WEBVTT - Gray: Markets are insanely competitive

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<v Speaker 1>This is a Bloomberg Business Flash and I'm Karen Moscow.

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<v Speaker 1>At twelve fifty two ten announced the euro and dollar

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<v Speaker 1>fourteen out nine. Again, what a eight point eight And

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<v Speaker 1>that's a Bloomberg business flash, Tom and mine Kara Moscow,

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<v Speaker 1>thank you so very much. Well, if he went to

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<v Speaker 1>the University of Chicago to get his NBA and a

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<v Speaker 1>pH d in finance, and along the way, Eugene Fama

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<v Speaker 1>was his dissertation advisor of the randomness of the markets? Uh,

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<v Speaker 1>something that Fauma would bring forward and uh, West Gray

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<v Speaker 1>decided they didn't have to be so random. I guess

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<v Speaker 1>he is the founder of Alpha architect uh and looks

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<v Speaker 1>at ways to um make your portfolio work a little

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<v Speaker 1>bit better. Uh. Let's start by asking what you what

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<v Speaker 1>did you learn from the Nobel Prize winner, Mr Fama,

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<v Speaker 1>and how did you take that into finding value in

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<v Speaker 1>the markets? Well, the number one thing I learned from

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<v Speaker 1>press for Fauma, he's still called Professor Fama because he's

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<v Speaker 1>a Nobel Prize winner, he's moe professor UM is that

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<v Speaker 1>basically markets are insanely competitive and it's really difficult to

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<v Speaker 1>beat the market. So if you're going to try to

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<v Speaker 1>devise strategies that presumed to do. So you really got

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<v Speaker 1>to think hard about what you're trying to do there,

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<v Speaker 1>And uh, you thought hard about it. So where did

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<v Speaker 1>you go? We went to value investing. So what I

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<v Speaker 1>did for my dissertation, which maybe wasn't the greatest idea

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<v Speaker 1>in the world considered, my advisor was the guy who

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<v Speaker 1>wrote all the research for the official market. I process

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<v Speaker 1>is I read four thousand stock pitches submitted to Value

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<v Speaker 1>Investor's Club. We do that by Wednesday. Yeah, you got it.

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<v Speaker 1>So I spent a year reading every single stock pitch

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<v Speaker 1>by all these hedgephone managers, and I co ate all

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<v Speaker 1>that data. All these folks were value minded and compiled

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<v Speaker 1>it all and I presented to you know, the main

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<v Speaker 1>man there, and I said, let's and value managers seem

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<v Speaker 1>to be the market. Yeah, with within this and within

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<v Speaker 1>the research. What I love about your work is a

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<v Speaker 1>work on back tests, which is the basic idea of folks.

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<v Speaker 1>I can't convey how important this is. Where pros back

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<v Speaker 1>tests like crazy, But you say, you've got to wait it.

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<v Speaker 1>You've got to You've got to provide a different importance

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<v Speaker 1>important nous to how you back tests discuss that. Sure. Yeah,

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<v Speaker 1>one of the things about back testing is that there's

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<v Speaker 1>a lot of science to it, but there's a lot

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<v Speaker 1>of art and there's a lot of warts in the data.

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<v Speaker 1>So unless you're actually buried in the raw data understanding

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<v Speaker 1>all the delisting problems, how to incorporate what happens when

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<v Speaker 1>a firm has a merger versus what happens when they

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<v Speaker 1>have a bankruptcy, Because when you're back test, you got

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<v Speaker 1>to know, Hey, this firm got delisted out of the database.

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<v Speaker 1>Both when bank up, you want to input you know,

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<v Speaker 1>negative of heart if it had a takeover, maybe you

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<v Speaker 1>want to input plus two sorry, plus twenty. And that's

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<v Speaker 1>going to have major implications on what you glean from

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<v Speaker 1>your back test and making sure you do that appropriately.

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<v Speaker 1>Um So, I think it's just really important that you're

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<v Speaker 1>in the weeds on understand the details of what you're

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<v Speaker 1>actually doing within the back test. Is the idea of

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<v Speaker 1>a Gaussian distribution, folks. At Gaussian is the Bell curve

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<v Speaker 1>and it can move with what are called cross moments.

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<v Speaker 1>The suppleness of the Gaussian curve. How much of a

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<v Speaker 1>slave is your quant world to the simplicity of a

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<v Speaker 1>Bell curve? Or do you have a humility that there's

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<v Speaker 1>a lot of other probability distributions that that are out there.

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<v Speaker 1>I definitely agree that a normal distribution does not define

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<v Speaker 1>the world at all when it comes to stock markets,

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<v Speaker 1>primarily because humans are involved, so you have a lot

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<v Speaker 1>bigger tail events on both the downside and on the upside.

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<v Speaker 1>So we're our models basically aren't really driven at all

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<v Speaker 1>by statistical normal distributions. Our models are all about understanding

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<v Speaker 1>psychology and then how can we leverage quantitative tools to

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<v Speaker 1>essentially that we don't suffer from the psychology problems of

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<v Speaker 1>all those in the heart of the heart of this

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<v Speaker 1>working with the honor, i should say, working with the

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<v Speaker 1>windsor for Fama is you also had to put up

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<v Speaker 1>with Taylor, Levitt and the rest of the mafia out

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<v Speaker 1>of Chicago. How does behavioral finance fold into your mathiness? Well,

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<v Speaker 1>and that that's actually one of the great things about

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<v Speaker 1>the University of Chicago's You have the extremes. You've got

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<v Speaker 1>Richard Taylor on one end, who says that Eugene Fauma

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<v Speaker 1>is you know, full of it, and then you have

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<v Speaker 1>hu Chief Fama who wins the Nobel Prize for saying

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<v Speaker 1>that prizes always reflect fundamentals. So there's just a lot

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<v Speaker 1>of intellectual battlegrounds out there, and I think the truth

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<v Speaker 1>by life somewhere in the middle, and that's pretty much

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<v Speaker 1>what we do. We say, listen, markets are really really competitive,

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<v Speaker 1>but the harsh reality of the world is that humans

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<v Speaker 1>are not hundred percent rational and that causes prices to

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<v Speaker 1>sometimes not fully reflect fundamentals. And you know, value investing

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<v Speaker 1>is just one example of where that's a strategy that's

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<v Speaker 1>been talked about for a hundred years. You know, it's

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<v Speaker 1>an open secret and it continues to work, but you've

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<v Speaker 1>got to have the horizon and the discipline to actually

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<v Speaker 1>stick to it for it to actually work for you.

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<v Speaker 1>All right, Does that leave it up to the robots now,

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<v Speaker 1>the robot advisors? Um? I think yeah, it does. I

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<v Speaker 1>think one of the biggest challenge of investing, especially as

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<v Speaker 1>we get more news, more ability to trade on instant,

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<v Speaker 1>more availability of data or you know, anyone sitting in

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<v Speaker 1>their underwear and their garage can be at one now

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<v Speaker 1>it allows people to actually act and one of the

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<v Speaker 1>biggest issues in investing is being disciplined and being able

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<v Speaker 1>to follow a process through thick and thin and not

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<v Speaker 1>be able to act. So I think robo technologies and

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<v Speaker 1>and just technology in general leveraging sydramatic decision making. This

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<v Speaker 1>helps us almost perfecting ourselves for making decisions. Dr Gray.

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<v Speaker 1>One last question cubs their white sox on my on

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<v Speaker 1>a white sox. I was on the south side, my

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<v Speaker 1>wife was on the north side. I have to go

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<v Speaker 1>with south south side. It makes for a perfect marriage.

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<v Speaker 1>Wesley Gray, Thank you so much with important work on

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<v Speaker 1>quantitative value out of the University of Chicago. Coming up.

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<v Speaker 1>One of our favorite guests on the quiet middle market

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<v Speaker 1>of m and A. It's Bloomberg Surveillance. Were kind of

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