I have just finished reading Daniel Kahneman's bestseller "Thinking, Fast and Slow", and found it full of inspirations important for traders. This is no surprise, of course, since Kahneman won the 2002 Nobel prize in economics for his work on decision theory. Here are some of the notables:
1) Simple sum is often better than a linear regression fit. Remember my constant mantra that "simpler is better" when building trading models? I have always advocated linear regression over nonlinear models, but Kahneman went a step further. He said that in social science modeling (which of course includes financial markets modeling), assigning equal weights to the predictive factors is often superior to weighting them using multivariate linear regressors when applied to out-of-sample data.
2) Overconfidence in corporate acquisitions. Managers of acquiring companies often believe that they are better than the managers of acquirees. This overconfidence has several causes: there is an illusion of control which overemphasizes the role of skill and neglects the role of luck, and there is a focus on what one knows and a neglect of what one does not, etc. The market already knows this: the stock of the acquirer usually suffers a sell-off upon announcement of the acquisition, because the result of any acquisition is more often bad than good, but the question is whether it has sufficiently discounted this phenomenon. Would shorting the stock of an acquirer at the completion of an acquisition and holding the short position for, say, 5 years, hedging this position with SPY, be profitable?
3) Premortem. After designing a trading strategy, it is always useful to write a brief imaginary history of how it has become an unmitigated financial disaster for you a few years from now. This will likely reveal scenarios that you have not previously thought of, and triggering additional risk management measures.
4) Risk seeking in the face of losses. Suppose you are running a strategy that has a fixed holding period. Have you ever extended this holding period when the position is losing, in the hope that the position will recoup some of its losses? I have, and the result was double the loss I would have suffered had I exited on time. Apparently this is a very common suboptimal behavioral bias: this is why many defendants with a weak legal case often risk continued litigations instead of accepting an unfavorable settlement.
5) Why do we often demand Sharpe ratio >=2? Psychological experiments have shown that people find the pain of losing $1 can only be compensated by the pleasure of winning $2. So if we equate standard deviation as the average drawdown of a strategy, then we need to have twice the average return!
Many businesses have profited from arbitraging the difference between rational decisions and biased decisions that people commonly made. (For e.g. lottery franchises benefit from people overweighting the probability of winning, sellers of extended warranties benefit from buyers' risk-aversion.) I wonder if there are still opportunities left for rational traders to take advantage of the biased decisions of irrational traders?
Monday, February 13, 2012
Monday, January 30, 2012
What worked in 2011?
We all know that 2011 was a bad year for many hedge funds, with the average fund down 5%. But what type of strategies did well, and what did particularly poorly? The numbers are out: Forex funds lose more than average, down 6%. In fact, 71 out of 77 Forex funds tracked by a Citigroup currency analyst were down in 2011. And the winners are? Statarb funds, with a 5% averge return.
This superior performance of statarb funds is quite a contrast from the last financial crisis 2007-9. Then, most of the big factor-driven statarb models failed miserably. What caused this difference? Is it because the risk management techniques of big funds have improved? Or maybe that's because in 2011, the deviation from factor returns mean-revert within a few days, so those statarb models that re-balance on a daily basis can benefit from the buying/selling opportunity at steep discount/premium?
To settle this question, let me report the 2011 backtest results (without transaction costs) of running Andrew Lo's prototype mean-reversion model : ranking stocks based on their previous day's returns, shorting the top decile and buying the bottom one, rebalancing only at the close. (Click on chart to make it larger.)
The APR in 2011 was 18.6%. Note in particular its performance since the crisis began officially on 20110808: despite a steep drawdown, the overall performance was spectacular! Clearly, high volatility benefited a prototypical statarb strategy, and the out-performance has not much to do with improved risk management.
You might wonder what would happen if we had used the intraday version of this strategy instead: enter all positions at the open, and exit them all at the close? I tried it: the performance is surprisingly similar to the interday strategy. So intraday vs. interday volatility or mean-reversion does not seem to play a part in last year's equities market. Contrasting this with the performance of Forex models, it is clear that high volatilities benefited statarb models while they hurt FX models.
In the next article or two, I will explore the 2011 performance of some other equities mean-reverting models that I used to trade. But what about your models? If you have some thoughts on what worked and what didn't in 2011, please share them with us in the comments section.
This superior performance of statarb funds is quite a contrast from the last financial crisis 2007-9. Then, most of the big factor-driven statarb models failed miserably. What caused this difference? Is it because the risk management techniques of big funds have improved? Or maybe that's because in 2011, the deviation from factor returns mean-revert within a few days, so those statarb models that re-balance on a daily basis can benefit from the buying/selling opportunity at steep discount/premium?
To settle this question, let me report the 2011 backtest results (without transaction costs) of running Andrew Lo's prototype mean-reversion model : ranking stocks based on their previous day's returns, shorting the top decile and buying the bottom one, rebalancing only at the close. (Click on chart to make it larger.)
You might wonder what would happen if we had used the intraday version of this strategy instead: enter all positions at the open, and exit them all at the close? I tried it: the performance is surprisingly similar to the interday strategy. So intraday vs. interday volatility or mean-reversion does not seem to play a part in last year's equities market. Contrasting this with the performance of Forex models, it is clear that high volatilities benefited statarb models while they hurt FX models.
In the next article or two, I will explore the 2011 performance of some other equities mean-reverting models that I used to trade. But what about your models? If you have some thoughts on what worked and what didn't in 2011, please share them with us in the comments section.
Tuesday, December 27, 2011
Risk indicators
During the financial crisis of 2008, I wrote about how I watched some risk indicators such as the VIX or the TED spread to decide what leverage I should use for my trading strategies. It turns out that this procedure is just as critical for the current crisis that began in August 2011. In fact, more than leverage-determinants, they can be used as the all-important variable that determines whether a certain strategy should be run at all. (What's the point of running a model that you think will lose money with low leverage?)
There are now more than a few of these risk indicators to pick from. Besides the VIX and the TED, there are the VSTOXX (EURO STOXX 50 Volatility), the VXY (JPMorgan G7 Volatility Index), the EM-VXY (JPMorgan Emerging Market Volatility Index), the ETF's ONN and OFF, and probably many more that I haven't heard of yet.
A lot of academic research has been done on whether we can devise "regime switching" models based on some complicated pattern-recognition algorithms to decide whether a market is in a certain "regime" which favors this or that particular model or parameter set. And often, these regime switching models rely on the recognition of some complicated set of patterns in the historical price series. Sorry to say, I have not found any of these complex regime switching model to have any real out-of-sample predictive power. On the other hand, my research shows that some of the aforementioned simple risk indicators will indeed prevent some trading models from falling off the cliff.
But which of these indicators are applicable to which model? This is not so obvious. For example, you might think that the EM-VXY would be an ideal leading indicator for Forex trading models that involve emerging market currencies, but I have found that it is only a contemporaneous (and thus useless) indicator to mine. Another example, I said during the 2008 financial crisis that VIX seems to be a useless contemporaneous indicator for equities trading models, but strangely, it is a good leading indicator for FX models. In contrast, the TED spread that everyone were obsessed about in 2008 shot up to over 300 bps then, but never went beyond 100 bps this time around. So really only rigorous backtesting can guide us here.
What risk indicators do you use? And have you really backtested their efficacies? Your comments would be very welcome here.
There are now more than a few of these risk indicators to pick from. Besides the VIX and the TED, there are the VSTOXX (EURO STOXX 50 Volatility), the VXY (JPMorgan G7 Volatility Index), the EM-VXY (JPMorgan Emerging Market Volatility Index), the ETF's ONN and OFF, and probably many more that I haven't heard of yet.
A lot of academic research has been done on whether we can devise "regime switching" models based on some complicated pattern-recognition algorithms to decide whether a market is in a certain "regime" which favors this or that particular model or parameter set. And often, these regime switching models rely on the recognition of some complicated set of patterns in the historical price series. Sorry to say, I have not found any of these complex regime switching model to have any real out-of-sample predictive power. On the other hand, my research shows that some of the aforementioned simple risk indicators will indeed prevent some trading models from falling off the cliff.
But which of these indicators are applicable to which model? This is not so obvious. For example, you might think that the EM-VXY would be an ideal leading indicator for Forex trading models that involve emerging market currencies, but I have found that it is only a contemporaneous (and thus useless) indicator to mine. Another example, I said during the 2008 financial crisis that VIX seems to be a useless contemporaneous indicator for equities trading models, but strangely, it is a good leading indicator for FX models. In contrast, the TED spread that everyone were obsessed about in 2008 shot up to over 300 bps then, but never went beyond 100 bps this time around. So really only rigorous backtesting can guide us here.
What risk indicators do you use? And have you really backtested their efficacies? Your comments would be very welcome here.
Friday, November 11, 2011
Trading platform and EC2 revisited
Recently I opened a discussion on the various software platforms which allow the programmers among us to build trading strategies easily. Here is one other addition: Quantopian. It is only in alpha stage, but I did get a preview of its features:
1) You can code in Python, which is an easier language to learn than Java, but no less powerful. In fact, I know of a superb programmer who uses Python to backtest HF strategies.
2) It is web-based, which means you can take advantage of collocation on a server much more stable than your own desktops. (For those who worry about the confidentiality of your strategies, the founder indicated to me that they can run an image of the software on an Amazon EC2 account that you owned so they won't have access to your codes. As for the confidentiality of codes residing on EC2 itself, please see below*.)
3) It is event-driven (or for those who like the latest jargon: CEP-enabled), like all the Java API's that I discussed in the previous article.
4) They have 1-min US equities data for backtesting. Tick-level data will be available soon.
5) Toolboxes for common technical indicators, mathematical algorithms, etc. will be available soon.
6) They will run a competition for trading models which makes it easier for independent traders to become trading advisers to others, or to raise money for their own funds.
Unfortunately, live walk-forward testing is not yet available.
* Some readers have wondered whether it is safe to run their trading models on Amazon's EC2. Won't Amazon's employees have access to their wildly profitable strategies? The answer is no: Amazon's security policy:
1) You can code in Python, which is an easier language to learn than Java, but no less powerful. In fact, I know of a superb programmer who uses Python to backtest HF strategies.
2) It is web-based, which means you can take advantage of collocation on a server much more stable than your own desktops. (For those who worry about the confidentiality of your strategies, the founder indicated to me that they can run an image of the software on an Amazon EC2 account that you owned so they won't have access to your codes. As for the confidentiality of codes residing on EC2 itself, please see below*.)
3) It is event-driven (or for those who like the latest jargon: CEP-enabled), like all the Java API's that I discussed in the previous article.
4) They have 1-min US equities data for backtesting. Tick-level data will be available soon.
5) Toolboxes for common technical indicators, mathematical algorithms, etc. will be available soon.
6) They will run a competition for trading models which makes it easier for independent traders to become trading advisers to others, or to raise money for their own funds.
Unfortunately, live walk-forward testing is not yet available.
* Some readers have wondered whether it is safe to run their trading models on Amazon's EC2. Won't Amazon's employees have access to their wildly profitable strategies? The answer is no: Amazon's security policy:
Guest Operating System: Virtual instances are completely controlled by the customer. Customers have full root access
or administrative control over accounts, services, and applications. AWS does not have any access rights to customer
instances and cannot log into the guest OS....
Thanks to a reader OL from France who provided me with this info. He also told me that:
"So, I finally deployed my momentum strategy on a Linux instance of EC2 (which is free btw).
I wrote it based on the java demo application provided by Interactive Brokers and some parts of Algoquant.
So far, I use a European instance of EC2 which alas doesn't have the best latency to IB US servers (90 ms) but still better than my bedroom connection.
A test ping from a US instance to IB US servers results in only 15 ms ..."
So there you go: Java+Algoquant+IB+EC2=profit.
Friday, September 30, 2011
Stop loss, profit cap, survivorship bias, and black swans
I have long espoused the view that we should not impose stop-losses on mean-reverting strategies, nor profit caps on momentum strategies. My view on the latter has not changed, but it has evolved on the former.
My original reason for opposing stop-losses on mean-reverting strategy is this. Say you believe your specific price series is mean-reverting, and say you have entered into a long position when the price is low. Now, however, the price gets much lower, and you are suffering a large unrealized loss. Well, based on your mean-reverting belief, you should buy more instead of liquidating! Indeed, if you backtest the effect of stop-losses on mean-reverting strategies, you will almost inevitably find that they decrease the overall returns and even Sharpe ratios.
But what this simplistic view ignored is 1) survivorship bias, and 2) black swan events. (Hat tip: Ben, who prompted me to consider these two issues.)
1) We normally would only trade those price series with a mean-reverting strategy only if we see that the prices did eventually revert. No one would bother to trade those price series that used to mean-revert, but suddenly stopped doing so. But saying that stop-losses are harmful to mean-reverting strategies is ignoring the fact that some mean-reverting will stop working altogether and would not survive our strategies selection process.
2) Let's define black swan events as those that did not occur in your backtest period. For example, let's say you never had a loss of 20% in a single day. So if you backtest a stop-loss of 20%, it will have no effect whatsoever on your backtest performance. However, no one can say for sure that it won't occur in the future. So if you or your investors simply cannot tolerate a 20% loss, you should impose this as a stop-loss. (After all, your brokerage has already imposed a stop-loss of 100% on you whether you like it or not.)
We can in fact turn point 2) around when deciding what stop-loss to use: a stop-loss should be loose enough so that it should have no effect on the backtest performance, and of course tight enough so that it will not result in the demise of your trading career.
There is also the issue of whether to use stop-loss on the intraday drawdown, or to use it on the multiple-day drawdown. I would argue that only intraday stop-loss is important to prevent a black-swan loss. In practice, when a strategy has a string of non-catastrophic losses occurring over multiple days, resulting in a large, unprecedented, drawdown, the trader will typically re-examine the strategy, taking into account this most recent performance and tweak the strategy so that it could theoretically be avoided. This is almost like a multi-day stop-loss strategy, as we stop an old strategy and start a new, modified, one. (Though the modified strategy might still recommend that you keep holding the current position!)
Now why am I still holding dear to the principle that one should not impose profit-caps on momentum strategies? Why, the possibility of black swan events again! But this time, any black swan can only result in unprecedented one-day gain instead of loss, since we should always have stop-losses on momentum strategies. We certainly don't want to impose a profit-cap to rule out this possibility!
My original reason for opposing stop-losses on mean-reverting strategy is this. Say you believe your specific price series is mean-reverting, and say you have entered into a long position when the price is low. Now, however, the price gets much lower, and you are suffering a large unrealized loss. Well, based on your mean-reverting belief, you should buy more instead of liquidating! Indeed, if you backtest the effect of stop-losses on mean-reverting strategies, you will almost inevitably find that they decrease the overall returns and even Sharpe ratios.
But what this simplistic view ignored is 1) survivorship bias, and 2) black swan events. (Hat tip: Ben, who prompted me to consider these two issues.)
1) We normally would only trade those price series with a mean-reverting strategy only if we see that the prices did eventually revert. No one would bother to trade those price series that used to mean-revert, but suddenly stopped doing so. But saying that stop-losses are harmful to mean-reverting strategies is ignoring the fact that some mean-reverting will stop working altogether and would not survive our strategies selection process.
2) Let's define black swan events as those that did not occur in your backtest period. For example, let's say you never had a loss of 20% in a single day. So if you backtest a stop-loss of 20%, it will have no effect whatsoever on your backtest performance. However, no one can say for sure that it won't occur in the future. So if you or your investors simply cannot tolerate a 20% loss, you should impose this as a stop-loss. (After all, your brokerage has already imposed a stop-loss of 100% on you whether you like it or not.)
We can in fact turn point 2) around when deciding what stop-loss to use: a stop-loss should be loose enough so that it should have no effect on the backtest performance, and of course tight enough so that it will not result in the demise of your trading career.
There is also the issue of whether to use stop-loss on the intraday drawdown, or to use it on the multiple-day drawdown. I would argue that only intraday stop-loss is important to prevent a black-swan loss. In practice, when a strategy has a string of non-catastrophic losses occurring over multiple days, resulting in a large, unprecedented, drawdown, the trader will typically re-examine the strategy, taking into account this most recent performance and tweak the strategy so that it could theoretically be avoided. This is almost like a multi-day stop-loss strategy, as we stop an old strategy and start a new, modified, one. (Though the modified strategy might still recommend that you keep holding the current position!)
Now why am I still holding dear to the principle that one should not impose profit-caps on momentum strategies? Why, the possibility of black swan events again! But this time, any black swan can only result in unprecedented one-day gain instead of loss, since we should always have stop-losses on momentum strategies. We certainly don't want to impose a profit-cap to rule out this possibility!
Sunday, September 18, 2011
More on automated trading platforms
The ideal software platform for automating backtesting and executing your algorithmic trading strategies depends mainly on your level of programming expertise and your budget. If you are a competent programmer in, say, Java or C#, there is nothing to prevent you from utilizing the API offered (usually for free) by many brokerages to automate execution. And of course, it is also easy for you to write a separate backtesting program utilizing historical data. However, even for programmer-traders, there are a couple of inconveniences in developing these programs from scratch:
A) Every time we change brokerages, we have to re-write parts of the low-level functions that utilize the brokerage's API;
B) The automated trading program cannot be used to backtest unless a simulator is built to feed the historical data into the program as if they were live. To reduce bugs, it is better to have the same code that both backtests and trades live.
This is where a number of open-source algorithmic trading development platforms come in. These platforms all assume that the user is a Java programmer. But they eliminate the hassles A) and B) above as they serve as the layer that shield you from the details of the brokerage's API, and let you go from backtesting to live trading mode with a figurative turn of a key. I have taken a tour of one such platforms Marketcetera, and will highlight some features here:
1) It has a trading GUI with features similar to that of IB's TWS. This will be useful if your own brokerage's GUI is dysfunctional.
2) Complex Event Processing (CEP) is available as a module. CEP is essentially a way for you to easily specify what kind of market/pricing events should trigger a trading action. For e.g., "BUY if ask price is below 20-min moving average." Of course, you could have written this trading rule in a callback function, but to retrieve the 20-min MA on-demand could be quite messy. CEP solves that data retrieval problem for you by storing only those data that is needed by your registered trading rules.
3) It can use either FIX or a brokerage's API for connection. Available brokerage connectors include Interactive Brokers and Lime Brokerage.
4) It offers a news feed, which can be used by your trading algorithms to trigger trading actions if you use Java's string processing utilities to parse the stories properly.
5) The monthly cost ranges from $3,500 - $4,500.
If Marketcera is beyond your budget, you can check out AlgoTrader. It has advantages 1)-3) but not 4) listed above, and is completely free. I invite readers who have tried these or other similar automated trading platforms to comment their user experience here.
P.S. For those of us who use Matlab to automate our executions, a reader pointed out there is a new product MATTICK that allows you to send order via the FIX protocol which should let us trade with a great variety of brokerages.
A) Every time we change brokerages, we have to re-write parts of the low-level functions that utilize the brokerage's API;
B) The automated trading program cannot be used to backtest unless a simulator is built to feed the historical data into the program as if they were live. To reduce bugs, it is better to have the same code that both backtests and trades live.
This is where a number of open-source algorithmic trading development platforms come in. These platforms all assume that the user is a Java programmer. But they eliminate the hassles A) and B) above as they serve as the layer that shield you from the details of the brokerage's API, and let you go from backtesting to live trading mode with a figurative turn of a key. I have taken a tour of one such platforms Marketcetera, and will highlight some features here:
1) It has a trading GUI with features similar to that of IB's TWS. This will be useful if your own brokerage's GUI is dysfunctional.
2) Complex Event Processing (CEP) is available as a module. CEP is essentially a way for you to easily specify what kind of market/pricing events should trigger a trading action. For e.g., "BUY if ask price is below 20-min moving average." Of course, you could have written this trading rule in a callback function, but to retrieve the 20-min MA on-demand could be quite messy. CEP solves that data retrieval problem for you by storing only those data that is needed by your registered trading rules.
3) It can use either FIX or a brokerage's API for connection. Available brokerage connectors include Interactive Brokers and Lime Brokerage.
4) It offers a news feed, which can be used by your trading algorithms to trigger trading actions if you use Java's string processing utilities to parse the stories properly.
5) The monthly cost ranges from $3,500 - $4,500.
If Marketcera is beyond your budget, you can check out AlgoTrader. It has advantages 1)-3) but not 4) listed above, and is completely free. I invite readers who have tried these or other similar automated trading platforms to comment their user experience here.
P.S. For those of us who use Matlab to automate our executions, a reader pointed out there is a new product MATTICK that allows you to send order via the FIX protocol which should let us trade with a great variety of brokerages.
Saturday, July 23, 2011
Sorry, your return is too high for us
I enjoyed reading Richard Wilson's The Hedge Fund Book (Richard also runs the Hedge Fund Blogger site). To be clear: it is purely marketing-oriented. It doesn't tell you how to find a successful trading strategy, but its focus is to tell you how to market your fund to investors once you have a successful strategy. To that end, it does a pretty good job in conveying what might be conventional wisdom to seasoned fund managers. (For e.g., don't bother to market to institutional investors if your AUM is less than $100M.) The book is filled with quite engaging interviews with fund managers, fund marketers, and other fund service providers (including our very own administrator Fund Associates). If Scott Patterson's The Quants is about the gods of hedge funds, this book is for and about the mortals.
One paragraph in the book stood out: "I've worked closely on the third-party marketing and capital introduction/prime brokerage side of the business, and I often see both types of firms deny clients service [to funds with high returns and high risk] ... Nobody wants to be associated with a manager aiming at 30 percent a month returns."
Maybe not aiming at, but what's wrong with achieving a 30 percent a month returns? I have actually met institutional investors who don't want to look at a fund that actually achieved double-digit monthly returns. Presumably that's because they believe that a high return automatically implies high risk, and also presumably a high leverage as well. I would argue that there are 2 reasons not to completely dismiss such funds out-of-hand:
1) Leverage should not be determined arbitrarily, but should be based on the minimum of what's dictated by half-Kelly (see my extensive discussions of Kelly formula on this blog and in my book) and what's dictated by the maximum single-day drawdown seen historically or in VaR simulations. And if this minimum still turns out to be higher than what most institutional investors are comfortable with, one should be bold enough to adopt it in your fund.
2) As an investor, there is an easy way to control leverage and risk: just apply Constant Proportion Portfolio Insurance (a concept also discussed elsewhere on this blog). For example, if the fund manager tells you the fund employs a constant 10x leverage (as dictated by the risk analysis outlined in 1) and you are only comfortable with 5x leverage, just invest half your capital into the fund, and keep the other half as cash in your bank account! Going forward, if the fund loses money, your effective leverage would have decreased to below 5x. Say you invested $1M into the fund, and kept $1M in the bank. And say the fund lost $0.5M. Your total equity is now $1.5M, and the fund manager is supposed to trade a $0.5M*10=$5M portfolio. Your effective leverage is now only 3.33x, well within your tolerance. Now if instead, the fund made money, you can immediately withdraw some of the profits to keep your effective leverage at 5x. So, say the fund made $0.5M. Your equity is now $2.5M, and the fund manager is supposed to trade a $1.5M*10=$15M portfolio. If you don't withdraw, this would increase your effective leverage to 6x. But if you immediately withdraw $0.25M, then the fund manager will trade a $1.25M*10=$12.5M portfolio, giving you an effective leverage of the desired 5x.
If you are an investor in hedge funds, please let us know what you think of this scheme in the comments section!
One paragraph in the book stood out: "I've worked closely on the third-party marketing and capital introduction/prime brokerage side of the business, and I often see both types of firms deny clients service [to funds with high returns and high risk] ... Nobody wants to be associated with a manager aiming at 30 percent a month returns."
Maybe not aiming at, but what's wrong with achieving a 30 percent a month returns? I have actually met institutional investors who don't want to look at a fund that actually achieved double-digit monthly returns. Presumably that's because they believe that a high return automatically implies high risk, and also presumably a high leverage as well. I would argue that there are 2 reasons not to completely dismiss such funds out-of-hand:
1) Leverage should not be determined arbitrarily, but should be based on the minimum of what's dictated by half-Kelly (see my extensive discussions of Kelly formula on this blog and in my book) and what's dictated by the maximum single-day drawdown seen historically or in VaR simulations. And if this minimum still turns out to be higher than what most institutional investors are comfortable with, one should be bold enough to adopt it in your fund.
2) As an investor, there is an easy way to control leverage and risk: just apply Constant Proportion Portfolio Insurance (a concept also discussed elsewhere on this blog). For example, if the fund manager tells you the fund employs a constant 10x leverage (as dictated by the risk analysis outlined in 1) and you are only comfortable with 5x leverage, just invest half your capital into the fund, and keep the other half as cash in your bank account! Going forward, if the fund loses money, your effective leverage would have decreased to below 5x. Say you invested $1M into the fund, and kept $1M in the bank. And say the fund lost $0.5M. Your total equity is now $1.5M, and the fund manager is supposed to trade a $0.5M*10=$5M portfolio. Your effective leverage is now only 3.33x, well within your tolerance. Now if instead, the fund made money, you can immediately withdraw some of the profits to keep your effective leverage at 5x. So, say the fund made $0.5M. Your equity is now $2.5M, and the fund manager is supposed to trade a $1.5M*10=$15M portfolio. If you don't withdraw, this would increase your effective leverage to 6x. But if you immediately withdraw $0.25M, then the fund manager will trade a $1.25M*10=$12.5M portfolio, giving you an effective leverage of the desired 5x.
If you are an investor in hedge funds, please let us know what you think of this scheme in the comments section!
Monday, July 18, 2011
The social utility of hedge funds
There is an article in the New Yorker magazine profiling Bridgewater Associates, the world's biggest global macro hedge fund. Inevitably, we come to the awkward question: "If hedge-fund managers are playing a zero-sum game, what is their social utility?"
I thought about this question a lot in the past, and I used to agree with many others that the social utility of hedge funds, or trading in general, is to provide liquidity to the markets. And a good economic case can be made that the more liquid a market is, the higher the utility it is to all participants. However, based on recent experience of flash crash and other unfortunate mishaps, we find out that traders typically do not provide liquidity when it is needed most! So this answer becomes quite unsatisfactory.
In trying to come up with a better reply, I though it is curious that few people asked "What is the purpose of having a Department of Defence?" since wars between nations are typically also zero-sum games, yet we greatly honour those who serve in the armed forces (in contrast to our feelings for hedge fund managers).
To me, clearly the answer with the best moral justification is that, in both cases, there is great social utility in defending either your clients' comfortable retirement from financial meltdown (e.g. due to governmental or corporate mismanagement), or in defending your country from foreign aggression. More specifically, the purpose of hedge funds is to reduce long-term volatility in your clients' net worth. (I would like to say "reduce risks to your clients' net worth", but that would be a bit too optimistic!)
I emphasize long-term volatility, because of course trading generates a lot of daily or hourly volatility in your clients' equity. But I do not believe that such short-term volatility affects ones' life goals. On the other hand, a 3-or-more-year drawdown in a typical buy-and-hold portfolio can wreck havoc with many lives.
I thought about this question a lot in the past, and I used to agree with many others that the social utility of hedge funds, or trading in general, is to provide liquidity to the markets. And a good economic case can be made that the more liquid a market is, the higher the utility it is to all participants. However, based on recent experience of flash crash and other unfortunate mishaps, we find out that traders typically do not provide liquidity when it is needed most! So this answer becomes quite unsatisfactory.
In trying to come up with a better reply, I though it is curious that few people asked "What is the purpose of having a Department of Defence?" since wars between nations are typically also zero-sum games, yet we greatly honour those who serve in the armed forces (in contrast to our feelings for hedge fund managers).
To me, clearly the answer with the best moral justification is that, in both cases, there is great social utility in defending either your clients' comfortable retirement from financial meltdown (e.g. due to governmental or corporate mismanagement), or in defending your country from foreign aggression. More specifically, the purpose of hedge funds is to reduce long-term volatility in your clients' net worth. (I would like to say "reduce risks to your clients' net worth", but that would be a bit too optimistic!)
I emphasize long-term volatility, because of course trading generates a lot of daily or hourly volatility in your clients' equity. But I do not believe that such short-term volatility affects ones' life goals. On the other hand, a 3-or-more-year drawdown in a typical buy-and-hold portfolio can wreck havoc with many lives.
If one day, the markets become so quiescent that few hedge funds can generate higher Sharpe ratio than a buy-and-hold portfolio (as indeed seems to be the case with the US equities markets these days), then yes, most hedge fund managers should just quit, instead of hogging intellectual resources from our best universities.
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