Statistical Interactions and Performance Attribution

Tuesday, May 29, 2007 9:00 AM

About two months ago, I attended a talk at the New York Society of Security Analysts (NYSSA) on performance attribution for investment portfolios. Although there was some discussion of attribution of fixed income portfolios, most of discussion was focused on equity portfolios and two Brinson models. The Brinson models basically divide performance attribution into two dimensions: returns attributable to overall allocation decisions, and returns attributable to security selection decisions. The interesting wrinkle to this analysis is that these effects interact with each other, and as a former quantitative instructor, I thought it was interesting that a room full of experienced analysts was still having trouble understanding interactions.

This commentary is about interactions and how to understand them, but first, I need to fill in a little background on performance attribution.

For the Brinson models and equity portfolios, allocation and security selection are less self evident than they would seem. Asset allocation is the attempt to improve investment returns or control risk by selecting which groups of securities are likely to perform well and how they behave relative to each other. Typically, these groups of securities are defined by industry (e.g. transportation, financials, consumer durables, etc.), but they could equally be defined by countries (e.g. US, Japan, China, etc.), or even something less traditional, such as segments of a global production chain or based fundamental factors like market capitalization. Once one has defined the categories that define “allocation,” the Brinson models essentially divide a portfolio’s performance into the portion of returns that can be attributed to decisions “between categories” (allocation), and the portion of returns that can be attributed to decisions “within categories” (security selection). An interesting implication is that the exact same portfolio could have different amounts of attribution to security selection, depending on how one has defined the categories. For this reason, incidentally, I have always found the Brinson techniques intellectually interesting, but ultimately unsatisfying, because the categories seem more or less arbitrary, and may not adequately capture where an investor’s true talent lies.

The interesting thing is that the total performance of a portfolio isn’t simply the sum of the allocation effect and the security selection effect, it is the sum of these two effects plus their interaction. It is actually possible to outperform a benchmark portfolio in every category consistently, but if the wrong categories end up having too much weight, the portfolio underperforms the benchmark as a whole, because of the interaction. Mathematically, the interaction effect appears as a portion of allocation return multiplied by a portion of selection return, and while most of the NYSSA audience had little trouble understanding the mathematics behind the interaction, there was a surprising degree of difficulty grasping the intuition of what was happening.

Sometimes the easiest way to describe mathematical interaction is by comparison with drug interactions. For example, having some wine with dinner might be a nice way to relax in the evening. Taking an antihistamine might be a sensible way to control allergies, which could also affect your ability to relax in the evening. But taking an antihistamine AND having a bottle of wine with dinner could create an entirely new effect - say, convulsions or a heart attack. Most certainly not relaxing. The interaction is something that neither drug induces on its own, but happens when both are taken together. Another way to think about interaction is a synergy, or, in this case, a dys-synergy. These examples suggest that the total effect of one component depends on the total effect of the other.

[Disclaimer: this example is hypothetical for the point of illustration, please read your antihistamine indications carefully; most will advise against taking with alcohol, but I do not know what specific interaction the drug companies are worried about.]


There are essentially two ways to interpret interactions in a causal framework: 1) understanding interactions as forms of synergy; and 2) understanding interactions as moderated effects. These are mathematically equivalent formulations, for the most part, but the way an interaction is interpreted can go a long way to making it understandable.

Suppose you have a model with an interaction term (term Dxy in this example).

R = A + Bx + Cy + Dxy

This formulation is consistent with the idea of synergy or dys-synergy, depending on wether term D is positive or negative. If D is positive, then x and y together have a greater influence on R than either would in isolation (synergy), and if d is negative, then x and y together have a lesser inlfuence on R than one would guess from each of their individual effects (dys-synergy, or mutual interference). This interpretation assumes that coefficient B and C are positive; the story would need to be more carefully constructed if one or both signs were negative.

Now, for a different interpretation, one could factor Y out of the terms Cy + Dxy, and get

R = A + Bx + (C + Dx)y

Which can be interpreted in the following way: R is a function of x and y, but the effect of y depends on the value of x. In a causal framework, where you suggest that x and y actually cause R, you can say that “the effect of y is moderated by the value of x,” or in other words, the degree to which y influences R is a function of the value of x.

Of course, one could equally have factored out x from term B and D of the equation and decided that the influence of x is a function of y. This is a choice up to the analyst and depends on which method of factoring makes the most intuitive sense. In practice, the easiest way is to think clearly about which variable is having its effect moderated by the other, and then factor out the variable that is being moderated.


So what do interactions mean in a situation like Brinson’s allocation models? To take the synergy approach, it suggests that if you are both a good asset allocator and a good security selector, you may achieve even greater returns than your individual skill on each dimension might suggest. Therefore, it is important to have an investment process that allows you to capture that greater return. How much greater depends on a number of things, including investment policy, constraints, etc., but as a rule of thumb, other things equal, it is probably a better strategy to try to improve the weaker dimension than to continue to improve the stronger. This is a conclusion that runs counter to most managerial instincts, particularly in an industry focused on differentiation and maintaining a unique edge. However, there is no reason that managerial techniques cannot organize specific edges into a strong overall process that includes more mundane but necessary decisions. These structures can retain the value of having one or more unique edges, but the process must consciously integrate the interacting factors, and possibly despite inter-firm tensions or rivalries.

Taking a moderated effects approach, the interactions in the Brinson models suggest that the degree to which security selection skill affects returns depends (at least in part) on one’s allocation skill, and one’s allocation skill depends in part on one’s security selection skill. Therefore the managerial process by which one merges selection skill with allocation skill is extremely influential in determining the overall portfolio result. This is essentially the same conclusion as they synergy formulation, but it is often easier to grasp and can often lead to deeper insights into one’s underlying model.

Statistical Interactions and Performance Attribution

Tuesday, May 29, 2007 9:00 AM

About two months ago, I attended a talk at the New York Society of Security Analysts (NYSSA) on performance attribution for investment portfolios. Although there was some discussion of attribution of fixed income portfolios, most of discussion was focused on equity portfolios and two Brinson models. The Brinson models basically divide performance attribution into two dimensions: returns attributable to overall allocation decisions, and returns attributable to security selection decisions. The interesting wrinkle to this analysis is that these effects interact with each other, and as a former quantitative instructor, I thought it was interesting that a room full of experienced analysts was still having trouble understanding interactions.

This commentary is about interactions and how to understand them, but first, I need to fill in a little background on performance attribution.

For the Brinson models and equity portfolios, allocation and security selection are less self evident than they would seem. Asset allocation is the attempt to improve investment returns or control risk by selecting which groups of securities are likely to perform well and how they behave relative to each other. Typically, these groups of securities are defined by industry (e.g. transportation, financials, consumer durables, etc.), but they could equally be defined by countries (e.g. US, Japan, China, etc.), or even something less traditional, such as segments of a global production chain or based fundamental factors like market capitalization. Once one has defined the categories that define “allocation,” the Brinson models essentially divide a portfolio’s performance into the portion of returns that can be attributed to decisions “between categories” (allocation), and the portion of returns that can be attributed to decisions “within categories” (security selection). An interesting implication is that the exact same portfolio could have different amounts of attribution to security selection, depending on how one has defined the categories. For this reason, incidentally, I have always found the Brinson techniques intellectually interesting, but ultimately unsatisfying, because the categories seem more or less arbitrary, and may not adequately capture where an investor’s true talent lies.

The interesting thing is that the total performance of a portfolio isn’t simply the sum of the allocation effect and the security selection effect, it is the sum of these two effects plus their interaction. It is actually possible to outperform a benchmark portfolio in every category consistently, but if the wrong categories end up having too much weight, the portfolio underperforms the benchmark as a whole, because of the interaction. Mathematically, the interaction effect appears as a portion of allocation return multiplied by a portion of selection return, and while most of the NYSSA audience had little trouble understanding the mathematics behind the interaction, there was a surprising degree of difficulty grasping the intuition of what was happening.

Sometimes the easiest way to describe mathematical interaction is by comparison with drug interactions. For example, having some wine with dinner might be a nice way to relax in the evening. Taking an antihistamine might be a sensible way to control allergies, which could also affect your ability to relax in the evening. But taking an antihistamine AND having a bottle of wine with dinner could create an entirely new effect - say, convulsions or a heart attack. Most certainly not relaxing. The interaction is something that neither drug induces on its own, but happens when both are taken together. Another way to think about interaction is a synergy, or, in this case, a dys-synergy. These examples suggest that the total effect of one component depends on the total effect of the other.

[Disclaimer: this example is hypothetical for the point of illustration, please read your antihistamine indications carefully; most will advise against taking with alcohol, but I do not know what specific interaction the drug companies are worried about.]


There are essentially two ways to interpret interactions in a causal framework: 1) understanding interactions as forms of synergy; and 2) understanding interactions as moderated effects. These are mathematically equivalent formulations, for the most part, but the way an interaction is interpreted can go a long way to making it understandable.

Suppose you have a model with an interaction term (term Dxy in this example).

R = A + Bx + Cy + Dxy

This formulation is consistent with the idea of synergy or dys-synergy, depending on wether term D is positive or negative. If D is positive, then x and y together have a greater influence on R than either would in isolation (synergy), and if d is negative, then x and y together have a lesser inlfuence on R than one would guess from each of their individual effects (dys-synergy, or mutual interference). This interpretation assumes that coefficient B and C are positive; the story would need to be more carefully constructed if one or both signs were negative.

Now, for a different interpretation, one could factor Y out of the terms Cy + Dxy, and get

R = A + Bx + (C + Dx)y

Which can be interpreted in the following way: R is a function of x and y, but the effect of y depends on the value of x. In a causal framework, where you suggest that x and y actually cause R, you can say that “the effect of y is moderated by the value of x,” or in other words, the degree to which y influences R is a function of the value of x.

Of course, one could equally have factored out x from term B and D of the equation and decided that the influence of x is a function of y. This is a choice up to the analyst and depends on which method of factoring makes the most intuitive sense. In practice, the easiest way is to think clearly about which variable is having its effect moderated by the other, and then factor out the variable that is being moderated.


So what do interactions mean in a situation like Brinson’s allocation models? To take the synergy approach, it suggests that if you are both a good asset allocator and a good security selector, you may achieve even greater returns than your individual skill on each dimension might suggest. Therefore, it is important to have an investment process that allows you to capture that greater return. How much greater depends on a number of things, including investment policy, constraints, etc., but as a rule of thumb, other things equal, it is probably a better strategy to try to improve the weaker dimension than to continue to improve the stronger. This is a conclusion that runs counter to most managerial instincts, particularly in an industry focused on differentiation and maintaining a unique edge. However, there is no reason that managerial techniques cannot organize specific edges into a strong overall process that includes more mundane but necessary decisions. These structures can retain the value of having one or more unique edges, but the process must consciously integrate the interacting factors, and possibly despite inter-firm tensions or rivalries.

Taking a moderated effects approach, the interactions in the Brinson models suggest that the degree to which security selection skill affects returns depends (at least in part) on one’s allocation skill, and one’s allocation skill depends in part on one’s security selection skill. Therefore the managerial process by which one merges selection skill with allocation skill is extremely influential in determining the overall portfolio result. This is essentially the same conclusion as they synergy formulation, but it is often easier to grasp and can often lead to deeper insights into one’s underlying model.

Causal Modeling and Quantitative Investing

May 21, 2007 7:40 AM

When I was a professor at Columbia University, I taught quantitative analysis (basically experimental design, statistics and econometrics) to masters’ level students in the program in Environmental Science, Policy, and Management. What made this course interesting is that environmental science is one of the most multi-disciplinary fields imaginable. With my background in the social sciences, teaching a course in statistics to students with backgrounds ranging from electrical engineering to art history became an exercise in understanding how different fields approach quantification. A physics undergraduate myself, I knew that under certain circumstances, mathematical modeling offered enormous predictive power, but as a professor in politics, I could also tell that there were times when quantification severely tested one’s suspension of disbelief.

In the social sciences (sociology, political science, and some psychology), statistical methods are most often used to test causal hypotheses. Without highly controlled experiments - some of which would be unethical to perform even if feasible - one can never truly escape from the challenge that correlation is a necessary but not sufficient condition to establish causality, and the result is that statistical analysis in the social fields is only a supplementary analysis to other qualitative forms of argument: it is never definitive. The essence of statistical analysis was to search for evidence of “what causes what” in the presence of extraneous noise from other sources, and a major part of the analysis was making sure the quantitative model was specified in a way that genuinely reflected the researcher’s understanding of what was actually going on.

As I moved to financial analysis, however, it was striking how little causal analysis appears in quantitative finance. The essence of finance can be boiled down to the fact that no sustainable return on investment can exceed the so-called risk free rate without taking on some level of risk, risk being defined, at minimum, as the possibility of earning less than this rate. Intelligent investing thus boils down to the efficient use of risk exposure: given that one must take risk to gain excess returns, is one getting as much return as possible for the amount of risk that is taken. In other words: given that there is risk, is the investor profiting from intelligent educated guesses, or is he or she just taking big chances and hoping to get lucky (what most people refer to as speculating).

As a result, much statistical analysis in finance focuses more on how much noise remains in the system, rather than on how much return can be predicted using specific causal models. Variables are used to predict returns, to be sure, but the analysis of how and why they should affect returns often seems amazingly thin. This initially came as a surprise, since good investors presumably make good predictions of future events. And predictability and residual noise are obviously related, insofar as having more of one leads to less of the other. However, a key insight of portfolio theory points out that better causal understanding of return drivers is not the only way to remove risk from the system.

Since the key issue is how efficiently an investor can convert his or her exposure to risk into some kind of expected return, minimizing residual noise can be almost more important than predicting expected returns, and whether one has a causally accurate model may actually seem less critical than whether one’s prediction equation is simply stable over time and reasonably accurate. But quantitative finance has also found that the correlation between asset returns is a key to reducing overall risk, and it is the use of these correlations to reduce an investment portfolio’s risk that has probably been modern portfolio theory’s most significant insight over the last 50 years. The use of correlation, diversification, and hedging has made it possible to reduce risk exposures dramatically without substantially reducing available returns.

For example, if one has two assets that are perfectly negatively correlated, one can potentially lock in a return with zero risk. That’s right, zero. It is not necessary to understand the causal structure guiding these assets’ valuations - it is sufficient to establish that they are negatively correlated. If one can establish that there is indeed a perfect negative correlation, it is possible to argue that they should return no more than the value of the risk free rate, or else one would have a perpetual money-making machine, which is logically inconsistent. In effect, the rate of riskless moneymaking would become the new risk-free rate, and other asset values would readjust accordingly. If this weren’t the case, and such a correlation existed, it would lead to return at zero risk (arbitrage), and since it should be possible to leverage an investment to increase returns, one could, theoretically, generate any payoff imaginable.

The key issue here is that none of this profit depends on a causal model that drives asset returns, other than the observation that two assets are negatively correlated, and possibly a causal understanding of why one price would be inversely related to another. The fact that one can improve the efficiency of risk exposure by exploiting these correlations has meant that the causal perspective has tended to be neglected in quantitative finance, because it has been possible - and profitable - to improve risk efficiency without requiring an understanding of causal effects.

Does this mean that a causal modeling perspective is useless for quantitative financial professionals? I don’t think so, especially in a changing global economy. In the past few decades, economists have focused on equilibrium models over causal models because of the problems in concluding causality from correlations, a logical step which equilibrium models do not require. Finance professionals may have simply followed suit and found that the equilibrium approach generates sufficiently handsome profits. However, the exclusion of potentially causal ideas from standard modeling practice simply because they are “mutable“ or soft comes back to haunt ”hard quants“ in the form of “regime shifts,” where the key parameters relevant to investing (e.g. correlations) can change suddenly. It is true that causal models may or may not be able to predict these regime shifts, but the causal perspective does include a greater self-awareness of what external conditions or events might make a model applicable or not, because a large part of the causal perspective is asking whether a particular model’s specification maps correctly to the modeler’s understanding of the way the world works.

Behavioral finance, a relatively new branch of finance and economics, has started to bring causality back into the equation. I will talk more about behavioral finance and other causal approaches in a future blog.

Causal Modeling and Quantitative Investing

May 21, 2007 7:40 AM

When I was a professor at Columbia University, I taught quantitative analysis (basically experimental design, statistics and econometrics) to masters’ level students in the program in Environmental Science, Policy, and Management. What made this course interesting is that environmental science is one of the most multi-disciplinary fields imaginable. With my background in the social sciences, teaching a course in statistics to students with backgrounds ranging from electrical engineering to art history became an exercise in understanding how different fields approach quantification. A physics undergraduate myself, I knew that under certain circumstances, mathematical modeling offered enormous predictive power, but as a professor in politics, I could also tell that there were times when quantification severely tested one’s suspension of disbelief.

In the social sciences (sociology, political science, and some psychology), statistical methods are most often used to test causal hypotheses. Without highly controlled experiments - some of which would be unethical to perform even if feasible - one can never truly escape from the challenge that correlation is a necessary but not sufficient condition to establish causality, and the result is that statistical analysis in the social fields is only a supplementary analysis to other qualitative forms of argument: it is never definitive. The essence of statistical analysis was to search for evidence of “what causes what” in the presence of extraneous noise from other sources, and a major part of the analysis was making sure the quantitative model was specified in a way that genuinely reflected the researcher’s understanding of what was actually going on.

As I moved to financial analysis, however, it was striking how little causal analysis appears in quantitative finance. The essence of finance can be boiled down to the fact that no sustainable return on investment can exceed the so-called risk free rate without taking on some level of risk, risk being defined, at minimum, as the possibility of earning less than this rate. Intelligent investing thus boils down to the efficient use of risk exposure: given that one must take risk to gain excess returns, is one getting as much return as possible for the amount of risk that is taken. In other words: given that there is risk, is the investor profiting from intelligent educated guesses, or is he or she just taking big chances and hoping to get lucky (what most people refer to as speculating).

As a result, much statistical analysis in finance focuses more on how much noise remains in the system, rather than on how much return can be predicted using specific causal models. Variables are used to predict returns, to be sure, but the analysis of how and why they should affect returns often seems amazingly thin. This initially came as a surprise, since good investors presumably make good predictions of future events. And predictability and residual noise are obviously related, insofar as having more of one leads to less of the other. However, a key insight of portfolio theory points out that better causal understanding of return drivers is not the only way to remove risk from the system.

Since the key issue is how efficiently an investor can convert his or her exposure to risk into some kind of expected return, minimizing residual noise can be almost more important than predicting expected returns, and whether one has a causally accurate model may actually seem less critical than whether one’s prediction equation is simply stable over time and reasonably accurate. But quantitative finance has also found that the correlation between asset returns is a key to reducing overall risk, and it is the use of these correlations to reduce an investment portfolio’s risk that has probably been modern portfolio theory’s most significant insight over the last 50 years. The use of correlation, diversification, and hedging has made it possible to reduce risk exposures dramatically without substantially reducing available returns.

For example, if one has two assets that are perfectly negatively correlated, one can potentially lock in a return with zero risk. That’s right, zero. It is not necessary to understand the causal structure guiding these assets’ valuations - it is sufficient to establish that they are negatively correlated. If one can establish that there is indeed a perfect negative correlation, it is possible to argue that they should return no more than the value of the risk free rate, or else one would have a perpetual money-making machine, which is logically inconsistent. In effect, the rate of riskless moneymaking would become the new risk-free rate, and other asset values would readjust accordingly. If this weren’t the case, and such a correlation existed, it would lead to return at zero risk (arbitrage), and since it should be possible to leverage an investment to increase returns, one could, theoretically, generate any payoff imaginable.

The key issue here is that none of this profit depends on a causal model that drives asset returns, other than the observation that two assets are negatively correlated, and possibly a causal understanding of why one price would be inversely related to another. The fact that one can improve the efficiency of risk exposure by exploiting these correlations has meant that the causal perspective has tended to be neglected in quantitative finance, because it has been possible - and profitable - to improve risk efficiency without requiring an understanding of causal effects.

Does this mean that a causal modeling perspective is useless for quantitative financial professionals? I don’t think so, especially in a changing global economy. In the past few decades, economists have focused on equilibrium models over causal models because of the problems in concluding causality from correlations, a logical step which equilibrium models do not require. Finance professionals may have simply followed suit and found that the equilibrium approach generates sufficiently handsome profits. However, the exclusion of potentially causal ideas from standard modeling practice simply because they are “mutable“ or soft comes back to haunt ”hard quants“ in the form of “regime shifts,” where the key parameters relevant to investing (e.g. correlations) can change suddenly. It is true that causal models may or may not be able to predict these regime shifts, but the causal perspective does include a greater self-awareness of what external conditions or events might make a model applicable or not, because a large part of the causal perspective is asking whether a particular model’s specification maps correctly to the modeler’s understanding of the way the world works.

Behavioral finance, a relatively new branch of finance and economics, has started to bring causality back into the equation. I will talk more about behavioral finance and other causal approaches in a future blog.

Global Macro Investing Style

May 14, 2007 9:40 PM

In my transition from researching the political economy of developing countries to methods of investment management, I’ve had to think carefully about how to apply years of experience and insight into emerging markets into investment theses that can be acted upon. Of the different investment styles, what is termed “Global Macro” investing seemed to be the investment style that fit my background best. However, my quantitative background pulled me in a different direction, with different dynamics, and I have been reconciling the conclusions that I have drawn from each.

Global Macro investing is an investment style that seeks to profit from large shifts in global (or, sometimes, national) economies. Probably the most famous practitioner of the global macro style is George Soros, who made a profit of $2 billion dollars in less than one week in 1992 by betting that the Bank of England would allow the pound-deutschemark exchange rate to depart from the trading band that had been necessary to keep the pound on track for European integration. To win this bet on skill rather than luck takes, among other things, an understanding of financial and economic drivers, an understanding of the political behavior of key actors, and an enormous stomach for risk, since the opportunity to gain $2 billion is also the opportunity to lose $2 billion.

The challenge of global macro investing, aside from the obvious question of doing correct political-economic analysis, is that good macro investment theses come relatively rarely. In Steven Drobny’s 2006 book, Inside the House of Money, several macro investors comment that they have only one or two major trading ideas per quarter. Even allowing for some professional modesty on the part of the interviewers, that amounts to perhaps 4-12 trading ideas per year. If one assumes that not all trading ideas are going to be correct or accurate all the time, a good global macro investor needs three key ingredients for success: 1) the ability to identify macro opportunities before the majority other market actors (vision), 2) a large probability of each vision being correct and timed correctly (skill), and 3) the ability to risk considerable capital on these views (stomach).

Robert Jaeger shows in his book, All About Hedge Funds, that global macro hedge funds (what he terms “global asset allocators”) have historically had both a high rates of return, about 17% (probably affected by both reporting and survivor biases), and a large dispersion in annual returns, with astandard deviation close to 11% (probably not so affected by these biases). This data covers the period from January 1990 to December 2001. The global macro funds in Jaeger’s study had a Sharpe Ratio - an indicator of how efficiently a strategy converts risk into return - in line with other major hedge fund strategies (approximately 1.0), and larger than many equity indices, but clearly, investors in macro funds had to have a fairly high tolerance for risk, because in any one year, there may be only 4-12 major actionable trading ideas, and small differences in how accurately the investor understands the world can easily translate into large gains or losses. It is one thing when a bad year means that your fund earned 8% instead of 12%; it’s entirely another matter when it means you lost 10% instead of gaining 30%. Even though it is possible to reduce those swings by investing only a part of one’s total assets in such a strategy, these wide swings will raise eyebrows.

Much quantitative investing, particularly relative value / market neutral styles, take a different approach to trading decisions. I’ll address some of these in a future blog, and then talk about how these approaches might interact profitably.

Bruce

Global Macro Investing Style

May 14, 2007 9:40 PM

In my transition from researching the political economy of developing countries to methods of investment management, I’ve had to think carefully about how to apply years of experience and insight into emerging markets into investment theses that can be acted upon. Of the different investment styles, what is termed “Global Macro” investing seemed to be the investment style that fit my background best. However, my quantitative background pulled me in a different direction, with different dynamics, and I have been reconciling the conclusions that I have drawn from each.

Global Macro investing is an investment style that seeks to profit from large shifts in global (or, sometimes, national) economies. Probably the most famous practitioner of the global macro style is George Soros, who made a profit of $2 billion dollars in less than one week in 1992 by betting that the Bank of England would allow the pound-deutschemark exchange rate to depart from the trading band that had been necessary to keep the pound on track for European integration. To win this bet on skill rather than luck takes, among other things, an understanding of financial and economic drivers, an understanding of the political behavior of key actors, and an enormous stomach for risk, since the opportunity to gain $2 billion is also the opportunity to lose $2 billion.

The challenge of global macro investing, aside from the obvious question of doing correct political-economic analysis, is that good macro investment theses come relatively rarely. In Steven Drobny’s 2006 book, Inside the House of Money, several macro investors comment that they have only one or two major trading ideas per quarter. Even allowing for some professional modesty on the part of the interviewers, that amounts to perhaps 4-12 trading ideas per year. If one assumes that not all trading ideas are going to be correct or accurate all the time, a good global macro investor needs three key ingredients for success: 1) the ability to identify macro opportunities before the majority other market actors (vision), 2) a large probability of each vision being correct and timed correctly (skill), and 3) the ability to risk considerable capital on these views (stomach).

Robert Jaeger shows in his book, All About Hedge Funds, that global macro hedge funds (what he terms “global asset allocators”) have historically had both a high rates of return, about 17% (probably affected by both reporting and survivor biases), and a large dispersion in annual returns, with astandard deviation close to 11% (probably not so affected by these biases). This data covers the period from January 1990 to December 2001. The global macro funds in Jaeger’s study had a Sharpe Ratio - an indicator of how efficiently a strategy converts risk into return - in line with other major hedge fund strategies (approximately 1.0), and larger than many equity indices, but clearly, investors in macro funds had to have a fairly high tolerance for risk, because in any one year, there may be only 4-12 major actionable trading ideas, and small differences in how accurately the investor understands the world can easily translate into large gains or losses. It is one thing when a bad year means that your fund earned 8% instead of 12%; it’s entirely another matter when it means you lost 10% instead of gaining 30%. Even though it is possible to reduce those swings by investing only a part of one’s total assets in such a strategy, these wide swings will raise eyebrows.

Much quantitative investing, particularly relative value / market neutral styles, take a different approach to trading decisions. I’ll address some of these in a future blog, and then talk about how these approaches might interact profitably.

Bruce

New Blogger.com Blog

May 14, 2007 9:07 PM

Blogger.com, the host for this weblog, has relaunched its API, so this blog is being relaunched as well. This is a test entry to see if this blog works.

New Blogger.com Blog

May 14, 2007 9:07 PM

Blogger.com, the host for this weblog, has relaunched its API, so this blog is being relaunched as well. This is a test entry to see if this blog works.