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Speaker 1: T dot com put Knowledge to Work. Hello and welcome

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Speaker 1: to another episode of the Odd Lots Podcast. I'm Joe

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Speaker 1: Wisenthal and I'm Tracy Alloway. So, Tracy, our podcast is

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Speaker 1: supposed to be it's there. It's a markets podcast, that's

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Speaker 1: what we both cover. But it seems like markets have

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Speaker 1: been kind of quiet lately. Don't you think, Oh my god,

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Speaker 1: tell me about it. Um there. There's only so many

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Speaker 1: times we can write about falling volatility and range bound

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Speaker 1: markets and like new highs and stocks. It's really really

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Speaker 1: frustrating as someone whose job it is to actually write

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Speaker 1: about these things. Right, we have we have a call

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Speaker 1: every day that we're on and we chat about the

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Speaker 1: themes and the markets, and we're like, all right, what's

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Speaker 1: the theme today? And every day it's like, uh, low

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Speaker 1: volatility again, when our rates going to move and break

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Speaker 1: out in a certain direction. It's getting a little repetitive. Yes,

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Speaker 1: why are you reminding me of the futility of art challs.

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Speaker 1: It'll change eventually, you know. Right now we seem to

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Speaker 1: be in this mode where there's no volatility in almost

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Speaker 1: any asset class. But it's always good to be reminded that,

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Speaker 1: you know, things change. There's you go through periods where

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Speaker 1: things are very quiet and then things are crazy, and

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Speaker 1: then when things are crazy, it feels like things will

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Speaker 1: never be quiet again. But you know, things go in cycles.

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Speaker 1: One can only hope. I guess the key thing here

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Speaker 1: is the timing, right, Like, how do we know when

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Speaker 1: things are going to change? That is always that's always

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Speaker 1: the trick. And if you knew the timing then you

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Speaker 1: would you would do very well in the markets. But nice,

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Speaker 1: So why are we talking about this? So today we

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Speaker 1: have a guest I'm very excited to talk about. He

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Speaker 1: is a long time veteran of the markets, lots of

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Speaker 1: experience in trading hedge funds. He's seen lots of these

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Speaker 1: different cycles over time. Uh, And in fact, he's also

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Speaker 1: devoted some of his research specifically to studying market cycles

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Speaker 1: and the patterns that repeat over and over again and

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Speaker 1: figuring out how to time them. And um, you know,

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Speaker 1: I think, uh, it's sort of the perfect the perfect

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Speaker 1: guests to sort of figure out where we are and

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Speaker 1: what could be where we could go next. I'm yeah,

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Speaker 1: this sounds great, and he'll be able to tell us

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Speaker 1: when markets are going to get exciting again, right hopefully, hopefully,

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Speaker 1: hopefully we'll be able to get him to tell us

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Speaker 1: to the day when markets will get excited again. But

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Speaker 1: we'll see if that happens. Our guest is Peter Borish.

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Speaker 1: He's a strategist at the Quad Group. He I've had

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Speaker 1: him on the TV show a couple of times, one

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Speaker 1: of my favorite guests, and so I was really excited

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Speaker 1: about the chance to talk longer with him and to

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Speaker 1: get to know a little bit more about his background,

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Speaker 1: which is extremely interesting. So I'll bring him in now. Peter,

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Speaker 1: thank you very much for joining us. Well, it's a pleasure.

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Speaker 1: It's really an honor when you talk about how uninspiring

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Speaker 1: and how uninteresting the markets are, that you can have

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Speaker 1: a guest that can join right in incredibly uninspiring and uninteresting.

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Speaker 1: Now it's just the opposite. We were bringing you in

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Speaker 1: because we hope that you'll remind us that just because

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Speaker 1: it feels a little quiet right now, uh, it won't

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Speaker 1: stay this way forever. So just the it's just the opposite.

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Speaker 1: But um, before we get into it, tell us a

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Speaker 1: little bit about your background. You've been a you're I

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Speaker 1: think you're a legitimate veteran in this industry at this point,

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Speaker 1: and so tell us how you got into trading and

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Speaker 1: markets and sort of your your path through. Well, first

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Speaker 1: of all, thank you. It's really fun to be here.

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Speaker 1: And I do very much like to go greater and

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Speaker 1: in depth and and and bring some substance to these

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Speaker 1: issues which are complicated. I sort of bring everything back

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Speaker 1: to Michigan. I'm a big Michigan guy. I went there

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Speaker 1: for undergraduate in graduate school, and I was very fortunate

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Speaker 1: to get a job at the New York Fed at

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Speaker 1: the real recession, which is in two i finished graduate school.

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Speaker 1: My career arc has been one of pure luck. I

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Speaker 1: started at the New York Fed, as I said, in

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Speaker 1: two that is the summer that SMP Future started, and

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Speaker 1: I was in research. And then I went down and

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Speaker 1: people didn't understand these new futures markets, and they created

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Speaker 1: a futures and options group right outside the desk where

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Speaker 1: they traded foreign exchange. And then three years later I

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Speaker 1: was recruited by this young guy from Memphis, coming off

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Speaker 1: the floor of the Cotton Exchange, who was starting something

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Speaker 1: at the time which people didn't really know about called

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Speaker 1: a hedge fund, by the name of Paul Tutor Jones,

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Speaker 1: and I was sort of his first research professional at

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Speaker 1: Tutor Investment Corporation, and we were lucky to apply what

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Speaker 1: I would say the discipline and methodology of futures markets

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Speaker 1: as financial futures around the world were being developed. So

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Speaker 1: the SMP Crude Royal started in eighty five, the Japanese

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Speaker 1: futures came on in in the later eighties, and then

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Speaker 1: you had the European futures markets in the early nineties,

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Speaker 1: and that's when it became very much a twenty four

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Speaker 1: hour world, not just in foreign exchange of course, but

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Speaker 1: now in all the markets, and with the advent of

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Speaker 1: stock indecks, futures, treasury futures, UH trading in the inner

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Speaker 1: actions among them. So give us some insight to what

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Speaker 1: trading was alike then and how the rules of future

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Speaker 1: markets futures markets kind of differed from other types of markets. Well,

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Speaker 1: the thing about futures markets, which are everything is a mirror.

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Speaker 1: It's what's a blessing can be accurse, but in futures

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Speaker 1: markets because they have this performance margin that you put up,

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Speaker 1: so there's an embedded uh more leverage in terms of

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Speaker 1: trading those markets relative to equity markets, So your risk

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Speaker 1: management has to be far more sophisticated because of if

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Speaker 1: there's volatility in one of those markets, then uh you

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Speaker 1: can lose money much more quickly. The success and every

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Speaker 1: trading business is about worrying about risk, not about the

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Speaker 1: reward so much, because it's if you can limit your risk,

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Speaker 1: if you can stay in trade for another day, then

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Speaker 1: you have the opportunity to be successful. We're always talking

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Speaker 1: about that. We're interested in people that want to make money,

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Speaker 1: not wanting to be right, and making money means limiting

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Speaker 1: your losses. So that approach that most of the future traders.

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Speaker 1: So if you think of Paul Jones, if you think

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Speaker 1: of Louis Bacon, if you think of of Bruce Covener,

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Speaker 1: even George Sorrows, all of these people started and we're

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Speaker 1: more active in the futures markets, and the sophistication of

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Speaker 1: those risk management tools then could be applied to other

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Speaker 1: markets as they came online. So it's a certain discipline

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Speaker 1: that those guys had in terms of not losing, not

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Speaker 1: being carried on at a stretcher, being able to survive

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Speaker 1: to the next day. That really sort of made them

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Speaker 1: the cream of the crop. Yes, we always talk about

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Speaker 1: and and I sit down with all our traders now

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Speaker 1: that it's discipline before vision. You know when we talk

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Speaker 1: in your introduction, you were saying, well, the markets kind

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Speaker 1: of boring, and I think this is gonna happen, and

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Speaker 1: I think that's gonna happen. And I try to distinguish

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Speaker 1: very much between research and a discipline approach to markets

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Speaker 1: versus gossip. I'm a Mets fan. We can gossip about baseball.

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Speaker 1: The season just started their one and oh if I

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Speaker 1: project that out, they're gonna go a hundred and sixty

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Speaker 1: two and zero, And you would say, wait a second,

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Speaker 1: that's kind of ridiculous, that's not gonna happen. Well, Amazon's

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Speaker 1: up today, it was up yesterday. I guess it's gonna

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Speaker 1: be up every day. We also know that's ridiculous. So

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Speaker 1: the logic of I know I'm going to be right,

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Speaker 1: this is what's going to happen. No, you are wrong,

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Speaker 1: the market is right. That's where risk management and discipline

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Speaker 1: comes into play. You started working for Paul Tutor Jones.

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Speaker 1: I think it's and two years later was the famous

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Speaker 1: crash of October or about two years later. And not

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Speaker 1: only did Paul Tutor did that? Did your fund do

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Speaker 1: extraordinarily well in that crash? And having called it right,

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Speaker 1: I believe, uh, Paul himself credited the work that you

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Speaker 1: did for helping the fund be on the right side

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Speaker 1: and anticipate that crash. So tell us a little bit

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Speaker 1: about specifically the research you were doing for him and

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Speaker 1: how you were able to anticipate what, you know, considered

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Speaker 1: one of the most pivotal market events in financial history. Sure,

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Speaker 1: I want to back up one second. Served fortunately that

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Speaker 1: what we thought was going to happen economically as a

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Speaker 1: result of the crash, in terms of you know, deflationary

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Speaker 1: pressures and things did not happen. Uh. So that was

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Speaker 1: very much a positive because we thought that the economy

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Speaker 1: would contract far more than it did. But it goes

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Speaker 1: into the cycles where we were is that we were

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Speaker 1: looking at data and cycles, and back then their computing

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Speaker 1: power was expensive, ATA was expensive, trading was expensive. And

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Speaker 1: one of the great things that one has to give

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Speaker 1: credit to Paul and the other people at Tutor was

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Speaker 1: the investment in all of those things. We were early

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Speaker 1: users of data computing power, and so we put this

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Speaker 1: together and and and I build a model, and we

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Speaker 1: were looking at early days. You know today you pull

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Speaker 1: up your Bloomberg, you can pull correlations up on anything,

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Speaker 1: a cross correlations, inverted matrices that back then it was

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Speaker 1: very difficult. We were doing that. We saw this pattern

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Speaker 1: which was incredible in terms of where we were both.

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Speaker 1: We started with the economic thought of technology innovation and

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Speaker 1: what was happening back in the early eighties relative to

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Speaker 1: what was happening with the innovation and technology in the twenties,

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Speaker 1: and then the markets were tracking that very much. And

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Speaker 1: when we first started this, the projection was sort of

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Speaker 1: it would go into early UH and then the data

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Speaker 1: and the patterns indicated that the market was likely to break.

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Speaker 1: One of the things about it was with the advent

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Speaker 1: of these derivative markets and futures markets that there's some

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Speaker 1: embedded misunderstanding. One can argue that to a certain extent

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Speaker 1: with some of these new volatility products. It's a little

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Speaker 1: bit like anybody that has a five year old. You

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Speaker 1: think you could talk to them, you think they're rational,

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Speaker 1: but they're not fully rational, and as markets develop and

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Speaker 1: people think they understand them, they don't always do that.

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Speaker 1: So that was part of the UH embedded sort of

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Speaker 1: market UH construction. The way that it worked in the

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Speaker 1: terms portfolio insurance and the assumption that there was always

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Speaker 1: going to be liquidity that led to even more acceleration

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Speaker 1: to the downside. So we were very, very fortunate, UH.

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Speaker 1: And all credit has to go to UH Paul and

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Speaker 1: the execution team at TUTOR, because even if I was

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Speaker 1: right and I gave the exact low and the exact high,

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Speaker 1: nothing goes in a straight line. And he's a far

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Speaker 1: better trader than I will ever be, so he would

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Speaker 1: make far more money. And UH we were fortunate as

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Speaker 1: a fund to benefit from that. And I think that

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Speaker 1: benefit of the entire industry in understanding the importance of

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Speaker 1: both risk management and understanding that these markets have a

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Speaker 1: place where they can be used for hedging. Peter, give

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Speaker 1: us some more insight into this idea of cycles, because um,

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Speaker 1: you know, I started researching this. UH. Joe basically gave

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Speaker 1: me some homework and told me to go read some articles.

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Speaker 1: So I've been learning about Martin Arms Armstrong and Edward

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Speaker 1: Dewey and thinking about Fibonacci sequences and things like that.

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Speaker 1: It kind of has a long history, right, Yes, I

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Speaker 1: am a a a firm believer in in in cycles.

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Speaker 1: Nothing works exactly, of course, but it goes back to

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Speaker 1: the nature of us as human beings, which is fear

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Speaker 1: versus greed, complacency versus uncertainty. And I look at where

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Speaker 1: we are right now, and and this is something I

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Speaker 1: talked about in Bloomberg Markets right after the election, that

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Speaker 1: if you're a student of history, so you can't be

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Speaker 1: a student of markets without being a student history. And

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Speaker 1: there's always these long waves that appear to be obvious

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Speaker 1: after the fact. So by the way that I'm one

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Speaker 1: of the greatest traders of yesterday, I can tell you

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Speaker 1: exactly what happened. So after the fact, my batting average

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Speaker 1: is amazing. It's that pesky uncertain future that makes this

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Speaker 1: business much more difficult. So what am I referring to? So, oh, well,

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Speaker 1: the advent of of you know, Apple, Amazon, and the

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Speaker 1: obstitution effect. So if you line them all up, one

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Speaker 1: of the questions have they created more wealth than they've

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Speaker 1: destroyed in terms of stores, in terms of other uh markets,

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Speaker 1: Whether it's best Buy or BlackBerry in terms of Apple,

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Speaker 1: in terms of the retail stores that you're seeing. Now,

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Speaker 1: these are long waves and this is a cycle that's

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Speaker 1: taking place so in Bloomberg markets. To me, the broader

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Speaker 1: cycle that we're seeing in one of the most famous

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Speaker 1: ones historically are the Chndrati of wave and the schoom Painter.

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Speaker 1: Schum Painter is a famous economist that talked about this

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Speaker 1: creative destruction. And where we are if you think about it,

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Speaker 1: is the Berlin Wall went up. Ironically, it started its

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Speaker 1: construction August thirteen, UH Ninette below in the stock market,

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Speaker 1: by the way, was August thirteen of Fibonacci, twenty one

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Speaker 1: years later. So if we talk at eighty two, excuse

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Speaker 1: me sixty two, and then you move forward twenty seven years. UH.

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Speaker 1: Ronald Reagan's most famous line was Gorbatov, tear down this wall.

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Speaker 1: The wall came down, uh November nine, twenty seven years

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Speaker 1: after that, November nine, ninety two thousand and sixteen, UH,

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Speaker 1: President Trump is elected. Now, if you look at history,

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Speaker 1: we haven't seen too many economies that have grown uh

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Speaker 1: by building walls and and looking inward. I like to say,

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Speaker 1: how the Great Wall of China work out, so we're

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Speaker 1: here potentially at the end of another long cycle. It

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Speaker 1: completes from sixty two to eighty nine to sixteen contralty

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Speaker 1: of fifty four years. Now, that just keeps something very

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Speaker 1: deep in the back of your mind, because that has

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Speaker 1: nothing to do with trading some p futures today, where

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Speaker 1: you know, if there are twenty three sixty do I

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Speaker 1: think they're going to before I think they're going to

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Speaker 1: But in terms of the Ralph Laurent announcement in the UH,

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Speaker 1: the pay less hues closing more stores, and you're seeing

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Speaker 1: that and you're saying, Okay, the deflationary pressures continue to

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Speaker 1: build up. We talked about a DP this morning and

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Speaker 1: being strong. Where are all these retail workers going to go?

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Speaker 1: Where's the marginal consumption going to be? From what we've

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Speaker 1: seen in this last cycle, which has not been addressed

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Speaker 1: at a policy perspective nor per se in the markets,

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Speaker 1: which is the things that you don't need have gone

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Speaker 1: down in price, the things that you do need have

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Speaker 1: gone up. So what do you need, education, healthcare? What

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Speaker 1: you don't need? I can skip a good meal, and

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Speaker 1: I can get an iPad, I can get an iPhone

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Speaker 1: because for a few hundred dollars, that's the difference. The

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Speaker 1: things that you don't need have really gone down the

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Speaker 1: quality of life, whether it's a fifty five inch television

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Speaker 1: or not. So that's what the dichotomy, and that's what's

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Speaker 1: leading some of these deflationary pressures, and you're seeing that

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Speaker 1: through lower real wage growth and the bond markets telling

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Speaker 1: you that as well. So just to wrap up, because

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Speaker 1: there are a lot of important ideas there, one thing

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Speaker 1: that really stuck out to me was this idea. You know,

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Speaker 1: as you said it, from the construction of the Berlin

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Speaker 1: Wall through the election of Donald Trump Key, events have happened,

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Speaker 1: as it turns out, on interesting annual or interesting intervals.

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Speaker 1: You mentioned the Fibonacci sequence, which is of course a

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Speaker 1: well known sequence that also appears in nature. You see

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Speaker 1: it in flowers and stuff. So the idea being that

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Speaker 1: these various events in history have a sort of deep

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Speaker 1: natural rhythm to them, and that it's sort of not

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Speaker 1: an accident that they appear at these certain intervals. Well,

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Speaker 1: think about us as as human beings. We we go

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Speaker 1: through cycles and things take place at at also that

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Speaker 1: natural rhythm. Uh, But it's really a build up of

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Speaker 1: of time, and that the innovation takes place over a cycle,

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Speaker 1: so that we were always planting the seeds today for

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Speaker 1: the next substitute, and and and it's it's funny so

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Speaker 1: you think of, well, thirteen years old right on. I'm Jewish,

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Speaker 1: so you have a bar mitzvah when you look at

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Speaker 1: twenty one, which is a year, you know, Huh, they're

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Speaker 1: both Fibonacci numbers as well. It's kind of it's I

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Speaker 1: don't know why it's there. That's not I'm not smart

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Speaker 1: enough to figure that out. But I just try to

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Speaker 1: sit back and be an observer, which is why I

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Speaker 1: said before, Uh, if you want to be uh student

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Speaker 1: of market, you have to be a student of history.

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Speaker 1: But you also have to be a student of people

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Speaker 1: because of the behavior. If we go back to the

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Speaker 1: markets for one moment, the one thing that was missing

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Speaker 1: to sort of indicate a potential inflection point or top

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Speaker 1: before the election was sentiment. And now sentiment is off

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Speaker 1: the charts. Everybody is particularly bullish. That to me is

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Speaker 1: a little bit of a contrary signal. The market hasn't

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Speaker 1: gone anywhere. We talked about uh, you know after the election,

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Speaker 1: when I was on uh that likely five percent move

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Speaker 1: to one thousand in the dow around March expiration. That

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Speaker 1: some of the largest turning points have taken place in March,

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Speaker 1: and that's what we've seen. And we haven't taken out

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Speaker 1: those highs yet. From March one, we've been meandering the

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Speaker 1: nastic Has you had that divergence between the nasty A

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Speaker 1: in the SMP back at the two thousand high uh,

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Speaker 1: and everybody talked about how you know, sort of under

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Speaker 1: President Obama there was all this uncertainty. There was an uncertainty.

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Speaker 1: They laid out a path, there was a route. You

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Speaker 1: may not have liked it, but you kind of knew

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Speaker 1: with Dodd Frankin. Now the uncertainty is even wider. So

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Speaker 1: it's likely that what's happened previously is unlikely to continue.

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Speaker 1: And we make this mistake all the time as participants

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Speaker 1: in the marketplaces. I said earlier is trying to, you know,

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Speaker 1: draw one line and assume that it's going to be

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Speaker 1: a linear UH movement, which is why I said the

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Speaker 1: Mets will be undefeated this year, which will be great.

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Speaker 1: I want to take a quick break for a word

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Speaker 1: from our sponsor, but knowledge to work and grow your

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Speaker 1: at c i T dot com Put knowledge to work.

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Speaker 1: And we're back with Peter Borsch of the Quad Group.

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Speaker 1: We've been talking about markets and history and cycles. Um

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Speaker 1: I wanna. Tracy in her last question to you talked

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Speaker 1: about some of the early people who worked on who

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Speaker 1: started seeing cycles and markets and economics. You mentioned Edward Dewey.

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Speaker 1: Who was he? And uh, what did what did he

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Speaker 1: learned in his work and what have you learned from

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Speaker 1: studying his work? So Edward Dewey was actually worked for

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Speaker 1: the US government and was one of the early people

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Speaker 1: that innovated and collected government data. His passion was cycles,

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Speaker 1: and he started this foundation called the Foundation for the

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Speaker 1: Study of Cycles. I served on so when I was

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Speaker 1: at tutor again, we would scour the world for data

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Speaker 1: literally because you couldn't download it there. There wasn't the internet.

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Speaker 1: I mean I flew to Zurich to collect, you know,

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Speaker 1: foreign exchange data, and got around and turned around the

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Speaker 1: next day, and and and and came back and we

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Speaker 1: would hire summer interns to punch in all that data

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Speaker 1: in spreadsheets. Dewey was doing all this by hand. How

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Speaker 1: I met Tom Demark if you talk about another data person.

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Speaker 1: He was in Wisconsin and he had by far and

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Speaker 1: away the cleanest data. He had these to mark chart

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Speaker 1: books that he would put out which were better than

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Speaker 1: Value Lyne and others. And we needed clean data. And

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Speaker 1: that's how we met. And we would try to gather

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Speaker 1: every book that we could that went over history and

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Speaker 1: collected data both from the original source, whether it was

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Speaker 1: doubt owns and in their library or people like Edward

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Speaker 1: Dewey or Martin Armstrong and others that were, you know,

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Speaker 1: passionate about clean data. What were some of the most

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Speaker 1: interesting um sort of cycles or data sets that you

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Speaker 1: can remember either collecting or studying over the years. Well,

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Speaker 1: the most important one in terms of doing the model

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Speaker 1: was getting the UH open high low and clothes, which

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Speaker 1: was unusual at that time for the Dow Jones. And

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Speaker 1: there was also Saturday sessions, so we needed to have

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Speaker 1: all that. You couldn't make all these assumptions. We wanted

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Speaker 1: to go to the pure source. At the same time,

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Speaker 1: when of SMP futures started trading and even then understanding

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Speaker 1: the SMP cash index versus the futures index, and we

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Speaker 1: would look for movements in and in fair value as well.

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Speaker 1: That was an early thing people doing index arbitrage because

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Speaker 1: that was a sentiment indicator to a certain extent. When

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Speaker 1: people were selling futures and they went to a discount,

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Speaker 1: then that probably was an indication of too much negativity

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Speaker 1: out there. Uh And so that was another area of

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Speaker 1: data that we would collect. But we also did things

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Speaker 1: which related to economic fundamental data which is relevant even

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Speaker 1: to today. So the unemployment number comes out on Friday,

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Speaker 1: that's the number that the market sees. But if you

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Speaker 1: go back and you look at your database and you say,

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Speaker 1: what's the unemployment for uh March that comes out this

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Speaker 1: Friday in April, the number you pull off the database

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Speaker 1: is the revised number. That's not the number that the

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Speaker 1: market saw. We would have to go back and we

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Speaker 1: would work with people that did newsletters and things like that.

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Speaker 1: We wanted to see. We wanted three numbers. We wanted

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Speaker 1: the number of what the expected number was, what the

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Speaker 1: actual number that the market saw, and then finally what

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Speaker 1: the revisions. So we needed that all that data you

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Speaker 1: keep expanding the size of your database. So we then

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Speaker 1: had to invest in smarter uh technology people to build

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Speaker 1: those databases. It took a serious investment. But that's where

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Speaker 1: people often make a lot of mistakes that they just

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Speaker 1: download data from the internet and they go, oh, I'm

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Speaker 1: going to run a model on this. No, it's completely irrelevant.

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Speaker 1: Joe was telling me before we went on the air

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Speaker 1: about the ADP number. The number that the market saw

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Speaker 1: last month was just revised down by fifty thou Well,

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Speaker 1: that is what a revision. That's enormous, And if you're

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Speaker 1: building your model on something that's different than the market

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Speaker 1: first saw, you're likely to have mistakes. So you really

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Speaker 1: have to roll up your sleeves and get into the

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Speaker 1: weeds on this stuff. It's it's not easy. It takes

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Speaker 1: a tremendous amount of investment, which is one reason why

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Speaker 1: today the quantitative firms that are successful get bigger because

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Speaker 1: they can invest in the time, the data, the cost

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Speaker 1: that's involved in building a really outstanding model. I find

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Speaker 1: this really fascinating because obviously, to some extent, we take

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Speaker 1: for granted the ability to just pull up data, even

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Speaker 1: revised data. It's not that hard to find, but the

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Speaker 1: idea of really having to do legwork to get it all.

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Speaker 1: And in your case, you know, talking about going back

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Speaker 1: to the early days of the Dow Jones and figuring

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Speaker 1: out those quotes when they had the Saturday session. What

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Speaker 1: did that tell you when you when you say it's

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Speaker 1: you found this data, you had some of the best

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Speaker 1: data anywhere. Then you had to actually do something with it,

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Speaker 1: because it's not enough to just have it. So what

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Speaker 1: were the kind of things that looking back at that

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Speaker 1: old Dow data and finding Saturday numbers and high low

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Speaker 1: and clothes that other people hadn't seen. What were the

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Speaker 1: kind of insights that enabled you to look at that

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Speaker 1: and then profit in present day markets? Well there, So

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Speaker 1: there's one other thing I want to add. And when

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Speaker 1: it comes to data, because when you look at the

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Speaker 1: high low close of the Dow, and if you took

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Speaker 1: the high low close of the thirty components of the Dow,

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Speaker 1: they would not be the same. So we went a

431
00:27:16,720 --> 00:27:20,000
Speaker 1: step further. There was a theoretical high and the actual

432
00:27:20,119 --> 00:27:24,760
Speaker 1: high because the theoretical high H means that the high

433
00:27:25,080 --> 00:27:29,280
Speaker 1: print for each of the individual components doesn't happen at

434
00:27:29,320 --> 00:27:32,480
Speaker 1: the same time that the actual high for the doubt

435
00:27:32,520 --> 00:27:36,240
Speaker 1: Jones index takes place. So we thought, originally we could

436
00:27:36,280 --> 00:27:40,280
Speaker 1: just get the thirty components and create our own Dow index.

437
00:27:40,320 --> 00:27:43,040
Speaker 1: It's a price weighted index. You you you, you know,

438
00:27:43,160 --> 00:27:45,879
Speaker 1: you get the divisor and and build it. No, that

439
00:27:45,960 --> 00:27:49,000
Speaker 1: didn't work. So there were mistakes there. Each point along

440
00:27:49,040 --> 00:27:52,560
Speaker 1: the way. You have to realize that you're likely to

441
00:27:52,600 --> 00:27:54,800
Speaker 1: make a mistake one of the things that you will

442
00:27:54,800 --> 00:27:57,240
Speaker 1: apply models and you think, okay, I'm ready to do it.

443
00:27:58,080 --> 00:28:01,720
Speaker 1: And no, today, when I'm looking at models, I've never

444
00:28:01,760 --> 00:28:04,440
Speaker 1: seen a bad simulated model. Nobody ever comes to me

445
00:28:04,560 --> 00:28:07,040
Speaker 1: and says, you know, Pete, this is the worst model

446
00:28:07,080 --> 00:28:09,679
Speaker 1: you ever saw. It's got a minus three sharp ratio.

447
00:28:10,080 --> 00:28:12,760
Speaker 1: You should invest in it because it can only get better. No,

448
00:28:13,080 --> 00:28:17,320
Speaker 1: because there's what I call this creeping intellectual cheating. It's

449
00:28:17,320 --> 00:28:20,359
Speaker 1: not that you intend to cheat, but because you don't

450
00:28:20,400 --> 00:28:24,520
Speaker 1: fully understand because you haven't made those mistakes yet, that

451
00:28:24,680 --> 00:28:30,200
Speaker 1: things are over optimized. And and where that's where experience

452
00:28:30,200 --> 00:28:33,480
Speaker 1: comes into place. So when you ask about these things, yeah,

453
00:28:33,680 --> 00:28:37,760
Speaker 1: that's where having a person that's implementing the model, like

454
00:28:37,800 --> 00:28:41,200
Speaker 1: a great trader like Paul Jones and the team around

455
00:28:41,280 --> 00:28:45,240
Speaker 1: him you have to pick it apart. And so I

456
00:28:45,320 --> 00:28:47,320
Speaker 1: would think that I would have a great answer, and

457
00:28:47,360 --> 00:28:50,040
Speaker 1: sometimes I'd work, you know, on shorter term stuff all

458
00:28:50,160 --> 00:28:53,720
Speaker 1: weekend and I'm like all excited about this the market open,

459
00:28:55,200 --> 00:28:57,200
Speaker 1: it's in the trash can, and I'm like, wow, I

460
00:28:57,200 --> 00:28:59,080
Speaker 1: could have had a much more fun weekend and staying

461
00:28:59,080 --> 00:29:01,920
Speaker 1: in the office all we again working. So that's that's

462
00:29:01,960 --> 00:29:04,480
Speaker 1: part of it too, is realizing it's a very humble

463
00:29:04,960 --> 00:29:08,240
Speaker 1: business that you think you're smart, but you're really not

464
00:29:08,360 --> 00:29:11,160
Speaker 1: that smart. So all those little different subtle things with

465
00:29:11,280 --> 00:29:13,720
Speaker 1: the data, as you just talked about putting us in

466
00:29:13,880 --> 00:29:16,880
Speaker 1: open high low clothes trying to do that. So the

467
00:29:17,000 --> 00:29:18,920
Speaker 1: question that we had to do and talk to dal

468
00:29:19,040 --> 00:29:21,880
Speaker 1: Jones and go back is is was it the actual

469
00:29:22,000 --> 00:29:24,680
Speaker 1: high was it a theoretical high? Because those are two

470
00:29:24,680 --> 00:29:27,400
Speaker 1: different things. We'll talk to us about how best to

471
00:29:28,120 --> 00:29:32,520
Speaker 1: use models or cycles because a lot of these underscore

472
00:29:32,640 --> 00:29:35,400
Speaker 1: modern finance, right, like there are models everywhere, there are

473
00:29:35,440 --> 00:29:39,360
Speaker 1: all these quantitative funds. Um technical analysis is really popular,

474
00:29:39,760 --> 00:29:43,160
Speaker 1: but people always levy a bunch of criticisms at those things.

475
00:29:43,200 --> 00:29:46,479
Speaker 1: You know they can be wrong or history doesn't always

476
00:29:46,480 --> 00:29:49,719
Speaker 1: repeat itself. This time is different, So how do you

477
00:29:49,880 --> 00:29:55,120
Speaker 1: actually apply those things? So a model or cycle, it's

478
00:29:55,160 --> 00:29:58,719
Speaker 1: literally it's a map. You pull up Google Maps and

479
00:29:58,800 --> 00:30:02,120
Speaker 1: you're gonna go and you're going a look. But until

480
00:30:02,240 --> 00:30:04,960
Speaker 1: they've perfected self driving cars, you still need to be

481
00:30:04,960 --> 00:30:08,320
Speaker 1: behind the wheel because there is likely to be a

482
00:30:08,360 --> 00:30:13,280
Speaker 1: pothole or there's likely that the weather is gonna change

483
00:30:13,680 --> 00:30:16,680
Speaker 1: and a bridge is gonna be out, and therefore you

484
00:30:16,720 --> 00:30:19,920
Speaker 1: have to uh change your route. You kind of the

485
00:30:19,960 --> 00:30:23,000
Speaker 1: map and the model tells you here's where I think

486
00:30:23,040 --> 00:30:24,960
Speaker 1: we're going from A to B, but it's not a

487
00:30:25,000 --> 00:30:30,000
Speaker 1: specific uh timing of that aspect. That's where your risk

488
00:30:30,080 --> 00:30:33,360
Speaker 1: management comes into play. And that's where you probe and

489
00:30:33,440 --> 00:30:37,840
Speaker 1: you probe and you probe. And so let's say I'm

490
00:30:37,880 --> 00:30:41,360
Speaker 1: super negative here, I'm like, Okay, I'm gonna sell, but

491
00:30:41,600 --> 00:30:43,520
Speaker 1: if we make new highs, I'm going to get out

492
00:30:44,120 --> 00:30:47,000
Speaker 1: because then my thesis is wrong. So if I risk

493
00:30:47,040 --> 00:30:49,240
Speaker 1: a little bit, if I'm wrong ten times in a row,

494
00:30:49,840 --> 00:30:52,760
Speaker 1: but then when i'm right, I can make it all back.

495
00:30:53,240 --> 00:30:56,480
Speaker 1: That's how you want to trade. Unfortunately, most people participate

496
00:30:56,520 --> 00:31:00,440
Speaker 1: in the market and it's upside down because they think

497
00:31:00,480 --> 00:31:02,920
Speaker 1: they're right. So they're like Okay, well we just made

498
00:31:02,920 --> 00:31:05,920
Speaker 1: new highs. It's just a little bit, it's no big deal.

499
00:31:06,080 --> 00:31:08,800
Speaker 1: Or I'm going to get out, or the market shortcovering

500
00:31:08,840 --> 00:31:11,080
Speaker 1: in front of the unemployment number we talked about earlier

501
00:31:11,120 --> 00:31:14,920
Speaker 1: on Friday, so I'll wait. Then you lose your discipline.

502
00:31:15,120 --> 00:31:18,760
Speaker 1: It's all about maintaining your discipline and your process and

503
00:31:18,800 --> 00:31:22,160
Speaker 1: what the cycles can say and where the roadmap is,

504
00:31:22,200 --> 00:31:25,959
Speaker 1: both whether it's a an economic cycle or whether you

505
00:31:26,000 --> 00:31:29,000
Speaker 1: think it's a fundamental cycle, or whether you think it's

506
00:31:29,000 --> 00:31:32,680
Speaker 1: a political cycle. It's when they come together. When I

507
00:31:32,800 --> 00:31:38,240
Speaker 1: sit with our traders and portfolio managers, the best approach

508
00:31:38,520 --> 00:31:43,640
Speaker 1: is when both the fundamentals and the technicals are coming together.

509
00:31:43,920 --> 00:31:46,880
Speaker 1: So we can argue now that the fundamentals are disconnected,

510
00:31:47,240 --> 00:31:49,920
Speaker 1: but they can go for a longer period of time.

511
00:31:49,960 --> 00:31:52,560
Speaker 1: What the technicals say is get out of the way,

512
00:31:52,640 --> 00:31:56,760
Speaker 1: because this is it's not telling you that it's breaking down.

513
00:31:57,120 --> 00:31:59,560
Speaker 1: When they come together, that's where you do it. A

514
00:31:59,640 --> 00:32:02,800
Speaker 1: great trader, and I've been fortunate to be around some

515
00:32:02,880 --> 00:32:06,560
Speaker 1: of the best, that's when they push it. My favorite

516
00:32:06,600 --> 00:32:10,120
Speaker 1: line is by Stanley Drucker Miller, which always says you

517
00:32:10,160 --> 00:32:15,360
Speaker 1: have to earn the right to trade big and when

518
00:32:15,400 --> 00:32:18,800
Speaker 1: you're there and that's when you put on the big position,

519
00:32:19,120 --> 00:32:22,440
Speaker 1: when the things line up, not when you think you

520
00:32:22,520 --> 00:32:24,560
Speaker 1: know more than the market, because you never do. You'r

521
00:32:24,720 --> 00:32:27,880
Speaker 1: there's one of you and there's millions of people participating

522
00:32:27,920 --> 00:32:31,520
Speaker 1: in the market. Something I've always been curious about. You know,

523
00:32:31,560 --> 00:32:35,040
Speaker 1: you talked about these long term cycles and things that

524
00:32:35,120 --> 00:32:37,680
Speaker 1: go on many years. You also see and I think

525
00:32:37,760 --> 00:32:39,920
Speaker 1: you kind of alluded to it, but you also see

526
00:32:39,960 --> 00:32:43,720
Speaker 1: these very short term ones. And you see charts that

527
00:32:43,760 --> 00:32:46,840
Speaker 1: are literally are intra day and that someone will annotate

528
00:32:46,880 --> 00:32:49,560
Speaker 1: and that will have five waves of a cycle within

529
00:32:49,640 --> 00:32:51,880
Speaker 1: the course of a day or the course of a week.

530
00:32:52,440 --> 00:32:55,880
Speaker 1: Do you see this sort of like fractal nature of cycles.

531
00:32:55,880 --> 00:32:59,680
Speaker 1: So we talked about Fibonacci sequences starting in the early sixties,

532
00:32:59,720 --> 00:33:04,480
Speaker 1: But do you find value in finding those same patterns

533
00:33:04,480 --> 00:33:07,640
Speaker 1: and the span of a few hours or a day

534
00:33:07,840 --> 00:33:11,880
Speaker 1: or a week. Well, so when you talk about five waves,

535
00:33:11,880 --> 00:33:15,160
Speaker 1: that you talk about, uh, the Elliott wave and the

536
00:33:15,200 --> 00:33:18,680
Speaker 1: motion again, that's a great way to have a discipline

537
00:33:18,720 --> 00:33:23,280
Speaker 1: to approach the market and look for potential inflection points.

538
00:33:23,600 --> 00:33:26,600
Speaker 1: In today's world with the speed of technology, what I

539
00:33:26,680 --> 00:33:29,800
Speaker 1: call man versus machine and shorter run trading, that's not

540
00:33:29,880 --> 00:33:33,520
Speaker 1: a space that we can compete in at quad group

541
00:33:33,640 --> 00:33:37,920
Speaker 1: and in long run trading, you basically become it's difficult

542
00:33:37,960 --> 00:33:40,240
Speaker 1: to outperform the index. Why do you want to pay

543
00:33:40,280 --> 00:33:43,160
Speaker 1: me two percent to be long Google? There's lots of

544
00:33:43,160 --> 00:33:46,040
Speaker 1: other ways markets work. So whether it's an et F

545
00:33:46,120 --> 00:33:49,200
Speaker 1: for directly or through yourself. Where we try to be

546
00:33:49,440 --> 00:33:53,120
Speaker 1: is that combination of man and machine one week to

547
00:33:53,320 --> 00:33:56,480
Speaker 1: three months what I call an earning cycle. And yes,

548
00:33:56,880 --> 00:34:00,560
Speaker 1: these things are all applicable for that, particularly when you

549
00:34:00,600 --> 00:34:05,320
Speaker 1: think about an earning cycle and in a market movement.

550
00:34:05,680 --> 00:34:08,839
Speaker 1: Now on the way down, this is where nothing goes

551
00:34:08,840 --> 00:34:11,000
Speaker 1: straight down or straight up. But if you think about

552
00:34:11,000 --> 00:34:13,719
Speaker 1: some of the retail stocks, and if you look at

553
00:34:13,760 --> 00:34:16,080
Speaker 1: Macy's and then it went down and oh someone's gonna

554
00:34:16,080 --> 00:34:18,000
Speaker 1: buy it, or there's an act and then there's a pop,

555
00:34:18,560 --> 00:34:21,719
Speaker 1: they tend to go back down because you know what

556
00:34:21,880 --> 00:34:24,320
Speaker 1: are they always say, the trend is your friend. Human

557
00:34:24,400 --> 00:34:27,480
Speaker 1: nature is we want to be smart. I've learned long

558
00:34:27,520 --> 00:34:31,239
Speaker 1: ago that I'm not, so I try to stay with

559
00:34:31,320 --> 00:34:33,680
Speaker 1: the trend and I'm not smart enough to pick the

560
00:34:33,719 --> 00:34:37,040
Speaker 1: bottom or pick the top. But that's where if you're

561
00:34:37,080 --> 00:34:40,120
Speaker 1: trying to do that something like the Elliott wave where

562
00:34:40,160 --> 00:34:43,120
Speaker 1: you have disciplined or what we talked about before, where

563
00:34:43,120 --> 00:34:45,040
Speaker 1: if I think the market is topping and I'm gonna

564
00:34:45,040 --> 00:34:48,640
Speaker 1: sell it, then I need to have a risk management

565
00:34:48,640 --> 00:34:50,439
Speaker 1: tool that's going to take me out so I don't

566
00:34:50,480 --> 00:34:54,400
Speaker 1: get buried. Okay, one more question. I know you just

567
00:34:54,480 --> 00:34:56,759
Speaker 1: said that you don't want to try to pick the

568
00:34:56,800 --> 00:34:59,560
Speaker 1: top or the bottom, So I'm going to rephrase this slightly.

569
00:35:00,520 --> 00:35:03,280
Speaker 1: When do you think markets are going to get more interesting?

570
00:35:05,600 --> 00:35:09,520
Speaker 1: I think we're in the process of them getting more interesting.

571
00:35:09,600 --> 00:35:12,680
Speaker 1: So we worked out this game plan, as we said

572
00:35:12,719 --> 00:35:14,719
Speaker 1: at the end of the year, and I've spoken about

573
00:35:14,760 --> 00:35:17,200
Speaker 1: and I just mentioned about how we thought that that

574
00:35:17,360 --> 00:35:19,400
Speaker 1: would go five percent from the election, it would get

575
00:35:19,400 --> 00:35:23,799
Speaker 1: to twenty one by March expiration. We're here and we've

576
00:35:23,840 --> 00:35:27,720
Speaker 1: been in this trading range. When you do something ahead

577
00:35:27,719 --> 00:35:31,960
Speaker 1: of time, when you do it intellectually and unemotionally, then

578
00:35:32,000 --> 00:35:33,600
Speaker 1: when you get to the point where you have to

579
00:35:33,640 --> 00:35:37,799
Speaker 1: implement it, you can think about a thousand reasons why

580
00:35:37,880 --> 00:35:41,560
Speaker 1: it's not gonna work out. We try to maintain our

581
00:35:41,640 --> 00:35:45,040
Speaker 1: discipline and say the thesis when I created it on

582
00:35:45,160 --> 00:35:48,120
Speaker 1: emotionally is going to work out until the market tells

583
00:35:48,160 --> 00:35:50,600
Speaker 1: me I'm wrong. So right now I think we're at

584
00:35:50,640 --> 00:35:55,680
Speaker 1: that inflection point. I think we're rolling over and until

585
00:35:55,760 --> 00:35:59,160
Speaker 1: the market tells me I'm wrong, And that would be

586
00:35:59,600 --> 00:36:03,360
Speaker 1: you know, the Dow making new highs uh closing above

587
00:36:03,440 --> 00:36:07,520
Speaker 1: the high, and the SMPS closing above March one high,

588
00:36:07,640 --> 00:36:11,080
Speaker 1: which could easily happen by the way between now and

589
00:36:11,080 --> 00:36:15,920
Speaker 1: and Friday afternoon, assuming the unemployment is very strong. But again,

590
00:36:16,000 --> 00:36:18,879
Speaker 1: I look at the bond market, I look at dollar yen,

591
00:36:19,400 --> 00:36:23,080
Speaker 1: I look at the commodity markets, and if I recommend

592
00:36:23,120 --> 00:36:26,160
Speaker 1: for all your listeners to look at the be calm.

593
00:36:26,640 --> 00:36:29,880
Speaker 1: I'm a big fan of the commodity index and what

594
00:36:30,080 --> 00:36:33,600
Speaker 1: that's been telling us that there's underlying weakness. I think

595
00:36:33,640 --> 00:36:36,640
Speaker 1: that is a perfect note to wrap up the conversation,

596
00:36:36,800 --> 00:36:40,680
Speaker 1: starting from your early work at the New York Fed too,

597
00:36:41,120 --> 00:36:44,600
Speaker 1: where we are exactly today in the markets. Peter Boris

598
00:36:44,640 --> 00:36:48,000
Speaker 1: really appreciate you coming on, fascinating conversation with pleasure. Thank

599
00:36:48,000 --> 00:37:01,400
Speaker 1: you very much. So trades see, are you are you

600
00:37:01,440 --> 00:37:05,839
Speaker 1: optimistic now that perhaps our morning market conversations will soon

601
00:37:05,960 --> 00:37:08,239
Speaker 1: start to get a little bit more interesting. I mean,

602
00:37:08,440 --> 00:37:11,560
Speaker 1: I don't want a big sell off, like I don't

603
00:37:11,600 --> 00:37:15,960
Speaker 1: want people to lose money, but something other than low

604
00:37:16,040 --> 00:37:20,360
Speaker 1: volatility and range bound markets would be very, very welcome.

605
00:37:20,640 --> 00:37:23,000
Speaker 1: I mean, I have to say, we do this a

606
00:37:23,000 --> 00:37:25,920
Speaker 1: lot on odd lots, right, We talk about past history

607
00:37:25,960 --> 00:37:28,880
Speaker 1: because we think it tells us something about the present.

608
00:37:29,120 --> 00:37:33,160
Speaker 1: So I am into the cycle idea. I'm not sure

609
00:37:33,440 --> 00:37:38,480
Speaker 1: I'm totally into, you know, the notion that Fibonacci sequences

610
00:37:38,560 --> 00:37:41,759
Speaker 1: hold like the secret to nature and you know, the

611
00:37:41,760 --> 00:37:44,960
Speaker 1: inner workings of markets. But I feel like there might

612
00:37:45,000 --> 00:37:47,919
Speaker 1: be something there maybe well, you know, as you say,

613
00:37:47,920 --> 00:37:51,440
Speaker 1: we talk all the time on the podcast about historical

614
00:37:51,600 --> 00:37:55,440
Speaker 1: episodes in markets, and so it intrigues me these this

615
00:37:55,520 --> 00:37:58,480
Speaker 1: idea of taking it to the next level and to say, Okay,

616
00:37:58,600 --> 00:38:02,120
Speaker 1: we acknowledge that at least to some extent, history repeats

617
00:38:02,239 --> 00:38:07,040
Speaker 1: or rhymes or whatever. So is it plausible to quantify

618
00:38:07,239 --> 00:38:10,920
Speaker 1: those repetitions as opposed to just saying that history repeats

619
00:38:11,040 --> 00:38:13,799
Speaker 1: and leaving it at that. Can you take it to

620
00:38:13,880 --> 00:38:16,760
Speaker 1: the next level and say, okay, well, let's let's actually

621
00:38:16,920 --> 00:38:19,799
Speaker 1: put some rigor behind this and see if history can

622
00:38:19,840 --> 00:38:22,839
Speaker 1: provide real guides to right now Yeah, that's a really

623
00:38:22,880 --> 00:38:25,160
Speaker 1: good way of putting it. Um. The other thing that

624
00:38:25,239 --> 00:38:29,440
Speaker 1: interested me was the idea of the importance of data,

625
00:38:29,760 --> 00:38:31,719
Speaker 1: I guess, and the notion of you know, flying to

626
00:38:31,840 --> 00:38:34,040
Speaker 1: Zurich to get a data set that maybe not many

627
00:38:34,040 --> 00:38:37,200
Speaker 1: other people have. And that's something that we've talked about

628
00:38:37,239 --> 00:38:41,440
Speaker 1: previously on the show. How proprietary data seems to becoming

629
00:38:41,440 --> 00:38:45,239
Speaker 1: more and more important in the market, right, Yeah, we've

630
00:38:45,239 --> 00:38:47,480
Speaker 1: talked about it, and I think a number of different

631
00:38:47,560 --> 00:38:50,000
Speaker 1: ways on the show. I think we've talked about satellites

632
00:38:50,120 --> 00:38:52,919
Speaker 1: and the attempts to get faster real time data. We've

633
00:38:52,920 --> 00:38:57,400
Speaker 1: talked about the bond market and how difficult it is

634
00:38:57,480 --> 00:39:01,319
Speaker 1: to really get clean sort of real time data on

635
00:39:01,400 --> 00:39:03,919
Speaker 1: what's going on there. And I think it's always sort

636
00:39:03,920 --> 00:39:06,959
Speaker 1: of worth reminding that in this current age, we take

637
00:39:07,000 --> 00:39:10,520
Speaker 1: the existence of data for granted, like it's oxygen or water,

638
00:39:11,160 --> 00:39:13,360
Speaker 1: but that even still, you know, I think we actually

639
00:39:13,400 --> 00:39:15,560
Speaker 1: have a hope we're trying to get another episode in

640
00:39:15,600 --> 00:39:19,560
Speaker 1: the future. There's still important economic data points that people

641
00:39:19,560 --> 00:39:22,279
Speaker 1: look at all the time, where people have to call

642
00:39:22,400 --> 00:39:26,040
Speaker 1: up operators on the phone or message him and say, hey,

643
00:39:26,040 --> 00:39:28,319
Speaker 1: what's the price of this today? Like It doesn't just

644
00:39:28,360 --> 00:39:31,480
Speaker 1: appear on a blinking screen. It really takes legwork to

645
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Speaker 1: get it right. Okay, well, don't give our future episodes,

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Speaker 1: but we will do more on this. Well, this has

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Speaker 1: been another episode of the Odd Lots podcast. I'm Joe Wisn'thal.

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Speaker 1: You can follow me on Twitter at the Stalwart and

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00:39:45,280 --> 00:39:48,600
Speaker 1: I'm Tracy Alloway. I'm on Twitter at Tracy Halloway and

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00:39:48,640 --> 00:39:51,479
Speaker 1: Peters on Twitter too. He should tweet more, but he's

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00:39:51,560 --> 00:40:11,640
Speaker 1: at p Borish. Thanks for listening. Put knowledge to work

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00:40:11,640 --> 00:40:14,840
Speaker 1: and grow your business with c i T. From transportation

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Speaker 1: to healthcare to manufacturing. C i T offers commercial lending, leasing,

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Speaker 1: and treasury management services for small and middle market businesses.

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Speaker 1: Learn more at c i T dot com. Put Knowledge

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Speaker 1: to Work