To the first approximation, investment is a roulette. The house (your broker) always wins thanks to fees, while you have 50/50 chances. This was shown pretty clearly in first part of the movie Wolf of Wall St, for instance. If you exchange dollars for euros today, and then exchange them back some other day when you feel like it, then ignoring the fees your expectation value of the difference in the dollar amounts is zero. Even the distribution is fairly uniform. Here by expectation value we mean our knowledge: we know about this number as much as we know about a random variable with zero mean and a normal distribution. In the same way, if you buy Apple stock today, and then sell it some day next year when you feel like it, there's no knowledge in science whether it's gonna be up or down then.
To the second approximation, S&P 500 appears to grow. So you have a small positive expectation value in your scientific knowledge for the purchase of Apple stock, that is essentially drowned in dispersion (random fluctuations). Buying S&P 500 index reduces that dispersion by a bit, so you expect to see that small positive expectation value after 3-5 years. The distribution is also skewed - it grows faster typically, but there are crises where it falls dramatically. Crises happen every 10 years or so. That is what has been observed in US for the last 100 years, and in other countries seemingly independently for some time. So a consensus seems to be that it will continue this way throughout the history of humanity. Here people confuse the more tangible concepts of economic growth, consumption, GDP, quality of life, population growth with the artificial concept of market price of S&P 500 (or any other index) stocks. They are actually not related by anything hard-wired in the system, or any inherent property of human psyche. The relationship between them is more like the relationship between the gold stored in fort Knox and the number of dollar bills in circulation. It used to be kept due to conservatism of the people in charge, but it can be completely abandoned whenever it becomes convenient. The value of dollar bills is actually completely independent of amount of gold in practice.
In the same way the price of stocks can in principle become independent from the wellbeing of the companies they represent. We are already in the age where computer algorithms are trading with each other, and the only thing they care about is beating each other, not the concept of value. There are regulators that enforce certain constraints on those algorithms, but the main constraint is against volatility, not against the deconstruction of meaning. So a more pessimistic picture of the future is that traditional investors will slowly become disappointed in value investing, and the prices of stocks will level out in accordance of regulations. The computer algorithms will have a lot of fun trading with each other, but stocks will become just another meaningless electronic currency that fluctuates within prescribed bounds, but is somewhat useless even for the leadership of the company that issued it. This transition is likely to happen around these years, so we might see the last impressive growth in the stock prices around the world. It's probably a good idea to ride this wave, it might be the last one.
To motivate a little bit why stock is decoupled from value, let me just list obvious things:
The company issues stocks, but does not have any obligation to pay dividends on it, or buy or sell it in any way. The company may have ups and downs, the profits may be redistributed as bonuses to the leadership or invested in a company of CEO's wife, either way the stockholders see little of it. Essentially once the stock is issued, it's just a piece of paper. The voting rights don't matter because someone holds 51% anyways. You can't interact with the company in any significant way using the stock you own. For all intents and purposes, what's happening is just a crowd of people that have in their mind some idea of what price this or that piece of paper has. It's all in their heads. There's no fixed meaning of that piece of paper. If the crowd suddenly decides as a whole to do something else with it, there's no stopping force. Of course real crowd has inertia - there are always conservative people who stick to old ways. But the majority is pretty flexible. And what the majority is doing is going on a wild goose chase of pattern-seeking, sometimes glorified as machine learning or advanced statistics. Of course, those approaches provide good tools to tell whether the pattern is really there, or you're just imagining it. However the malpractice of using these tools in finance introduces several layers of selection biases, after which the original results of those tools become meaningless. First the author of the algorithm or strategy discards a bunch of strategies that didn't work. Then the hedge fund fires the workers whose strategies didn't perform. Then the hedge funds that didn't perform disappear and new ones are created. Finally the investors look at hedge funds, choose the ones that made the best impression using what they call "due diligence". Why is this multi-level scheme of choosing which strategy will the money follow bad? For instance, if at least one of the links in the chain is untrustworthy, the whole thing falls apart, and the huge chunk of money in possession of that given investor is invested according to a terrible, meaningless, essentially random strategy. Moreover, due to constant rotation of algorithms, quants, hedge funds, and investors themselves, there's no memory in the system. If one tries to ask: how well do hedge funds perform in general? Or: how well do our employees perform in general? One gets an essentially random number that can happen to be positive, which is when it will be used in an advertisement brochure, but it also can happen to be negative, which is when it is not talked about. Selection bias is when we mention a few success stories like Steve Jobs, but never talk about 99% of failure stories. In the same way, the employee may not be 100% honest about his selection of the algorithm and not 100% blind in his crossvalidation. The company's choice of who to fire and whose algorithm to invest money into is usually rushed, and the good statistics are exaggerated (p-value hacking). The successful hedge funds get media and investor attention, while the failed ones disappear quietly, and nobody really counts how many of them were there. Successful investors have a lot of money and a personally confirmed belief that the whole system works so the majority of money in the future is distributed by people whose personal experience was not representative of the system as a whole. Of course locally this industry looks legit, and every quant believe himself to be smarter than the other guy, and his statistical tools to be flawless. So unless you compare his experiences to the experiences of another quant who also believed he was smart but lost, everything seems to be working. Look at people who were fired from hedge funds. Are they really objectively less smart than the ones that work there today? Or were they just unlucky? When there's a hedge fund with flawless 30-year record that you see, how many other hedge funds were there that lost money and lost investors so you don't see them?
For the technical and fundamental traders of the 90'ies, the numbers really added up to zero as they should, ignoring the "making money from air" effect (overall S&P 500 growth, and people who grew thanks to market manipulation, fees, bubbles aka pyramid schemes). That's not bad by itself, in every competition there are winners and losers. It's even not surprising to have zero-sum game where the total wealth remains the same. Still you would expect that the person who professionally trades, as in he convinced somebody to pay him fees to trade their money, will perform better than the random number generator. This was not the case - the industry was so deeply rotten that no selection based on the promo materials and no due diligence were able to correlate with the positive performance of the professional investors. They all were in it for the fees after all. Now there's a myth that the machine trading hedge funds are more honest about their performance reports. And some of them definitely are. But the scientific hypotheses is that the algorithmic trading is equally rotten so that from the outside point of view there's no way to tell apart the fakes from the real ones. They themselves don't know if they are fake sometimes, because quants who have doubts about it just get fired, or not get hired in the first place.
Anyways, even if there's a positive returns promise that we can believe from some of the algorithmic trading, it is not accessible to ordinary investors. You need to have a lot of money to even begin talking to some of the hedge funds. There are platforms which allow you to use openly available algorithms and reproduce whatever the hedge funds are doing, at your own risk. But no statistically significant good strategies really exist. The trick with statistical significance is that for strong enough noise, nothing is statistically significant. Moreover, the more complicated your strategy is, the smaller the noise that will completely ruin it's significance. Suppose your strategy has n parameters that can be 0 or 1. So you have 2^n strategies. The base probability is 1/2^n for each of those to be the best one. Suppose the strategies true expectation value of working at all is e<<1, and dispersion 1. Then for it to be picked, you need a sample of poly(n)/e points. But when you write a strategy, you make a lot of choices, so n is pretty big actually. For any interesting algorithm, the sample size needed is enormous - much bigger than the trading data available. And all the simple algorithms were already been found and washed out. So the robots have to choose algorithms in the pool where there's no statistically significant difference between them. This is just for illustrative purposes and is oversimplified.
I don't think the idea of robots fighting each other in a game of insufficient information is that far-fetched. It's like horse-races. You need to spend a lot of time and money on your horse for it to not lose immediately, but your competitors are doing the same, and in the end there's no way to predict who's gonna win. In the same way, an amateur-made algorithm will immediately lose on the fees, but even the professionally made algorithms are essentially giving back random performance. Of course that performance is properly smooth, so they create an illusion of doing something intelligent (or otherwise they would have been discarded at the selection process), but they can smoothly loose money just as well as they can smoothly win.
To the second approximation, S&P 500 appears to grow. So you have a small positive expectation value in your scientific knowledge for the purchase of Apple stock, that is essentially drowned in dispersion (random fluctuations). Buying S&P 500 index reduces that dispersion by a bit, so you expect to see that small positive expectation value after 3-5 years. The distribution is also skewed - it grows faster typically, but there are crises where it falls dramatically. Crises happen every 10 years or so. That is what has been observed in US for the last 100 years, and in other countries seemingly independently for some time. So a consensus seems to be that it will continue this way throughout the history of humanity. Here people confuse the more tangible concepts of economic growth, consumption, GDP, quality of life, population growth with the artificial concept of market price of S&P 500 (or any other index) stocks. They are actually not related by anything hard-wired in the system, or any inherent property of human psyche. The relationship between them is more like the relationship between the gold stored in fort Knox and the number of dollar bills in circulation. It used to be kept due to conservatism of the people in charge, but it can be completely abandoned whenever it becomes convenient. The value of dollar bills is actually completely independent of amount of gold in practice.
In the same way the price of stocks can in principle become independent from the wellbeing of the companies they represent. We are already in the age where computer algorithms are trading with each other, and the only thing they care about is beating each other, not the concept of value. There are regulators that enforce certain constraints on those algorithms, but the main constraint is against volatility, not against the deconstruction of meaning. So a more pessimistic picture of the future is that traditional investors will slowly become disappointed in value investing, and the prices of stocks will level out in accordance of regulations. The computer algorithms will have a lot of fun trading with each other, but stocks will become just another meaningless electronic currency that fluctuates within prescribed bounds, but is somewhat useless even for the leadership of the company that issued it. This transition is likely to happen around these years, so we might see the last impressive growth in the stock prices around the world. It's probably a good idea to ride this wave, it might be the last one.
To motivate a little bit why stock is decoupled from value, let me just list obvious things:
The company issues stocks, but does not have any obligation to pay dividends on it, or buy or sell it in any way. The company may have ups and downs, the profits may be redistributed as bonuses to the leadership or invested in a company of CEO's wife, either way the stockholders see little of it. Essentially once the stock is issued, it's just a piece of paper. The voting rights don't matter because someone holds 51% anyways. You can't interact with the company in any significant way using the stock you own. For all intents and purposes, what's happening is just a crowd of people that have in their mind some idea of what price this or that piece of paper has. It's all in their heads. There's no fixed meaning of that piece of paper. If the crowd suddenly decides as a whole to do something else with it, there's no stopping force. Of course real crowd has inertia - there are always conservative people who stick to old ways. But the majority is pretty flexible. And what the majority is doing is going on a wild goose chase of pattern-seeking, sometimes glorified as machine learning or advanced statistics. Of course, those approaches provide good tools to tell whether the pattern is really there, or you're just imagining it. However the malpractice of using these tools in finance introduces several layers of selection biases, after which the original results of those tools become meaningless. First the author of the algorithm or strategy discards a bunch of strategies that didn't work. Then the hedge fund fires the workers whose strategies didn't perform. Then the hedge funds that didn't perform disappear and new ones are created. Finally the investors look at hedge funds, choose the ones that made the best impression using what they call "due diligence". Why is this multi-level scheme of choosing which strategy will the money follow bad? For instance, if at least one of the links in the chain is untrustworthy, the whole thing falls apart, and the huge chunk of money in possession of that given investor is invested according to a terrible, meaningless, essentially random strategy. Moreover, due to constant rotation of algorithms, quants, hedge funds, and investors themselves, there's no memory in the system. If one tries to ask: how well do hedge funds perform in general? Or: how well do our employees perform in general? One gets an essentially random number that can happen to be positive, which is when it will be used in an advertisement brochure, but it also can happen to be negative, which is when it is not talked about. Selection bias is when we mention a few success stories like Steve Jobs, but never talk about 99% of failure stories. In the same way, the employee may not be 100% honest about his selection of the algorithm and not 100% blind in his crossvalidation. The company's choice of who to fire and whose algorithm to invest money into is usually rushed, and the good statistics are exaggerated (p-value hacking). The successful hedge funds get media and investor attention, while the failed ones disappear quietly, and nobody really counts how many of them were there. Successful investors have a lot of money and a personally confirmed belief that the whole system works so the majority of money in the future is distributed by people whose personal experience was not representative of the system as a whole. Of course locally this industry looks legit, and every quant believe himself to be smarter than the other guy, and his statistical tools to be flawless. So unless you compare his experiences to the experiences of another quant who also believed he was smart but lost, everything seems to be working. Look at people who were fired from hedge funds. Are they really objectively less smart than the ones that work there today? Or were they just unlucky? When there's a hedge fund with flawless 30-year record that you see, how many other hedge funds were there that lost money and lost investors so you don't see them?
For the technical and fundamental traders of the 90'ies, the numbers really added up to zero as they should, ignoring the "making money from air" effect (overall S&P 500 growth, and people who grew thanks to market manipulation, fees, bubbles aka pyramid schemes). That's not bad by itself, in every competition there are winners and losers. It's even not surprising to have zero-sum game where the total wealth remains the same. Still you would expect that the person who professionally trades, as in he convinced somebody to pay him fees to trade their money, will perform better than the random number generator. This was not the case - the industry was so deeply rotten that no selection based on the promo materials and no due diligence were able to correlate with the positive performance of the professional investors. They all were in it for the fees after all. Now there's a myth that the machine trading hedge funds are more honest about their performance reports. And some of them definitely are. But the scientific hypotheses is that the algorithmic trading is equally rotten so that from the outside point of view there's no way to tell apart the fakes from the real ones. They themselves don't know if they are fake sometimes, because quants who have doubts about it just get fired, or not get hired in the first place.
Anyways, even if there's a positive returns promise that we can believe from some of the algorithmic trading, it is not accessible to ordinary investors. You need to have a lot of money to even begin talking to some of the hedge funds. There are platforms which allow you to use openly available algorithms and reproduce whatever the hedge funds are doing, at your own risk. But no statistically significant good strategies really exist. The trick with statistical significance is that for strong enough noise, nothing is statistically significant. Moreover, the more complicated your strategy is, the smaller the noise that will completely ruin it's significance. Suppose your strategy has n parameters that can be 0 or 1. So you have 2^n strategies. The base probability is 1/2^n for each of those to be the best one. Suppose the strategies true expectation value of working at all is e<<1, and dispersion 1. Then for it to be picked, you need a sample of poly(n)/e points. But when you write a strategy, you make a lot of choices, so n is pretty big actually. For any interesting algorithm, the sample size needed is enormous - much bigger than the trading data available. And all the simple algorithms were already been found and washed out. So the robots have to choose algorithms in the pool where there's no statistically significant difference between them. This is just for illustrative purposes and is oversimplified.
I don't think the idea of robots fighting each other in a game of insufficient information is that far-fetched. It's like horse-races. You need to spend a lot of time and money on your horse for it to not lose immediately, but your competitors are doing the same, and in the end there's no way to predict who's gonna win. In the same way, an amateur-made algorithm will immediately lose on the fees, but even the professionally made algorithms are essentially giving back random performance. Of course that performance is properly smooth, so they create an illusion of doing something intelligent (or otherwise they would have been discarded at the selection process), but they can smoothly loose money just as well as they can smoothly win.
No comments:
Post a Comment