Speaker 1: But knowledge to work and grow your business with c
Speaker 1: i T from transportation to healthcare to manufacturing. C i
Speaker 1: T offers commercial lending, leasing, and treasury management services for
Speaker 1: small and middle market businesses. Learn more at c i
Speaker 1: T dot com put Knowledge to Work. Hello and welcome
Speaker 1: to another episode of the Odd Lots Podcast. I'm Joe
Speaker 1: Wisenthal and I'm Tracy Alloway. So, Tracy, our podcast is
Speaker 1: supposed to be it's there. It's a markets podcast, that's
Speaker 1: what we both cover. But it seems like markets have
Speaker 1: been kind of quiet lately. Don't you think, Oh my god,
Speaker 1: tell me about it. Um there. There's only so many
Speaker 1: times we can write about falling volatility and range bound
Speaker 1: markets and like new highs and stocks. It's really really
Speaker 1: frustrating as someone whose job it is to actually write
Speaker 1: about these things. Right, we have we have a call
Speaker 1: every day that we're on and we chat about the
Speaker 1: themes and the markets, and we're like, all right, what's
Speaker 1: the theme today? And every day it's like, uh, low
Speaker 1: volatility again, when our rates going to move and break
Speaker 1: out in a certain direction. It's getting a little repetitive. Yes,
Speaker 1: why are you reminding me of the futility of art challs.
Speaker 1: It'll change eventually, you know. Right now we seem to
Speaker 1: be in this mode where there's no volatility in almost
Speaker 1: any asset class. But it's always good to be reminded that,
Speaker 1: you know, things change. There's you go through periods where
Speaker 1: things are very quiet and then things are crazy, and
Speaker 1: then when things are crazy, it feels like things will
Speaker 1: never be quiet again. But you know, things go in cycles.
Speaker 1: One can only hope. I guess the key thing here
Speaker 1: is the timing, right, Like, how do we know when
Speaker 1: things are going to change? That is always that's always
Speaker 1: the trick. And if you knew the timing then you
Speaker 1: would you would do very well in the markets. But nice,
Speaker 1: So why are we talking about this? So today we
Speaker 1: have a guest I'm very excited to talk about. He
Speaker 1: is a long time veteran of the markets, lots of
Speaker 1: experience in trading hedge funds. He's seen lots of these
Speaker 1: different cycles over time. Uh, And in fact, he's also
Speaker 1: devoted some of his research specifically to studying market cycles
Speaker 1: and the patterns that repeat over and over again and
Speaker 1: figuring out how to time them. And um, you know,
Speaker 1: I think, uh, it's sort of the perfect the perfect
Speaker 1: guests to sort of figure out where we are and
Speaker 1: what could be where we could go next. I'm yeah,
Speaker 1: this sounds great, and he'll be able to tell us
Speaker 1: when markets are going to get exciting again, right hopefully, hopefully,
Speaker 1: hopefully we'll be able to get him to tell us
Speaker 1: to the day when markets will get excited again. But
Speaker 1: we'll see if that happens. Our guest is Peter Borish.
Speaker 1: He's a strategist at the Quad Group. He I've had
Speaker 1: him on the TV show a couple of times, one
Speaker 1: of my favorite guests, and so I was really excited
Speaker 1: about the chance to talk longer with him and to
Speaker 1: get to know a little bit more about his background,
Speaker 1: which is extremely interesting. So I'll bring him in now. Peter,
Speaker 1: thank you very much for joining us. Well, it's a pleasure.
Speaker 1: It's really an honor when you talk about how uninspiring
Speaker 1: and how uninteresting the markets are, that you can have
Speaker 1: a guest that can join right in incredibly uninspiring and uninteresting.
Speaker 1: Now it's just the opposite. We were bringing you in
Speaker 1: because we hope that you'll remind us that just because
Speaker 1: it feels a little quiet right now, uh, it won't
Speaker 1: stay this way forever. So just the it's just the opposite.
Speaker 1: But um, before we get into it, tell us a
Speaker 1: little bit about your background. You've been a you're I
Speaker 1: think you're a legitimate veteran in this industry at this point,
Speaker 1: and so tell us how you got into trading and
Speaker 1: markets and sort of your your path through. Well, first
Speaker 1: of all, thank you. It's really fun to be here.
Speaker 1: And I do very much like to go greater and
Speaker 1: in depth and and and bring some substance to these
Speaker 1: issues which are complicated. I sort of bring everything back
Speaker 1: to Michigan. I'm a big Michigan guy. I went there
Speaker 1: for undergraduate in graduate school, and I was very fortunate
Speaker 1: to get a job at the New York Fed at
Speaker 1: the real recession, which is in two i finished graduate school.
Speaker 1: My career arc has been one of pure luck. I
Speaker 1: started at the New York Fed, as I said, in
Speaker 1: two that is the summer that SMP Future started, and
Speaker 1: I was in research. And then I went down and
Speaker 1: people didn't understand these new futures markets, and they created
Speaker 1: a futures and options group right outside the desk where
Speaker 1: they traded foreign exchange. And then three years later I
Speaker 1: was recruited by this young guy from Memphis, coming off
Speaker 1: the floor of the Cotton Exchange, who was starting something
Speaker 1: at the time which people didn't really know about called
Speaker 1: a hedge fund, by the name of Paul Tutor Jones,
Speaker 1: and I was sort of his first research professional at
Speaker 1: Tutor Investment Corporation, and we were lucky to apply what
Speaker 1: I would say the discipline and methodology of futures markets
Speaker 1: as financial futures around the world were being developed. So
Speaker 1: the SMP Crude Royal started in eighty five, the Japanese
Speaker 1: futures came on in in the later eighties, and then
Speaker 1: you had the European futures markets in the early nineties,
Speaker 1: and that's when it became very much a twenty four
Speaker 1: hour world, not just in foreign exchange of course, but
Speaker 1: now in all the markets, and with the advent of
Speaker 1: stock indecks, futures, treasury futures, UH trading in the inner
Speaker 1: actions among them. So give us some insight to what
Speaker 1: trading was alike then and how the rules of future
Speaker 1: markets futures markets kind of differed from other types of markets. Well,
Speaker 1: the thing about futures markets, which are everything is a mirror.
Speaker 1: It's what's a blessing can be accurse, but in futures
Speaker 1: markets because they have this performance margin that you put up,
Speaker 1: so there's an embedded uh more leverage in terms of
Speaker 1: trading those markets relative to equity markets, So your risk
Speaker 1: management has to be far more sophisticated because of if
Speaker 1: there's volatility in one of those markets, then uh you
Speaker 1: can lose money much more quickly. The success and every
Speaker 1: trading business is about worrying about risk, not about the
Speaker 1: reward so much, because it's if you can limit your risk,
Speaker 1: if you can stay in trade for another day, then
Speaker 1: you have the opportunity to be successful. We're always talking
Speaker 1: about that. We're interested in people that want to make money,
Speaker 1: not wanting to be right, and making money means limiting
Speaker 1: your losses. So that approach that most of the future traders.
Speaker 1: So if you think of Paul Jones, if you think
Speaker 1: of Louis Bacon, if you think of of Bruce Covener,
Speaker 1: even George Sorrows, all of these people started and we're
Speaker 1: more active in the futures markets, and the sophistication of
Speaker 1: those risk management tools then could be applied to other
Speaker 1: markets as they came online. So it's a certain discipline
Speaker 1: that those guys had in terms of not losing, not
Speaker 1: being carried on at a stretcher, being able to survive
Speaker 1: to the next day. That really sort of made them
Speaker 1: the cream of the crop. Yes, we always talk about
Speaker 1: and and I sit down with all our traders now
Speaker 1: that it's discipline before vision. You know when we talk
Speaker 1: in your introduction, you were saying, well, the markets kind
Speaker 1: of boring, and I think this is gonna happen, and
Speaker 1: I think that's gonna happen. And I try to distinguish
Speaker 1: very much between research and a discipline approach to markets
Speaker 1: versus gossip. I'm a Mets fan. We can gossip about baseball.
Speaker 1: The season just started their one and oh if I
Speaker 1: project that out, they're gonna go a hundred and sixty
Speaker 1: two and zero, And you would say, wait a second,
Speaker 1: that's kind of ridiculous, that's not gonna happen. Well, Amazon's
Speaker 1: up today, it was up yesterday. I guess it's gonna
Speaker 1: be up every day. We also know that's ridiculous. So
Speaker 1: the logic of I know I'm going to be right,
Speaker 1: this is what's going to happen. No, you are wrong,
Speaker 1: the market is right. That's where risk management and discipline
Speaker 1: comes into play. You started working for Paul Tutor Jones.
Speaker 1: I think it's and two years later was the famous
Speaker 1: crash of October or about two years later. And not
Speaker 1: only did Paul Tutor did that? Did your fund do
Speaker 1: extraordinarily well in that crash? And having called it right,
Speaker 1: I believe, uh, Paul himself credited the work that you
Speaker 1: did for helping the fund be on the right side
Speaker 1: and anticipate that crash. So tell us a little bit
Speaker 1: about specifically the research you were doing for him and
Speaker 1: how you were able to anticipate what, you know, considered
Speaker 1: one of the most pivotal market events in financial history. Sure,
Speaker 1: I want to back up one second. Served fortunately that
Speaker 1: what we thought was going to happen economically as a
Speaker 1: result of the crash, in terms of you know, deflationary
Speaker 1: pressures and things did not happen. Uh. So that was
Speaker 1: very much a positive because we thought that the economy
Speaker 1: would contract far more than it did. But it goes
Speaker 1: into the cycles where we were is that we were
Speaker 1: looking at data and cycles, and back then their computing
Speaker 1: power was expensive, ATA was expensive, trading was expensive. And
Speaker 1: one of the great things that one has to give
Speaker 1: credit to Paul and the other people at Tutor was
Speaker 1: the investment in all of those things. We were early
Speaker 1: users of data computing power, and so we put this
Speaker 1: together and and and I build a model, and we
Speaker 1: were looking at early days. You know today you pull
Speaker 1: up your Bloomberg, you can pull correlations up on anything,
Speaker 1: a cross correlations, inverted matrices that back then it was
Speaker 1: very difficult. We were doing that. We saw this pattern
Speaker 1: which was incredible in terms of where we were both.
Speaker 1: We started with the economic thought of technology innovation and
Speaker 1: what was happening back in the early eighties relative to
Speaker 1: what was happening with the innovation and technology in the twenties,
Speaker 1: and then the markets were tracking that very much. And
Speaker 1: when we first started this, the projection was sort of
Speaker 1: it would go into early UH and then the data
Speaker 1: and the patterns indicated that the market was likely to break.
Speaker 1: One of the things about it was with the advent
Speaker 1: of these derivative markets and futures markets that there's some
Speaker 1: embedded misunderstanding. One can argue that to a certain extent
Speaker 1: with some of these new volatility products. It's a little
Speaker 1: bit like anybody that has a five year old. You
Speaker 1: think you could talk to them, you think they're rational,
Speaker 1: but they're not fully rational, and as markets develop and
Speaker 1: people think they understand them, they don't always do that.
Speaker 1: So that was part of the UH embedded sort of
Speaker 1: market UH construction. The way that it worked in the
Speaker 1: terms portfolio insurance and the assumption that there was always
Speaker 1: going to be liquidity that led to even more acceleration
Speaker 1: to the downside. So we were very, very fortunate, UH.
Speaker 1: And all credit has to go to UH Paul and
Speaker 1: the execution team at TUTOR, because even if I was
Speaker 1: right and I gave the exact low and the exact high,
Speaker 1: nothing goes in a straight line. And he's a far
Speaker 1: better trader than I will ever be, so he would
Speaker 1: make far more money. And UH we were fortunate as
Speaker 1: a fund to benefit from that. And I think that
Speaker 1: benefit of the entire industry in understanding the importance of
Speaker 1: both risk management and understanding that these markets have a
Speaker 1: place where they can be used for hedging. Peter, give
Speaker 1: us some more insight into this idea of cycles, because um,
Speaker 1: you know, I started researching this. UH. Joe basically gave
Speaker 1: me some homework and told me to go read some articles.
Speaker 1: So I've been learning about Martin Arms Armstrong and Edward
Speaker 1: Dewey and thinking about Fibonacci sequences and things like that.
Speaker 1: It kind of has a long history, right, Yes, I
Speaker 1: am a a a firm believer in in in cycles.
Speaker 1: Nothing works exactly, of course, but it goes back to
Speaker 1: the nature of us as human beings, which is fear
Speaker 1: versus greed, complacency versus uncertainty. And I look at where
Speaker 1: we are right now, and and this is something I
Speaker 1: talked about in Bloomberg Markets right after the election, that
Speaker 1: if you're a student of history, so you can't be
Speaker 1: a student of markets without being a student history. And
Speaker 1: there's always these long waves that appear to be obvious
Speaker 1: after the fact. So by the way that I'm one
Speaker 1: of the greatest traders of yesterday, I can tell you
Speaker 1: exactly what happened. So after the fact, my batting average
Speaker 1: is amazing. It's that pesky uncertain future that makes this
Speaker 1: business much more difficult. So what am I referring to? So, oh, well,
Speaker 1: the advent of of you know, Apple, Amazon, and the
Speaker 1: obstitution effect. So if you line them all up, one
Speaker 1: of the questions have they created more wealth than they've
Speaker 1: destroyed in terms of stores, in terms of other uh markets,
Speaker 1: Whether it's best Buy or BlackBerry in terms of Apple,
Speaker 1: in terms of the retail stores that you're seeing. Now,
Speaker 1: these are long waves and this is a cycle that's
Speaker 1: taking place so in Bloomberg markets. To me, the broader
Speaker 1: cycle that we're seeing in one of the most famous
Speaker 1: ones historically are the Chndrati of wave and the schoom Painter.
Speaker 1: Schum Painter is a famous economist that talked about this
Speaker 1: creative destruction. And where we are if you think about it,
Speaker 1: is the Berlin Wall went up. Ironically, it started its
Speaker 1: construction August thirteen, UH Ninette below in the stock market,
Speaker 1: by the way, was August thirteen of Fibonacci, twenty one
Speaker 1: years later. So if we talk at eighty two, excuse
Speaker 1: me sixty two, and then you move forward twenty seven years. UH.
Speaker 1: Ronald Reagan's most famous line was Gorbatov, tear down this wall.
Speaker 1: The wall came down, uh November nine, twenty seven years
Speaker 1: after that, November nine, ninety two thousand and sixteen, UH,
Speaker 1: President Trump is elected. Now, if you look at history,
Speaker 1: we haven't seen too many economies that have grown uh
Speaker 1: by building walls and and looking inward. I like to say,
Speaker 1: how the Great Wall of China work out, so we're
Speaker 1: here potentially at the end of another long cycle. It
Speaker 1: completes from sixty two to eighty nine to sixteen contralty
Speaker 1: of fifty four years. Now, that just keeps something very
Speaker 1: deep in the back of your mind, because that has
Speaker 1: nothing to do with trading some p futures today, where
Speaker 1: you know, if there are twenty three sixty do I
Speaker 1: think they're going to before I think they're going to
Speaker 1: But in terms of the Ralph Laurent announcement in the UH,
Speaker 1: the pay less hues closing more stores, and you're seeing
Speaker 1: that and you're saying, Okay, the deflationary pressures continue to
Speaker 1: build up. We talked about a DP this morning and
Speaker 1: being strong. Where are all these retail workers going to go?
Speaker 1: Where's the marginal consumption going to be? From what we've
Speaker 1: seen in this last cycle, which has not been addressed
Speaker 1: at a policy perspective nor per se in the markets,
Speaker 1: which is the things that you don't need have gone
Speaker 1: down in price, the things that you do need have
Speaker 1: gone up. So what do you need, education, healthcare? What
Speaker 1: you don't need? I can skip a good meal, and
Speaker 1: I can get an iPad, I can get an iPhone
Speaker 1: because for a few hundred dollars, that's the difference. The
Speaker 1: things that you don't need have really gone down the
Speaker 1: quality of life, whether it's a fifty five inch television
Speaker 1: or not. So that's what the dichotomy, and that's what's
Speaker 1: leading some of these deflationary pressures, and you're seeing that
Speaker 1: through lower real wage growth and the bond markets telling
Speaker 1: you that as well. So just to wrap up, because
Speaker 1: there are a lot of important ideas there, one thing
Speaker 1: that really stuck out to me was this idea. You know,
Speaker 1: as you said it, from the construction of the Berlin
Speaker 1: Wall through the election of Donald Trump Key, events have happened,
Speaker 1: as it turns out, on interesting annual or interesting intervals.
Speaker 1: You mentioned the Fibonacci sequence, which is of course a
Speaker 1: well known sequence that also appears in nature. You see
Speaker 1: it in flowers and stuff. So the idea being that
Speaker 1: these various events in history have a sort of deep
Speaker 1: natural rhythm to them, and that it's sort of not
Speaker 1: an accident that they appear at these certain intervals. Well,
Speaker 1: think about us as as human beings. We we go
Speaker 1: through cycles and things take place at at also that
Speaker 1: natural rhythm. Uh, But it's really a build up of
Speaker 1: of time, and that the innovation takes place over a cycle,
Speaker 1: so that we were always planting the seeds today for
Speaker 1: the next substitute, and and and it's it's funny so
Speaker 1: you think of, well, thirteen years old right on. I'm Jewish,
Speaker 1: so you have a bar mitzvah when you look at
Speaker 1: twenty one, which is a year, you know, Huh, they're
Speaker 1: both Fibonacci numbers as well. It's kind of it's I
Speaker 1: don't know why it's there. That's not I'm not smart
Speaker 1: enough to figure that out. But I just try to
Speaker 1: sit back and be an observer, which is why I
Speaker 1: said before, Uh, if you want to be uh student
Speaker 1: of market, you have to be a student of history.
Speaker 1: But you also have to be a student of people
Speaker 1: because of the behavior. If we go back to the
Speaker 1: markets for one moment, the one thing that was missing
Speaker 1: to sort of indicate a potential inflection point or top
Speaker 1: before the election was sentiment. And now sentiment is off
Speaker 1: the charts. Everybody is particularly bullish. That to me is
Speaker 1: a little bit of a contrary signal. The market hasn't
Speaker 1: gone anywhere. We talked about uh, you know after the election,
Speaker 1: when I was on uh that likely five percent move
Speaker 1: to one thousand in the dow around March expiration. That
Speaker 1: some of the largest turning points have taken place in March,
Speaker 1: and that's what we've seen. And we haven't taken out
Speaker 1: those highs yet. From March one, we've been meandering the
Speaker 1: nastic Has you had that divergence between the nasty A
Speaker 1: in the SMP back at the two thousand high uh,
Speaker 1: and everybody talked about how you know, sort of under
Speaker 1: President Obama there was all this uncertainty. There was an uncertainty.
Speaker 1: They laid out a path, there was a route. You
Speaker 1: may not have liked it, but you kind of knew
Speaker 1: with Dodd Frankin. Now the uncertainty is even wider. So
Speaker 1: it's likely that what's happened previously is unlikely to continue.
Speaker 1: And we make this mistake all the time as participants
Speaker 1: in the marketplaces. I said earlier is trying to, you know,
Speaker 1: draw one line and assume that it's going to be
Speaker 1: a linear UH movement, which is why I said the
Speaker 1: Mets will be undefeated this year, which will be great.
Speaker 1: I want to take a quick break for a word
Speaker 1: from our sponsor, but knowledge to work and grow your
Speaker 1: business with c i T from transportation to healthcare to manufacturing.
Speaker 1: C i T offers commercial lending, the seeing and treasury
Speaker 1: management services for small and middle market businesses. Learn more
Speaker 1: at c i T dot com Put knowledge to work.
Speaker 1: And we're back with Peter Borsch of the Quad Group.
Speaker 1: We've been talking about markets and history and cycles. Um
Speaker 1: I wanna. Tracy in her last question to you talked
Speaker 1: about some of the early people who worked on who
Speaker 1: started seeing cycles and markets and economics. You mentioned Edward Dewey.
Speaker 1: Who was he? And uh, what did what did he
Speaker 1: learned in his work and what have you learned from
Speaker 1: studying his work? So Edward Dewey was actually worked for
Speaker 1: the US government and was one of the early people
Speaker 1: that innovated and collected government data. His passion was cycles,
Speaker 1: and he started this foundation called the Foundation for the
Speaker 1: Study of Cycles. I served on so when I was
Speaker 1: at tutor again, we would scour the world for data
Speaker 1: literally because you couldn't download it there. There wasn't the internet.
Speaker 1: I mean I flew to Zurich to collect, you know,
Speaker 1: foreign exchange data, and got around and turned around the
Speaker 1: next day, and and and and came back and we
Speaker 1: would hire summer interns to punch in all that data
Speaker 1: in spreadsheets. Dewey was doing all this by hand. How
Speaker 1: I met Tom Demark if you talk about another data person.
Speaker 1: He was in Wisconsin and he had by far and
Speaker 1: away the cleanest data. He had these to mark chart
Speaker 1: books that he would put out which were better than
Speaker 1: Value Lyne and others. And we needed clean data. And
Speaker 1: that's how we met. And we would try to gather
Speaker 1: every book that we could that went over history and
Speaker 1: collected data both from the original source, whether it was
Speaker 1: doubt owns and in their library or people like Edward
Speaker 1: Dewey or Martin Armstrong and others that were, you know,
Speaker 1: passionate about clean data. What were some of the most
Speaker 1: interesting um sort of cycles or data sets that you
Speaker 1: can remember either collecting or studying over the years. Well,
Speaker 1: the most important one in terms of doing the model
Speaker 1: was getting the UH open high low and clothes, which
Speaker 1: was unusual at that time for the Dow Jones. And
Speaker 1: there was also Saturday sessions, so we needed to have
Speaker 1: all that. You couldn't make all these assumptions. We wanted
Speaker 1: to go to the pure source. At the same time,
Speaker 1: when of SMP futures started trading and even then understanding
Speaker 1: the SMP cash index versus the futures index, and we
Speaker 1: would look for movements in and in fair value as well.
Speaker 1: That was an early thing people doing index arbitrage because
Speaker 1: that was a sentiment indicator to a certain extent. When
Speaker 1: people were selling futures and they went to a discount,
Speaker 1: then that probably was an indication of too much negativity
Speaker 1: out there. Uh And so that was another area of
Speaker 1: data that we would collect. But we also did things
Speaker 1: which related to economic fundamental data which is relevant even
Speaker 1: to today. So the unemployment number comes out on Friday,
Speaker 1: that's the number that the market sees. But if you
Speaker 1: go back and you look at your database and you say,
Speaker 1: what's the unemployment for uh March that comes out this
Speaker 1: Friday in April, the number you pull off the database
Speaker 1: is the revised number. That's not the number that the
Speaker 1: market saw. We would have to go back and we
Speaker 1: would work with people that did newsletters and things like that.
Speaker 1: We wanted to see. We wanted three numbers. We wanted
Speaker 1: the number of what the expected number was, what the
Speaker 1: actual number that the market saw, and then finally what
Speaker 1: the revisions. So we needed that all that data you
Speaker 1: keep expanding the size of your database. So we then
Speaker 1: had to invest in smarter uh technology people to build
Speaker 1: those databases. It took a serious investment. But that's where
Speaker 1: people often make a lot of mistakes that they just
Speaker 1: download data from the internet and they go, oh, I'm
Speaker 1: going to run a model on this. No, it's completely irrelevant.
Speaker 1: Joe was telling me before we went on the air
Speaker 1: about the ADP number. The number that the market saw
Speaker 1: last month was just revised down by fifty thou Well,
Speaker 1: that is what a revision. That's enormous, And if you're
Speaker 1: building your model on something that's different than the market
Speaker 1: first saw, you're likely to have mistakes. So you really
Speaker 1: have to roll up your sleeves and get into the
Speaker 1: weeds on this stuff. It's it's not easy. It takes
Speaker 1: a tremendous amount of investment, which is one reason why
Speaker 1: today the quantitative firms that are successful get bigger because
Speaker 1: they can invest in the time, the data, the cost
Speaker 1: that's involved in building a really outstanding model. I find
Speaker 1: this really fascinating because obviously, to some extent, we take
Speaker 1: for granted the ability to just pull up data, even
Speaker 1: revised data. It's not that hard to find, but the
Speaker 1: idea of really having to do legwork to get it all.
Speaker 1: And in your case, you know, talking about going back
Speaker 1: to the early days of the Dow Jones and figuring
Speaker 1: out those quotes when they had the Saturday session. What
Speaker 1: did that tell you when you when you say it's
Speaker 1: you found this data, you had some of the best
Speaker 1: data anywhere. Then you had to actually do something with it,
Speaker 1: because it's not enough to just have it. So what
Speaker 1: were the kind of things that looking back at that
Speaker 1: old Dow data and finding Saturday numbers and high low
Speaker 1: and clothes that other people hadn't seen. What were the
Speaker 1: kind of insights that enabled you to look at that
Speaker 1: and then profit in present day markets? Well there, So
Speaker 1: there's one other thing I want to add. And when
Speaker 1: it comes to data, because when you look at the
Speaker 1: high low close of the Dow, and if you took
Speaker 1: the high low close of the thirty components of the Dow,
Speaker 1: they would not be the same. So we went a
Speaker 1: step further. There was a theoretical high and the actual
Speaker 1: high because the theoretical high H means that the high
Speaker 1: print for each of the individual components doesn't happen at
Speaker 1: the same time that the actual high for the doubt
Speaker 1: Jones index takes place. So we thought, originally we could
Speaker 1: just get the thirty components and create our own Dow index.
Speaker 1: It's a price weighted index. You you you, you know,
Speaker 1: you get the divisor and and build it. No, that
Speaker 1: didn't work. So there were mistakes there. Each point along
Speaker 1: the way. You have to realize that you're likely to
Speaker 1: make a mistake one of the things that you will
Speaker 1: apply models and you think, okay, I'm ready to do it.
Speaker 1: And no, today, when I'm looking at models, I've never
Speaker 1: seen a bad simulated model. Nobody ever comes to me
Speaker 1: and says, you know, Pete, this is the worst model
Speaker 1: you ever saw. It's got a minus three sharp ratio.
Speaker 1: You should invest in it because it can only get better. No,
Speaker 1: because there's what I call this creeping intellectual cheating. It's
Speaker 1: not that you intend to cheat, but because you don't
Speaker 1: fully understand because you haven't made those mistakes yet, that
Speaker 1: things are over optimized. And and where that's where experience
Speaker 1: comes into place. So when you ask about these things, yeah,
Speaker 1: that's where having a person that's implementing the model, like
Speaker 1: a great trader like Paul Jones and the team around
Speaker 1: him you have to pick it apart. And so I
Speaker 1: would think that I would have a great answer, and
Speaker 1: sometimes I'd work, you know, on shorter term stuff all
Speaker 1: weekend and I'm like all excited about this the market open,
Speaker 1: it's in the trash can, and I'm like, wow, I
Speaker 1: could have had a much more fun weekend and staying
Speaker 1: in the office all we again working. So that's that's
Speaker 1: part of it too, is realizing it's a very humble
Speaker 1: business that you think you're smart, but you're really not
Speaker 1: that smart. So all those little different subtle things with
Speaker 1: the data, as you just talked about putting us in
Speaker 1: open high low clothes trying to do that. So the
Speaker 1: question that we had to do and talk to dal
Speaker 1: Jones and go back is is was it the actual
Speaker 1: high was it a theoretical high? Because those are two
Speaker 1: different things. We'll talk to us about how best to
Speaker 1: use models or cycles because a lot of these underscore
Speaker 1: modern finance, right, like there are models everywhere, there are
Speaker 1: all these quantitative funds. Um technical analysis is really popular,
Speaker 1: but people always levy a bunch of criticisms at those things.
Speaker 1: You know they can be wrong or history doesn't always
Speaker 1: repeat itself. This time is different, So how do you
Speaker 1: actually apply those things? So a model or cycle, it's
Speaker 1: literally it's a map. You pull up Google Maps and
Speaker 1: you're gonna go and you're going a look. But until
Speaker 1: they've perfected self driving cars, you still need to be
Speaker 1: behind the wheel because there is likely to be a
Speaker 1: pothole or there's likely that the weather is gonna change
Speaker 1: and a bridge is gonna be out, and therefore you
Speaker 1: have to uh change your route. You kind of the
Speaker 1: map and the model tells you here's where I think
Speaker 1: we're going from A to B, but it's not a
Speaker 1: specific uh timing of that aspect. That's where your risk
Speaker 1: management comes into play. And that's where you probe and
Speaker 1: you probe and you probe. And so let's say I'm
Speaker 1: super negative here, I'm like, Okay, I'm gonna sell, but
Speaker 1: if we make new highs, I'm going to get out
Speaker 1: because then my thesis is wrong. So if I risk
Speaker 1: a little bit, if I'm wrong ten times in a row,
Speaker 1: but then when i'm right, I can make it all back.
Speaker 1: That's how you want to trade. Unfortunately, most people participate
Speaker 1: in the market and it's upside down because they think
Speaker 1: they're right. So they're like Okay, well we just made
Speaker 1: new highs. It's just a little bit, it's no big deal.
Speaker 1: Or I'm going to get out, or the market shortcovering
Speaker 1: in front of the unemployment number we talked about earlier
Speaker 1: on Friday, so I'll wait. Then you lose your discipline.
Speaker 1: It's all about maintaining your discipline and your process and
Speaker 1: what the cycles can say and where the roadmap is,
Speaker 1: both whether it's a an economic cycle or whether you
Speaker 1: think it's a fundamental cycle, or whether you think it's
Speaker 1: a political cycle. It's when they come together. When I
Speaker 1: sit with our traders and portfolio managers, the best approach
Speaker 1: is when both the fundamentals and the technicals are coming together.
Speaker 1: So we can argue now that the fundamentals are disconnected,
Speaker 1: but they can go for a longer period of time.
Speaker 1: What the technicals say is get out of the way,
Speaker 1: because this is it's not telling you that it's breaking down.
Speaker 1: When they come together, that's where you do it. A
Speaker 1: great trader, and I've been fortunate to be around some
Speaker 1: of the best, that's when they push it. My favorite
Speaker 1: line is by Stanley Drucker Miller, which always says you
Speaker 1: have to earn the right to trade big and when
Speaker 1: you're there and that's when you put on the big position,
Speaker 1: when the things line up, not when you think you
Speaker 1: know more than the market, because you never do. You'r
Speaker 1: there's one of you and there's millions of people participating
Speaker 1: in the market. Something I've always been curious about. You know,
Speaker 1: you talked about these long term cycles and things that
Speaker 1: go on many years. You also see and I think
Speaker 1: you kind of alluded to it, but you also see
Speaker 1: these very short term ones. And you see charts that
Speaker 1: are literally are intra day and that someone will annotate
Speaker 1: and that will have five waves of a cycle within
Speaker 1: the course of a day or the course of a week.
Speaker 1: Do you see this sort of like fractal nature of cycles.
Speaker 1: So we talked about Fibonacci sequences starting in the early sixties,
Speaker 1: But do you find value in finding those same patterns
Speaker 1: and the span of a few hours or a day
Speaker 1: or a week. Well, so when you talk about five waves,
Speaker 1: that you talk about, uh, the Elliott wave and the
Speaker 1: motion again, that's a great way to have a discipline
Speaker 1: to approach the market and look for potential inflection points.
Speaker 1: In today's world with the speed of technology, what I
Speaker 1: call man versus machine and shorter run trading, that's not
Speaker 1: a space that we can compete in at quad group
Speaker 1: and in long run trading, you basically become it's difficult
Speaker 1: to outperform the index. Why do you want to pay
Speaker 1: me two percent to be long Google? There's lots of
Speaker 1: other ways markets work. So whether it's an et F
Speaker 1: for directly or through yourself. Where we try to be
Speaker 1: is that combination of man and machine one week to
Speaker 1: three months what I call an earning cycle. And yes,
Speaker 1: these things are all applicable for that, particularly when you
Speaker 1: think about an earning cycle and in a market movement.
Speaker 1: Now on the way down, this is where nothing goes
Speaker 1: straight down or straight up. But if you think about
Speaker 1: some of the retail stocks, and if you look at
Speaker 1: Macy's and then it went down and oh someone's gonna
Speaker 1: buy it, or there's an act and then there's a pop,
Speaker 1: they tend to go back down because you know what
Speaker 1: are they always say, the trend is your friend. Human
Speaker 1: nature is we want to be smart. I've learned long
Speaker 1: ago that I'm not, so I try to stay with
Speaker 1: the trend and I'm not smart enough to pick the
Speaker 1: bottom or pick the top. But that's where if you're
Speaker 1: trying to do that something like the Elliott wave where
Speaker 1: you have disciplined or what we talked about before, where
Speaker 1: if I think the market is topping and I'm gonna
Speaker 1: sell it, then I need to have a risk management
Speaker 1: tool that's going to take me out so I don't
Speaker 1: get buried. Okay, one more question. I know you just
Speaker 1: said that you don't want to try to pick the
Speaker 1: top or the bottom, So I'm going to rephrase this slightly.
Speaker 1: When do you think markets are going to get more interesting?
Speaker 1: I think we're in the process of them getting more interesting.
Speaker 1: So we worked out this game plan, as we said
Speaker 1: at the end of the year, and I've spoken about
Speaker 1: and I just mentioned about how we thought that that
Speaker 1: would go five percent from the election, it would get
Speaker 1: to twenty one by March expiration. We're here and we've
Speaker 1: been in this trading range. When you do something ahead
Speaker 1: of time, when you do it intellectually and unemotionally, then
Speaker 1: when you get to the point where you have to
Speaker 1: implement it, you can think about a thousand reasons why
Speaker 1: it's not gonna work out. We try to maintain our
Speaker 1: discipline and say the thesis when I created it on
Speaker 1: emotionally is going to work out until the market tells
Speaker 1: me I'm wrong. So right now I think we're at
Speaker 1: that inflection point. I think we're rolling over and until
Speaker 1: the market tells me I'm wrong, And that would be
Speaker 1: you know, the Dow making new highs uh closing above
Speaker 1: the high, and the SMPS closing above March one high,
Speaker 1: which could easily happen by the way between now and
Speaker 1: and Friday afternoon, assuming the unemployment is very strong. But again,
Speaker 1: I look at the bond market, I look at dollar yen,
Speaker 1: I look at the commodity markets, and if I recommend
Speaker 1: for all your listeners to look at the be calm.
Speaker 1: I'm a big fan of the commodity index and what
Speaker 1: that's been telling us that there's underlying weakness. I think
Speaker 1: that is a perfect note to wrap up the conversation,
Speaker 1: starting from your early work at the New York Fed too,
Speaker 1: where we are exactly today in the markets. Peter Boris
Speaker 1: really appreciate you coming on, fascinating conversation with pleasure. Thank
Speaker 1: you very much. So trades see, are you are you
Speaker 1: optimistic now that perhaps our morning market conversations will soon
Speaker 1: start to get a little bit more interesting. I mean,
Speaker 1: I don't want a big sell off, like I don't
Speaker 1: want people to lose money, but something other than low
Speaker 1: volatility and range bound markets would be very, very welcome.
Speaker 1: I mean, I have to say, we do this a
Speaker 1: lot on odd lots, right, We talk about past history
Speaker 1: because we think it tells us something about the present.
Speaker 1: So I am into the cycle idea. I'm not sure
Speaker 1: I'm totally into, you know, the notion that Fibonacci sequences
Speaker 1: hold like the secret to nature and you know, the
Speaker 1: inner workings of markets. But I feel like there might
Speaker 1: be something there maybe well, you know, as you say,
Speaker 1: we talk all the time on the podcast about historical
Speaker 1: episodes in markets, and so it intrigues me these this
Speaker 1: idea of taking it to the next level and to say, Okay,
Speaker 1: we acknowledge that at least to some extent, history repeats
Speaker 1: or rhymes or whatever. So is it plausible to quantify
Speaker 1: those repetitions as opposed to just saying that history repeats
Speaker 1: and leaving it at that. Can you take it to
Speaker 1: the next level and say, okay, well, let's let's actually
Speaker 1: put some rigor behind this and see if history can
Speaker 1: provide real guides to right now Yeah, that's a really
Speaker 1: good way of putting it. Um. The other thing that
Speaker 1: interested me was the idea of the importance of data,
Speaker 1: I guess, and the notion of you know, flying to
Speaker 1: Zurich to get a data set that maybe not many
Speaker 1: other people have. And that's something that we've talked about
Speaker 1: previously on the show. How proprietary data seems to becoming
Speaker 1: more and more important in the market, right, Yeah, we've
Speaker 1: talked about it, and I think a number of different
Speaker 1: ways on the show. I think we've talked about satellites
Speaker 1: and the attempts to get faster real time data. We've
Speaker 1: talked about the bond market and how difficult it is
Speaker 1: to really get clean sort of real time data on
Speaker 1: what's going on there. And I think it's always sort
Speaker 1: of worth reminding that in this current age, we take
Speaker 1: the existence of data for granted, like it's oxygen or water,
Speaker 1: but that even still, you know, I think we actually
Speaker 1: have a hope we're trying to get another episode in
Speaker 1: the future. There's still important economic data points that people
Speaker 1: look at all the time, where people have to call
Speaker 1: up operators on the phone or message him and say, hey,
Speaker 1: what's the price of this today? Like It doesn't just
Speaker 1: appear on a blinking screen. It really takes legwork to
Speaker 1: get it right. Okay, well, don't give our future episodes,
Speaker 1: but we will do more on this. Well, this has
Speaker 1: been another episode of the Odd Lots podcast. I'm Joe Wisn'thal.
Speaker 1: You can follow me on Twitter at the Stalwart and
Speaker 1: I'm Tracy Alloway. I'm on Twitter at Tracy Halloway and
Speaker 1: Peters on Twitter too. He should tweet more, but he's
Speaker 1: at p Borish. Thanks for listening. Put knowledge to work
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