think forward think research channel the opinions expressed in the following program are strictly those of the speaker they do not necessarily reflect the views of the National Science Foundation from the National Science Foundation where discoveries begin this is frontier discussions of today's most exciting research subjects by distinguished scientists and engineers working at the frontiers of knowledge I'd like to start by thanking my crush man Keisha and by Hedy and the National Science Foundation for inviting me here today it's a it's both a pleasure and an a real privilege to be delivering this lecture particularly because this is from the engineering Directorate and as Mike mentioned my affiliation is actually with the Sloan School of Management not with the School of Engineering now as many of you know MIT is a real engineering powerhouse and I first heard about MIT when I was in elementary school because my older sister was an undergraduate at MIT and I remember very very vivid memories of visiting MIT and as an elementary school student and being extraordinarily impressed with virtually everything about it including the pinball Center and the Student Center but what really got me about MIT and ultimately what drew me there was something that my sister told me at the time she said you know MIT is such a quantitative place that all of the buildings at the Institute are numbered and people refer to the buildings not by their names but by their numbers and moreover the numbers actually have a logic to them and I said well what is that logic and she said well you know what from what she's learned as a freshman she said that the numerical value is inversely proportional to the the standing of the department that's in the buildings and so for example building one is the president's office as well as the mathematics department mathematics the queen of all sciences building two is physics and I think electrical engineering computer science is building three or four and on down the line and I thought this was a terribly logical way of arranging your your construction and I remembered that so years later when I was being recruited by the Sloan School of Management I was very excited talked to the Dean and you know was ready to accept the offer and I asked him at the last minute I said can you tell me what what building are we in and he said building be 52 so that was a bit discouraging but nevertheless the reason I decided to join was because financial engineering had been pioneered at the Sloan School by the folks like Paul Samuelson Bob Merton and many others that came before I did and so it was certainly with a degree of concern and alarm that when the financial crisis broke I came across all sorts of commentary from various quarters about how quantitative methods are responsible for this crisis and the financial engineering is the root of all evil and that we should kill all the quants and at first you know my reaction was puzzlement because it seemed to me that blaming quantitative methods for the financial crisis is sort of like blaming accounting in the real number real number system for accounting fraud you know you don't want to blame the methods you want to blame the people that are using the methods inappropriately so I decided to try to dig in a little bit more at what the underlying causes are of this kind of financial crisis now I'm sure that many of you have heard much more than you want to hear about financial crisis by now so what I thought I would do today in the next half hour or so is to provide a somewhat different perspective on the crisis I'll give you a very quick overview of it I've actually now been able to compress the entire crisis into one slide I'm very impressed about that but then I'd like to dig in a little bit and focus specifically on the financial technology underlying the crisis and I use that term very deliberately because I will argue that technology is ultimately at the root of all crises in all disciplines not just financial so in order for me to make that case I need to illustrate to you in extremely concrete terms and excruciating detail exactly what the technology is so we're going to do that we're going to do a little financial engineering and after I illustrate it I'm going to turn to the second theme of the talk which is how crisis gets created when technologies can get ahead of the human abilities to manage them responsibly so with that is the introduction this is the crisis in one slide as many of you know the crisis really had its roots in the u.s. residential real estate market in the 1990s we had a low interest rate environment we had an ownership society where people were encouraged to buy homes there was a lot of building and so mortgages were issued for all of these homeowners to buy these homes then these mortgages were repackaged and sold to other investors and many of these investors were banks money market funds insurance companies pension funds and so on and ultimately when interest rates started to go up and housing prices started to come down these homeowners could no longer afford to pay these mortgages and they defaulted and those defaults caused a cascade of defaults among banks insurance companies and money market funds to the point where there was a general loss of confidence by the public and the government had to step in which is where we are today so in a nutshell that's the crisis but the much bigger question is how could this have happened to us one of the most sophisticated financial centers of the world how could how could we have succumbed to these kinds of temptations now I don't know how many of you watch CNN regularly but right around the time of the Lehman Brothers debacle and the beginnings of the crisis CNN started a segment that they called the Hall of Shame and it didn't last very long but during the week or two that they featured it they would actually show the images of individuals that they felt were responsible for the crisis so for example Dick Fuld the CEO of Lehman Brothers was featured prominently in the Hall of Shame I think he was a you know public enemy number one they had Christopher Cox the former director of the SEC and others and they stopped this feature after shortly after they started and I think the reason they did so is because ultimately they ran out of room for the Hall of Shame because pretty soon they'd figure that everybody ended up in that Hall of Shame and if you actually did an honest job of trying to trace the ultimate culprits well here's the list that I've compiled so far you can blame homeowners commercial banks investment banks mortgage lenders brokers services trustees credit rating agencies insurance companies investors regulators government-sponsored enterprises and ultimately politicians every one of these groups can share some of the blame and it's not to say that it's all the same and everybody was equally responsible but the fact is that this financial crisis that we're in is quite a bit more complicated than it may first appear and there isn't a single smoking gun or a single culprit that we can arrest and put in prison and be assured that we won't ever have to deal with this again so in particular what I want to focus on today is the role of the quants or models and make the distinction between models and mania in trying to understand the ultimate sources of this crisis now as I said before they do that we need to go through a particular example so I hope you'll bear with me for the next five or ten minutes I'm going to drag you through a very simple example but in gory detail about a particular financial technology that is absolutely critical for the crisis but it's also critical for the wealth and growth that we've experienced over the last couple of decades and it's a brilliant innovation in financial engineering so before I do that let me describe to you what the problems were in the financial markets as a kind of a motivation for this innovation so obviously lots of lawsuits are being filed even as we speak about the crisis and surely there was wrongdoing that needs to be brought to light but so it's very hard to actually get anybody to say what happened and how it happened and why it happened but it turns out that in August of last year The Economist magazine actually published an anonymous article which is ver unusual for the economists to do they published in an anonymous article by the chief risk officer of a major financial institution that's in the midst of this crisis and this anonymous chief risk officer described a bit about what happened and what the circumstances were so let me just read this to you quickly there'll be some words that you won't understand but after I described the simple example that i'ma give you you'll understand it completely like most banks we owned a portfolio of different tranches of collateralized debt obligations I'll explain that in a few minutes which are packages of asset-backed securities our business and risk strategy was to buy pools of assets mainly bonds where housed them on our own balance sheet and structure them into CDOs and finally distribute them to end investors we were most eager to sell the non-investment grade tranches and our risk approvals were conditional on reducing these to 0 we would allow positions of the top rated triple-a and super senior even better than triple-a tranches to be held on our own balance sheets as the default risk was deemed to be well protected by all the lower tranches which would have to absorb any prior losses now that that sounds like a lot of gobbledygook but what I want you to take from this is the simple fact that this particular institution which got into a lot of trouble with these securities their strategy was to hold the triple-a or better than triple-a stuff and to get rid of the lower than triple-a stuff what all of that means I'm going to make clear in a few minutes ok so just keep that in the back of your mind and now let me turn to the example this is going to be an example involving the magic the technology of financial securities ation this really is a genuine breakthrough in financial engineering and it's going to be so simple that you're going to be amazed that people consider to breakthrough but in a minute I think you'll see why I want to start with a piece of paper this green piece of paper that's essentially an IOU it's a it's a loan and the bearer of this piece of paper will get paid $1,000 that's what the loan promises okay so you can think of it as a mortgage as an auto loan as a student loan credit card receivable lots of different things fit into this category or a bond except that it's a risky bond meaning that there's a chance that whoever issued the piece of paper to the holder they may not actually make good on the promise of paying back $1,000 okay so that means that there's some risk you might get $1,000 but you might get nothing the piece of paper might default and let's suppose that the probabilities are 90% you'll get paid and 10% that you won't now with those kind of probabilities you can come up with a relatively simple model of what this piece of paper is worth what do you think it should be worth what $900 you got that because you computed the expected value well maybe it's a little bit less because maybe you need a little bit of sweetener in order to take that risk but you probably wouldn't pay more than 900 so for very simple benchmark purposes let's say it's worth 900 okay we'll come back to that later it's not critical exactly what it is worth now let's suppose that we have two pieces of paper that are absolutely identical both loans that pay the holder $1,000 and with 90 percent probability so they're both worth $900 what I'm about to do is to take these two pieces of paper that and put them into a portfolio now what does that mean other than drawing a red circle around them well what I'm about to do is to say that I've now got two identical pieces of paper that when I put them into a portfolio I can then look at the portfolio as a separate and independent entity and then issue obligations on this portfolio okay now before I do that and talk about how I do that let me ask you is this piece of paper particularly attractive I mean would you pay $900 for this piece of paper how many people would pay $900 three eight 850 800 oh yeah okay fine so it's getting some action here by by the show of hands it's clear that this is not a particularly attractive investment opportunity I mean you know if you paid $900 and you get a thousand that's a 10 percent return that's a pretty good return nowadays but the problem is you don't get 10 percent for sure sometimes you get zero right so you've got to think about that okay what I'm going to show you is with the magic of securitization I'm going to be able to do something that nobody's been able to do before this innovation occurred which is akin to the following analogy you've often heard that the the children of two ugly parents are rarely attractive right doesn't apply to anybody here of course but but what I'm going to show you is that I'm going to create two children from these two ugly parents one of whom will be a beautiful supermodel and the other will be a deformed Quasimodo and that will be the magic of securitization so let me let me show you how do we do this I've now got a portfolio that owns these two pieces of paper and I'm going to assume that these two pieces of paper are statistically independent in other words whether or not one default has no bearing on whether the other defaults okay there are two separate coin tosses each with the probability 90% of heads 10% tails okay once I make that assumption I'm going to come back to that assumption later that's going to be key so keep that in the back of your mind once I make that assumption it turns out that I actually can figure out the statistics of this portfolio completely and here it is the portfolio has three possible economic values it's either worth $2,000 if both of the loans pay off or it's worth nothing both of the loans default or it's worth a thousand if only one of the two pays off and the probabilities are given right there 81% because with 90 percent chance each of them pays off and if they're statistically independent then 0.9 times 0.9 is 0.8 1 and similarly if both of them default the chances are 0.1 times 0.1 which is 0.01 so there's only a 1% chance that this thing is worth zero okay now that I've got the statistics of this portfolio down I can then engage in the magic of securitization and here's how securitization works with this portfolio as the asset that backs my two new pieces of paper I'm going to issue now two new bonds and the bonds are nothing more than claims on the portfolio okay the blue bond pays the holder a thousand dollars based upon the results of this portfolio so as long as this portfolio is worth at least a thousand dollars that blue bond will make good on its promise the orange bond is the same thing it has a face value or payoff of a thousand dollars but it's a different color for a reason it's a different color because it's junior to the blue bond meaning the orange bond is second in line the blue bond has to be paid first before the orange bond can get its payment and that's part of the contractual agreement so I've created two different securities by simply changing the rules and using these two green identical securities as the underlying collateral or value for this set of claims now why is this such a big innovation well let's see the probability of the blue bond getting paid is what well it's whenever that portfolio is worth at least a thousand and there are two states in the world where that portfolio is worth at least a thousand other they both of the green bonds pay off or one of the two green bonds pays off and the probability of those two events occurring is 99 percent the blue bond pays off 99 percent of the time it's got a 1 percent chance of default what about the orange bond the orange bond on the other hand has a 10 percent chance of getting 0 but then also another sorry one percent chance and then 18 percent chance it's got a 19 percent chance of defaulting and getting 0 because the only time the orange bond gets paid $1,000 is if both of the green bonds pay off right so the green the the orange bond is much much riskier than the blue bond and the blue bond is much safer than either the orange or the green bonds the blue bond is that super model I was talking about the orange bond is that Quasimodo that nobody wants right well almost nobody will find a home for it in a minute now let me let me tell you why this is significant why this is such an innovation in order to do that I need to tell you limit about the bond market in particular the corporate bond market and how risky are safe these are and Moody's will rate bonds in different categories depending on how risky they feel the bond is so the rating categories are triple a double a single a B double a B a and B and the Triple A bonds are the safest the best bets Triple A bonds almost never default one five years after they're issued the default rate is something like two one hundredths of one percent after 20 years if you wait long enough then the default rate for Triple A bonds is about two and a half percent okay what that tells you is that the blue bonds that I just created they're very similar to Triple A bonds aren't they they only default 1% the time on the other hand those orange bonds that I created they default 19 percent of the time 19 percent corresponds to something like BA the be a component of corporate bonds ends up being very risky and by the way they're so risky that certain entities like insurance companies pension funds money market funds they're not allowed to buy them they are known as below investment-grade anything below B double a bylaw cannot be used as an investment if you are an ERISA pension fund so what have I done with the securitization I've taken two relatively unattractive green bonds and I have created two new bonds one of which is incredibly attractive and another of which is really really risky now I need to tell you one more piece of information before we go to the net logical conclusion why this is such a big deal even though a one percent default rate is pretty low there are certain institutions that still cannot bear that kind of a risk like a Savings Bank or a money market fund where they want to protect principal even 1% is too much of a risk so the geniuses that came up with this idea said you know what we have a solution for that what we're going to do is we're going to buy insurance for you on these 1% triple-a bonds the insurance will guarantee that in the event of default in the unlikely event of default the insurance company will pay you back the principal how about that and to make sure that this is a really serious and believable insurance contract we're going to find a very big very stable very well known insurance company like I don't know how about AIG so we now have even better than triple-a Securities we've got securities that have a 1% chance of default but on top of that we've got AIG backing them so the probability of default is even less than 1% what what's the chances that a IG can go under my god now you see where I'm going with this this innovation has taken two relatively unattractive green bonds and has created a huge market for this kind of security who's going to buy this well insurance companies pension funds money market funds sovereign wealth funds and retail investors huge demand we can now tap in to the demand that is latent in the financial marketplace but wait a minute what about these who's going to buy these Quasimodo bonds these really risky bonds well it turns out that in the last 20 years a market has grown for that - and you know who those buyers are hedge funds why because hedge funds are looking for a lot more action they want investments on steroids they don't want to earn 5 or 10% that's not of interest to them they want to earn 15 20 30 % but you can't earn that kind of money unless you take lots of risk that's a lot of risk how much risk well let's let's do the math what's the pricing of these securities well we can work it out we've got the probabilities and we've got the payoffs so just using plain old expected values look what you get the blue bonds are worth nine hundred and ninety dollars that's a very high price given that you're going to be paid back a thousand dollars right so you're not earning much of a return but on the other hand look you're not taking much risk because it's only got a 1% chance of default and with super senior protection you've even got less risk on the other hand look at the orange bonds the prices for those are eight hundred and ten dollars if you invest in one of those and you happen to get lucky and your bond pays off you've made a twenty percent rate of return that's a pretty nice return right yeah absolutely and you know what you get there you get something called a CDO squared and believe it or not those were marketed as well because why not exactly and so you get the weird phenomenon that with a pool of green bonds that are absolutely identical and not particularly attractive you can actually create a huge supply of Triple A securities if you're assuming that they're in correlated right so that's a big assumption isn't it we're going to get to that so now let me tell you about the crisis this same anonymous risk manager the economists in that same article later on he writes about what happened to this Bank during the crisis in May of 2005 we held triple-a tranches expecting that the rise in value and sold non-investment grade tranches expecting them to go down but the reverse happened of what we had expected triple-a tranches went down and non-investment grade tranches went up imagine that resulting in losses as we mark the positions to market and then he goes on to say this is entirely counterintuitive we couldn't understand it it was really bizarre well I'm about to explain to you just how counterintuitive it was it's actually quite simple with this example suppose that the assumption that these two green bonds were statistically independent suppose that we were wrong suppose that the correlations were not zero but they actually were one now you might ask why should the correlations go up if you think of these green bonds as mortgages on various homes why should the default in Stockton California have anything to do with defaults in Miami Florida or Reno Nevada or Phoenix Arizona they should be independent roughly speaking that's if the housing market is going up if the national housing market is declining well then you can see how those correlations might change now let me show you a little bit about the housing market and then we can get to the the point of this for a period of about 50 years US housing was right around 110 and somehow during the late 1990s something happened and things took off the housing market went nuts and if you ask the question how far would this real index have to decline in order to get us back to that 50-year steady state average you're looking at a 45 percent real drop or a 35 percent nominal drop from the peak of the housing market okay now I'm going to show you what happened to housing over the last 20 years using a nominal index this now does not account for inflation this is the S&P case-shiller 10 city housing market for the last 20 years from 1987 to 2009 and you can see that except for a little blip in 8990 the housing market pretty much rose continuously until June of 2006 and then started its decline and where are we today the most recent observation which is two months lag because housing markets tend to be less liquid we don't have as good data we're here and so the housing market has come back a little bit but if you look at the drop from the peak to the trough we actually have dropped about 33% so we're now finally back down to about where we've been and in fact the futures market that shows where financial prognosticators predict where we'll be suggest that there'll be some additional recovery so if you've been looking to buy that vacation home now may be a good time to look but the point of this is that when we are on a rollercoaster ride going up well the correlations of default don't have anything to do with each other but when we're going down by a lot the correlations can go up and if you ask the question well how come people didn't figure this out well suppose you're sitting here in 19 2006 and you're looking back 20 years if you use 20 years of historical data there's no period in US history during those 20 years where correlations were significant all right now let's go back to the correlation example suppose that these two green loans are not only not independent let's say that they're perfectly dependent if they're perfectly dependent then there are only two outcomes either zero or 2004 the portfolio in that case the orange and the blue pieces of paper they have the same probability of default what does that mean that means that the blue piece of paper which we thought was 1 percent default rate is actually now 10 percent the default rate for those blue pieces of paper those supermodels has increased by a factor of 10 on the other hand those lucky hedge funds that wanted to take on lots of risk they thought they were taking risks of 19 percent default but actually in the end the default rates went down to 10 percent so this increased correlation has the following bizarre effect for the blue pieces of paper that we paid $990 for we just lost 10 percent of our investment it's now worth 900 those orange pieces of paper that the hedge funds purchase for 810 dollars they just made 10% because in this regime where the correlations go to one where you have phase-locking behavior in these regimes both pieces of paper are worth the same so the Triple A stuff lost money and the BA stuff made money and the banks insurance companies money market funds hedge funds they all exhibited these kinds of losses and gains over the course of the last couple of years that's where the financial crisis came from it was financial innovation pumping huge amounts of money into the housing market using these kinds of technologies and CDO Squared's taking the bad stuff and repackaging it again and again and again and after a while it became so complex that nobody really understood what they had and what it was worth because the market ultimately broke down okay that's where we are today now let me turn to the second theme of my talk which is what do we make of it how do we explain beyond this where crisis comes from and to do that I actually would like to focus on crisis in other industries besides finance in particular in technology sensitive industries it turns out that in 1984 sociologist by the name of Charles Perrault came up with an explanation which he developed in a book called normal accidents Perot said that accidents in technology intensive industries not only can happen but in fact will happen with regularity because of two things Perot identified complexity and tight coupling as the preconditions for normal accidents complexity meaning nonlinearities and tight coupling meaning multiple stages of a process which depend intimately on prior stages executing correctly and he didn't look at financial services but he looked at chemical industries nuclear power plants the space program and in all of these industries he identified these can do two conditions as contributing to it now question how complex are these blue and orange pieces of paper these collateralized debt obligations well this is a chart that was issued by the FDIC that describes all of the elements that you need in order to issue a collateralized debt obligation you need a mortgage broker lender borrower servicer issuer trustee underwriter rating agency credit and hence provider and investors that's a lot of moving parts pretty complex but what's even more complex is the regulatory environment that we operate in and let me give you an illustration of that does anybody recognize what that is any guesses as to what this is well let me tell you because I don't think in this audience we have any or bankers this is paragraph 624 of the basel ii accord for how to compute the appropriate amount of regulatory capital you need to maintain as a bank yeah now obviously in this audience equations like this don't faze anybody but remember that the folks that are supposed to be implementing this are not PhDs in nuclear physics there are Countians lawyers clerks they're not dumb but they're not trained to understand nonlinear dynamical systems and how they can interact in surprising ways but I would argue that that the two conditions that Perot outlines complexity and tight coupling is not enough we actually need one more and the condition that I've added to this is the absence of negative feedback over an extended period of time because ultimately and Perot doesn't really go into why normal accidents occur he just outlined the conditions under which they do I would argue that it's really human behavior that ultimately causes these kinds of accidents and human behavior is such that over a period of time if nothing bad happens we ultimately end up under estimating the risks of the system and we become complacent about the risks that were taking and I'll make that argument more precise in a few minutes but the argument here is that human behavior not just of investors but of managers of legislators of regulators of credit rating agencies all of us participated in some extent to this crisis when we are focused on one aspect of our environment our brain works in tremendously sophisticated ways it actually filters out everything else as it should because our brain doesn't have infinite capacity and so in order for you to focus on one thing there's a cost to that you can't focus on some but something else we now I think understand that human behavior has limitations and in particular when you're focused on profit maybe just maybe you might not be focusing as much on risk I'm going to ask each of you to imagine that you are the chief risk officer of Lehman Brothers and I'm going to imagine that it's 2005 it's before the crisis and I want to suppose that you knew in advance that the housing market was headed for a downturn and that Lehman Brothers with its extraordinary exposures would be a very very poorly positioned for that kind of an outcome what would you do about it what could you do about it as chief risk officer of Lehman Brothers now I don't know anything about Lehman Brothers so this is not inside information I have no idea what actually happened but let's just do the thought experiment suppose that you knew that the real estate market was headed for trouble and there was going to be some dislocation and by the way this is not as as unrealistic a thought experiment as you might think because in January of 2005 Bob Shiller was quoted in CNN money.com as saying that there was a real estate bubble in September of 2005 yours truly was featured in the New York Times in an article that there was a hedge fund shakeout that was coming that we detected some significant dislocation that was about to occur in the hedge fund industry and so there was publicly available information at least from academics Raghu Rajan others have published papers in 2004 2005 and 2006 that there are problems coming and suppose that you believed it what would you have done well let's go down the list you might have approached the CEO to whom you report Dick Fuld and you would have said dick I really think that we ought to shut down the mortgage business because we've made a lot of money that's all great but based on what I've heard and what I've read were headed for a disaster of epic proportion so we've got to get out while we can you know what if we decide to you he would have said let me see if I understand you want to shut down the business that has been the most profitable among law businesses for the last several years that has produced record earnings for our company and shareholders and that accounts for a significant fraction of your bonus and mine you want to do what not going to happen shareholders would have a fit okay so then you say well let's not let's not shut it down let's cut it back by half let's take half the risk off well he would have said let me see if we'd reduce the risk by half we reduce the bonus pool by half which means that the very best people in our group the people that have already been recruited by all of our competitors and who are already thinking about leaving to start their own hedge funds and taking half of our clients with them now they're really going to leave we're not going to do that either so finally you throw up your hands in frustration and say you know what I'll take matters into my own hands now I don't know any chief risk officer that actually can do this but let's suppose you could let's suppose that you were actually authorized to trade on the firm's behalf to hedge the exposure in mortgages by betting against those blue and orange pieces of paper if you were to do that in size large enough to affect the bottom line of Lehman Brothers to really protect them from that downside you know what have happened you would have lost spectacular amounts of money in 2005 2006 and the first half of 2007 at which point you would have long been fired and rightly so for losing a lot of shareholder wealth because you don't have perfect market timing nobody has perfect market timing and timing is everything when it comes to these kind of decisions someone once said that the difference between salad and garbage is timing the point of this example is that the psychology of greed makes financial crisis unavoidable I would argue that financial crises are the outcome of two factors human behavior coupled with free enterprise if you're willing to get rid of either one of those you can get rid of crises but as long as we have human behavior and we engage in free enterprise we're going to have greed ultimately overtake our ability to manage this from a social societal point of view now just because financial crises are inevitable and if you want more evidence since 1974 there have been 18 national level banking crises around the world and the common themes among these crises are according to Rogoff and Reinhart rising housing and stock markets capital inflows large public debt to GDP ratio and enormous amounts of financial liberalisation that sound familiar that's been our history for the last 20 years but just because you can't avoid crises it doesn't mean that there's nothing we can do I would argue that you can actually do a lot to prepare for crises because it's not financial losses that create crisis so it's when the wrong people lose money and buy wrong people I don't just mean you and me I mean people that aren't prepared to lose money like money market funds like savings banks like pension funds when when those entities take risks that they shouldn't when they don't understand those risks that's when crisis occurs so let me conclude since we're running out of time by pointing out that there's a lot that we can do to prevent the most serious repercussions of crisis just like you can't rule out hurricanes you can't legislate away hurricanes there's a lot that you can do to reduce their damage if you're properly prepared I have a list here of things that we can do to prepare for crisis and the things that are highlighted in red are things that we could have done all along that we didn't need any new legislation or new agencies to do but we simply decided not to do them because we were making too much money and having too good a time so I want to just highlight two items on this list though for this audience one is that I proposed in other writings that what we really need at the very outset is to create an organization very much like the National Transportation Safety Board for financial crashes because we don't really know enough to be able to even formulate proper policy reform we need to study every single crash just the way the NTSB does produce a report and ultimately allow that report to guide policy interestingly the NTSB has no regulatory authority the FAA regulates the airline industry but the NTSB has enormous impact in being able to provide a summary of what happened how it happened why it happened and what to do to make it not happen again and in many cases the NTSB has actually provided a very important counterbalance to the FAA when the FAA gets a little bit too cozy with the industry that's the role that I think we need for a financial regulation the second point I want to highlight and then I'll conclude is that we actually need smarter regulation we don't need more regulation because the banking industry is the among the most highly regulated of all industries we need smarter regulation which means we need smarter people which means we need to invest in the knowledge base and that's why I'm so delighted to be here at NSF because that's what NSF does to give you a little bit of an illustration of the lack of expertise in financial services I did a little calculation here I looked at the MIT report for the number of PhDs produced by the School of Engineering and in 2007 the School of Engineering produced 337 PhDs in engineering just PhDs of those PhDs the majority were funded by government grants NSF Department of Defense and so on during that same year the Sloan School of Management we produced four PhDs and they were paid for by MIT not by government grants and so what I hope we take out of this crisis is not that we need less people engaged in finance but we need more people smarter people engaged in finance at every level because over the next few years we're going to be rebuilding the financial infrastructure that will support us for the next century in fact if you ask the question where did the current regulations come from that we actually operate under today they were written in the 1930s after the last great disaster that occurred so for the next couple of years we're going to be rewriting the regulations that will serve us I hope for the next century and the hope is that we're going to have smarter people people in this audience at that table helping to rewrite those regulations thank you think forward think research Channel
Excellent talk by quant-god Andrew Lo. Thanks.
I see some flaws in the talk, most especially: the probabilities of default he starts with are pure guesses. The precise estimate 10% default prob is a sham; hiding behind these sorts of probabilities is the implicit view that "the future is a good guide to the past, let's look at this guys statistics and based on past data, estimate the precise risk we're dealing with". This is Taleb's Turkey problem, it's extremely dangerous. NO MENTION AT ALL in the talk.
I do agree with many of his suggestions (No too big to fail, educating people in high school about the system [if I ever have kids, I'll do it even sooner]. But one suggestion is deeply wrong from my point of view. He argues for high-flying scientists (e.g. quantum physicists, etc) to REGULATE the system. I believe the exact opposite is needed: we need clueless bozos regulating banking.
Bear with me: mathematical men and women will rapidly understand all formulas are structures within it. The good side of that is that they may find some flaws. But there's a very bad side to it. They may become fascinated with the minutia and forget basic principles (that the middle class never would).
Say, some random taxi driver might say: "I dont care what the investment bankers do; I don't care if they blow up. I do care if their blowing up spreads to others. I do care if savings banks and retirement funds blow up. So separate these activities cleanly; take out leverage from the savers (and the public), don't trust any "AAA" stuff at facevalue, insure all the way from insures who cannot insure anything from investment banking. And if some bank starts making outstanding profits, assuming it's taking mountains of hidden risk and take it out of the savings system".
The savings system should be the most conservative people in the globe. Don't smooth me out with this implicit analogy with high-tech, through the use of words like "innovation", "talent", and even "magic".
The only "talent" these people have is in creating a huge uranium pile and watching, bedazzled, and it blows up.