Andrew W. Lo on "Adaptive Markets: Financial Evolution at the Speed of Thought"

hi good evening everyone I'm Jim Kelly director of the gabelli Center for global security analysis here at Fordham and it's my pleasure to welcome you all to Fordham and to co-sponsor this wonderful event with the Museum of American Finance we both share a common mission financial education there's is much broader than ours we're focused on the university community but we certainly overlap and we have mutual interests and this is a perfect expression of of one of those areas we can work together as you can see in the brochure we handed out about the gabelli Center our activities our objective is primarily promote value investing and behavioral economics is central to value investing in fact we have a course at the undergraduate level in behavioral finance for our students as part of our value investing concentration so we're delighted to have professor Lowe here to lecture on adaptive markets which is highly relevant to our curriculum so that's my welcome and thank you professor and to the leadership of the gabelli school here at Fordham - dean donnel rapa tele and - serese Chatterjee we are so sincerely appreciative at the museum we had a flood ten days ago as most of you know the archives are fine Alexander Hamilton's report on public credit is fine but the structure took a lot of water and on short notice professors Kelly and Chatterjee helped us immeasurably to relocate to this beautiful Center so thank you very much for that I also want to thank Bloomberg for education as you know we are live-streaming with Bloomberg this is our third event with Bloomberg and we want to thank Rob language and Blair Wilkie for making that possible everyone in our audience has a car to 3x5 card please fill out your questions and we'll collect them later for professor Lowe now turning our attention to tonight's program our investors and markets rational and efficient or irrational and inefficient well professor Lowe has some thoughts on that topic in his book adaptive markets that he's going to share with us tonight he is the Charles E and Susan Tijeras professor at the MIT Sloan School of Management he's also the director of the MIT laboratory for financial engineering he's published numerous articles in academic journals and he has authored several books including the econometrics of financial markets a non random walk down Wall Street hedge funds an analytical perspective and the evolution of technical analysis he's won numerous awards including the Alfred P sloan Foundation fellowship the Paul a Samuelson award the American Association for individual investors award and the Graham and Dodd Awards so please join me in a warm welcome but before we do that I just want to point out the professor Richard Silla is the chairman of the board of the Museum of American Finance and John Herzog is the founder and chairman emeritus of the museum and now Professor Andrew Lowe so can everybody hear me without this microphone ok ok great well I want to first start by thanking the Museum of American Finance for inviting me here today and organizing this event and of course the gabelli School of Business for co-hosting and sponsoring this it's a great pleasure and privilege for me to be here because in a way I am a product of American Finance in fact when I first came across the title of the museum I was convinced that it was a typo and that it was meant to be the American Museum of Finance as opposed to the Museum of American Finance but the fact is that actually there is a uniquely American form of finance and all of you I suspect know that Alexander Hamilton played a pretty critical role in creating that new form of finance in fact I think Richard and David have a book coming out on Alexander Hamilton that's a must-read so it's very appropriate that this is an event for me because I really do feel beholden to that system that framework that gave me the opportunity to be here today and to be in the career that I am I'm also particularly grateful to be here at Fordham University because I actually grew up in New York City I emigrated to the United States when I was five years old went to public schools throughout went to high school in the Bronx at the Bronx High School of Science and Fordham University at the time was just in the Bronx and it was a mainstay of many students a number of the high school students made their way over to afford him a number of the college students came by and mentored us and it's wonderful to see how Fordham has grown and spread and has succeeded over the course of the last few years so I'm really happy to be talking about my book also because there are some really interesting connections between adaptive markets and value investing and what all of you are doing here I also want to thank the folks online for joining and Bloomberg for sponsoring this event I'm hoping that you'll see the various different connections not only with finance but with broader areas of learning and I'm gonna try to draw that out over the course of the next half hour or so so let me begin by telling a little bit about why I wrote the book it wasn't really planned in fact for those of you who have a copy of the book I need to first apologize immediately for its length I I generally don't go on and on but my publisher told me something before I handed in the manuscript he said that he said remember Andrew every equation you include in the book will reduce your readership by 50% so I took that to heart and there's not a single equation in the book as a result it's 500 pages long so so I apologize but the reason that it ended up being that length is because it's a chronicle of my own journey and frustration with the state of affairs that even today still affects financial economics and the state of affairs I suspect most of you know is the fact that most of us who grew up in this kind of a neoclassical financial economics type of a paradigm were taught the efficient markets hypothesis the idea that prices fully and everywhere and always reflect all available information and at the opposite end of that spectrum is the fact that people are people and we engage in all sorts of human biases foibles and irrationalities there's a pretty wide gulf between these two schools of thought and as a graduate student I was introduced to both of them and it was really difficult to choose you know it's kind of like a child listening to his or her parents arguing you know you just want it to stop and get along and you know that they love each other but they just you know can't you know can't come to terms so I decided over the course of probably now going on 25 years to try to reconcile these two warring parties and that's really the the nature of this book what I wanted to do was to describe to you the sort of the different paths that I ended up taking to try to reconcile these two schools of thought and really that that path started off as a graduate student when I started reading a lot of the psychology literature like many of you I began with the idea that markets are efficient people behave in a rational manner and from that literature I was brought to the psychology literature that showed all sorts of experiments behavioral economics behavioral finance that demonstrated that people did not react in the ways that we would predict using expectation of utility functions and so on and so that digression to behavioral finance and psych ecology naturally brought me to the cognitive neurosciences that of course brought me then to artificial intelligence and the theory of bounded rationality which eventually brought me to evolutionary biology in ecology and ultimately when you synthesize all these various different schools of thought you get what I call for lack of a better term the adaptive markets hypothesis so with your permission what I'm gonna do is to take you through a little bit of that meandering path but that's really what the 500 pages consists of it consists of my own personal journey through these various different literature's because I think like you I started out being a great skeptic of behavior and how all of these pieces might fit together in some kind of a framework so that's where we're going and let me just give you a quick summary ultimately what I'm going to do is to reconcile these two fighting parents and show you that there is a framework that allows us to think about both behavior and rational deliberation in the same breath and so here's the quick summary the adaptive market hypothesis begins with the acknowledgement that the traditional paradigm of investment analysis what we know and love and use in our day-to-day practices is not wrong but it's incomplete it doesn't capture the fact that in a very dynamic environment we're not going to see the same kind of relationships as in a static environment so when things are stable then stable investment policies make sense but when things are highly dynamic well then actually things don't stay the same and people adapt to those kinds of changes and the fact is that right now we are living a very dynamic economy with lots of things changing even day-to-day and so the reason that many of these theories look like they're not working is not because they're wrong but because they're not being applied to the right context and we need a meta theory to allow us to integrate all of these ideas that's the idea behind the adaptive markets hypothesis you know the old saw that it's the economy stupid politicians have been saying for years that it's the economy that drive political events well I think that evolutionary biologists should start telling economists you know what it's the environment that actually drives all of these various different kinds of behaviors and so what I'm going to try to do in the next half hour or so is to give you by way of a few examples how it is that this new framework allows us to integrate what we know and love the traditional investment framework with all of the various anomalies that is becoming harder and harder to ignore so in order to do that I want to start with the traditional investment framework and that investment framework really has to do with a creation myth that we finance professors more than anybody else perpetrate on our students and the myth goes like this in the beginning and by the beginning I mean 1964 in the beginning bill Sharpe said let there be beta and there was beta and it was good this framework this single factor model that allows us to price systemic risk and separate that from unique investment acumen namely alpha you gave us all sorts of opportunities to create new financial products and services most importantly though it allowed us to discharge our fiduciary duties to our clients by giving us a rational framework for thinking about how to invest it democratized finance for the masses very important innovation now this innovation didn't come free there was a cost and in particular the cost was a set of assumptions we had to make some assumptions in order to generate that kind of a simple linear relationship between risk and reward and what are those assumptions well we had to assume that the relationship between risk and reward is linear we had to assume that the parameters were stable over time and space and that we could estimate them and be able to make use of them and most importantly we had to assume that markets were rational that people were efficient in how they priced these kinds risks what if it's the case that some of these assumptions don't hold well it turns out that if any one of these assumptions is violated you do not get the simple risk-reward relationship it gets more complicated and in some cases when some of these assumptions are violated at the same time you don't get anything that looks even closely like the kind of a risk-reward relationship that we used in practice now I have to say that again the framework is not wrong all economic theories are meant to be approximations to a much more complex reality and so the question really is how good is the approximation how large are the approximation errors and so the first thing I want to point out is that this framework that we all know and love don't worry you don't need to throw it out because it turns out that for a very long time this was an excellent approximation to a much more complicated reality and to illustrate that I'm going to show you this graph that probably looks unfamiliar to you but every single one of you I suspect has already seen this graph you just don't know it this is a graph of the cumulative performance of a $1 investment in the S&P 500 from 1926 to the present now the reason you don't recognize this it's because I've plotted it on a semi logarithmic scale and the reason I did that was because on a log scale it turns out that equal vertical distances correspond to equal rates of return and so if you've got an investment that has roughly the same rate of return over time it looks like a straight line on this graph and when you look at this graph you see that the United States stock market has been one of the most consistent investments in the history of capital markets for a period of about five or six decades the US stock market looks like a straight line it turns out that during this period the assumptions that I showed you on the previous page were an excellent approximation to this much more complicated reality doesn't much matter where you invest during that period of the 1930s to the early 2000s you would have earned approximately a risk premium of about 8% with an approximate standard deviation of about 15 to 20 percent pretty reliable risk reward trade-off during that period of time but take a look at the last 15 years do you believe that the last 15 years is just like the previous 50 if you do then you don't need any other theories the traditional efficient markets hypothesis cap M and all of the various different implications are just fine but I would argue that the last 15 or 20 years it does look a little bit different and there are reasons that I'll give you in a few minutes why I think they're very different and in case you want to see a better example of this kind of a difference let me show you Japan so this is the Japanese stock market from the 1940s to the present and they also had their period of about 20 or 30 years of very very stable performance in the Japanese stock market but starting in the 1980s they ran into a problem as I'm sure all of you know and the Lost Decade became the lost to decades and now we're running on the lost three decades in Japan so once again you look at this and you ask yourself well do you think that the last 30 years was just a minor blip in an otherwise up we're trending curve or did something change I would argue that something's changed and so the adaptive markets hypothesis takes change seriously and asks the question what are you gonna do about that change how are you gonna respond how will you adapt to that change so the basic idea behind the adaptive market hypothesis really follows that of evolutionary biology and the great evolutionary biologist theodosius dobzhansky once said that nothing in biology makes sense except in the of evolution and so I'm gonna steal his phrase and repurpose it for our current context which is that nothing in the financial industry makes sense except in the light of adaptive markets and I'm gonna try to convince you of that by giving you some examples so first of all before we go on let me define what I mean by adaptive markets so these are some very basic principles that are outlined in the book about what adaptive markets is we start with the assumption that people act in their own self-interest I think that most economists would agree with that statement but starting from point 2 onwards we depart from the traditional economic theories because we are gonna acknowledge that we make mistakes we're not perfect and we don't engage in rational expectations we suffer from all sorts of behavioral biases however we learn and adapt from those mistakes and it's that adaptation and the process of natural selection that ultimately governs market dynamics so it's really evolutionary theory applied to financial markets and when I say applied I don't mean as a metaphor or as an analogy I mean literally it is part of evolutionary biology we are all animals and it turns out that one of the ways that we've adapted to dealing with an otherwise hostile environment is capital markets that's a tool that we use just like the opposable thumb so I'm gonna give you some examples of that and then I'm looking forward to the discussion I'm sure that it's gonna be by far the most unlikely portion of this evening to get your comments and reactions to this so I want to first start by describing a little bit about human behavior and particular I want to talk about what investors really want I suspect many of you already know that and if you've seen this example before please don't give it away to the rest of the audience I'm gonna show you four financial investments I'm not gonna tell you what they are I'm not even gonna tell you over what time period they span I'm simply going to show you the cumulative performance of a one dollar investment in each of these four assets and I'm gonna ask you to pick one of these four for your retirement portfolio or for your parents grandparents retirement portfolios okay so let me show you what they are so we've got the green line blue line red line and the black line and you see they all start out at a dollar but they have very very different risk reward characteristics the green line goes from $1 to $2 not very interesting although you don't know what the investment period is I'm going to tell you that these are a matter of many years now this is not days or weeks it's years that we're talking about and so the Green Line is not all that interesting but it's also not nearly as volatile as say the red line the red line turns a dollar into about four and a half but way more ups and downs the blue line is even more profitable but also quite a bit more volatile and the black line is somewhere in the middle so this is all about your own personal risk preferences right what would you like for your retirement portfolio if you could have one and only one you can't mix and match these so how many people by a show of hands would want the green investment anybody all right one person for the green investment okay how about the red investment anybody want the red investment Wow nobody wants the rip so I want you all to remember this moment because after I tell you what it is you're gonna have some rethinking to do all right all right how about the blue investment how many people want the blue investment okay Wow we got the hedge fund managers it came out tonight and now how about the black investment yeah so in all of the audience's that I've presented this to that is by far the most popular investment because it's got seemingly the most attractive trade-off between risk and reward well let me tell you what these four investments are first of all the time period that we're talking about is 1992 2008 so that's the time period and the for investments are these the Green Line is US Treasury bills safest asset in the world at least until February 6th but not very interesting from a performance perspective you're not gonna make a lot of money so probably not a great thing to invest in for retirement unless you our way past retirement and looking really to preserve wealth now what about the red line that none of you picked you know what that is the red line is the sp500 most of you already have investments in that so you need to start rebalancing your portfolio now after this but you can see that more volatile but actually you would have done well had you put your money in the SP in 2008 the blue line is the single stock Pfizer the pharmaceutical company way more volatile but also has done very well since 2008 now what about the most popular investment the asset that all of you picked well this is the returns to a private fund called the Fairfield century fund this for those of you who are hedge fund aficionados was the feeder fund for the Bernie Madoff Ponzi scheme which is why I had to stop it in 2008 now you know how the Ponzi scheme got as big as it did it's human nature we are all drawn like a moth to a flame we are drawn to investments with high yield and low risk right high Sharpe ratios and in many cases that gets us into trouble it's human nature but it's more complicated than this you know because we actually adapt to changing perceived risk and I want to show you an illustration of that from a very very old academic study it was done in 1975 by a University of Chicago economist by the name of Sam peltzman so this might sound like a very boring title the effects of automobile safety regulation but this is one of the most exciting papers in the history of the economics literature and I'll tell you why in 1975 peltzman being a good Chicago economist that he was decided to ask the question how much value has government created in mandating all of these automobile safety regulations because ultimately those costs are passed on to the consumer so we end up paying for those costs things like reinforced bumpers padded dashboards collapsible steering column seatbelts lap belts so on and so forth every one of these safety regulations that was mandated by law so then all automobile manufacturers had to install them ultimately they raised the prices of these cars and we paid for them so the natural question that he asked was okay well fine we're gonna pay for them but what's the benefit how many lives have been saved by all of these mandated safety regulations so he decided to look he looked at the number of highway deaths before during and after these pieces of legislation and what he found was absolutely shocking he found that there were no lives saved none with all of these safety regulations now that's not exactly right what he found was that initially right after the safety regulations were imposed the number of deaths from traffic accidents did decline and then after a few years it went right back up to where it was before in fact it was only one instance where he found that it looked like there was a relatively permanent decline in the number of automobile occupant deaths and the reason I put it in those terms was because in that instance he found that those decreases in deaths were offset by increases in the number of pedestrian deaths so what he concluded is that every time one of these safety regulations was imposed initially it reduced the number of deaths and then people adapted because they were driving safer cars they simply drove faster more recklessly and so his conclusion was if you want people to drive more safely what you should do is to take away all of these safety devices and install sharp spikes on the dashboards pointing at the driver we adapt we adapt to all of these kinds of conditions now obviously this kind of a conclusion was incredibly controversial and a lot of people argue against this so-called peltzman effect they argue that you know what you didn't take into account the kind of driving is it city driving or urban or rural driving you didn't take into account the skill of the driver the education of the driver the nature of the venue is this roar during morning rush hour or evening commutes or during the middle of the day there are all sorts of factors that they claimed he did not take into account and so this paper literally launched hundreds of additional studies to verify or refute the peltzman effect in some cases they confirmed it in other cases they contradicted it and it wasn't until 2007 that finally two economists came up with one particular venue where all of these other effects were controlled for right do you know what that venue was can you guess where you can basically control for all of these other effects and so therefore what you're focusing on is getting to your destination a little bit sooner what would that venue be what is it yeah race exactly racecar driving so in 2007 these two economists sobel and Nesbit they analyzed the number of accidents in NASCAR racing when they introduced safety measures and it turns out that this is a real problem for NASCAR because in that setting every time they introduce a safety device titanium reinforced struts or additional protection for roll cages every time they introduce these safety enhancements the number of accidents and deaths went up didn't stay the same it increased if the only thing that matters is getting to your destination a little bit sooner than your competitors if I tell you that your race car is slightly safer you will push that to the limit and beyond it is human nature now this has some pretty significant implications for finance particularly today when we happen to be sitting at a time where we are near historic lows in stock market volatility given how low the volatility is equity markets investors are adapting to that and what do you think they're gonna do what do you think happens when all of a sudden I give you something that now has a higher Sharpe ratio that has lower perceived risk you're gonna want to do more of that right that's what's happening right now as we sit here and enjoy this wonderful bull market and wonderfully low level of volatility of course what goes up eventually does come down and so as we start adapting to this kind of a new low level of volatility there is a rude awakening that we're facing so something to keep in mind so that's one important implication of adaptive markets markets are not the same over time and the population of investors they're not the same over time they change and they adapt and sometimes that adaptation is positive and beneficial but other times that kind of adaptation can be quite dangerous and detrimental so I'm gonna give you another example about this new world order of investments and describe to you a whole bunch of different implications of efficient markets and how that compares to adaptive markets I won't have time to go through all of these but I want to give you at least one example to show you that all of the things that we take for granted as being naturally an implication of the current framework that we live in things like long-only investments are just fine you don't have to worry about shorting or that you can control your risk by asset allocation or that you can invest for the stocks in the long run every single one of these tenets of modern finance has a different interpretation and a different implication under the lens of adaptive markets so I want to focus just on one of them which is the notion of passive investing and indexation that's a hot topic because obviously the amount of money that's been flowing into index products ETFs futures and other related instruments has been dramatic over the course of the last several years in fact Vanguard just passed its four trillion dollar mark it's a kind of a milestone for how investors are thinking about investing so I want to start by talking about what exactly an index is what is an index what's the definition of an index can anybody give me a quick one liner of what an index is to you what does it mean for a practical perspective yeah exactly and typically one of those weights do we know market cap right exactly a market cap weighted portfolio that's it that that's the the natural knee-jerk reaction of what an index is and the idea behind the index is that we're not using any investment acumen or alpha it's meant to be passive you're gonna just buy it and hold it and leave it there that's really what an index has come to mean in the industry now who gave us that definition who if you had to identify one person that was responsible for the passive investment management industry who would you place that honor on Jack Bogle absolutely the founder of Vanguard I think there's universal agreement that Jack Bogle is the man when you ask Jack Bogle though he actually gives credit to somebody else now Vogel was the first to create the first passive mutual fund but he wasn't the first to engage in passive investing in a graduation speech in 1997 mr. Bogle gave credit to these two individuals Jack McCloud and Bill Foust of Wells Fargo Bank the basic ideas go back a few years earlier in 69 to 71 Wells Fargo Bank had worked from academic models to develop the principles and techniques leading to index investing McCown and Fauss pioneered the effort which led to the construction of a six million dollar index account for the pension fund of Samsonite corporation with the strategy based on an equal weighted index of New York Stock Exchange equities its execution was described as a nightmare and ultimately it was abandoned in favor of market cap weights now when I read this I was blown away because I didn't understand what the big deal was what was so hard about a portfolio of a hundred equally weighted NYSC stocks and you know what the problem was in 1969 if you constructed a portfolio of 100 equally weighted stocks at the end of the month that portfolio is no longer equally weighted right because the prices the stocks of the prices that went up there over weighted and this the the stocks of the prices that went down there under weighted and so you have to rebalance that portfolio every month and it turns out that rebalancing a portfolio of a hundred stocks every month in 1969 was an operational nightmare remember that in 1969 a spreadsheet was a piece of paper with lines on it and somebody had to do the back office accounting reconciliation and all the trading kind of of calculations it took a month to trade a hundred stocks and do the clearing literally a month and so ultimately it was just too hard it was technologically too difficult they gave it up and they decided to go to market cap weighting market cap weighting has this wonderful property that once you construct it you never have to touch it because once a market cap weighted portfolio always a market cap weighted portfolio the only thing you have to do is deal with index inclusions and deletions but that's it it's buy and hold you never have to touch it so this was really striking to me because what this tells me is that there's no magic behind market cap weighting we've decided that market cap weighting is a good thing to do because of technological constraints that's why we do it what if I told you that technology has advanced what if I told you that it was possible to trade quicker cheaper and to do all the back-office reconciliation in calculations at a touch of a button what if all of this could be automated well if that were the case may be we wouldn't choose market cap waiting any longer would we maybe we would pick something else so what I'd like to do is propose to you a different definition of what an index is and here's my definition an index is any portfolio that has three characteristics and here are the three number one it is totally transparent meaning everybody knows how it was constructed two it's investable meaning that you could put a hundred million dollars to work in that portfolio and actually get the return that the index will show over the course of a month and three most importantly it's totally systematic rules based and everybody knows the rules those three are what the key characteristics are for what we think of as an index because for passive investing what you want to be able to say is I don't want to give my money to you the manager because look what I can get with this portfolio that requires nothing more than my sticking it into this very very simple vehicle in order for you to be able to say no I'm not gonna give the money to you I'm gonna put it here you need these three things to happen so I'm gonna give you a surprise quiz now I'm gonna ask you to apply this definition and tell me whether or not the following are actually indexes based upon my definition not the industry's definition so here's the first question a value weighted average in other words a market cap weighted portfolio is that an index under my definition yes or no correct yes what about an equal weighted average like the very first index portfolio for Samsonite is that also an index according to my definition absolutely okay what about a target date fund you know the fun where we change the asset allocation as a function of how old you are how old you are called close to retirement is a target a fun and index fund according to my definition no you sure yes well it depends do you know the glide path if you know the glide path if you know the asset allocation rule it is because it's totally transparent its systematic and it's investable all right what about the FHFA house price index is that an index it not only is it not transparent it's certainly not investable right no that's not an it Nix what about the hedge fund industry as parent right and last but not least a couple more trend-following futures is that an index well the answer is maybe it depends is that rules based is it totally transparent in that case it is but many of the trend-following futures mutual funds have certain proprietary components so no those are not so it depends how about large cap risk managed core product again it depends maybe whether it's systemic whether it's transparent you get the idea though right why am i taking through all this it's because it turns out that this new definition of an index opens up a whole ocean of possibilities for the investment community and for the investor in order to give you an appreciation of that I'm gonna take an analogy from music particularly from audio files out there I don't know how many of you are really serious about listening to music but if you are you will probably recognize this does anybody know what this is yeah it's a graphic equalizer this is a device that my audiophile friends tell me is a must-have what it does is that it allows you to control the amount of sound coming out of your speakers at different frequencies so that you can tailor the listening experience to your own particular needs now I don't really know what my needs are for these different frequencies but he tells me that it's actually very important sometimes you want more bass sometimes you want more trouble this will allow you to do that imagine if we had this device for our investment portfolio we had a an equalizer that allowed us to dial up or down different characteristics so let me give you an example there's a very big spectrum of difference between a hedge fund and an index fund passive versus active right and one way to think about that is alpha this notion of unique investment acumen hedge funds have it at least they claim they do those skeptics don't believe it they would prefer to put money in passive index funds where there is no alpha it's all about beta and so sometime during the 1960s and 70s we as a society decided to be skeptical about alpha and we decided to dial down the Alpha and go focusing on beta right but at the same time that we did that we also decided to relinquish our roles as risk managers the typical index fund has no risk management I mean it's it's you get the sp500 that's your portfolio if the S&P 500 goes down by 50% that's what you're gonna get you own that 50% loss and by the way the S&P did go down by 50% between 2008 and early 2009 so that's not just a hypothetical example no risk management it's an index passive investing if it goes down you go down with it and sure enough that's exactly what you see on this graphic equalizer chart those two things are linked if you want risk management the place to get it is back in the hedge fund community because hedge funds manage their risk in an extrude excruciating detail it turns out that this is a false dichotomy we do not have to stand for this there's no reason why we can break we cannot break this link we can actually now thanks to technology we can now change that and dial down the Alpha while maintaining our risk management so this is an example of adaptive markets at technology has given us number one the ability to go away from market cap leading but number two to change our mix of alpha versus beta and Sigma variance risk and in fact if you take this to its logical conclusions what you get is what I call full spectrum investing there are many different characteristics of a portfolio liquidity credit exposure foreign currency exposure so on and so forth and right now we've got very little in between those two extremes across these characteristics there is a whole ocean of untapped investment opportunities and so with the proper tools with the proper technology we can dial up or down those kinds of different exposures in order to tailor the investment experience for each and every one of you here today so what am I talking about well here's the vision precision indexes you've all heard about precision medicine the idea that I can tune the particular therapy to your specific genetic makeup well there's no reason why we can't do that from the financial perspective instead of the sp500 or the the Russell 1000 imagine having your individual index tailored to your particular circumstances imagine that index constructed to take into account your income your expenses your life goals all your constraints this is what I call really smart beta and imagine if we can automate this so the product is just running all the time now you might think this is like Robo advisors it's not Robo advisors we are not there yet it turns out that we're close but we have not achieved this we have the hardware we have the software we have the telecommunications but there's a missing piece and that missing piece is that we don't have the algorithms we do not understand how to create these kinds of strategies now I tell you that this idea is not new so I'm gonna read to you something from an article that was published a few years ago titled personal indexes in the concluding paragraph of this article this author wrote the following artificial intelligence and active management are not at odds with indexation but instead imply a more sophisticated set of indexes and portfolio management policies for the typical investors something each of us can look forward to perhaps within the next decade who was this incredibly prescient and brilliant author who wrote these words yours truly I but I wrote this in 2001 so you could argue I was a bit off I'm late but but actually no I think that you know as of 2011 we had a number of products that were automated in a number of ways but we're not there yet and the reason we're not there yet is because of this it's not because of artificial intelligence we have plenty of artificial intelligence what's missing is artificial stupidity we don't yet know how to model human behavior in an algorithmic way and until and unless we can do that until we can actually model not how people ought to behave but how people actually behave we're not going to be able to develop the products and services that address those human foibles and inconsistencies in irrationalities so I'm gonna give you an example of this by talking about artificial intelligence this is why I have to go from human behavior to a computer science I want to describe to you what's happened over the course of the last few years in AI because something remarkable has occurred that ultimately will have an impact on financial markets and to make that point I'm going to give you an example that distinguishes between human versus artificial intelligence so the example of AI that I want to bring to bear is something that all of us have dealt with before and that has to do with online shopping a few years ago I started getting interested in biomedicine and the healthcare industry so I decided to order a book on one of the most successful biotech companies in the industry Biogen and Genentech and so Genentech's book was relatively easy to purchase I went on Amazon and I clicked on you know add to the shopping cart and the moment I did that Amazon does this thing which I just absolutely detest you know what it is what they did was they showed to me five books that people who bought that book also bought and I had to have two more of those it's really really nasty nasty piece of technology very effective this is an example not of old AI but of new AI this is very different than how we used to think about AI in the 1970s and 80s and I know that because I was there then and I actually worked a bit in that field and I can tell you right now that things are so different and let me tell how they're different in the old days in the 1980s AI was tantamount to constructing what are called expert systems how many people have heard of the term expert systems it has a very interesting division in the show of hands everybody under the age of 40 will not have heard of that term if you're over 40 my guess is you will an expert system was a rather lengthy piece of software that tried to replicate a particular aspect of human behavior and it did so by trying to account for every possible contingency and then coming up the optimal response to that contingency very much like what an economist would think we ought to behave like as a human and so a good example is a piece of software that created a robotic arm to play ping pong I was quite excited about this because I loved ping pong when I was young but I could never find anybody to play with me so I would I wanted to get a robotic arm didn't exist but there was some faculty that were doing research on it and I think was Carnegie Mellon that developed the first robotic arm that did this they gate they created a robotic arm to play ping pong not very well but well enough to be able to hit the ball across and play you know reasonably consistently over a period of minutes that piece of software was it must have been a two hundred thousand lines of code which back in the 1980s was a very very big piece of code and what it did was to first of all identify the trajectory of the ball using Newton's laws of gravity move the ping-pong paddle in just the right way calculate the appropriate angle of deflection and the coefficient of friction on the powell surface and use the appropriate Newton's of force to be able to move that ball across the net it was an incredibly complicated piece of code and it worked sort of it couldn't beat serious players but it at least get the ball over the net that was AI back in the 1980s what's AI today well today you know what they do they take cameras and they film Olympic level ping-pong players and they film them for a few hours and they take that footage and they store it and they decompose it into all the possible different positions angles and various kinds of position of moves and they get the robotic arm simply to replay that data in other words they use large amounts of data and they simply draw from that database an appropriate example to control the robotic arm the amount of code very very small a few tens of thousands of lines the amount of data hundreds of gigabytes of video data big data and machine learning have completely transformed the space and back in the 1980s we couldn't do this because we didn't have the ability to store data I don't know how many you remember but I bought one of the first IBM pcs and I paid $3,000 for the PC it was an extra thousand dollars if you wanted an extra hundred and 25 K of memory what we're doing today with storage being so cheap and using machine learning methods on large amounts of data is actually much closer to how human intelligence works we are building machines that are closer and closer to how we think and that's both exciting and frightening so let me give you a couple of examples of that and I'll wrap up so I'm gonna give you an example of how we all engage in this kind of very similar pattern recognition the way that Amazon and other online retailers do and I'll start with something that all of us are implicitly good at which is identifying friend versus foe this is something that from an evolutionary perspective was hardwired into us at the very early stages otherwise we wouldn't be here today to talk about it so I'm going to show you an image and ask you to identify as quickly as possible whether or not this image is friend or foe is it threatening or not threatening alright here we go friend or foe friend foe friend who knows you can't tell right all you see is a bunch of blotches but friends that tells me that you all of you are very friendly here because your natural reaction is friends in other audiences the immediate reaction is foe I don't know what it is I'm scared of it it can take it out but you can't really tell not enough data in this rather pixelated image how about this friend or foe whoa Wow interesting that was a big change okay well let me show you one more image with even more data and here it is so this is yours truly being stalked by a ninja so definitely looks like a foe but actually upon further reflection and examination the ninja is not real this was taken at the Washington DC spy museum I took a selfie with this ninja statue and you can tell that it is not at all threatening data big data makes a big difference so having a high-resolution image is pretty important for determining friend or foe it turns out that we do this in a more refined way when we're meeting people to see whether or not they're friendly or not and so I want to give you a more refined example of this and the refining example has to do with cocktail parties that you might happen upon over the course of an evening as you go from person and person and group to group you'll learn things about different people and that allows you to determine friend or foe in a much more sophisticated manner so let me just give you a specific example at the cocktail party you'll run into people and engage in casual conversation and during the course of an evening evenings conversation you'll talk about things you'll learn stuff about the other party things like what their gender is their sexual orientation race ethnicity age group current home state religious affiliation so on and so forth and in parentheses are the broad number of categories of each so two genders two sexual orientations roughly four combinations and so on so I'm gonna do a little experiment here I'm going to tell you about two specific individuals that I've met and after I tell you about them I'm gonna ask you to make some judgments about them okay and the two people I'm gonna tell you about are Jose and Susan so Jose happens to be a young professional gay Latino male who lives in state of California no religious affiliation is a Democrat middle class and has an MBA now that's Jose now to tell you about Susan Susan is a middle-aged white heterosexual female from the state of Texas Christian Republican affluent and has a Bachelor of Arts that's Susan okay I'm gonna ask you three questions now about these two individuals first question you're doing an internet startup in Silicon Valley and you need to hire somebody to help out with that effort who would you rather hire Jose or Susan how many people would hire Jose as their internet startup colleague how many of you will hire Susan okay second question second question you are organizing a fundraiser for breast cancer campaign we want to raise money to help deal with breast cancer who would you hire to help you organize that fundraiser how many people hire Jose for that fundraiser how many little hire Susan for that fundraiser okay third question you're working at the Internal Revenue Service as an auditor and you need to audit one of these two individuals you can't do both but you need to figure out which one of them is more likely to be cheating on his or her taxes how many of you would audit Jose how many of you would audit Susan Wow that's remarkable I I can't believe how judgmental you people are I mean you know you don't know these people but yet you're making decisions about who to hire who to throw in jail for tax evasion now it's true I asked you but you didn't really hesitate did you what you're doing is exactly what Amazon does you are actually using machine learning and Big Data except it's human learning in this case you are searching your database of all the people that you know who have done fundraisers and seeing which one is most like Susan versus Jose and how the outcome went and picking on the basis of that it's human nature and it's not perfect by any means but it's better than nothing and the reason I know it's better than nothing is that we are here today to talk about it we survived this is a survival mechanism and it's a very sophisticated one how sophisticated well if I go through all the different combinations of the types of people that you will now be able to categorize them into the number of buckets you now have in your brain because of these characteristics if you do the math there are three hundred and forty five thousand six hundred unique combinations of personality types your database this is more pixels than in a 600-800 photograph so just by talking to people and finding out a few facts about them you can put them into various different buckets and then search your database to see which of those buckets have been more successful for fundraising and which is been more successful for doing startups the problem with this algorithm that all of us use is that it's based on very very sparse data what I mean by sparse data is how many people here have met more than three hundred and forty five thousand six hundred people in their lives I actually had one marketing person raise his hand when I guess but what that means is that for most of us virtually all of these cells all of these buckets are empty we have no data and this is one of the real problems with fake news fake news fills in these missing cells and if you get them filled in with the wrong information it will affect and change your behavior in dramatic ways we all exhibit biases based upon missing cells we're all going to face gender bias racial bias religious bias I'm not excusing it but I think we can explain it and if you understand it you can do something about it what we need to do is to change the database if you want to eliminate bias we need to go into those cells and change the entries into something that's correct as opposed to misleading so it's it's more than this though it's not just cells it's not just data its interpretation of the data so the last two examples that I'm gonna give you a no wrap up is that we respond not to data but to narrative to interpretations of the data so just like I explained you're not just looking at you know statistical relationships you're trying to predict whether or not Susan or Jose is gonna be good in one Coxon or another in that kind of prediction you need this narrative and it turns out that narrative the stories we tell ourselves about how people behave that can actually affect our reality so I want to illustrate that to you with an example and the example has to do with this does anybody recognize this image yeah what is that very good exactly thank you it is Harvard Square how many people here visited Harvard Square how many people have ever driven into Harvard Square and how many people of those who've driven have not been able to find parking in Harvard Square so until recently my rule of thumb living in in Cambridge and in Boston is never ever ever drive into Harvard Square you could take the team take the bus take an uber do not drive your own car into Harvard Square you will never find parking and I use this rule of thumb until recently when I was taught an algorithm for dramatically increasing my chances of getting parking in Harvard Square and I'm going to share that algorithm with you today because most of you don't live in Boston and will not compete with me but you know this algorithm actually worked here in New York so if you want to get parking in Times Square this will work too and let me tell what the algorithm is before you enter the place where you want to get parking before you do that either at a stop light or pull aside and be safe so you can do this close your eyes and utter the following incantation the incantation is rabbi Mahoney rabbi Mahoney rabbi Mahoney you say that three times and you will magically be able to get parking with much more likelihood than before now you're I know a few of your giggling you know you know you don't believe me and you know I didn't believe this either when I first heard it and then I tried it and it really works now you may be too embarrassed to even try it so if you are here's a tip tell one of your friends and have them try it and they'll come back and tell you and thank you for this incredible insight you know and here sure enough is my parking space that I got by using this but what's more interesting is not that it works but why it works and it took me a while to figure this out I actually literally went through the process of trying this a few times measuring you know how long it took me to get a space and you know how it went about it and after doing all of that I finally realized what was going on and it actually has to do with narrative it turns out that when I went through this process and uttered the incantation the very act of doing so implicitly caused a small portion of my brain to acknowledge that no it was not impossible to get a parking space in Harvard Square that that there was a glimmer of hope no matter how small it now existed in my brain because otherwise I wouldn't bother doing the incantation and go through this really stupid exercise but I did so and because of that change in my narrative because I changed my narrative from oh it's impossible to get parking - you know it's very very unlikely but you know what there's a slight chance that I might be able to do it because of that narrative change I noticed something when I drove into Harvard Square I drove more slowly I looked more carefully down the side of the road to see whether or not any brake lights would turn on and indicate a car is about to pull out I would now allow pedestrians to cross in front of me instead of trying to run them over because they might be going to a car and giving me a space I changed my behavior because of that narrative and the change in behavior actually increased the chances that I would get a space sometimes things need to be believed in to be seen and that's a remarkable lesson that I learn from this exercise so what we think of as reality is not nearly as real as we think reality is often what we make it and the narratives that we use in all of our daily lives narrative is key and so I want to conclude by talking about the subtitle of my book evolution at the speed of thought what I'm getting at there is that narrative changes as a function of circumstances and it's critical for us to manage that narrative so that we end up doing the things that we want to achieve as opposed to allowing those narratives those pieces of fake news to control us so I want to leave you with one final story about the power of narrative and open it up for questions and discussion and that has to do with a hiker by the name of Erin Lee Ralston who on April the 26th of 2003 was hiking in a remote part of Utah called Blue John Canyon this is part of Utah that where there's no cell phone coverage there are no highways there's nobody around for hundreds of miles you are literally out completely in the desert and he was a experienced hiker but during the course of his hiking he slipped down a crevasse fell and a 600 pound boulder fell on top of him and pinned his right arm to the face of the crevasse wall you probably know who I'm talking about because he wrote a book that was called between a rock and a hard place in a movie was made about him with James Franco called 127 hours he was pinned for a hundred and twenty seven hours in that crevasse before he decided to do what most of us consider unthinkable he took out a dull multi-tool and amputated his right arm below the elbow how did he do that I don't mean literally how I'm not going to show you any video of that but I mean how does somebody do that how does somebody decide after five days that okay now it's time for me to cut my right arm off and it was a gruesome gruesome process that I won't describe to you but but it took some incredible amount of fortitude to do that well if you read the book he describes how he did it it has to do with narrative and this is the narrative that that he used a blonde three-year-old boy in a red polo shirt comes running across a sunlit hardwood floor and what I somehow know is my future home by the same intuitive perception I know the boys my own I been to scoop him into my left arm using my handless right arm to balance him and we laugh together as I swing him up to my shoulder then with a shock the vision blinks out I'm back in the canyon echoes of his joyful sounds resonating in my mind creating a subconscious reassurance that somehow I will survive this entrapment despite having already come to accept that I will die where I stand before help arrives now I believe I will live that boy that belief changes everything for me now what's amazing about the story is that in 2003 Aaron Lee Ralston was not married did not have a girlfriend did not have a kid this is a total figment of his overactive imagination heating up married till six years later and a year after that he had a son and ultimately realized his vision but it was that narrative that got him to do this financed desperately needs new narratives since the financial crisis finance has received a very very bad rap and part of it is deserved but at the same time finance is the core of fueling innovation and we don't want to cut our noses off to spite our faces and I'm afraid that we're in the midst of that process because finance has been so heavily criticized regulators practitioners we're all in this midst of self-discovery and recrimination and I want to argue that finance is not the problem it ultimately could very well be the solution of any of society's problems and so with the help of the Museum of American Finance and the gabelli School of Business with all of you I think we can all get to a better narrative thank you [Applause] happy to take questions or comments however we would like to do this when we start with you oh sorry yep yeah because there are people online that may not be oh yeah so it just coincidentally this morning I was in a coaching session on narrative coaching which a whole new evolving field I kept thinking throughout your talk about what you said at the beginning and you mentioned the word environment and all the time you were talking about human behavior and the ability to create algorithms in order to model that behavior I kept thinking but without the environment right you can't really you can't really make very accurate expectations so I think what's so interesting about your work and I guess the question is is this is this true or not but what's interesting about your work is now your focus on language as a way to recreate reality to create different new worlds new behaviors absolutely I think that language and environment are really critical for a lot of not only behaviors but a lot of the dynamics and evolution of financial markets so this is something that economists rarely get involved in because it just seems too fuzzy and qualitative but the fact is that we're now able to start quantifying some of these qualitative features thanks to machine learning Big Data and the AI revolution so I think that over the the next five or 10 years we're gonna see a confluence where narrative coaching might ultimately end up becoming as much of a science as nutrition has become for somebody like Tom Brady who you know is just a master at being able to manage his his body and you know his skills oh the mic is not working so let me this is a great question risk models have relatively short memories and therefore have forgotten volatility how do we constructively study of the potential negative portfolio outcomes given our forgetful assistants so I think that someone once said that those who don't study history are condemned to repeat it and I think that's a very important lesson to be learned we actually have memories for a reason so memory itself is an evolutionary adaptation right it allows us to keep a larger database of narratives from which we can draw upon now memory is not always a good thing sometimes if you can never forget those of you who can never forgive somebody because you can never forget what they did to you that can sometimes be detrimental to your mental health but but more often than not having good memories are going to be an important part of this adaptive process so now taking that to our risk analytics if we don't include enough memory in our risk models we are going to be condemned to repeat some of the past mistakes at the same time we have to balance those kinds of memories and acknowledge that you know what the memories of the 1920s and 30s those were pretty tough times but maybe today is different for a variety of reasons so that adaptation has to occur in a somewhat more systematic fashion second question an example of a new narrative for investing a conflict of profitable present so yeah let me let me describe the new narrative that I have in mind the narrative for new investments is the fact that all of these risk reward relationships that we know and love they're not stable over time and circumstances the environment does matter and so there's certain period we're investment opportunities are plentiful and other periods where they're much harder to come by and the very best managers out there you already know this instinctively value investing is a good example we know that value investing works well in many circumstances but we also know that there's a value growth cycle and there are periods where value doesn't seem to make any sense it was one of the great hedge fund managers Julie and Robertson I think a value investor in his own right Julian Robertson made billions of dollars for his investors and for himself over many years and at some point in the midst of the internet bubble Robertson said you know what this doesn't make sense I keep losing money because I don't bet on companies that that don't have any earnings they don't have any value as far as I can tell and yet they keep going up in price I must be a dinosaur I don't know what's going on I'm shutting down the Tiger fund he shut down that hedge fund six months before the internet bubble burst timing is critical in these kinds of things someone once said that the difference between salad and garbage is timing and so we don't have perfect timing we need to be able to adjust and adapt in appropriate ways the questions if one links your car safety model with your Harvard Square car parking exam incantation there's a new slower more observant narrative will counter the risk-taking temptation of a frothy market yeah absolutely it can in other words if you have the right narrative that you're going to be investing for the long run and that you don't care about short-run fluctuations you know they're gonna happen and you're willing to stick it out then absolutely that narrative can see you through some very difficult times the problem is that that narrative is not suited for everybody out there because if you're a 65 year old you don't have the luxury of adopting that narrative because it matters to a great deal what's gonna happen in the next three to five years so we need to figure out exactly what kind of narrative is appropriate what what kind of environments and what kind of individuals and I think that's another example of what I consider the new narrative investing one size no longer fits all it had to fit all in the days where technology didn't allow us to come up with precision indices but today in today's day and age we can use all of these various technologies to create some very sophisticated products but we need to get the behavior right first the technology already exists what doesn't yet exist is our ability to take that technology and apply it seriously to a realistic model of human behavior yes yes you want to try using the mic oh there's another one in the meantime while we're getting that I'm looking at the cumulative return of the American stock market the deviation from linearity coincides with the rise of hft algo trading are the inefficiencies largely a result of human behavior or computer models yes sir so I think it's a little bit unfair to blame the blip on high-frequency trading I'm going to characterize it more as the rise of technology and the interaction between technology and human behavior in other words we've built ourselves a fantastic super race car but can any human really handle it can any human have the reaction times to handle a race car and they take it to its logical extreme a modern f-16 fighter jet now can no longer be controlled manually by a fighter pilot there is no setting on an f-16 that is manual because an f-16 is so powerful that humans cannot control it at that speed and so it has to be done by computer assist so I would suggest that yes that blit you saw in those cumulant returns it's due to the fact that technology is moving in a way that's going faster and faster so it's it's it's Moore's law versus Murphy's Law and I think Murphy's Law is winning lately so yes in a period of change where the external environment might be evolving how do you either purge your memory or your data bank and refresh it so you can respond to it and serve how do you know and since you won't have the history necessarily how can you be forward-looking and being predictive of what to expect yeah it's really hard so let me repeat the question for those online the question was how are you able to refresh your memories in a changing world so that you're not always responding to old history lessons that are no longer relevant and you know the short answer is it's hard and in many cases it's not possible unless and until you actually develop a deeper more sophisticated narrative of what's going on so let me give you a case in point the mortgage crisis that gave us the financial crisis of 2007 in 2009 2008 was the the epicenter but it started before and it continued after that financial crisis occurred partly because we had the wrong narrative and the narrative that we had going up into the 1990s and early 2000s was the US housing market cannot go down nationally different parts of the US housing market can easily go down by a lot but over the course of a five decade period prior to the 1990s so going back to the 1930s we did not have a period of national home price level decline that did not happen in the data so never mind if you had a five decade memory even if you had a two or three decade memory you would never remember a period where housing markets went down at the same time so exactly the problem that you suggested we didn't have that in our databases so nobody ever thought that it could happen now of course we know in retrospect that it did happen and it happened in large scale that national home prices went down by fifteen or twenty percent from peak to trough what do you do in that instance well the answer is you need to develop a more sophisticated narrative and what is that narrative well somebody like John Paulson who bet against the housing market he spent a lot of time doing research on the underlying housing market going piece by piece looking at the various different homeownership rates the amount of leverage who is holding what paper what banks how much leverage they were what the default rates were what could happen so it took a lot of time and effort for him to develop a more sophisticated narrative and he bet on that narrative and he won now there are examples of hedge fund managers that bet on that exact same narrative two years too early and they got wiped out and so this is where timing matters so the only answer that I can give you is an answer that the human race has developed which is you better get smarter and I wish I could tell you how to do that go to business school that will help but apart from that you need to develop that more sophisticated narrative and there many different ways of doing it talking to people looking at various different histories doing calculations simulations analysis but in the end it is an adaptive process fraught with error and in frustration so let me uh III think we're running out of time I've kept people wait one more question so I'm gonna just read to you this question here what are the methods for examining our own sparse matrices broadly as investors that's a wonderful question to end on because it's something that I think all of us need to struggle with and not just as investors but as humans the answer to that from the adaptive markets perspective is unfortunately to learn to live with discomfort and disagreement you know one of the things that drives the fake news industry is the fact that all of us Democrats Republicans independents all of us we are all the same way in the following sense we all like to be right and we all want to be told what we believe we want to hear what we already think because it makes us feel good it makes us feel smart it makes us feel right that is an incredibly dangerous addictive drug that we all need to be aware of the opposite side of that statement is that none of us wants to be told that we're wrong and yet that's how we learn my mother used to tell me you never learn anything from success and I thought that she was only telling me that to comfort me because I failed so often when I was younger but she was right failure when you learn something that you didn't already know when I tell you something that doesn't agree with you you need to spend extra effort adjudicating that conflict and it takes time effort and it's not very pleasant but it's absolutely critical for developing a better narrative so how do we get rid of that fake news and how do we fill out that 360 5,600 element matrix it's by constantly challenging ourselves if you think bitcoin is the next best thing to slice bread you want to put all your money in Bitcoin maybe you're right but you know what you should spend your time talking to people who disagree with you and try to understand why they disagree with you and think it carefully and argue against yourself if you think that you know we're in a bull market that will never end maybe you're right but you should spend some time thinking about it and talking to people who disagree with you and explain why it is that if you go back in history maybe there's a problem that's coming up and you need to deal with this is a very tiring process because what it means that you have to spend a lot of time feeling dumb and those of you who are in the financial markets those of you who trade or invest for a living you know what I'm talking about because if you are right fifty five percent of the time you are a genius which means that 45 percent of the time you're dealing with failure and it's very tough to do that get over it learn to live with it learn to embrace it and then you will be adaptive as well thank you [Applause] thank you yes great watershed it thank you

Channel: Museum of American Finance

Views: 8,965

Rating: 4.9447002 out of 5

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Id: swWBVVYeA7s

Length: 84min 22sec (5062 seconds)

Published: Thu Jan 25 2018

Reddit Comments

[u/HoleyProfit]

Everyone has heard of the book, "A random walk down wall street" (And if you've not, just post any analysis on any market ever and someone will refer you to it to explain why you're wrong) - but a lesser known book is, "A non-random walk down wall street" - it's lesser known because it's mostly maths written in letters ... and that's confusing.

But here's a video from the writer of the book. It's a very informative view on the efficient markets hypothesis, alternative ways the markets can behave and how the underlying market psychology shifts over time.