,799
Speaker 3: a data driven FED. Right in a world where the

626
00:39:55,840 --> 00:39:59,920
Speaker 3: Fed's the only headline and the FED is giving forward guidance,

627
00:40:00,200 --> 00:40:03,720
Speaker 3: it's really easy to have low vall and for everyone

628
00:40:03,760 --> 00:40:08,520
Speaker 3: to just ride momentum. But in a normal world where

629
00:40:08,560 --> 00:40:12,440
Speaker 3: the FED has to respond to economic data, you and

630
00:40:12,520 --> 00:40:16,400
Speaker 3: I know economic data is a manifestation of human behavior.

631
00:40:16,400 --> 00:40:19,759
Speaker 3: It's volatile, right, So the FED is going to be

632
00:40:19,800 --> 00:40:23,480
Speaker 3: more volatile. Policy is going to be more volatile. It

633
00:40:23,680 --> 00:40:27,759
Speaker 3: means your interest rate curve, your yield curve, needs to

634
00:40:27,800 --> 00:40:31,759
Speaker 3: have some term premium in it. Remember that. And that's

635
00:40:31,840 --> 00:40:35,160
Speaker 3: part of the Great normalization. You know. I do the

636
00:40:35,200 --> 00:40:38,520
Speaker 3: math when I do some of my chats with the

637
00:40:38,560 --> 00:40:43,480
Speaker 3: younger folks on the team, and I say, okay, real growth, inflation,

638
00:40:44,120 --> 00:40:48,480
Speaker 3: term premium, see this thing. It's been zero or negative

639
00:40:48,480 --> 00:40:51,000
Speaker 3: for the last fifteen years. That's not normal.

640
00:40:51,200 --> 00:40:54,279
Speaker 2: So wait, you're saying the thirty year bond should pay

641
00:40:54,280 --> 00:40:56,839
Speaker 2: a higher yield than the ten year bond, higher than

642
00:40:56,840 --> 00:41:00,480
Speaker 2: the two year. Yes, I'm not familiar with. It's been

643
00:41:00,800 --> 00:41:02,799
Speaker 2: opposite for me, exactly. It's so hard.

644
00:41:02,800 --> 00:41:03,240
Speaker 3: Exactly.

645
00:41:03,360 --> 00:41:07,800
Speaker 2: So another quote of yours, which I assume is related

646
00:41:07,800 --> 00:41:11,000
Speaker 2: to this, is the era of set it and forget

647
00:41:11,040 --> 00:41:13,600
Speaker 2: it is over? Is that what we're saying here?

648
00:41:13,760 --> 00:41:17,040
Speaker 3: Yes, exactly. So you know what comes out of this

649
00:41:17,239 --> 00:41:20,560
Speaker 3: idea of the Great normalization is it's also an era

650
00:41:21,040 --> 00:41:25,399
Speaker 3: where we can't just passively close our eyes. By the

651
00:41:25,440 --> 00:41:28,239
Speaker 3: S and P five hundred market cap weighted index and

652
00:41:28,360 --> 00:41:32,320
Speaker 3: go to bed. It was a great fifteen year run.

653
00:41:32,840 --> 00:41:37,120
Speaker 3: But our view is that as cost of capital readjusts

654
00:41:37,360 --> 00:41:41,279
Speaker 3: as it's actually a positive number. This is where the

655
00:41:41,320 --> 00:41:45,400
Speaker 3: skill of corporate management starts to differentiate winners and losers.

656
00:41:45,440 --> 00:41:48,880
Speaker 3: And we move back to a world, right, and you

657
00:41:48,960 --> 00:41:51,480
Speaker 3: and I grew up in this world, that that fun

658
00:41:51,600 --> 00:41:55,239
Speaker 3: world where you're actually stock picking, where the research that

659
00:41:55,520 --> 00:42:00,520
Speaker 3: individual fundamental analysts were doing mattered and you had to say, hey, hey,

660
00:42:00,800 --> 00:42:03,399
Speaker 3: these guys are going to win because these management teams

661
00:42:03,400 --> 00:42:07,880
Speaker 3: are taking strategies that can work, and these management teams

662
00:42:08,040 --> 00:42:08,960
Speaker 3: are dropping the ball.

663
00:42:09,320 --> 00:42:13,960
Speaker 2: Huh. Really really super interesting given all of these changes

664
00:42:14,000 --> 00:42:17,640
Speaker 2: that we're witnessing. And again this is something else you've

665
00:42:17,640 --> 00:42:21,960
Speaker 2: written about, how do you separate the signal from the noise?

666
00:42:22,040 --> 00:42:26,680
Speaker 2: What's your process for filtering out what's just the noisy

667
00:42:26,800 --> 00:42:31,000
Speaker 2: data that's within the margin of error or just slightly

668
00:42:31,040 --> 00:42:35,080
Speaker 2: beyond and genuine important market information.

669
00:42:35,360 --> 00:42:37,520
Speaker 3: So this is the art, right, This is the art

670
00:42:37,640 --> 00:42:40,640
Speaker 3: of all of it, is separating the noise in the signal.

671
00:42:40,760 --> 00:42:45,880
Speaker 3: For us, the signal is always operates ultimately on just

672
00:42:45,920 --> 00:42:49,239
Speaker 3: two axes. Is what's really going on in terms of

673
00:42:49,280 --> 00:42:52,479
Speaker 3: the rate of change? In growth and what is going

674
00:42:52,520 --> 00:42:55,120
Speaker 3: on in terms of the rate of change of inflation,

675
00:42:55,600 --> 00:42:58,120
Speaker 3: because the rate of change of inflation is going to

676
00:42:58,200 --> 00:43:02,520
Speaker 3: give you an indication of policy bias, of rate bias.

677
00:43:02,960 --> 00:43:05,080
Speaker 3: And if you can focus on those two things and

678
00:43:05,239 --> 00:43:07,480
Speaker 3: every single piece of data you get, you say, what

679
00:43:07,480 --> 00:43:09,600
Speaker 3: does this mean for growth? What does this mean for inflation?

680
00:43:10,320 --> 00:43:12,640
Speaker 3: You can try to keep yourself sane at night.

681
00:43:13,000 --> 00:43:18,920
Speaker 2: Huh So, I'm curious as to February was a tough month.

682
00:43:19,760 --> 00:43:23,160
Speaker 2: We've seen volatility spike now up to twenty three or so.

683
00:43:23,200 --> 00:43:25,960
Speaker 2: I haven't even looked at it today with markets off

684
00:43:26,000 --> 00:43:30,760
Speaker 2: a couple of percent. The questions you're getting from clients,

685
00:43:31,160 --> 00:43:33,560
Speaker 2: what are you hearing, What are you hearing about tariffs,

686
00:43:33,640 --> 00:43:37,920
Speaker 2: about the post election regime change, about what's going on

687
00:43:38,040 --> 00:43:41,719
Speaker 2: in geopolitics, what's lighting your phone up? And what are

688
00:43:41,719 --> 00:43:42,640
Speaker 2: you telling these folks?

689
00:43:43,120 --> 00:43:45,560
Speaker 3: You know, obviously we would love to spend the bulk

690
00:43:45,600 --> 00:43:50,000
Speaker 3: of our time talking about asset allocation as it corresponds

691
00:43:50,040 --> 00:43:55,000
Speaker 3: to growth and inflation. Unfortunately, exactly to your point, Barry,

692
00:43:55,000 --> 00:43:58,640
Speaker 3: we're spending a disproportionate amount of time out of our

693
00:43:58,680 --> 00:44:04,759
Speaker 3: comfort zone being asked to respond to our understanding and

694
00:44:04,920 --> 00:44:09,840
Speaker 3: our expectations for the economic impacts of policy, and what

695
00:44:10,000 --> 00:44:15,200
Speaker 3: has complicated things, as you know, is that this administration

696
00:44:15,719 --> 00:44:20,319
Speaker 3: has chosen to implement policy fast and furious, and in

697
00:44:20,400 --> 00:44:24,360
Speaker 3: many cases quote unquote in parallel, right. I think that,

698
00:44:25,200 --> 00:44:28,680
Speaker 3: you know, coming off of the election, coming off of

699
00:44:28,719 --> 00:44:33,080
Speaker 3: the campaign season, a lot of us were trying, you know,

700
00:44:33,160 --> 00:44:36,400
Speaker 3: to build models based on well, they're going to sequence

701
00:44:36,520 --> 00:44:39,239
Speaker 3: things right, They're gonna you know, deliver some of the

702
00:44:39,280 --> 00:44:42,080
Speaker 3: bad news early and then you know the candy will

703
00:44:42,160 --> 00:44:46,720
Speaker 3: come at the end. I think what we're experiencing, especially

704
00:44:46,760 --> 00:44:50,200
Speaker 3: after the last fifteen years of this kind of one

705
00:44:50,320 --> 00:44:53,160
Speaker 3: or two note market right where it's been, what is

706
00:44:53,200 --> 00:44:57,799
Speaker 3: the FED saying, oh generative AI looks like good headlines

707
00:44:58,280 --> 00:45:04,040
Speaker 3: to seventeen lines a day of policy flood the zone.

708
00:45:04,280 --> 00:45:08,160
Speaker 3: So clients are asking for certainty, they're asking for clarity,

709
00:45:08,200 --> 00:45:11,600
Speaker 3: and it's hard. I'm going to be honest with you. So, look,

710
00:45:11,760 --> 00:45:14,480
Speaker 3: we're in the camp and this is a pure economic view.

711
00:45:14,560 --> 00:45:19,239
Speaker 3: I hope I'm not going to be accused of being political.

712
00:45:20,360 --> 00:45:24,719
Speaker 3: Pure economists will tell you that tariffs, particularly if implemented

713
00:45:24,760 --> 00:45:27,920
Speaker 3: over long periods of time and to the extent that

714
00:45:27,960 --> 00:45:33,439
Speaker 3: they cause trade war reciprocity tend to be destructive to

715
00:45:33,440 --> 00:45:37,759
Speaker 3: total global trade, and aggregate tend to be a one

716
00:45:37,800 --> 00:45:43,800
Speaker 3: time inflationary problem and tend you know, to to really

717
00:45:44,000 --> 00:45:47,480
Speaker 3: you know, kind of hurt the efficiency of markets, and

718
00:45:47,560 --> 00:45:50,120
Speaker 3: so I think we're seeing some of that. I think

719
00:45:50,160 --> 00:45:54,279
Speaker 3: it's very hard for CEOs and CFOs today to be

720
00:45:54,360 --> 00:45:59,359
Speaker 3: making decisions not knowing what the policy duration is going

721
00:45:59,400 --> 00:46:01,520
Speaker 3: to be. It's one thing to have a policy and say, okay,

722
00:46:01,520 --> 00:46:05,120
Speaker 3: we're deregulating X, or here's the new tax policy for

723
00:46:05,160 --> 00:46:07,960
Speaker 3: the next four years. I can work with that. When

724
00:46:08,000 --> 00:46:11,600
Speaker 3: you tell me we're having twenty five percent tariffs on lumber,

725
00:46:12,800 --> 00:46:16,960
Speaker 3: well how long, how much? Where aw's it going? You know?

726
00:46:17,920 --> 00:46:21,880
Speaker 3: I think that's the big question is is the inconsistency

727
00:46:21,920 --> 00:46:25,040
Speaker 3: of it and the questions of is this a negotiating tactic,

728
00:46:25,360 --> 00:46:29,280
Speaker 3: what are we negotiating for? How do I model it?

729
00:46:29,360 --> 00:46:32,680
Speaker 2: That kind of thing, And you know, it's really hard

730
00:46:32,920 --> 00:46:36,600
Speaker 2: to get a handle on this because let's just use

731
00:46:36,840 --> 00:46:41,080
Speaker 2: Canada and Mexico. The first tariff was floated and then

732
00:46:41,080 --> 00:46:43,799
Speaker 2: it was quickly resolved and it felt, oh, this is

733
00:46:43,880 --> 00:46:49,080
Speaker 2: just a negotiating tactic. The effect of the second twenty

734
00:46:49,120 --> 00:46:52,760
Speaker 2: five percent tariffs on Mexico and Canada and ten percent

735
00:46:52,840 --> 00:46:59,040
Speaker 2: tariffs on China. And it's not only surprising that it

736
00:46:59,120 --> 00:47:04,640
Speaker 2: was done, it's kind of perplexing. What are we getting

737
00:47:04,680 --> 00:47:08,719
Speaker 2: out of the tariffs with Canada When you look at

738
00:47:09,680 --> 00:47:13,520
Speaker 2: some of the supposed bases for this. The fentanyl that

739
00:47:13,560 --> 00:47:16,919
Speaker 2: comes into the United States is mostly brought in by

740
00:47:17,239 --> 00:47:22,520
Speaker 2: US citizens and smugglers. It's not coming in from either

741
00:47:22,600 --> 00:47:28,080
Speaker 2: Canadian lumber or oil, or televisions that are being built

742
00:47:28,080 --> 00:47:32,160
Speaker 2: in Mexico and sent over the border. It's you know,

743
00:47:32,880 --> 00:47:37,360
Speaker 2: it's kind of odd, especially given the North American Free

744
00:47:37,400 --> 00:47:44,680
Speaker 2: Trade Agreement that was negotiated to replace NAFTA was Trump's tready.

745
00:47:44,880 --> 00:47:47,960
Speaker 2: So the whole thing is kind of you know, clients

746
00:47:48,000 --> 00:47:50,040
Speaker 2: don't like to hear you say I have no idea

747
00:47:50,120 --> 00:47:54,560
Speaker 2: what's going on, and beware of people who say they do.

748
00:47:55,080 --> 00:47:59,080
Speaker 2: But it really feels like this is sort of arbitrary

749
00:47:59,160 --> 00:48:02,960
Speaker 2: and capricious, and we don't really know how this resolves.

750
00:48:03,000 --> 00:48:05,239
Speaker 2: It's sort of grit your teeth and write it out.

751
00:48:05,440 --> 00:48:08,600
Speaker 2: Is brace yourself, mauthora. That's what it feels like.

752
00:48:08,800 --> 00:48:12,640
Speaker 3: Just hold on and the way I always frame things,

753
00:48:12,680 --> 00:48:15,600
Speaker 3: as I say to people, look what kind of risk

754
00:48:15,719 --> 00:48:20,319
Speaker 3: premiums are there in the markets? When stocks are very expensive,

755
00:48:20,560 --> 00:48:24,000
Speaker 3: as they have been for a while, here, it tells

756
00:48:24,040 --> 00:48:27,480
Speaker 3: you risk premiums are tight. Right, things are quote unquote

757
00:48:27,480 --> 00:48:31,479
Speaker 3: price for perfection. When credit spreads are tight, it tells

758
00:48:31,520 --> 00:48:35,279
Speaker 3: you people are not requiring a premium for fear or

759
00:48:35,320 --> 00:48:40,279
Speaker 3: default or uncertainty. Right when there are no term premiums

760
00:48:40,400 --> 00:48:43,440
Speaker 3: in the in the United States Treasury curve, it's telling

761
00:48:43,480 --> 00:48:46,520
Speaker 3: you the same thing. So look, if this were all

762
00:48:46,719 --> 00:48:51,279
Speaker 3: happening against a backdrop where stocks were selling it fifteen times,

763
00:48:51,760 --> 00:48:55,400
Speaker 3: where you know we had eight hundred you know, basis points,

764
00:48:55,440 --> 00:48:58,799
Speaker 3: spreads in high yield, all this kind of stuff, you

765
00:48:58,840 --> 00:49:02,320
Speaker 3: and I might be saying, Hey, guys, yes there's uncertainty,

766
00:49:02,320 --> 00:49:05,000
Speaker 3: but this is a buying opportunity. Look, you know things

767
00:49:05,000 --> 00:49:07,440
Speaker 3: are selling off off of fifteen multiple. Where do you

768
00:49:07,440 --> 00:49:09,680
Speaker 3: think they're going to land at thirteen? We're going to

769
00:49:09,719 --> 00:49:15,440
Speaker 3: buy here, but we're not there. Markets hate uncertainty, and

770
00:49:15,480 --> 00:49:19,520
Speaker 3: they really hate uncertainty. When things are priced for perfection.

771
00:49:20,000 --> 00:49:21,480
Speaker 2: Does it give you a lot of room for error?

772
00:49:21,719 --> 00:49:26,640
Speaker 2: So let's talk about something more positive. AI has been

773
00:49:26,640 --> 00:49:29,719
Speaker 2: the big story for the past couple of years. Let's

774
00:49:29,800 --> 00:49:33,279
Speaker 2: talk a little bit about that and other emerging technologies

775
00:49:33,360 --> 00:49:38,200
Speaker 2: or innovations you think might impact the investing landscape over

776
00:49:38,239 --> 00:49:40,239
Speaker 2: the next decade. What are you looking at?

777
00:49:40,400 --> 00:49:43,200
Speaker 3: Yeah, so we're looking at at a lot of things.

778
00:49:43,239 --> 00:49:48,359
Speaker 3: But look, clearly, generative AI is transformative, There's no doubt

779
00:49:48,400 --> 00:49:52,600
Speaker 3: about it. I think the conundrum for investors is how

780
00:49:52,640 --> 00:49:58,400
Speaker 3: do you stay ahead of the revolution itself? And what

781
00:49:58,440 --> 00:50:02,840
Speaker 3: I mean by that is that, you know, technology innovation

782
00:50:03,560 --> 00:50:08,640
Speaker 3: tends to follow very clear scripts over history, and by that,

783
00:50:08,800 --> 00:50:12,840
Speaker 3: I mean you tend to get the big infrastructure build,

784
00:50:13,480 --> 00:50:16,800
Speaker 3: then you get the software applications, and then you get

785
00:50:17,120 --> 00:50:23,800
Speaker 3: mass economy wide deployment, and in that sequence you get

786
00:50:24,120 --> 00:50:27,440
Speaker 3: new killer apps and the quote unquote the winners of

787
00:50:27,480 --> 00:50:32,400
Speaker 3: that era. I'm not entirely sure that all the winners

788
00:50:32,400 --> 00:50:36,279
Speaker 3: have been identified with regard to generative AI. And while

789
00:50:36,280 --> 00:50:41,600
Speaker 3: the Magnificent Seven are magnificent on many, many, many financial attributes,

790
00:50:41,640 --> 00:50:46,440
Speaker 3: on many innovation attributes, you know, I think the market

791
00:50:46,520 --> 00:50:49,720
Speaker 3: is telling you that maybe they are not the only

792
00:50:49,800 --> 00:50:54,560
Speaker 3: winners here, and that maybe the growth in the infrastructure

793
00:50:54,560 --> 00:50:57,880
Speaker 3: build doesn't go on forever. And certainly our experience with

794
00:50:57,960 --> 00:51:01,719
Speaker 3: the Internet validates that. So you know, what are we

795
00:51:01,840 --> 00:51:06,840
Speaker 3: super excited about right now? We're we're super excited about

796
00:51:06,880 --> 00:51:11,560
Speaker 3: some of these AI adopters. We're looking at areas, whether

797
00:51:11,640 --> 00:51:19,160
Speaker 3: it's document recognition, voice recognition, all these various applications, the agents.

798
00:51:19,560 --> 00:51:22,920
Speaker 3: You know, how we're going to deploy AI into learning

799
00:51:23,000 --> 00:51:27,879
Speaker 3: agents to help human beings do things. Almost become the

800
00:51:28,040 --> 00:51:31,719
Speaker 3: white collar robot, if you will. I think, you know,

801
00:51:31,800 --> 00:51:36,160
Speaker 3: that's all very interesting, But where AI is likely to

802
00:51:36,200 --> 00:51:39,200
Speaker 3: have some of its most profound impacts is in healthcare

803
00:51:39,600 --> 00:51:42,720
Speaker 3: and the extent to which we're going to be able

804
00:51:42,760 --> 00:51:47,640
Speaker 3: to use large language models just to process data and

805
00:51:47,760 --> 00:51:54,120
Speaker 3: personalize medicine and personalized diagnostics and solutions, treatment plans so

806
00:51:54,360 --> 00:51:55,000
Speaker 3: much faster.

807
00:51:55,880 --> 00:52:01,280
Speaker 2: I saw a fascinating video the other day about AI

808
00:52:01,640 --> 00:52:04,960
Speaker 2: being used. So when you look at the history of healthcare,

809
00:52:04,960 --> 00:52:07,160
Speaker 2: it really started out as a little bit of chemistry,

810
00:52:07,280 --> 00:52:10,600
Speaker 2: and then it became biology, and then it became genomics.

811
00:52:10,640 --> 00:52:14,000
Speaker 2: And one of the challenges is trying to figure out

812
00:52:14,080 --> 00:52:20,160
Speaker 2: how protein folds and how different molecules interact with the

813
00:52:20,200 --> 00:52:25,080
Speaker 2: body's receptors and immune system. And it turned out that

814
00:52:25,560 --> 00:52:29,760
Speaker 2: like for the prior fifty years, we've identified a few

815
00:52:29,960 --> 00:52:35,880
Speaker 2: thousand different combinations of molecules and protein foldings, which is

816
00:52:36,400 --> 00:52:40,880
Speaker 2: key to figuring out what the genetic code operates in

817
00:52:40,960 --> 00:52:43,800
Speaker 2: actual life. And so they went from like the library

818
00:52:43,840 --> 00:52:49,320
Speaker 2: of twenty five hundred protein folding protocols to using AI

819
00:52:49,480 --> 00:52:52,960
Speaker 2: identifying like four hundred things exactly. Like it's an insane

820
00:52:53,080 --> 00:52:57,279
Speaker 2: order of magnitude, and we've only begun figuring out how

821
00:52:57,320 --> 00:53:00,400
Speaker 2: do these different proteins work on different parts of the

822
00:53:00,400 --> 00:53:04,319
Speaker 2: body and response to different diseases, infections, virus. It's like,

823
00:53:05,200 --> 00:53:11,480
Speaker 2: it's shocking that these aren't headlines yet. Yeah, just academic research,

824
00:53:11,560 --> 00:53:16,879
Speaker 2: but it seems like when people are talking about longevity,

825
00:53:16,960 --> 00:53:20,239
Speaker 2: it's not the cold plunge that's right to do it.

826
00:53:20,239 --> 00:53:23,160
Speaker 2: It's going to be all of these half a million

827
00:53:23,239 --> 00:53:26,160
Speaker 2: news correct protein designs tell us a little bit about

828
00:53:26,480 --> 00:53:29,760
Speaker 2: the investment opportunities that exist in the healthcare space.

829
00:53:29,840 --> 00:53:33,200
Speaker 3: So right now, you know, healthcare is one of the

830
00:53:33,200 --> 00:53:37,520
Speaker 3: sectors that we have moved over weight. You know, clearly

831
00:53:37,600 --> 00:53:40,719
Speaker 3: the healthcare sector over the last you know, decade, and

832
00:53:40,960 --> 00:53:43,640
Speaker 3: much of this bull market in large part has been

833
00:53:43,719 --> 00:53:47,759
Speaker 3: left behind and valuations have been you know, with the

834
00:53:47,800 --> 00:53:52,880
Speaker 3: exception of some of the obesity drugs, the pharmaceutical industry

835
00:53:52,920 --> 00:53:57,000
Speaker 3: has been squashed by by worries about regulation, squashed by

836
00:53:57,000 --> 00:54:00,000
Speaker 3: the power of the insurance companies, you know, squashed by

837
00:54:00,080 --> 00:54:02,759
Speaker 3: patent xpery, you know, squashed by a lot a lot

838
00:54:02,800 --> 00:54:06,640
Speaker 3: of things. But we think that that valuations are there.

839
00:54:06,920 --> 00:54:08,880
Speaker 3: We think that that's a great place to invest, and

840
00:54:09,239 --> 00:54:12,719
Speaker 3: you can do it obviously through venture and in the

841
00:54:12,760 --> 00:54:17,160
Speaker 3: public markets. Other themes that were super super excited about

842
00:54:17,200 --> 00:54:20,880
Speaker 3: our defense and space and the and the conjoint between

843
00:54:20,920 --> 00:54:25,960
Speaker 3: those two. You know, this idea that ultimately the way

844
00:54:26,080 --> 00:54:29,160
Speaker 3: we think about weaponry, the way we think about defense,

845
00:54:29,280 --> 00:54:34,040
Speaker 3: will be humanless, not unlike you know, some of what

846
00:54:34,080 --> 00:54:37,080
Speaker 3: you see in the sci fi movies and Star Wars,

847
00:54:37,560 --> 00:54:42,759
Speaker 3: unmanned vehicles doing the very surgical games of war, if

848
00:54:42,800 --> 00:54:45,960
Speaker 3: you will. So I think, you know, that's something we're

849
00:54:46,400 --> 00:54:49,760
Speaker 3: super excited about. Some of the innovations in the energy space,

850
00:54:50,640 --> 00:54:54,600
Speaker 3: not so much purely around clean tech or powering data center,

851
00:54:55,320 --> 00:54:59,160
Speaker 3: but really thinking about how do we more creatively use

852
00:54:59,680 --> 00:55:02,080
Speaker 3: and we're deuced dependency on some of these rare earth

853
00:55:02,280 --> 00:55:09,359
Speaker 3: materials to create battery autonomous vehicles another one. So all

854
00:55:09,400 --> 00:55:12,759
Speaker 3: of these areas the very very fascinating time to be

855
00:55:12,840 --> 00:55:15,239
Speaker 3: an investor in new tech.

856
00:55:15,760 --> 00:55:20,439
Speaker 2: Yeah, you mentioned autonomous and defense. This giant New York

857
00:55:20,480 --> 00:55:24,080
Speaker 2: Times article came out about the war in Ukraine and

858
00:55:24,280 --> 00:55:29,680
Speaker 2: the transition from World War one and two type trench warfare,

859
00:55:30,080 --> 00:55:36,920
Speaker 2: armored vehicles, tanks. Exactly seventy percent of the casualties inflicted

860
00:55:36,960 --> 00:55:41,000
Speaker 2: in the war as of recently are being driven by

861
00:55:41,160 --> 00:55:46,960
Speaker 2: drunk It's absolutely futuristic sci fi. When warfare changes that rapidly,

862
00:55:47,080 --> 00:55:49,759
Speaker 2: it has to make you raise the question, how do

863
00:55:49,840 --> 00:55:52,040
Speaker 2: the geopolitical alignments change?

864
00:55:53,040 --> 00:55:53,239
Speaker 3: Here?

865
00:55:53,280 --> 00:55:56,320
Speaker 2: We are, how we are? How do the big defense

866
00:55:56,360 --> 00:56:00,920
Speaker 2: companies like there's a reason Pallanteer has been super hot

867
00:56:01,760 --> 00:56:06,440
Speaker 2: and not necessarily Luckheyed Martin or Boeing correct, It's really

868
00:56:06,520 --> 00:56:10,520
Speaker 2: quite fascinating. I have two personal questions yes, to ask

869
00:56:10,600 --> 00:56:13,640
Speaker 2: you before we get to our favorite questions. All right,

870
00:56:14,040 --> 00:56:17,840
Speaker 2: starting with you wake up every morning at five oh seven.

871
00:56:18,520 --> 00:56:22,719
Speaker 2: So first, why five oh seven. It's such a specific

872
00:56:22,960 --> 00:56:26,120
Speaker 2: number as opposed to just setting the alarm for five

873
00:56:26,280 --> 00:56:29,439
Speaker 2: or five thirty. And then if you're up at five

874
00:56:29,480 --> 00:56:32,240
Speaker 2: oh seven, give us a day in the life of Morgan,

875
00:56:32,280 --> 00:56:34,080
Speaker 2: Stanley's chief investment.

876
00:56:33,680 --> 00:56:39,640
Speaker 3: Oh jeez, So I'm extraordinarily superstitious about odd numbers.

877
00:56:39,960 --> 00:56:47,279
Speaker 2: Really, yes, wait, you were applied mathematics undergraduate. Yeap, that doesn't.

878
00:56:47,400 --> 00:56:52,240
Speaker 3: It's just screams. I guess it's part of my lived

879
00:56:52,360 --> 00:56:55,600
Speaker 3: experience is that, you know, I always say to people, hey,

880
00:56:55,640 --> 00:56:59,080
Speaker 3: it's an odd number year, We're good, you know, really,

881
00:56:59,280 --> 00:57:01,960
Speaker 3: Oh my god, I'm very I'm very so I'm.

882
00:57:01,800 --> 00:57:04,680
Speaker 2: Trying to remember the Nobel laureate in physics. I'm drawing

883
00:57:04,719 --> 00:57:07,720
Speaker 2: a blank on his name. Who a grad student visited

884
00:57:07,800 --> 00:57:12,080
Speaker 2: his house and there's a horseshoe over the doorway. Yeah,

885
00:57:12,120 --> 00:57:15,560
Speaker 2: And the grad student says, professor, you don't you don't

886
00:57:15,600 --> 00:57:19,320
Speaker 2: believe in lucky charms and things like that, And the

887
00:57:19,400 --> 00:57:22,480
Speaker 2: response was maybe it was plank, I'm not sure, but

888
00:57:22,520 --> 00:57:25,440
Speaker 2: the response was, I'm told it works whether you believe

889
00:57:25,480 --> 00:57:28,280
Speaker 2: in it or not, which is which is pretty charming.

890
00:57:28,760 --> 00:57:31,640
Speaker 2: So but I believe in it. Odd numbers.

891
00:57:32,800 --> 00:57:35,840
Speaker 3: Is really so it's an odd number. So so look,

892
00:57:35,920 --> 00:57:39,080
Speaker 3: it was something you know, back in the day, one

893
00:57:39,120 --> 00:57:41,440
Speaker 3: of my jobs was I was a director of research,

894
00:57:41,480 --> 00:57:43,480
Speaker 3: and so I always had to be at my desk

895
00:57:43,560 --> 00:57:46,120
Speaker 3: right at six point thirty. So I got into the

896
00:57:46,200 --> 00:57:48,800
Speaker 3: routine of, you know, up five oh seven, you know,

897
00:57:48,880 --> 00:57:51,520
Speaker 3: do the quick twenty minutes on the treadmill, grabbed the

898
00:57:51,520 --> 00:57:54,480
Speaker 3: coffee shower out the door, and so that's you know,

899
00:57:54,640 --> 00:57:57,480
Speaker 3: still still me. You know, old dogs, new tricks. It's

900
00:57:57,520 --> 00:57:58,760
Speaker 3: been it's been really hard.

901
00:57:59,240 --> 00:58:02,560
Speaker 2: And how different and is every day as Cio is like,

902
00:58:02,960 --> 00:58:04,800
Speaker 2: I like to sometimes ask, what's the day in the

903
00:58:04,880 --> 00:58:07,760
Speaker 2: life like? But I suspect no two days are the

904
00:58:07,800 --> 00:58:08,680
Speaker 2: same for you.

905
00:58:08,240 --> 00:58:10,800
Speaker 3: Now, No two days are the same. But but Barry,

906
00:58:10,880 --> 00:58:14,880
Speaker 3: let me just tell you. I I wake up at

907
00:58:14,920 --> 00:58:16,840
Speaker 3: five h seven every day and the very first thing

908
00:58:17,000 --> 00:58:19,560
Speaker 3: I say is I am blessed that I have the

909
00:58:19,640 --> 00:58:22,480
Speaker 3: career that I have, that I have the seat that

910
00:58:22,600 --> 00:58:26,160
Speaker 3: I have at this point in my life because I

911
00:58:26,200 --> 00:58:30,400
Speaker 3: am learning every day. No two days are the same.

912
00:58:31,080 --> 00:58:33,960
Speaker 3: I get to hang out with the most amazing people

913
00:58:34,480 --> 00:58:38,200
Speaker 3: like you, you know, like my colleagues at Morgan Stanley,

914
00:58:38,440 --> 00:58:41,640
Speaker 3: like my clients, all of whom are you know, so

915
00:58:41,640 --> 00:58:45,920
Speaker 3: so interesting and successful in different ways. Going to meetings

916
00:58:46,000 --> 00:58:49,360
Speaker 3: where you get to hear Scott Vaisant speak at the

917
00:58:49,520 --> 00:58:53,560
Speaker 3: New York Economics Club, and you know, you're just really

918
00:58:53,920 --> 00:58:57,320
Speaker 3: feel alive, You feel plugged into the world and what's

919
00:58:57,400 --> 00:59:01,520
Speaker 3: going on. So I feel blessed every day and No,

920
00:59:01,640 --> 00:59:02,760
Speaker 3: two days are the same.

921
00:59:03,000 --> 00:59:07,400
Speaker 2: So last last career question. You've been watching the state

922
00:59:07,440 --> 00:59:10,560
Speaker 2: of the economy, the markets, just what's going on in

923
00:59:10,560 --> 00:59:14,560
Speaker 2: the world for just about twenty five thirty years. What's

924
00:59:14,600 --> 00:59:19,720
Speaker 2: been the most significant shift you've observed in wealth management

925
00:59:20,280 --> 00:59:21,160
Speaker 2: over that period.

926
00:59:22,040 --> 00:59:28,240
Speaker 3: Wow, that's a fantastic question. Look, I think if there

927
00:59:28,280 --> 00:59:32,680
Speaker 3: was one theme that I would say over my thirty

928
00:59:32,760 --> 00:59:37,200
Speaker 3: year career that has characterized everything, it has been the

929
00:59:37,320 --> 00:59:45,120
Speaker 3: democratization of reasonably sophisticated product. Right, So whether you know,

930
00:59:45,200 --> 00:59:48,760
Speaker 3: you talk about you know, first coming into the business

931
00:59:48,840 --> 00:59:53,160
Speaker 3: and the advent of you know, first mutual funds was

932
00:59:53,200 --> 00:59:58,480
Speaker 3: about democratization of you know, diversified stock investing U and

933
00:59:58,520 --> 01:00:01,280
Speaker 3: then you know passive asting as a way to get

934
01:00:01,320 --> 01:00:05,560
Speaker 3: access to an index in a you know, more technology

935
01:00:05,640 --> 01:00:10,840
Speaker 3: efficient way. You talk about the original rollout of quote

936
01:00:10,920 --> 01:00:16,080
Speaker 3: unquote liquid alternatives or evergreen type products. Uh, and now

937
01:00:16,160 --> 01:00:19,120
Speaker 3: we're at the point where, you know, we're talking about

938
01:00:19,640 --> 01:00:25,000
Speaker 3: very sophisticated private equity private credit products being contemplated for

939
01:00:25,120 --> 01:00:29,760
Speaker 3: four one K plans and being packaged in these structures

940
01:00:29,800 --> 01:00:34,920
Speaker 3: to give folks periodic liquidity. So democratization of you know,

941
01:00:35,200 --> 01:00:39,680
Speaker 3: sophisticated alpha and beta that that once upon a time,

942
01:00:39,800 --> 01:00:41,880
Speaker 3: I think, you know, when I, you know, started in

943
01:00:41,920 --> 01:00:44,560
Speaker 3: the industry, people would say, well, there's the market and

944
01:00:44,600 --> 01:00:47,280
Speaker 3: then there's the extra stuff, and that's and you've got

945
01:00:47,280 --> 01:00:49,320
Speaker 3: to figure it out. And if you don't like that,

946
01:00:49,520 --> 01:00:55,360
Speaker 3: own some bonds. I think now it's it's the democratization

947
01:00:55,480 --> 01:01:00,880
Speaker 3: of very sophisticated access, of access to sophistic cada products.

948
01:01:00,960 --> 01:01:04,680
Speaker 2: So let's jump to my favorite questions that I ask

949
01:01:04,800 --> 01:01:07,960
Speaker 2: all of my guests, starting with what are you streaming

950
01:01:07,960 --> 01:01:10,320
Speaker 2: these days? What are you watching to relax or on

951
01:01:10,360 --> 01:01:12,880
Speaker 2: the treadmill or just to keep you entertained?

952
01:01:13,280 --> 01:01:17,000
Speaker 3: Love streaming. The most recent thing I finished was something

953
01:01:17,000 --> 01:01:22,600
Speaker 3: called Shrinking Yeah, so yeah, so good. I've been watching

954
01:01:22,680 --> 01:01:23,560
Speaker 3: Prime Targets.

955
01:01:23,880 --> 01:01:25,000
Speaker 2: What are Prime Targets?

956
01:01:25,120 --> 01:01:25,240
Speaker 1: So?

957
01:01:25,400 --> 01:01:31,600
Speaker 3: Prime Target is a show about a mathematician who's working

958
01:01:31,640 --> 01:01:35,640
Speaker 3: in Oxford who is working on a thesis to generate

959
01:01:36,400 --> 01:01:42,960
Speaker 3: prime number combinations and permutations that supposedly, if he's able

960
01:01:43,040 --> 01:01:46,760
Speaker 3: to develop this algorithm as part of his PhD thesis,

961
01:01:47,480 --> 01:01:52,800
Speaker 3: would unlock or give folks the ability to hack almost

962
01:01:52,880 --> 01:01:58,800
Speaker 3: any system. And so of course it becomes a scenario

963
01:01:58,880 --> 01:02:01,760
Speaker 3: where you know, there's the bad guys are chasing him

964
01:02:01,920 --> 01:02:04,680
Speaker 3: to try to get his thing, and of course, you know,

965
01:02:04,800 --> 01:02:08,400
Speaker 3: the national security agencies are trying are chasing him, and

966
01:02:08,880 --> 01:02:11,120
Speaker 3: it's kind of a spy versus spy kind of thing,

967
01:02:11,120 --> 01:02:13,800
Speaker 3: and it's a poor, innocent nerd guy in the middle.

968
01:02:14,080 --> 01:02:15,920
Speaker 2: And what is surface or surfacing?

969
01:02:16,200 --> 01:02:21,400
Speaker 3: So Surface is a is a show also on Apple TV.

970
01:02:22,760 --> 01:02:26,040
Speaker 3: It's in its second season. It's about a woman who

971
01:02:26,440 --> 01:02:29,240
Speaker 3: kind of fakes her death as a way of leaving

972
01:02:29,360 --> 01:02:32,760
Speaker 3: behind her life and going back to England. She'd been

973
01:02:32,840 --> 01:02:35,400
Speaker 3: living in the United States. She was married in a

974
01:02:35,400 --> 01:02:39,200
Speaker 3: marriage that wasn't great, and she fakes her death to

975
01:02:39,280 --> 01:02:43,240
Speaker 3: go back to England to investigate what she thinks was

976
01:02:43,360 --> 01:02:45,880
Speaker 3: her mother's murder when she was a kid.

977
01:02:46,240 --> 01:02:50,160
Speaker 2: Huh really interesting. Let's talk about your early mentors who

978
01:02:50,240 --> 01:02:51,760
Speaker 2: helped shape your career.

979
01:02:52,080 --> 01:02:56,280
Speaker 3: Sure you know Bernstein was that seminal place, So the

980
01:02:56,360 --> 01:03:01,160
Speaker 3: two I would I would speak to one Sanders. Lou

981
01:03:01,240 --> 01:03:06,600
Speaker 3: Sanders was the CEO at Sanford Bernstein. In my humble opinion,

982
01:03:06,760 --> 01:03:10,640
Speaker 3: one of the greatest value investors, uh, certainly that I

983
01:03:10,720 --> 01:03:16,680
Speaker 3: ever met in my career. Just brilliant numbers person, very

984
01:03:16,800 --> 01:03:21,720
Speaker 3: very high integrity. Taught me how to be objective, to

985
01:03:21,760 --> 01:03:24,240
Speaker 3: get the emotions out of it, to build the model,

986
01:03:24,360 --> 01:03:27,800
Speaker 3: and have the discipline to build the model. Sally Crotchek,

987
01:03:27,920 --> 01:03:30,560
Speaker 3: we talked about one of my best friends in the business.

988
01:03:31,640 --> 01:03:34,840
Speaker 3: You know, someone that I care a lot about, someone

989
01:03:34,880 --> 01:03:38,760
Speaker 3: who showed me how to lead. Although we were peers.

990
01:03:38,880 --> 01:03:44,400
Speaker 3: She has a natural charisma and natural instinct for leading people.

991
01:03:45,680 --> 01:03:48,600
Speaker 3: She and I kind of worked side by side through

992
01:03:48,760 --> 01:03:52,360
Speaker 3: the nine to eleven crisis. I learned a lot from

993
01:03:52,400 --> 01:03:57,560
Speaker 3: her in terms of what people need from leaders when

994
01:03:57,600 --> 01:04:00,760
Speaker 3: things are tough. They look to leaders to say the

995
01:04:00,800 --> 01:04:03,560
Speaker 3: right things and do the right things and be strong

996
01:04:03,640 --> 01:04:07,880
Speaker 3: people and not get you know, bogged into headlines or theories,

997
01:04:07,960 --> 01:04:11,040
Speaker 3: but just to say, remember what we're here to do.

998
01:04:11,640 --> 01:04:13,960
Speaker 2: Let's talk about books. What are some of your favorites?

999
01:04:14,000 --> 01:04:14,560
Speaker 2: What are you reading?

1000
01:04:14,600 --> 01:04:18,000
Speaker 3: Commons? What am I reading? So now this is going

1001
01:04:18,040 --> 01:04:21,880
Speaker 3: to reveal my politics. The last book I finished was

1002
01:04:21,920 --> 01:04:25,840
Speaker 3: a book called Prequel by Rachel Maddow, and it's a

1003
01:04:25,960 --> 01:04:30,000
Speaker 3: very in the middle of reading, it's fantastic, she said,

1004
01:04:30,120 --> 01:04:34,760
Speaker 3: it's captivating, and it's fantastic. And it's captivating and fantastic

1005
01:04:34,840 --> 01:04:39,240
Speaker 3: not for good reasons, but it lays out some of

1006
01:04:39,280 --> 01:04:46,480
Speaker 3: the dynamics of American history and American political dynamics between

1007
01:04:46,520 --> 01:04:49,160
Speaker 3: the wars between World War One and World War Two

1008
01:04:50,520 --> 01:04:54,560
Speaker 3: and the First America First movement in the United States

1009
01:04:55,560 --> 01:05:00,479
Speaker 3: that was very much against America ever getting into World

1010
01:05:00,560 --> 01:05:01,280
Speaker 3: War Two.

1011
01:05:01,840 --> 01:05:05,080
Speaker 2: Very isolationists, very anti yes, and.

1012
01:05:05,040 --> 01:05:08,120
Speaker 3: It was, and it was in this a way that's

1013
01:05:08,160 --> 01:05:12,080
Speaker 3: similar to our current political dynamic. It ended up bringing

1014
01:05:12,120 --> 01:05:17,480
Speaker 3: in some very different factions right where you had, interestingly,

1015
01:05:17,680 --> 01:05:21,680
Speaker 3: coalitions of people who ended up being a political block

1016
01:05:22,040 --> 01:05:24,800
Speaker 3: who came at things from very different points of view.

1017
01:05:24,840 --> 01:05:28,040
Speaker 3: So you had kind of the Father Coglin part of

1018
01:05:28,080 --> 01:05:32,240
Speaker 3: the movement. Father Coglin, for those who know, was a

1019
01:05:32,480 --> 01:05:40,040
Speaker 3: very very famous Sunday radio show Catholic preacher and sacifist. Correct, Yeah,

1020
01:05:40,120 --> 01:05:44,920
Speaker 3: but very isolationist. That was one dimension of it. And

1021
01:05:44,960 --> 01:05:47,439
Speaker 3: then you had, you know, kind of the anti communist

1022
01:05:47,560 --> 01:05:52,040
Speaker 3: and the anti immigrant sides of the party and some

1023
01:05:52,160 --> 01:05:56,200
Speaker 3: other other dimensions to it. But it's fascinating book. Prequel

1024
01:05:56,360 --> 01:05:58,160
Speaker 3: Rachel Mattow really recommend it.

1025
01:05:58,560 --> 01:06:02,120
Speaker 2: Our final two questions, what sort of advice would you

1026
01:06:02,160 --> 01:06:05,760
Speaker 2: give to a recent college grad interested in a career

1027
01:06:06,240 --> 01:06:10,840
Speaker 2: in either wealth management or finance or anything related to

1028
01:06:10,920 --> 01:06:11,440
Speaker 2: your work?

1029
01:06:11,760 --> 01:06:17,439
Speaker 3: Yeah, so, and people hate when I say this because

1030
01:06:17,480 --> 01:06:21,640
Speaker 3: it belies the path that I took. But I'm a

1031
01:06:21,680 --> 01:06:26,240
Speaker 3: big believer in liberal arts education. I don't think that

1032
01:06:26,320 --> 01:06:30,600
Speaker 3: to work on Wall Street, to be a great portfolio manager,

1033
01:06:30,720 --> 01:06:33,480
Speaker 3: to be a great you know, economist, to be a

1034
01:06:33,520 --> 01:06:38,160
Speaker 3: great strategist, that you have to study finance or business

1035
01:06:38,200 --> 01:06:41,680
Speaker 3: administration or go to the Wharton School of Business. I

1036
01:06:41,760 --> 01:06:44,320
Speaker 3: do not believe that. I believe we live in a

1037
01:06:44,360 --> 01:06:47,280
Speaker 3: world where if you know how to read books, if

1038
01:06:47,320 --> 01:06:49,480
Speaker 3: you know how to teach yourself things, if you know

1039
01:06:49,600 --> 01:06:52,760
Speaker 3: how to learn how to learn, you can have a

1040
01:06:52,800 --> 01:06:56,240
Speaker 3: phenomenal career. And it's exactly to your point, Barry, that

1041
01:06:56,720 --> 01:06:59,960
Speaker 3: you and I entered the business twenty five thirty years ago.

1042
01:07:00,080 --> 01:07:03,520
Speaker 3: Oh nothing's the same. It's all about adapting. And so

1043
01:07:03,720 --> 01:07:07,880
Speaker 3: if I tell folks, study what you love, study what

1044
01:07:07,920 --> 01:07:11,200
Speaker 3: you're passionate about, learn how to learn, and never lose

1045
01:07:11,240 --> 01:07:12,560
Speaker 3: that hunger for knowledge.

1046
01:07:12,960 --> 01:07:16,000
Speaker 2: Become an autodidact. Learn how to learn, learn how to

1047
01:07:16,560 --> 01:07:19,520
Speaker 2: what's going on? Our final question, what do you know

1048
01:07:19,560 --> 01:07:22,920
Speaker 2: about the world of investing today that you wish you

1049
01:07:22,960 --> 01:07:25,440
Speaker 2: knew thirty years ago when you were first getting started.

1050
01:07:25,600 --> 01:07:29,760
Speaker 2: And I don't mean by Amazon at iwin Apple at one.

1051
01:07:29,920 --> 01:07:34,360
Speaker 2: I mean, what broad principle did you learn along the

1052
01:07:34,360 --> 01:07:36,480
Speaker 2: way that would have been useful to have found out

1053
01:07:36,560 --> 01:07:37,640
Speaker 2: much earlier that.

1054
01:07:37,680 --> 01:07:39,880
Speaker 3: Being right is not what matters.

1055
01:07:40,440 --> 01:07:43,240
Speaker 2: A you're going to have to expound on that being.

1056
01:07:43,040 --> 01:07:46,360
Speaker 3: Right is not what matters. What matters in the long

1057
01:07:46,480 --> 01:07:52,240
Speaker 3: run is what Einstein said, you know, decades ago. Remember

1058
01:07:52,360 --> 01:07:57,480
Speaker 3: the power of compounding. If you save, if you're disciplined,

1059
01:07:57,680 --> 01:08:03,560
Speaker 3: if you just have a consistent plan, you will highly

1060
01:08:04,040 --> 01:08:08,360
Speaker 3: likely compound your wealth at at least seven and a

1061
01:08:08,400 --> 01:08:11,560
Speaker 3: half to eight percent per year, which means you will

1062
01:08:11,680 --> 01:08:16,439
Speaker 3: double your wealth every decade, double your savings. Whatever that

1063
01:08:16,640 --> 01:08:19,840
Speaker 3: is for most of us, if we're lucky enough we

1064
01:08:19,960 --> 01:08:24,679
Speaker 3: have you know, three four doublings in us. Just do that.

1065
01:08:25,280 --> 01:08:27,760
Speaker 3: And it's not to say that what I do all

1066
01:08:27,840 --> 01:08:31,000
Speaker 3: day doesn't matter, or what you do all day doesn't matter.

1067
01:08:31,360 --> 01:08:34,360
Speaker 3: It's just at the end of the day, we're trying

1068
01:08:34,400 --> 01:08:37,679
Speaker 3: to guide people. But as I say to my team,

1069
01:08:38,080 --> 01:08:40,200
Speaker 3: I know, the likelihood I'm going to be right on

1070
01:08:40,280 --> 01:08:44,120
Speaker 3: any given decision is at best fifty to fifty. What

1071
01:08:44,240 --> 01:08:46,360
Speaker 3: matters is do we have a good plan and are

1072
01:08:46,400 --> 01:08:49,200
Speaker 3: we being disciplined and consistent about it because compounding is

1073
01:08:49,240 --> 01:08:49,679
Speaker 3: your friend.

1074
01:08:50,439 --> 01:08:54,439
Speaker 2: Really fascinating stuff, Lisa, thank you for being so generous

1075
01:08:54,479 --> 01:08:57,879
Speaker 2: with your time. We have been speaking with Lisa Shallatt.

1076
01:08:58,160 --> 01:09:02,040
Speaker 2: She is chief investment officer at Morgan Stanley Wealth Management.

1077
01:09:02,600 --> 01:09:05,360
Speaker 2: If you enjoy this conversation, check out any of the

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Speaker 2: five hundred previous discussions we've had over the past ten years.

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Speaker 2: You can find those at iTunes, Spotify, YouTube, wherever you

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Speaker 2: find your favorite podcast. Be sure and check out my

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Speaker 2: new book, How Not to Invest The Ideas, numbers and

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Speaker 2: Behaviors That Destroy Wealth out everywhere March eighteenth. I would

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Speaker 2: be remiss if I did not thank the crack team,

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Speaker 2: and I'll just put these conversations together each week. Andrew

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Speaker 2: Gavin is my audio engineer, and A Luke is my producer.

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Speaker 2: Sage Bauman is a head of podcasts here at Bloomberg.

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Speaker 2: Sean Russo is my researcher. I'm Barry Prudhos. You've been

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Speaker 2: listening to Masters of Business. Oh, I'm Bloomberg Radio. Y