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Speaker 1: Many Future is up eight points a, Dolumiti futures up seventy,
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Speaker 1: Nimex screwed oil up nine tenths per cent, or thirty
Speaker 1: four cents to forty dollars seventh cents of Aarrol Comics
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Speaker 1: At twelve fifty two ten announced the euro and dollar
Speaker 1: fourteen out nine. Again, what a eight point eight And
Speaker 1: that's a Bloomberg business flash, Tom and mine Kara Moscow,
Speaker 1: thank you so very much. Well, if he went to
Speaker 1: the University of Chicago to get his NBA and a
Speaker 1: pH d in finance, and along the way, Eugene Fama
Speaker 1: was his dissertation advisor of the randomness of the markets? Uh,
Speaker 1: something that Fauma would bring forward and uh, West Gray
Speaker 1: decided they didn't have to be so random. I guess
Speaker 1: he is the founder of Alpha architect uh and looks
Speaker 1: at ways to um make your portfolio work a little
Speaker 1: bit better. Uh. Let's start by asking what you what
Speaker 1: did you learn from the Nobel Prize winner, Mr Fama,
Speaker 1: and how did you take that into finding value in
Speaker 1: the markets? Well, the number one thing I learned from
Speaker 1: press for Fauma, he's still called Professor Fama because he's
Speaker 1: a Nobel Prize winner, he's moe professor UM is that
Speaker 1: basically markets are insanely competitive and it's really difficult to
Speaker 1: beat the market. So if you're going to try to
Speaker 1: devise strategies that presumed to do. So you really got
Speaker 1: to think hard about what you're trying to do there,
Speaker 1: And uh, you thought hard about it. So where did
Speaker 1: you go? We went to value investing. So what I
Speaker 1: did for my dissertation, which maybe wasn't the greatest idea
Speaker 1: in the world considered, my advisor was the guy who
Speaker 1: wrote all the research for the official market. I process
Speaker 1: is I read four thousand stock pitches submitted to Value
Speaker 1: Investor's Club. We do that by Wednesday. Yeah, you got it.
Speaker 1: So I spent a year reading every single stock pitch
Speaker 1: by all these hedgephone managers, and I co ate all
Speaker 1: that data. All these folks were value minded and compiled
Speaker 1: it all and I presented to you know, the main
Speaker 1: man there, and I said, let's and value managers seem
Speaker 1: to be the market. Yeah, with within this and within
Speaker 1: the research. What I love about your work is a
Speaker 1: work on back tests, which is the basic idea of folks.
Speaker 1: I can't convey how important this is. Where pros back
Speaker 1: tests like crazy, But you say, you've got to wait it.
Speaker 1: You've got to You've got to provide a different importance
Speaker 1: important nous to how you back tests discuss that. Sure. Yeah,
Speaker 1: one of the things about back testing is that there's
Speaker 1: a lot of science to it, but there's a lot
Speaker 1: of art and there's a lot of warts in the data.
Speaker 1: So unless you're actually buried in the raw data understanding
Speaker 1: all the delisting problems, how to incorporate what happens when
Speaker 1: a firm has a merger versus what happens when they
Speaker 1: have a bankruptcy, Because when you're back test, you got
Speaker 1: to know, Hey, this firm got delisted out of the database.
Speaker 1: Both when bank up, you want to input you know,
Speaker 1: negative of heart if it had a takeover, maybe you
Speaker 1: want to input plus two sorry, plus twenty. And that's
Speaker 1: going to have major implications on what you glean from
Speaker 1: your back test and making sure you do that appropriately.
Speaker 1: Um So, I think it's just really important that you're
Speaker 1: in the weeds on understand the details of what you're
Speaker 1: actually doing within the back test. Is the idea of
Speaker 1: a Gaussian distribution, folks. At Gaussian is the Bell curve
Speaker 1: and it can move with what are called cross moments.
Speaker 1: The suppleness of the Gaussian curve. How much of a
Speaker 1: slave is your quant world to the simplicity of a
Speaker 1: Bell curve? Or do you have a humility that there's
Speaker 1: a lot of other probability distributions that that are out there.
Speaker 1: I definitely agree that a normal distribution does not define
Speaker 1: the world at all when it comes to stock markets,
Speaker 1: primarily because humans are involved, so you have a lot
Speaker 1: bigger tail events on both the downside and on the upside.
Speaker 1: So we're our models basically aren't really driven at all
Speaker 1: by statistical normal distributions. Our models are all about understanding
Speaker 1: psychology and then how can we leverage quantitative tools to
Speaker 1: essentially that we don't suffer from the psychology problems of
Speaker 1: all those in the heart of the heart of this
Speaker 1: working with the honor, i should say, working with the
Speaker 1: windsor for Fama is you also had to put up
Speaker 1: with Taylor, Levitt and the rest of the mafia out
Speaker 1: of Chicago. How does behavioral finance fold into your mathiness? Well,
Speaker 1: and that that's actually one of the great things about
Speaker 1: the University of Chicago's You have the extremes. You've got
Speaker 1: Richard Taylor on one end, who says that Eugene Fauma
Speaker 1: is you know, full of it, and then you have
Speaker 1: hu Chief Fama who wins the Nobel Prize for saying
Speaker 1: that prizes always reflect fundamentals. So there's just a lot
Speaker 1: of intellectual battlegrounds out there, and I think the truth
Speaker 1: by life somewhere in the middle, and that's pretty much
Speaker 1: what we do. We say, listen, markets are really really competitive,
Speaker 1: but the harsh reality of the world is that humans
Speaker 1: are not hundred percent rational and that causes prices to
Speaker 1: sometimes not fully reflect fundamentals. And you know, value investing
Speaker 1: is just one example of where that's a strategy that's
Speaker 1: been talked about for a hundred years. You know, it's
Speaker 1: an open secret and it continues to work, but you've
Speaker 1: got to have the horizon and the discipline to actually
Speaker 1: stick to it for it to actually work for you.
Speaker 1: All right, Does that leave it up to the robots now,
Speaker 1: the robot advisors? Um? I think yeah, it does. I
Speaker 1: think one of the biggest challenge of investing, especially as
Speaker 1: we get more news, more ability to trade on instant,
Speaker 1: more availability of data or you know, anyone sitting in
Speaker 1: their underwear and their garage can be at one now
Speaker 1: it allows people to actually act and one of the
Speaker 1: biggest issues in investing is being disciplined and being able
Speaker 1: to follow a process through thick and thin and not
Speaker 1: be able to act. So I think robo technologies and
Speaker 1: and just technology in general leveraging sydramatic decision making. This
Speaker 1: helps us almost perfecting ourselves for making decisions. Dr Gray.
Speaker 1: One last question cubs their white sox on my on
Speaker 1: a white sox. I was on the south side, my
Speaker 1: wife was on the north side. I have to go
Speaker 1: with south south side. It makes for a perfect marriage.
Speaker 1: Wesley Gray, Thank you so much with important work on
Speaker 1: quantitative value out of the University of Chicago. Coming up.
Speaker 1: One of our favorite guests on the quiet middle market
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