SPEAKER: This is a production of Cornell University Library.
MAUREEN O'HARA: Thank you all for coming out this afternoon. And thank you for inviting me to give a Chat in the Stacks. I think it's a neat idea. And when I was asked, I was delighted to come.
I want to talk about modern finance and what has been described to me as an ethically challenged field. And it's hard to disagree in some ways. Banks and financial service firms placed dead last yet again in this year's global ranking of trust in industries. A recent Harris poll found 68% of people surveyed disagreed that, in general, people on Wall Street are as honest and moral as other people.
Bloomberg, in a poll that just came out in July, found only 32% viewed Wall Street banks favorably. We were delighted to see that Congress came in below us at 29%. But insurance companies were dead last at 26%.
So those of us who are in academic finance-- and I also work in the industry a bit-- you tell yourself, oh, that's not true. But after a while, when you started seeing poll after poll, you begin to wonder if there's not something out there.
Here's another one I'll just share. This is the Chicago Trust Index, percentage of trust in financial institutions. The most recent survey fell back down. We're at 27%, which, needless to say, I think even Congress might beat us on this one. So it's disconcerting. And it's one of the reasons that I wrote the book.
And there are really two issues that I think are reflected in this. The first one is the one that is a bit trickier to deal with. But I think most people don't actually understand what modern finance is and how it works.
And I think modern finance, in many ways, really dates from the 1990s on. And as you think about what we do in finance today, it's not what people in the past did. So the notion of a loan and a mortgage and different sorts of specific entities is not actually the way modern finance works.
So one of the goals of the book was really to try and write at a level that we'll call the "educated layman" could read and really understand what we do. When you read that Goldman Sachs get sued for fraud and they used something called a synthetic CDO, what in the world did they do? Most people really can't understand that. But actually, it's not that hard. And one of the things I tried to do in the book was give people an ability to be able to think about that.
The second problem may be the more important one, which is that I think there's too many finance practitioners who do not understand or even recognize that there is an ethical component in finance. And for that, I think we in academic finance bear some of the explanation because we teach finance like engineering. It's a tool.
So you want me to build a contract that does x? No problem. I will build that contract. You want me to build something that can get around this pesky little law that says you can't do x? No problem. I can build something that'll go right around it, and no problem.
And if you think about that for a minute, it's like, wait a minute. Maybe this has led to a culture that simply leads us to ask whether I can do something, but not whether I should do something.
So I think we've begun, as the finance academic community, to rethink this. I think the banks have begun to rethink this because they're up to $323 billion in fines worldwide and $160-some billion in the US alone. So it's gotten to be an expensive hobby to avoid thinking about the ethical issues.
So you might ask, why? Why is this happening? And I just thought I'd share a couple of-- here's a theory that a lot of people like. This is the finance and the bad people theory. This is by Judge Richard Posner.
He argued that basically, firms that have short-term capital-- they have to compete ferociously. They know that it's a jungle out there. And so as a result, the business model of those sorts of firms, which he defines to be the financial firms, attracts people who have a taste for risk and attach a high utility of money.
And then he goes on, as you can read, the complexity of modern finance, the greed and gullibility of individual financial consumers, difficulty so many ordinary people have in understanding what's going on here facilitate financial sharp practices, enabling financial fraudsters to skirt criminal sanctions. So when you first read this, it's like, well, I don't know.
And then the second time you read it, as a finance professor, he seems to equate people who work in finance as financial fraudsters. And I think that is a little harsh. So let me give you an alternative view that I developed in this book. And it's that arbitrage plays a starring role. And I'm going to explain arbitrage a bit more in a minute.
But modern finance is not really, as I mentioned, about traditional contracts. It's about cash flows. So whether those cash flows are coming via interest payments on bonds, or they're cash flows coming from dividends on stock, or they're cash flows coming from mortgage payments, or they're cash flows coming any-- I don't care where the cash flows are coming from. Those are the building blocks of modern finance.
And we don't tend to look at things as, let's take these-- think of this as taking these cash flows and these cash flows. And we put them together. And maybe we added an option. And we added a credit default swap. And we do this, we do that, and voila. We create something. So you tell me the contract you want, and I'll create it using cash flows.
The problem is, in the process of doing so, the ethical dimensions get lost. And that's part of what I want to try and do in this book-- is first make it a little clearer about how modern finance works and then talk about those ethical dimensions.
And again, I particularly like this little cartoon. I don't know if any of you know xkcd. Here we have two people at a Mexican restaurant. They're the ones giving the chips away. If they don't see the arbitrage potential, sucks for them. In a deep sense, society only functions because we generally avoid taking these people out to dinner.
I really like this, right? Stealing chips from Mexican restaurants is really-- there's no law against it. But it's just not done. And I think there are lots of things out there that fit into that category, and particularly in finance.
And so as we think about this role that arbitrage plays, I want to make it a little bit clearer that arbitrage is more than just buy low and sell high. So a classic arbitrage is you have the same good trading in two places at different prices.
So if you remember back-- some of you it will be easier, some of your less easy-- some undergraduate class that you took. And you talked about gold in London having one price and gold in New York having another price. So you sell where it's expensive, and you buy where it's cheap. And that's an arbitrage.
And when you have the same good trading in two markets at different prices, then when you sell it at the high price and buy it at the low price, what have you done? You basically have gotten something for nothing. You were able to arbitrage. And in the process, you bring the market back in line. That's certainly still true. That's classic arbitrage.
But it's more than that now because now what we're going to do is-- in modern finance-- using the tools of modern finance, which are things like swaps, which are things like credit default swaps, which aren't actually swaps at all-- they're actually options. But using derivatives and other sorts of ways that we can change cash flows, what we can do is we can look at something like a natural security. And we can build a synthetic version.
So I'll give you an example. Suppose you wanted to hold-- you're running a big insurance company, like TIAA. And you want to buy AAA bond as an investment, nice and safe. great investment for an insurance company.
But what's the problem? We only have two AAA companies in the entire United States now. Back when [? Jean ?] was in school and others, we had 180. But we have two, right? Those are Microsoft and Johnson & Johnson. So if every insurance company wants to buy AAA bonds, the yields will be negative.
But here's the good news. We can make more. We can synthetically create a AAA bond by using a credit default swap and a little bit of financial magic. So what you can do in this new world of finance is great. It can do really neat things.
It can create products for a company like TIAA that allows us to better hedge the risks over time so that when you retire, you're going to have this big lump sum of money or big-- we hope-- annuity that you'll take. So the tools of finance today are fantastic. So what we can do is build synthetic versions of the contract that you want.
So where does arbitrage come in? I'm going to create this synthetic IBM bond. And I have a real IBM bond. And arbitrage keeps the two prices in the same place, just like the gold in London and the gold in New York. So arbitrage is going to be extraordinarily important in this world. We're going to build synthetic things.
And I want to argue the synthetic world is everywhere. But the notion of arbitrage permeates finance. All of option pricing theory is built on the concept of arbitrage. And when we teach our students, we often are teaching them to look for opportunities to arbitrage and make markets more efficient and yourself richer in the process.
So think about the LEGO idea as we're going to use these cash flows. All these little LEGO pieces are cash flows. And we're going to build what it is that you want. But when you do that, it's not always easy to see the lines. For example, was Goldman Sachs helping Greece get into the EU via financial engineering unethical? It's a really interesting question.
What they did was use these rather complex swaps to be able to take debt off their balance sheet and therefore get in underneath the rules that said a country couldn't have more than this percent debt on their sovereign balance sheet. Goldman came up with a way to do that. Was that ethical or not? We can talk about that later. What's that?
MAUREEN O'HARA: Well, we'll talk about that one later. It's an interesting question. And we'll come back because I know this is of particular interest. Here's one. Was JP Morgan manipulating the California energy market? Or were they optimizing against an inefficient algorithm?
If you talk to most of my friends in economics and finance, they'll say, hey, if the rules allow me to do it, so be it. If you don't like the outcome, change the rules. I think that's missing a lot of points. And we're actually going to look at the JP Morgan Chase in a minute to show you what I do in the book because the book goes through a variety of these. And we try and figure it out.
Are HFT strategies designed to take advantage of other HFTs ethical? Some of you may have read Michael Lewis's Flash Boys. Michael Lewis was adamant that the HFTs are horrible and should be banned from the face of the Earth.
But what's interesting is that most finance professionals like myself think that what he picked on in the book isn't a problem at all. There are problems. He doesn't know them. So we're going to come back and talk about them.
But the point of this is that some of these things are tricky. It's not like if I said to you, is it unethical go rob a bank? I think everybody would go, yeah, I think that's probably not good. Is it unethical to sell a widow, an orphan a security that's worthless? You'd say, yeah, that's probably unethical. They're easy ones.
But once you get into the world of natural securities and synthetic securities and, tell you what we'll do. We'll transform this cash flow. And we'll do that. And we'll move it over here. And we'll do that, It can be really tricky to keep track of what happened to the ethical guidelines.
So what are we going to try to do? We're trying to draw these lines. We're going to help little Dogbert here, who has this view that the easy ones are to define between the felons and the good people. The stuff that we're interested in is the gray area, or the weasels.
It's not exactly illegal. I'm actually teaching our MBAs. And the phrase, it's not exactly illegal, should be, I think, a red flag. Maybe this is not exactly the way you should be running your business.
So what's our quest? Our quest is to eat all those really good-looking cookies. But in the process, we're going to sort out the positive effects of when finance actually generates something for nothing-- when we actually can make everybody better off using these tools of finance, which we can.
But we need to sort out those activities from the activities that lead to the opposite outcome, where the financiers take all the gains and society pays the cost. And that's really what I think finance has to strive to do.
So what's the book do? Well, for those of you who don't maybe know modern finance, in about two chapters, it tries to tell you how modern finance works. And as I tell friends of mine who are not in finance in any way, shape, or form-- I say, look, if you read those chapters and you start getting a little lost, just keep going. Ignore the chapter for now. You'll come back to it later. And head into the rest of the book.
So the first part is, how does modern finance work? And it explains to you what swaps are and all sorts of things. And then it sets out some frameworks for evaluating the ethical limits of arbitrage.
And I think this is tricky in that we don't normally think of ethical-- buying low and selling high is not a moral decision. But using arbitrage to get around legal rules, using arbitrage to be able to take advantage of someone because it's so complex they have no idea what you're doing-- that has a moral dimension.
And then what we're going to do is-- the rest of the book ventures into the gray. And so it's a series of chapters that looks at essentially arbitraging the complexity, arbitraging for deception, arbitraging for a variety of things. There's a whole bunch, almost like little vignettes and case studies, including Goldman in Greece.
And here I'll have to make a confession. I'm a big football fan. It just shows you what kind of patience and forbearance I have. I'm a Bills fan. I know. And it's not been easy, has it?
MAUREEN O'HARA: I don't like hockey. So anyway, one of my favorite things on TV in the old days was when you watched Monday Night Football. They had those little segments called You Make the Call. And they'd show this play. And they say, well, did this violate the rules? And I loved that part.
So I wrote this part of the book thinking of that. You make the call. I tell you what happened and then set out what happened. And then you make the call. Did this cross the line or not? And then I offer my thoughts on whether it did or not because again, if it's easy, you don't need to ponder it. And so that's part of the challenge.
And then at the end of the book, we try and emerge out of the fog and talk about how to make finance more ethical. So that's the overview of the book. And I just thought to give you a taste of what we're doing today. I'm not going to explain the basic workings of modern finance because I have found that's a little dry in a setting here.
I'm actually not going to spend a lot of time on developing the ethical frameworks, although I found this really fun when I was writing the book because not everybody looks at the world the same way. But there's a surprising amount of unanimity across a wide range of ethical frameworks. And so I was trying to bring that out in the book.
And then what I am going to spend some time on today is I want to let you make the call and explain a little bit about some of these ethical issues and where they emerge. And then we'll briefly conclude. And then we'll open it up and talk about whatever people want to talk about.
So before I get there, though, I do want to talk a little bit about why. And again, why do we seem to have these problems in finance? And I really don't like the crappy people work in finance theory.
It may be true. But I don't believe it because I've worked in a lot of places. And most of the people I know are great. And they really don't view themselves that way. But why does it happen now? Why didn't it just happen in the 1920s or whatever? Maybe it did. It might have. It might have.
AUDIENCE: Wasn't there the crash of 19-something?
MAUREEN O'HARA: There was. But I think some of the ethical issues we see today are pretty unique to today. But let me talk about why I think today is different.
One thing is that almost everything today takes place in markets. The largest lender in the United States now for mortgage loans is Quicken Loans. You do that on the web. You don't meet anybody. You don't shake hands across a table anymore. So the nation of markets is important. Almost everything we do in finance now operates in an impersonal market.
The other challenge we have is that a lot of what happens in finance is complex. So you have what we call "delegated behavior agency problems." The quant who restructures the financial product is not the trader who interacts with the client or the senior guy who put the whole thing in place in the first place. And the question is, who in that group is responsible for the ethical dimensions? And I think in practice, the answer is none of them.
The other problem are complexity of products in corporate form. Goldman Sachs has 946 subsidiaries in tax havens alone. If you look at the structure of a major financial institution, there are thousands of subsidiaries. It's extraordinarily complex. And these things often go across markets. So as you try and sort through who's doing what, it gets pretty tricky.
AUDIENCE: Excuse me, can you explain "subsidiary"? What do you mean by that?
MAUREEN O'HARA: So you have a company. And then they have other companies. They have other divisions that are set up, in many cases, as separate companies. So Johnson & Johnson, for example, has 286 subsidiaries. Some of them make this product. Some of them make that product.
So you have a corporate structure up here. And then you have all these other little companies down here. And can you, as the corporate structure, make sure that every one of these little companies that you own at Johnson & Johnson are behaving the way Johnson & Johnson wants?
So in a bank, they have thousands of those. And a lot of them are located in-- it creates a great management challenge. And as a management professor, it's something I worry about a lot. And then there's in personality. You never see anybody. Statistical victims always seem a lot less compelling than real victims.
So for a moment, let's talk about, does it matter? So I'm going to give you something here you guys can think about. Isn't he cute? This is a mouse experiment that was run. And this is an experiment about how people change when they operate in markets. So here's the experiment. He's awfully cute.
So participants get to decide between-- this is one in Germany. And the lab that had all these mice had used them for various research projects. But those research projects are gone now. They're over. And so now what do we do with the mice?
So they set up this little experiment. And in the first experimental part of this, they had a group of people, let's say all of you. And they offered each person the following choice. You can have 10 euros, but one of the little mice is going to be killed. Or you can forego getting your 10 euros, and the mouse will be spared.
So they're going to do that treatment. Then they're going to do a second experiment, where they take half the room. And they say that you're going to be the sellers. And you're going to be the buyers. And so we're going to match up each seller with a buyer. And we're going to give the seller the property rights to the mouse. So each seller has the right to decide, if they will, the owner of the mouse.
If you and your buyer can agree on how to split the 20 euros, you get 20 euros. And the mouse is killed. Or if you guys agree that you don't want the money, then the mouse will be spared. Does everybody see the difference? In the first one, each individual gets to decide. And in the second one, the buyer and the seller together decide.
Here's some interesting results. When individuals were given this choice, 45% of them took the euros. And the mouse was toast. But on the other hand, almost 55% spared the mouse.
When you put those same people in a market setting, the mouse is toast. 72% of the time, that mouse hits the dust. In fact, to get individuals to kill the mouse at the same rate that they'll do it in a market, you had to pay them 47.5 euros.
Now, what does that mean? I admit that mice are not necessarily the same thing as people or trading and various other things. But I think one of things it points out is that once you get into market settings, the immediacy of some of these issues seem to fade. And in our case here, the poor mice hit the dust.
So what are the ethical limits of arbitrage? A lot of people say, well, let's just rely on the legal boundaries to determine when we cross the line. But that's probably not a good idea because we can simply arbitrage around them. That's what modern finance can do. You tell me what the rule is, I'll create a way, using my cash flow approach, to get around it.
So you don't have to go back as far as Aristotle. And this is the only thing I'm going to talk about-- the ethical limits. I'm going to let you guys decide on some of these things.
Aristotle pointed out that every action had technique and prudence and that every technical action has a moral component. And so one way to think about it is, every arbitrage, which is a technical action, has a moral component. And that should really be kept in mind as we think about these.
So we're going to run out of time because we're supposed to keep this to 35, 40 minutes. So let me just look at a couple of examples of the modern things that happened in markets. And the HFT ones are pretty easy that I'm going to show you, I think.
Michael Lewis in Flash Boys didn't like the fact that you could design an algorithm that would use machine learning to try and see if you could predict where the larger orders were going to trade and step in front of them. So in Michael Lewis's book, think about the problem of a large institution who's going to trade, say, 100,000 shares.
In our current market structure, we have 13 different stock exchanges. So typically what happens is you chop them up in the order. And you start sending them off to the exchanges. But they're not all right next to each other. So the orders take a little bit longer to get to some down the road-- like milliseconds, but still longer.
The HFT guys write algorithms to watch for patterns in the exchanges that have the shortest distance to go or the lowest latency. And then based on that, they run ahead and put in orders in the ones that are ahead in front of what they think are the orders that are coming. Is that unethical? What's interesting is almost everyone I know in finance says no.
People have watched markets for years. If you're a market watcher and you say, every time I see the market go over 60 three times a day I buy, that's kind of the same idea of, I'm training my machines to watch for a pattern. And then I'm going to trade. But you might disagree. Michael Lewis does.
But here are some things that are a bit more challenging. Here's what they do. So this is an algorithm that's been written to take advantage of another algorithm. So see those blue dots? That's an order that has been placed by a broker dealer for a client. So this client wants to buy. And so you see the market opens.
And over here, you're going to see-- those green dots are also orders. Those are being put in by a machine. So there's an HFT machine algorithm that's been written to put it in order and cancel it, put it an order and cancel it, put it an order and cancel it. That all happens within microseconds.
So if you look at those little lines, when you see three lines, there's an order. It gets canceled instantly. And then another order is put one tick above, another a tick above that. You can see as you go along at 30.08-- this is at 9:30 in the morning, eight seconds-- the blue order gets put. That's an order to buy. There isn't a seller out there right now. So that order is going to sit there.
But this quote dangler keeps putting orders in and canceling them, putting them in and canceling them. What's he trying to do? He's trying to fool the algorithm that has sent in the blue order into thinking that there is a lot of interest out there. And they're trying to raise the quote. And so he won't be able to buy. And so the blue orders stay there for a while.
But then that second algorithm gets sucked into thinking there actually is someone out there. And you can see that the quote dangler is basically-- there's no trades actually taking place on this yet. These are all just orders in the books. You can see he takes the price all the way up there. This is three minutes later.
And actually, the blue guy doesn't ever trade. He pulled his order, and he quit. So the green guy didn't succeed. What he was trying to do was induce him to bet against himself. And then when he gets high enough, the green guy will trade against him.
This is called being a quote dangler. This should be illegal, but it's almost impossible to catch. But it's clearly across the line. This guy's manipulating the market. He is simply trying to fool you into trading against him.
Here's another type of strategy. This is called a "momentum ignition strategy." So again, this is all done by computers. So what's happening here? The blue lines are orders that are being submitted and canceled, submitted and canceled, and submitted and canceled.
And what they're trying to do is-- and actually, in this case, these are tiny little trades, like trades of a share. So you can see what they're trying to do is they're trying to move the price up and down and up and down and up and down, getting wider and wider.
And why are they doing that? Because there are people who put what are called "stop orders" in the book. And when the price hits the stop order, when-- suppose you want to protect yourself. You own IBM. You put a stop order in at 60. IBM is trading at 70 right now. So unless the price hits 60, that order won't execute.
And so what they're trying to do is they're trying to find the stops in the book. And here they succeed. So you can see at some point, all of a sudden the price just falls through the floor because what they've done is they triggered all the stop orders. And what are they trying to do? They're trying to buy at the low point.
So is it ethical to write algorithms to do that? I don't think so. Is it ethical to write an algorithm to try and guess where people are going and try and go there first? I think so. But others may disagree. But these markets are tricky.
We're going to do one more. And then we're going to do Chase. And then we'll move on for a minute. Remember, this guy became famous because some people say he contributed to the flash crash.
But he's doing something much like that first diagram I showed you. He used a layering algorithm in futures. And what that means is he put lots of sell orders at prices three, four, and five ticks above the price. So he's trying to give you the idea there's a lot of depth out there.
But what's interesting about his algorithm is that it includes code that said, if the price ever gets close to these things, cancel the orders. Now, to write an algorithm that says, cancel if my order can ever execute, has got to be unethical. It should be illegal. So in this brave new world of the HFT and the world of modern finance, the kind of behaviors you see are really remarkable.
Let me give you one that's kind of my favorite. And I talk about this one with the MBAs because I think we train them to do exactly this. So JP Morgan Chase became the owner of 28 outdated power generating plants when they took over Bear Stearns in the crisis. So some of you may remember Bear Stearns failed. And the Fed got JP Morgan Chase to take them over.
But Bear Stearns was a big player in energy finance. So all of a sudden, JP Morgan Chase is now the proud owner of 28 electric generating plants. And they're all outdated. And they don't make any money. So how do we make them profitable?
Well, what they realized was, we could invest in those plants, spend lots of money, bring them up to date, and go that route. But they don't want to do that. Instead, they realized that the way we trade energy in the United States is really complicated. But it involves an auction. And it's an auction that is the world's most complicated auction involving-- there's a day-ahead auction, and then there's the day-of auction, and all kinds of things.
And it's run by a group called CAISO, at least in California. And that stands for the California Independent Power Authority or something along those lines. And CAISO runs these auctions in a way to try and make sure that there's going to be enough electricity in California. But some days it's really hot. And everybody turns on their air conditioning. And so we're going to need a lot more electricity.
And here's the kicker-- electricity can't be stored. So in order to come up with more electricity, we're going to have to induce some of these old power plants to ramp up and start producing electricity. And to do that, since they're expensive to run, we're going to have to have compensatory payments.
So JP Morgan realized that-- let's not think about the problem of generating electricity. Let's think about the problem of selling it in the auction. And so they developed bidding strategies to try and gather all these compensatory payments and, if possible, not to actually ever have to sell the electricity. They came up with 11 strategies. And all of them were within the rules of the auction.
So what would they do? Well, here's an example. Basically, remember, why is this arbitraged? They're arbitraging the algorithm. They don't care about the electricity market. They're arbitraging the algorithm. So what would they do?
Well, the way this market works is you submit a bid in what's called the day-ahead market-- so say Monday. So they submit a bid on Monday to be willing to produce electricity between the hours of 11:00 PM and 12:00 AM on Tuesday. And they'll sell it for minus 30 a megawatt hour.
And you say, minus 30? But the rules of the auction were designed to allow wind farms, who get subsidies from the government, to submit bids. And so for them, sometimes it's better to just sell even at minus 30. So they bid minus 30. And their bid is accepted.
So that means the next day, they're going to be producing this electricity at night from 11:00 to midnight. And they're going to obviously not make any money at minus 30. But why would they do this?
Well, here's the rule. It turns out that because power plants can't come up and down overnight or, for that matter, instantaneously, there's something called ramp up and ramp down rules. So the rules are that you have to allow a power plant to operate for three hours at a stretch. So once their bid got accepted for minus 30, the next day, they bid to provide electricity from midnight until 2:00 in the morning. Only now they want to be paid $999 per megawatt hour.
Now, what's the normal bid price that you get at this time in the morning? About $15 an hour. But this bid has to be accepted because that bid was accepted because of the contiguous ramp up, ramp down rule. So these are the kinds of strategies that JP Morgan has come up with. We'll put in a bid for minus 30. And now you're going to have to accept our bid for 999 because the rules of the auction say you've got to do it.
So they had 11 of these strategies. And they started making money hand over fist. So what do you think? Did this cross the line? Anybody think it didn't? Well, JP Morgan didn't think it did. However, the problem is, the regulator thought it did. And the Federal Energy Regulatory Commission charged them with market manipulation, arguing that they interfered with and distorted the well-functioning markets in CAISO.
Now, I think-- not everyone-- but most people looking at this go, are you kidding me? And people say, well, you know, they made the market better by revealing this flaw. And the argument I would make is, yeah, but no one else was trying to distort the market. I mean, this is a problem. They raised electricity costs for everybody else. JP Morgan's the only one who benefits from this behavior. So these are the kinds of questions.
Personally, I think we do train our finance students how to arbitrage in exactly this way. And what is particularly scary here is that the regulator, after JP Morgan developed the first two strategies, said, don't do it. And so they go, OK, I'll shut that one down. And then they develop another one. And then they develop another one and another one and another one until finally they get them for manipulation.
AUDIENCE: [INAUDIBLE] from what--
MAUREEN O'HARA: Enron did?
MAUREEN O'HARA: Yeah. One of the reasons FRC was able to go after them for this behavior was after Enron, which also manipulated everything-- after Enron, they changed the rule about what is manipulation in energy markets. And so they have a much broader rule. Enron played every game in the book. But they thought they had stopped that until JP Morgan came in. And this was just last year.
So I'm almost out of time. So these are just some examples of the sorts of things that we look at in the book and think about where exactly does the weasel zone start. I don't think finance is unstoppable. That is, I don't think that it's the case that you can't stop finance, that you can create a law that we'll always get around. But I think you have to think more carefully about how are you going to make it a source of good.
You can try and change the culture in finance. Some of you may know the Dutch were so mad after the crisis because they had to bail out two of their major banks. Now all 90,000 Dutch bankers have to swear an oath that says they won't take people's money and misbehave and things like that. Will that make Dutch bankers better bankers? I don't know. That's one approach.
I think you change the way you regulate. If you tell me what the rule is, I can tell you how to get around it because I can build a synthetic way to get around that rule. I think we need to change from a world of trying to be very specific towards a world of standards.
And that's why JP Morgan got caught. Because JP Morgan got caught because the rule about what is manipulation was so broad that even though every strategy they used was technically within the rules, the overall intent was to manipulate the market. And they got them.
And this flips the old rule that says, use standards of people are trustworthy and rules otherwise. I want to argue that you don't want to use rules in a modern market because the minute you tell me what the rule is, I can build my way around it.
You also have to recognize the importance of market acceptance. Goldman Sachs was trying to get into the Islamic finance market. And the market simply wouldn't do it. So the market can have a role. And finally, I'd say that I think if we have more focus on ethics by regulators, journalists, boards, managers, protesters-- go occupy Wall Street-- and even finance professors, we'll have greater awareness, discussion, debate, and hopefully change.
So let me end there with this, go back to our little thing. As a start, don't be this guy. Don't be the guy who is arbitraging the Mexican restaurant. So thank you.
AUDIENCE: Just to finish the JP Morgan one, the ratepayers really ended up transferring value to JP Morgan?
MAUREEN O'HARA: That's correct.
AUDIENCE: Thank you.
MAUREEN O'HARA: That's-- I mean, that's basically because [? Caso ?] is a nonprofit. So they cover their costs by raising the electricity rates to cover--
AUDIENCE: JP Morgan was cheating their own customers.
MAUREEN O'HARA: Some of them, yeah, but not the ones who were their shareholders. I know you want to talk about Greece, right?
AUDIENCE: Yeah, well two things. One is about Greece and the role that Goldman Sachs played. And the second-- if I can ask a second, you don't have to answer it-- is you mentioned how colleges are teaching how to go around. Is that what you-- Did I hear that?
MAUREEN O'HARA: What we teach people how to do is how to synthetically build things, right. So if I can teach-- and we do-- I mean, modern finance is all about saying, tell me what kind of cash flows and payments do you want, and I'll figure out a way to structure things to do that, right? And so because everything's being done with complex editions of derivatives and everything else, they usually don't fall on the law because the laws were written for sort of the traditional contracts.
So we don't teach people to be criminals. What we teach people tools that can allow them to create alternatives that are not explicitly prohibited by law, and that's the problem. And we don't tell people think about this before you do it, right. Think about whether or not building a synthetic alternative to something that's illegal is exactly the right idea. So that's the second question.
You want to go back to Greece? So here's the issue with Greece. What did Greece do, right. So Greece has lots of debt and they want to get into the eurozone, but the eurozone has rules that say you cannot have more than a certain percentage of debt to GDP.
So what does Goldman do? Goldman structures something called an elongated swap, which is a type of derivative that basically turns the debt from their balance sheet into a stream of derivative payments. Now, why does that work? It works because Eurostat, who's the group that wrote the rules, does not count derivatives in counting up the debt--
AUDIENCE: Off balance sheet.
MAUREEN O'HARA: Off balance sheet. So what you did-- exactly way to put it-- is you took something that was on your balance sheet and you got to take it off by turning it into a derivative. Now it's not on your balance sheet, so technically, you don't have that debt and you're able to qualify for entrance. Now the debt still there. It's just you're going to be paying it down the road and it was done by a derivative. Now was that ethical or not? So here's two--
AUDIENCE: [INAUDIBLE] suggested it?
MAUREEN O'HARA: Well, the government of Greece hired Goldman Sachs to help and come up with a solution. So let's start-- Goldman didn't say to Greece you know, hey, I have an idea. Why don't you sneak in. I mean, so the government of Greece hires Goldman and says help us, right.
One of the reasons that they thought to do this-- and again, let's put-- was that Italy was running into similar problems about four years before. They hired JP Morgan Chase. And JP Morgan Chase did something very similar for the government of Italy, all right. So first thing you say is it wasn't the first time this was done, because it was done for Italy already.
The second thing here that is important to understand is that the European agency had been asked about the treatment of derivatives and recognizing that being able to do this allowed people to get around these rules, but they decided not to change the rule. This is before Goldman helped Greece. So the regulators knew that you could do this with the rules. They opted to leave the rules in place.
Goldman wasn't the first bank to have done it at the bequest of the country. And so is this illegal? Rather, it's certainly not illegal, right. It's absolutely within the rules.
Was it unethical? I have to admit I come down on the side of saying, you know what? If the regulators all knew that you could do this and approved it, if it had been done for Italy, if the government of Greece hired Goldman to do it for them, it's hard to say that it's necessarily unethical.
AUDIENCE: Who were the regulators?
MAUREEN O'HARA: Well the regulators are part of the eurozone, right. So the eurozone, the government of the eurozone, are the people for the European Union. So all the European Union regulators. Now the argument you might make that it's unethical is but the people in Greece have ended up having to pay a tremendous price. But it was their elected leaders who put them into this mess, right.
So that's why I think these issues are interesting. Was it ethical? Was it unethical? I think it was not necessarily unethical. It certainly wasn't illegal. But the outcome has been terrible, so--
But, you know, other reasonable people can disagree. People can say no, you should have known that you shouldn't do this. But I think you should think about it.
AUDIENCE: With regard to the crisis, the most recent financial crisis, do you think that the lack of regulations and or ethics would have been mitigated if there weren't the moral hazard created by the implicit backing of the government for the banks?
MAUREEN O'HARA: Yeah. That's a tricky one and, you know, we definitely have a challenge, right. I mean, what's interesting is that most of the subprime mortgages that will fail were not actually insured. They were generated by some of the major Wall Street banks, but they weren't insured at the time. I think there's no question that the insurance and the too big to fail subsidy tend to gave the banks a hubris about what they could get away with.
But here's the problem, you're always going to have to have some sort of insurance in a banking system, right. An uninsured banking system is too unstable. So what do I think should happen. Well, you got to ask yourself, why did only one person go to jail, right. I mean, whether these things were ethical or not, many of them are completely illegal, but nobody goes to jail.
After the savings and loan crisis, more than 600 bankers went to jail. But after this, one. So I think you have laws, or maybe you need to write your laws better. And you need to throw some people in jail. I think that would help a lot.
AUDIENCE: And it would get some attention.
MAUREEN O'HARA: And it would get some attention.
AUDIENCE: Why do you think it didn't happen?
MAUREEN O'HARA: You know. I don't know. It didn't. Yeah.
AUDIENCE: You mentioned machine learning earlier on very briefly. I have a question on that. So basically now there's like, an army of people out there trying to find that magical alpha using machine learning, and when they find it they don't necessarily know what that alpha is. So in that context, how do you discuss these ethical issue if you don't even know why you get that alpha.
MAUREEN O'HARA: Well, I think that's an interesting question. I don't think there's anything wrong with machine learning and I don't think there's anything wrong with trying to find alpha, right. I mean, markets are markets. In every market, there's a buyer and a seller. And after every trade there's someone who, looking back, goes, oops, I wish I hadn't done that, or good me, I did.
So let's be real clear. I think markets are great. When you're using machine learning, I think there is an ethical issue, but it's not looking for alpha. It's when you have these programs that are built-- When you build an algorithm and the algorithm says, OK, I'm using my machines and they're telling me all these signals. And then when I get these signals, I'm going to either buy or sell, all right.
There's nothing wrong necessarily, but you have to be very careful about how you build these algorithms. You also have to be very careful about who you let use your pipes into these systems, because you can build an algorithm that can generate these kind of self-perpetuating price cycles, right. Because, for example, an algorithm that says the more the price drops, the more I'm going to sell, right. That's a terrible algorithm because the more the price drops, the more you're selling. That causes the price to drop even more.
So suppose you've come up with a strategy that says I'm going to build an algorithm that says whenever I see a price drop, we're going to start selling. And then the more I can get the price to drop, I'm going to sell more and more and more and more and more. And then as soon as I drive the price down to a certain level, then I'll buy. That's unethical, right. That is completely unethical because that's just saying I'm going to manipulate prices and take advantage of it.
What isn't unethical is to say, I'm going to build an algorithm that tries to look at patterns in volume. And when I see that volume peaks up earlier in the day than it normally does, I'm going to interpret that as there's probably been good news about this stock, because volume and news are often correlated. So I'm going to buy on volume and I'm going to sell when the market's quiet.
That I don't think is unethical because you have a model in your mind that says, I think stocks that have more information are stocks I want to buy and et cetera, et cetera. So that's the difference I see. And I'll tell you one thing that broker dealer firm's worry about. You build algorithms that actually end up feeding off of each other, because everybody builds their own algorithm and they don't actually ever check to see whether my algorithm is actually going to create your algorithm, and so that's where I think the ethics comes in. So keep building your machine learning, but just don't take the market down. Yeah.
AUDIENCE: The thing that surprised me when I read the flashpoints was how rapidly you could turn these sales and so forth. What do you think of a rule that says if you put a buy order in the market that you can't cancel it for 30 seconds or something like that. Or if you actually own the stock you can't sell it again for some reasonable-- 30 seconds isn't very long, but it's enough to defeat some of these algorithms where people are paying about $10 million to get a fiber network that gives them--
MAUREEN O'HARA: It won't defeat them and I'll tell you why. All right. Suppose that you have a rule that says if you put an order in the book-- you're willing to sell, for example, at 90 and there isn't anybody willing to buy at that price yet. So your order's sitting in the book and under your rule, it has to sit there for 30 seconds.
All right. Well suppose over that interval the price blipped up to 94. Well, you don't want to sell at 90 now that it's at 94. So what you're going to do in the minute that that 30 seconds is up you're going to try and cancel. But guess what? They're faster than you are. They have already put in an order to buy at your 90, which is going to happen as soon as that 30 seconds is up and before you can cancel.
So part of the challenge in this world is that when you put in rules that say orders have to have a particular life, you expose the person who puts it into adverse selection, because you're not fast enough and you never will be. So the New York Stock Exchange has just introduced an order just like you want. And you have to leave it there for-- it's like half a second or something, which sounds like, ridiculous but the only people using it are retail traders, and they all get taken advantage of.
No institution would ever do that, because they know just what's going to happen to them. So unless you're going to have your own fiber optic cable and you're going to co-locate in the New York Stock Exchange, I guarantee you the guy who goes against your order when it's against you is going to take advantage of you all day long. Yeah. So it's a bit of a challenge.
AUDIENCE: Time for one more maybe.
AUDIENCE: You raised the point about the Islamic markets. Could you explain that just a little bit. Was it the bank-- nobody was interested? There was no interest. It that what that--
MAUREEN O'HARA: So the Islamic markets are really interesting. And I don't claim to be a particular Islamic expert, but there's a variety of features in Islamic markets. You can't, for example, charge interest, right. And there's-- you know, Goldman Sachs wanted to set up something that was akin to a bond-- and the bond that would pay interest-- but you can't pay interest.
So they had structured this very complicated transaction that basically resulted in something paying interest. But because it had all these moving pieces, each piece was compliant. And they actually got a group of mullahs to sign off on this thing, right. But there's another piece of Islamic finance that says that you should be raising the money for causes that somehow benefit Islam. And Goldman was going to use the money for other stuff.
And so even though they had these mullahs who signed off on it, there were a variety of things where the rest of the Islamic market said, I don't care what they said. This is not a Sharia compliant contract. And so Goldman had to withdraw it. And then they went back to the drawing board and met two years later, they came back with something that was a little bit closer to what is allowed in Islamic finance and that actually worked.
So there is no-- You know, it is interesting that religious authorities have kind of signed off. There's no law against these sorts of contracts, but they couldn't sell it. And I think that's something that we underestimate that I think the market-- you know, people-- We can all look back-- I know you both can-- on firms like Bankers Trust that always kind of skirted the law.
And there was a big lawsuit involving Bankers Trust and Proctor and Gamble about a contract that Bankers Trust and Procter and Gamble-- And then the court sided with Bankers Trust, but that was the end because the market sided with Procter and Gamble, and nobody would deal with Bankers Trust.
AUDIENCE: Arthur Anderson does their accounts for them.
MAUREEN O'HARA: Right. So anyway, thank you all very much. It was a lot of fun. Everyone go be ethical.
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One of the key innovations of modern finance is its reliance on arbitrage, the practice of taking advantage of a price difference between two or more markets to generate profits and remove inefficiencies. When done correctly, arbitrage can create value at little or no cost. But it can also be an exploitative tool.
In a September 2017 Chats in the Stacks book talk at Mann Library, finance economist Maureen O’Hara presents her newest book to offer insights into some of the business practices that form murky gray areas in modern finance—practices that may be formally legal yet are of highly questionable ethical standard. Something for Nothing takes a humanistic approach to ethics in the financial industry to examine key cases such as the Goldman Greek transaction, Lehman Brothers’ attempt to cover up its debt, JPMorgan Chase’s maneuvers in California’s energy markets, Bernie Madoff’s trading strategies in the 1980s, and toxic loans in France.
Maureen O’Hara is Purcell Professor of Finance at the Johnson Graduate School of Management, Cornell University and also Professor of Finance at the University of Technology Sydney in Australia. An expert on market microstructure, market theory and the practice of trading, she also publishes widely in banking and financial intermediaries, law and finance, and experimental economics. Prof. O’Hara has served as President of the American Finance Association, the Western Finance Association and the Financial Management Association. She has been an active member of a variety of corporate boards and has served on a number of national and international government advisory boards and task forces, including the CFTC-SEC Emerging Regulatory Issues Task Force aka the “flash crash” committee and the SEC’s Equity Market Structure A.