Fault Lines by Prof. Rajan is one of an ever increasing number of books recounting the financial collapse of the past few years. Unlike many of the others, Prof Rajan is both knowledgeable and experienced having been at the IMF in a senior role during a portion of this period. Thus this book is written from the perspective of a highly credible professional as well as a hands on operative.
Overall it is well written and avoids the finger pointing polemics that we are forced to endure from the journalist types who have their points to make. Rajan writes in a clear and well structured manner and details the problems, as well as recommending solutions. As the title says the system has certain enduring fault lines that need to be avoided rather than rebuilding upon.
Chapter 1 is the introduction and he lays out the history well. Especially he has a balanced position on who should take the blame and on p. 42 he calls the Government and its actions as the "elephant in the room". He does not take the Progressive's stance and blame the lack of regulation as the sole cause and he does not take the Conservative cause agreeing that all regulation is an anathema. Like any complex system which we will never really understand there must be circuit breakers, and that means some form of balanced regulation. Rajan states on p 43 at the end of Chapter 1:
"Growing income inequality in the United States stemming from unequal access to quality education led to political pressure for more housing credit. This pressure created a serious fault line that distorted lending in the financial sector."
I would strongly disagree with this statement. The US has one of the most open educations systems in the world and despite the less than stellar grammar and secondary systems the university systems are without equal. The problem here was demanding that credit be given to anyone on the part of the Government. Frankly when the Government opens the faucet to individuals who have no idea what responsible lending even means it is in and of itself a recipe for a disaster.
Chapter 2 discusses the whole issue of exports and Rajan's personal recollections regarding the controlled economy of India are telling. India was and to some degree is still a socialist centrally controlled state. It is a window to what can go wrong in an economy centrally controlled. On p 50 he states: "The great Austrian economist Joseph Schumpeter argued that capitalism grew through innovation, with newcomers bringing creative new processes and techniques that destroyed the business of old incumbents." This is creative destruction. The Progressive movement of the early part of the 20th century rebelled against the railroad tycoons but understanding Schumpeter one could have just as easily said, "this too shall pass". Namely in a Hegelian sense each action has a reaction and a resolution. Rajan on pp 54-55 discusses the sometimes success of the old Soviet system. As I was wont to tell my Russian employees that I was trained in the Joe Stalin school of management, never fail, the results would be tragic!
Chapter 4 discusses the US and its "weak safety net" which is a double edged sword. We in the US have limited unemployment benefits. It is in many ways Darwinian in that it is also a force to drive people back to work or seek other alternatives. In Germany, where I ran one of my companies, you cannot fire anyone. It is impossible. That frankly is a barrier to entry for an entrepreneur. Only the large incumbents can work in such an environment. Rajan seems to vacillate between the benefits of the US approach and the need for more social benefits. He discusses the discretionary stimulus approach of the US where the Government chooses who to pay and who not to pay. These he alludes may be seen as political payoffs and may not in any substantial manner truly stimulate.
Chapter 7 is quite interesting. He opens the chapter, pp 124-125, with a simple explanation of the reasons for the collapse of the derivatives. Let me paraphrase:
Consider a company which buys a pool of ten mortgages, all most likely subprime. Now the chance of any one going under is 10%. That means on average only one of the 10 will not pay back. This does beg the question of what factual basis was used to determine this but alas that was left to Wall Street and the rating agencies. Now we create two tranches, bundles, one which get a great interest rate but bears the losses, and second which gets a lower but still good interest rate and has its losses hedged by the first tranche. This works well except that the model is wrong!
What really happens is a Markov chain where when the first guy goes bankrupt, then the probability of another going is not the same but higher, and when a second goes bust it goes even higher. This means that instead of the first tranche bearing all the risk, the risk is moved to the second tranche which never thought it would have any! And then an AIG insures the second, and we know that there is a high probability of at least a 50% loss, a number AIG would never have imagined. Dumb quants! Yes, and on pp 142-143 Rajan details the Trillin conjecture that the changes in Wall Street over the past 30 years resulted in the dumbest guys moving upward relative to the Merlin's mixing their brews in the quant rooms. Rajan rejects that conjecture somewhat but there is considerable truth in it...just look at some of the folks who left and ended up in Government.
Chapter 8 discussing the reforming of the financial world. On p 164 he details a suggestion which should be adopted, the altering of compensation to reflect the risk over time. In Chapter 9 he returns to how to improve things in the US and on p 189 he speaks of the major problem in secondary education, the lack of competent instructors. To teach in a public school you need an education degree. Even if you had a PhD, held faculty position in a half a dozen universities and taught for over twenty years you still needed to learn how to operate an overhead projector and prepare a lesson plan. Thus the lack of educational advantage he posits in Chapter 1 is in many ways a result of the teachers unions barriers to entry of competent folks. Yet he never takes that leap. On pp 192-193 he posits the expansions of unemployment and benefits. Here I would disagree. Just look at the results in Germany, Greece, and other countries. In Russia I could fire a bad employee in Greece he was there until the return of Homer!
Rajan overall does a superb job at presenting the problems, the continuing faults and discussing solutions and safeguards. He deals with facts and logic and he does not tell stories as is typical of the wandering journalist. This is worth a read and for some worth a detailed study.
Showing posts with label Finance. Show all posts
Showing posts with label Finance. Show all posts
Monday, May 17, 2010
Friday, May 7, 2010
High Frequency Trading and the Market
The Wall Street Journal reports on the high frequency traders and their potential impact on the market yesterday, especially exacerbating an already down trend.
A number of high-frequency firms stopped trading Thursday in the midst of the market plunge, possibly adding to the market's selloff. Tradebot Systems Inc., a large high-frequency firm based in Kansas City, Mo., closed down its computer trading systems when the Dow Jones Industrial Average had dropped about 500 points...Tradebot's system is designed to stop trading when the market becomes too volatile...
The problem is that these trades, rapid in and out trades, are predicated on three things; (i) the market is somewhat sticky and does not exhibit Wiener process independence, there is a correlation, albeit short term, and if a stock is going up it may continue to do so and if down then likewise, (ii) transaction costs are de minimis, (iii) proximity to the points of trade are critical since the correlations times are in nano seconds.
The problem is that no one knows the elements of instability in such a system or call it a game. They all have unstable modes, as we have noted many times before. You can optimize the linear modes but the nonlinear issues lead to instabilities, and all too often the whiz kids forget them, look at Long Term Capital.
We feel that high frequency trading, led by good old Goldman Sachs and their ilk, will sooner than later crash the entire market. 2008 will look like the good old days!
A number of high-frequency firms stopped trading Thursday in the midst of the market plunge, possibly adding to the market's selloff. Tradebot Systems Inc., a large high-frequency firm based in Kansas City, Mo., closed down its computer trading systems when the Dow Jones Industrial Average had dropped about 500 points...Tradebot's system is designed to stop trading when the market becomes too volatile...
The problem is that these trades, rapid in and out trades, are predicated on three things; (i) the market is somewhat sticky and does not exhibit Wiener process independence, there is a correlation, albeit short term, and if a stock is going up it may continue to do so and if down then likewise, (ii) transaction costs are de minimis, (iii) proximity to the points of trade are critical since the correlations times are in nano seconds.
The problem is that no one knows the elements of instability in such a system or call it a game. They all have unstable modes, as we have noted many times before. You can optimize the linear modes but the nonlinear issues lead to instabilities, and all too often the whiz kids forget them, look at Long Term Capital.
We feel that high frequency trading, led by good old Goldman Sachs and their ilk, will sooner than later crash the entire market. 2008 will look like the good old days!
Saturday, April 4, 2009
Every System Has Instabilities: Another Look at Johnson
Everywhere I look over the past few days I see a copy of Simon Johnson's paper The Quiet Coup. Even David Brooks at the Times has decided to write about it. Then there is the writing of Felix Salmon on the Li Formula which measures risk in complex portfolios. There is also the brilliant but under-publicized book by MacKenzie called An Engine, Not a Camera (MIT Press 2008) which looks at the 1987 crash from an historical and philosophical view. Finally there is also the overview of depressions and recessions recently by Muller.
However, the Johnson paper recounts what happened. But there is also a layer below this which is more fundamental. MacKenzie recounts the establishment of the mercantile exchange and the necessity of railroads and the requirement to have a commodicized whet or corn product so that corn was corn and wheat was wheat so that one could then write futures independent upon a specific bushel of corn or wheat. This established the first break in the nexus between the commodity, the good, the property is you will, and the financial instrument which was then collateralized against it.
A contract was no longer on a specific bushel of corn from a specific farmer. It was just corn of a certain specification. Yet what was preserved was the basic issue of money for goods, hard assets were always at the core. One had to remember that of you traded commodity contracts you never wanted to hold the contract at the end, it made you buy the wheat. Even if you lived on Fifth Avenue, the railroad care was now yours!
We now make a few observations which we believe extend the Johnson model and world view.
1. Financial Contracts Were Unlinked from Real Assets
If you have a mortgage on a house if all else fails you still own the house. If you have an option to buy eggs at the end you own the eggs, albeit costly, and you either make a large omelet or try to sell them at a loss. But when you created new financial instruments which were based on the performance of another set of financial instruments you moved from owning something to betting on a horse at Belmont. If you bought an option on an interest rate, albeit secured by some complex instrument, you just as easily could have bought a ticket for Lucky Larry in the 4th at Belmont. At the end of the race you won or lost and your relationship to the horse and the jockey remained unchanged, you had none. The development of these new instruments made more money for the purveyors but they were just bets. It was gambling. Everyone knew that. Land is land, that is why people like it, gold is gold, but a bet is just a bet.
2. Derivatives are Both Derived from Something Else and a Mathematical Derivative
Derivatives evolved as complex sets of new securities which were derivative from some set of reality based securities. A short, especially a naked short, is just that. But there is a second definition of derivative which applies. Namely the mathematical derivative. Namely dx/dt. We all know that if x(t) is a stock price of house price that dx(t)/dt is its derivative and we can get higher and higher derivatives. They go up fast and they go down really fast! Worse if x(t)=z(t)+w(t) where w is some noise in the system, well, hold onto your hat. Taking derivatives of noise is a sure guarantee to see things just go crazy. The white noise, Wiener process, that is assumed in the quants models when differentiated is unstable. The differentiate if n times and what do you get, Armageddon.
3. Every thing in the world is a System
Everything in life is a system. Cells are systems, genes are systems, the stock market is a system. A system is a set of interrelated and dependent states which can be measured and characterized and which has a dynamic set of interactions which evolved over a period of time. Some systems are easy to understand. A radio is a system where a signal comes via an antenna and is converted by a small set of electronics to a sound we hear. The simple observation about systems is that everything is connected. Every action has a consequence whether you know it or not. Tell a three year old not to touch a hot stove and then five minutes latter they do and come screaming that they burned their hand. Hopefully they do not do it again. However Financial managers seem to be unresponsive three year olds, they just keep touching the stove.
4. Systems Have Feedback Paths
The system we look at have paths that feed back on other elements. Push here and it pops up there. Some paths are negative and reduce effects and some are positive and increase them. Some paths are delayed and the delay can cause jerky hesitation. Some complex delayed feedback can cause instabilities.
5. Systems with Feedback are Unstable
Every engineer is shown a picture of the Seattle Tacoma Narrows Bridge collapse and told to beware of this phenomenon. There is unstable feedback that cause collapse. Engineers sped their careers looking at the past and looking to avoid these problems. That is why they over design things. That is why Apollo 13 returned. Financial managers are clueless. They are Masters of the Universe who never look behind. They could never be a spy, they never look back to see who is following them, and often times it is the instabilities of the very systems they are playing with. The quants were for the most part physicists and mathematicians. The engineers, real engineers, are just too cautious. I recount a prior article on my comments on the Black Scholes model in 1973.
6. Systems are Stochastic, Random
Every system has random elements. They are random either because we can never really model them or because things just happen. Cancer in many cases is a rand event. In Chronic Myelogeneous Leukemia there is a splitting of a chromosome in the stem cells in the bone marrow. A fragile part of one chromosome breaks off and attaches to another. This is considered random, it just happens. Perhaps if we knew more about it we could deterministically say it has a deterministic cause, but maybe it just happens. All systems have these characteristics. The Quants modelled randomness in a Gaussian manner. The Gaussian world assumes that every event is independent and that they add up in such a manner and that clustering of "stuff" is impossible. There are various theorems which lay this view out in detail. The Gaussian world is a kind world, bad things happen independently and infrequently. In the really world clustering of event are all too common. It is this un-modellable world that we live in.
7. Random Systems with Feedback Can Be Really Unstable
If deterministic systems can be unstable then random feedback systems can be really unstable.
8. "S..t" Happens! Unstable Systems can result from the confluence of Events.
Well all of the above may just sound risky enough but there is also the favorite problem, we engineers call it Murphy's Law, the unexpected. It always happens. I remember Apollo 13 which I had designed the optical guidance system at the last minute and I thought it would never be used. Who'd da thunk! Things just have the habit of clustering. It is like telephone traffic was on mothers day. We all knew it and we planned for it. But then when the second all digital switch was deployed in Beverly Hills in 1966 they forgot about the fact that 1 PM in Beverley Hills was 4 PM in New York and everyone called their stock broker that day because of the glitch in the Stock Market and the switch failed because of an overload. This stuff just happens.
Thus Johnson is spot on in his analysis and the world of Bonfire of the Vanities lingered. The Financial Managers never got the point. They just went further and further and created their own world akin to the Oligarchs of Russia. The Fannie Mae and Freddie Mac bonus problem is another example of this arrogance. It continues and it is permitted to continue. Johnson observed the human side of the problem. My points articulated here is an attempt to detail a few of the laws of nature related to this problem. By the way, the same will apply to the current budget process. Thus beware!
However, the Johnson paper recounts what happened. But there is also a layer below this which is more fundamental. MacKenzie recounts the establishment of the mercantile exchange and the necessity of railroads and the requirement to have a commodicized whet or corn product so that corn was corn and wheat was wheat so that one could then write futures independent upon a specific bushel of corn or wheat. This established the first break in the nexus between the commodity, the good, the property is you will, and the financial instrument which was then collateralized against it.
A contract was no longer on a specific bushel of corn from a specific farmer. It was just corn of a certain specification. Yet what was preserved was the basic issue of money for goods, hard assets were always at the core. One had to remember that of you traded commodity contracts you never wanted to hold the contract at the end, it made you buy the wheat. Even if you lived on Fifth Avenue, the railroad care was now yours!
We now make a few observations which we believe extend the Johnson model and world view.
1. Financial Contracts Were Unlinked from Real Assets
If you have a mortgage on a house if all else fails you still own the house. If you have an option to buy eggs at the end you own the eggs, albeit costly, and you either make a large omelet or try to sell them at a loss. But when you created new financial instruments which were based on the performance of another set of financial instruments you moved from owning something to betting on a horse at Belmont. If you bought an option on an interest rate, albeit secured by some complex instrument, you just as easily could have bought a ticket for Lucky Larry in the 4th at Belmont. At the end of the race you won or lost and your relationship to the horse and the jockey remained unchanged, you had none. The development of these new instruments made more money for the purveyors but they were just bets. It was gambling. Everyone knew that. Land is land, that is why people like it, gold is gold, but a bet is just a bet.
2. Derivatives are Both Derived from Something Else and a Mathematical Derivative
Derivatives evolved as complex sets of new securities which were derivative from some set of reality based securities. A short, especially a naked short, is just that. But there is a second definition of derivative which applies. Namely the mathematical derivative. Namely dx/dt. We all know that if x(t) is a stock price of house price that dx(t)/dt is its derivative and we can get higher and higher derivatives. They go up fast and they go down really fast! Worse if x(t)=z(t)+w(t) where w is some noise in the system, well, hold onto your hat. Taking derivatives of noise is a sure guarantee to see things just go crazy. The white noise, Wiener process, that is assumed in the quants models when differentiated is unstable. The differentiate if n times and what do you get, Armageddon.
3. Every thing in the world is a System
Everything in life is a system. Cells are systems, genes are systems, the stock market is a system. A system is a set of interrelated and dependent states which can be measured and characterized and which has a dynamic set of interactions which evolved over a period of time. Some systems are easy to understand. A radio is a system where a signal comes via an antenna and is converted by a small set of electronics to a sound we hear. The simple observation about systems is that everything is connected. Every action has a consequence whether you know it or not. Tell a three year old not to touch a hot stove and then five minutes latter they do and come screaming that they burned their hand. Hopefully they do not do it again. However Financial managers seem to be unresponsive three year olds, they just keep touching the stove.
4. Systems Have Feedback Paths
The system we look at have paths that feed back on other elements. Push here and it pops up there. Some paths are negative and reduce effects and some are positive and increase them. Some paths are delayed and the delay can cause jerky hesitation. Some complex delayed feedback can cause instabilities.
5. Systems with Feedback are Unstable
Every engineer is shown a picture of the Seattle Tacoma Narrows Bridge collapse and told to beware of this phenomenon. There is unstable feedback that cause collapse. Engineers sped their careers looking at the past and looking to avoid these problems. That is why they over design things. That is why Apollo 13 returned. Financial managers are clueless. They are Masters of the Universe who never look behind. They could never be a spy, they never look back to see who is following them, and often times it is the instabilities of the very systems they are playing with. The quants were for the most part physicists and mathematicians. The engineers, real engineers, are just too cautious. I recount a prior article on my comments on the Black Scholes model in 1973.
6. Systems are Stochastic, Random
Every system has random elements. They are random either because we can never really model them or because things just happen. Cancer in many cases is a rand event. In Chronic Myelogeneous Leukemia there is a splitting of a chromosome in the stem cells in the bone marrow. A fragile part of one chromosome breaks off and attaches to another. This is considered random, it just happens. Perhaps if we knew more about it we could deterministically say it has a deterministic cause, but maybe it just happens. All systems have these characteristics. The Quants modelled randomness in a Gaussian manner. The Gaussian world assumes that every event is independent and that they add up in such a manner and that clustering of "stuff" is impossible. There are various theorems which lay this view out in detail. The Gaussian world is a kind world, bad things happen independently and infrequently. In the really world clustering of event are all too common. It is this un-modellable world that we live in.
7. Random Systems with Feedback Can Be Really Unstable
If deterministic systems can be unstable then random feedback systems can be really unstable.
8. "S..t" Happens! Unstable Systems can result from the confluence of Events.
Well all of the above may just sound risky enough but there is also the favorite problem, we engineers call it Murphy's Law, the unexpected. It always happens. I remember Apollo 13 which I had designed the optical guidance system at the last minute and I thought it would never be used. Who'd da thunk! Things just have the habit of clustering. It is like telephone traffic was on mothers day. We all knew it and we planned for it. But then when the second all digital switch was deployed in Beverly Hills in 1966 they forgot about the fact that 1 PM in Beverley Hills was 4 PM in New York and everyone called their stock broker that day because of the glitch in the Stock Market and the switch failed because of an overload. This stuff just happens.
Thus Johnson is spot on in his analysis and the world of Bonfire of the Vanities lingered. The Financial Managers never got the point. They just went further and further and created their own world akin to the Oligarchs of Russia. The Fannie Mae and Freddie Mac bonus problem is another example of this arrogance. It continues and it is permitted to continue. Johnson observed the human side of the problem. My points articulated here is an attempt to detail a few of the laws of nature related to this problem. By the way, the same will apply to the current budget process. Thus beware!
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