Quantitative and Technical Analysis

Shhh! Don’t tell anyone. Late at night, when nobody is looking... I’ve secretly been reading books on Technical Analysis! Don’t tell the CFA Institute, or they might revoke my standing! (disclaimer: that last point was a joke)

It is interesting to come out of the closet as an agnostic about technical analysis, since many of my colleagues who practice quantitative methods or do the CFA curriculum point out that numerous academic studies have found questionable value in TA. The CFA level I curriculum has approximately 1000 pages dedicated to financial statements analysis and another 500 devoted to fundamental equity valuation techniques. These are no doubt extremely useful and much needed in the curriculum, but the attitude of the CFA Institute to TA is indicated by its paltry 100 pages or so dedicated to technical analysis, complete with the conclusion that it probably doesn’t work. Also, I don’t recall a single question related to TA out of 240 questions given on the actual Level I exam. When I mention that I’ve been investigating TA, I can feel my friends and colleagues are looking at me as if I’ve just lost 20 IQ points. It’s as if Isaac Newton’s investigation of alchemy meant that you couldn’t take the theory of calculus seriously (note: Isaac Newton did study alchemy).

My interest in Technical Analysis (henceforth TA) has several drivers. The immediate issue had to do with my Global Macro view that gold was a good investment. This was a fairly clear choice: immense pressure on the dollar from budget and current account deficits, concerns about inflation and energy prices, the concentration of wealth in the United States, and increasing security problems around the globe. When I lived in Brazil, if I saw these things happening there, I knew to get my assets out of the local currency. The US Dollar is a special case, as the world’s reserve currency, but the current administration is also in a class (of irresponsibility) of its own, which requires reviewing this special case more critically. These are the types of moments when fundamental assumptions about the way the world works tend to break, and so the macro opportunities are potentially quite good. So gold was (and probably still is) an excellent investment choice for a US based investor, but the main problem was that I had few tools to determine a target exit price for the commodity. Typical quantitative analysis demands a target price (i.e. expected value), and fundamental tools attempt to compute one, but are designed to work with equities or debt instruments. Technical trading rules allow one to base an exit on changes in price movements without necessarily predetermining an exit price (exits being different from stops, which exist for risk control).

I realized that the exit price problem is a common macro decision dilemma, and that for many people, placing global macro positions was really more like a trader’s framework than the mindset of a fundamental or quantitative investor. I had never considered the trading mentality myself, because - in my mind’s eye - I imagined traders as type-A personalities glued to a trading desk and yelling into phones on an exchange floor. This was not a good fit for my personality, which I see as more of the analyst-observer-actor type than the get-in-your-face-and-force-that-trade type. After reading Steven Drobny’s Inside the House of Money, however, I realized that much global macro work is done within a trader’s framework more than a portfolio manager’s framework; the main difference is that position cycles are usually executed over longer intervals, and therefore require less yelling over telephones.

...I should point out that there is a more quantitative approach to global macro strategy, called tactical asset allocation (TAA or GTAA). This strategy involves having a model that can predict short term deviations from long term expected returns (predict is the key term here), and therefore inform asset class or sector over- or under-weightings with respect to the long term strategic allocation, usually with the use of an optimizer. But we didn’t have a model that could output a target price for gold, so the GTAA approach was not viable....

Another motivation was my sense that large portions of quantitative community had gotten a little too smug with the superior performance of their models. On the one hand, many simply point out that “TA can’t be shown to work.” These guys obviously managed to get through the 100 pages of CFA material on TA and absorbed the conclusions. On the other hand, others have said “yes, we use technical indicators: we find that momentum helps our quantitative models.” But as far as I could find, momentum was the only technical indicator that quants had decided might have some predictive value. Now admittedly, funds don’t tend to publish their models as widely as Nobel Prize winners like Eugene Fama and Kenneth French, so if there were more technical indicators in quantitative models, they would be hard to find. (Goldman Sachs’ Mark Carhart is known for adding momentum to the Fama-French model successfully).

Now, one of the reasons that technical analysis isn’t supposed to work in quant-land and CFA-land is the “efficient markets hypothesis” or EMH. There are several versions of the EMH that range from weak to strong. The weakest form is a form that says “any information that past price data can offer you is already included in the current price of an asset (else it would have been acted upon already).” This effectively translates to “don’t bother looking at past price data (other than to estimate volatility),” and “charting techniques don’t add value.” It basically asserts something called Markov Property in mathematics: if a process has the Markov Property, only the most recent element in a sequence contains any useful information about the future. The Markov Property does help make statistical models easier to specify. Applied to asset prices, it also sounds like the traditional economist’s joke that there can’t be a ten dollar bill lying on the ground, else someone would have already gotten it: therefore no ten dollar bills can ever be found on the ground (consequently, nobody bothers looking, and it soon becomes obvious that there are no ten dollar bills on the ground, because no one has ever seen one... “excuse me, sir, how will you be taking your Black Swan tonight?”).

Having heard that technical analysis doesn’t work, that minimally efficient markets should preclude technical analysis from working, and then hearing many of the same analysts argue that momentum adds value to quantitative models makes me feel that someone isn’t “‘fessing up.“ For momentum to add value, the Markov Property can’t possibly hold, and therefore even the ”weak form“ of the EMH doesn’t seem to be weak enough. And, as someone who has experience working with market failures in environmental provision, political meddling in markets, and oligopolistic distortions, the idea that markets are efficient or ”only occasionally inefficient“ seems simply implausible to me. There have got to be inefficiencies, and these inefficiencies are potential sources of comparative investing advantage for those who understand them (not that I claim to understand all of them). And we know, instinctively, that when market conditions change, many people - if not most - need time to come to a decision about what that means for the price, and even institutions with professionally trained investors have organizational processes and constraints (e.g. risk management policies or staff) that preclude ”instantaneous simultaneous adjustments by all actors to incorporate new information.“ To me, this means that the main theoretical reason that technical analysis can’t possibly work is undone. The real question is, how long a window does the average investor (or professional investor) have to react to new market data and can this window permit any forms of TA to add value?

Now, I certainly think there may be good TA and bad TA, just as there is good statistical analysis and bad statistical analysis in quantitative work. Personally, I have always been suspicious of TA explanations that sound like ”Crouching tiger on lonely mountain scares market bull by morning mists,“ but what I have found from my reading of technical indicators is that there are genuine causal mechanisms being hypothesized for many technical formations in terms of what market participants are actually doing. Now, maybe these hypotheses are wrong, but it does bring TA out of the realm of astrology and witchcraft into the realm of social psychology, which is a field that has a scientific methodological tradition. Most of the popular quantitative techniques for valuation rely on equilibrium models, which have a compelling logic of their own, but they are surprisingly devoid of causal-structure more complex than ”participants desire to make a buck, and might use arbitrage to do it.“

It seems eminently plausible that a diversity of valuations and valuation techniques - not to mention varied institutional investing decision processes and constraints - could make buying and selling pressures ”lumpy“ over given price ranges, in the sense that the interaction between buyers and sellers is not a smooth or even a monotonic function of price. If supply and demand pressures are indeed lumpy, this would lead to non-linear effects on price changes, and these price changes would themselves affect future valuation. Consider this: the urgency with which one considers selling an asset may depend on how quickly its price has dropped. If others may also be feeling urgent needs to sell the asset, because they suddenly are not sure that they understand their investment or their risk, then the asset value at the next interval is likely to be smaller still, even though - in theory - the next price change should be independent of the last price change. Which scenario here has a more plausible cause-effect mechanism? Price changes are independent of each other because of equilibrium, or price changes alter people’s confidence in their investment process. Even in highly quantitative shops that trust their risk models, the difficulties that quant funds faced in august caused many PMs to dial down their tracking error because they no longer trusted the valuations. When they did that, portfolios were unable to recover as much as the market bounceback would suggest they had.

Where this all leads is that there may be non-linear determinants of price changes (i.e. the ”lumpiness of supply and demand“) that, in turn, affect future valuations, which, in turn, affect future price changes. This is a classic situation in which physicists and mathematicians would expect to find deterministic chaos. What then might TA charting patterns be? Very possibly, head-and-shoulders patterns, double-tops, double bottoms, wedges, etc. are the strange attractors that come out of this non-linear, recursive, lumpiness in prices subsequently affecting valuations. Different attractors correspond to different semi-stable states of the market, and very likely reflect different psychological states of market participants that affect how lumpy price ranges are. Over a large range, these prices probably are constrained by general equilibrium limits, which would explain why theories like CAPM or the APT methodology (note that APT is really a methodology, and not a true theory) appear to be minimally plausible (and also why they probably perform better at longer time frames that smooth out short-term disruptions).

In any case, I am relatively new to technical analysis techniques, and I am sure there are plenty of silly ones out there, but I am not convinced that this ground should be dismissed as quickly as the quantitative and fundamental communities do. There are very defensible reasons to think that at least some of TA could be valuable.

As I proceed further, I promise to tell you more about what I find (though I may not publish my TA model either). I may indeed eventually come to the conclusion that TA doesn’t have any value after all, but even that would be a valuable conclusion if that’s where this leads.

Quantitative and Technical Analysis

Shhh! Don’t tell anyone. Late at night, when nobody is looking... I’ve secretly been reading books on Technical Analysis! Don’t tell the CFA Institute, or they might revoke my standing! (disclaimer: that last point was a joke)

It is interesting to come out of the closet as an agnostic about technical analysis, since many of my colleagues who practice quantitative methods or do the CFA curriculum point out that numerous academic studies have found questionable value in TA. The CFA level I curriculum has approximately 1000 pages dedicated to financial statements analysis and another 500 devoted to fundamental equity valuation techniques. These are no doubt extremely useful and much needed in the curriculum, but the attitude of the CFA Institute to TA is indicated by its paltry 100 pages or so dedicated to technical analysis, complete with the conclusion that it probably doesn’t work. Also, I don’t recall a single question related to TA out of 240 questions given on the actual Level I exam. When I mention that I’ve been investigating TA, I can feel my friends and colleagues are looking at me as if I’ve just lost 20 IQ points. It’s as if Isaac Newton’s investigation of alchemy meant that you couldn’t take the theory of calculus seriously (note: Isaac Newton did study alchemy).

My interest in Technical Analysis (henceforth TA) has several drivers. The immediate issue had to do with my Global Macro view that gold was a good investment. This was a fairly clear choice: immense pressure on the dollar from budget and current account deficits, concerns about inflation and energy prices, the concentration of wealth in the United States, and increasing security problems around the globe. When I lived in Brazil, if I saw these things happening there, I knew to get my assets out of the local currency. The US Dollar is a special case, as the world’s reserve currency, but the current administration is also in a class (of irresponsibility) of its own, which requires reviewing this special case more critically. These are the types of moments when fundamental assumptions about the way the world works tend to break, and so the macro opportunities are potentially quite good. So gold was (and probably still is) an excellent investment choice for a US based investor, but the main problem was that I had few tools to determine a target exit price for the commodity. Typical quantitative analysis demands a target price (i.e. expected value), and fundamental tools attempt to compute one, but are designed to work with equities or debt instruments. Technical trading rules allow one to base an exit on changes in price movements without necessarily predetermining an exit price (exits being different from stops, which exist for risk control).

I realized that the exit price problem is a common macro decision dilemma, and that for many people, placing global macro positions was really more like a trader’s framework than the mindset of a fundamental or quantitative investor. I had never considered the trading mentality myself, because - in my mind’s eye - I imagined traders as type-A personalities glued to a trading desk and yelling into phones on an exchange floor. This was not a good fit for my personality, which I see as more of the analyst-observer-actor type than the get-in-your-face-and-force-that-trade type. After reading Steven Drobny’s Inside the House of Money, however, I realized that much global macro work is done within a trader’s framework more than a portfolio manager’s framework; the main difference is that position cycles are usually executed over longer intervals, and therefore require less yelling over telephones.

...I should point out that there is a more quantitative approach to global macro strategy, called tactical asset allocation (TAA or GTAA). This strategy involves having a model that can predict short term deviations from long term expected returns (predict is the key term here), and therefore inform asset class or sector over- or under-weightings with respect to the long term strategic allocation, usually with the use of an optimizer. But we didn’t have a model that could output a target price for gold, so the GTAA approach was not viable....

Another motivation was my sense that large portions of quantitative community had gotten a little too smug with the superior performance of their models. On the one hand, many simply point out that “TA can’t be shown to work.” These guys obviously managed to get through the 100 pages of CFA material on TA and absorbed the conclusions. On the other hand, others have said “yes, we use technical indicators: we find that momentum helps our quantitative models.” But as far as I could find, momentum was the only technical indicator that quants had decided might have some predictive value. Now admittedly, funds don’t tend to publish their models as widely as Nobel Prize winners like Eugene Fama and Kenneth French, so if there were more technical indicators in quantitative models, they would be hard to find. (Goldman Sachs’ Mark Carhart is known for adding momentum to the Fama-French model successfully).

Now, one of the reasons that technical analysis isn’t supposed to work in quant-land and CFA-land is the “efficient markets hypothesis” or EMH. There are several versions of the EMH that range from weak to strong. The weakest form is a form that says “any information that past price data can offer you is already included in the current price of an asset (else it would have been acted upon already).” This effectively translates to “don’t bother looking at past price data (other than to estimate volatility),” and “charting techniques don’t add value.” It basically asserts something called Markov Property in mathematics: if a process has the Markov Property, only the most recent element in a sequence contains any useful information about the future. The Markov Property does help make statistical models easier to specify. Applied to asset prices, it also sounds like the traditional economist’s joke that there can’t be a ten dollar bill lying on the ground, else someone would have already gotten it: therefore no ten dollar bills can ever be found on the ground (consequently, nobody bothers looking, and it soon becomes obvious that there are no ten dollar bills on the ground, because no one has ever seen one... “excuse me, sir, how will you be taking your Black Swan tonight?”).

Having heard that technical analysis doesn’t work, that minimally efficient markets should preclude technical analysis from working, and then hearing many of the same analysts argue that momentum adds value to quantitative models makes me feel that someone isn’t “‘fessing up.“ For momentum to add value, the Markov Property can’t possibly hold, and therefore even the ”weak form“ of the EMH doesn’t seem to be weak enough. And, as someone who has experience working with market failures in environmental provision, political meddling in markets, and oligopolistic distortions, the idea that markets are efficient or ”only occasionally inefficient“ seems simply implausible to me. There have got to be inefficiencies, and these inefficiencies are potential sources of comparative investing advantage for those who understand them (not that I claim to understand all of them). And we know, instinctively, that when market conditions change, many people - if not most - need time to come to a decision about what that means for the price, and even institutions with professionally trained investors have organizational processes and constraints (e.g. risk management policies or staff) that preclude ”instantaneous simultaneous adjustments by all actors to incorporate new information.“ To me, this means that the main theoretical reason that technical analysis can’t possibly work is undone. The real question is, how long a window does the average investor (or professional investor) have to react to new market data and can this window permit any forms of TA to add value?

Now, I certainly think there may be good TA and bad TA, just as there is good statistical analysis and bad statistical analysis in quantitative work. Personally, I have always been suspicious of TA explanations that sound like ”Crouching tiger on lonely mountain scares market bull by morning mists,“ but what I have found from my reading of technical indicators is that there are genuine causal mechanisms being hypothesized for many technical formations in terms of what market participants are actually doing. Now, maybe these hypotheses are wrong, but it does bring TA out of the realm of astrology and witchcraft into the realm of social psychology, which is a field that has a scientific methodological tradition. Most of the popular quantitative techniques for valuation rely on equilibrium models, which have a compelling logic of their own, but they are surprisingly devoid of causal-structure more complex than ”participants desire to make a buck, and might use arbitrage to do it.“

It seems eminently plausible that a diversity of valuations and valuation techniques - not to mention varied institutional investing decision processes and constraints - could make buying and selling pressures ”lumpy“ over given price ranges, in the sense that the interaction between buyers and sellers is not a smooth or even a monotonic function of price. If supply and demand pressures are indeed lumpy, this would lead to non-linear effects on price changes, and these price changes would themselves affect future valuation. Consider this: the urgency with which one considers selling an asset may depend on how quickly its price has dropped. If others may also be feeling urgent needs to sell the asset, because they suddenly are not sure that they understand their investment or their risk, then the asset value at the next interval is likely to be smaller still, even though - in theory - the next price change should be independent of the last price change. Which scenario here has a more plausible cause-effect mechanism? Price changes are independent of each other because of equilibrium, or price changes alter people’s confidence in their investment process. Even in highly quantitative shops that trust their risk models, the difficulties that quant funds faced in august caused many PMs to dial down their tracking error because they no longer trusted the valuations. When they did that, portfolios were unable to recover as much as the market bounceback would suggest they had.

Where this all leads is that there may be non-linear determinants of price changes (i.e. the ”lumpiness of supply and demand“) that, in turn, affect future valuations, which, in turn, affect future price changes. This is a classic situation in which physicists and mathematicians would expect to find deterministic chaos. What then might TA charting patterns be? Very possibly, head-and-shoulders patterns, double-tops, double bottoms, wedges, etc. are the strange attractors that come out of this non-linear, recursive, lumpiness in prices subsequently affecting valuations. Different attractors correspond to different semi-stable states of the market, and very likely reflect different psychological states of market participants that affect how lumpy price ranges are. Over a large range, these prices probably are constrained by general equilibrium limits, which would explain why theories like CAPM or the APT methodology (note that APT is really a methodology, and not a true theory) appear to be minimally plausible (and also why they probably perform better at longer time frames that smooth out short-term disruptions).

In any case, I am relatively new to technical analysis techniques, and I am sure there are plenty of silly ones out there, but I am not convinced that this ground should be dismissed as quickly as the quantitative and fundamental communities do. There are very defensible reasons to think that at least some of TA could be valuable.

As I proceed further, I promise to tell you more about what I find (though I may not publish my TA model either). I may indeed eventually come to the conclusion that TA doesn’t have any value after all, but even that would be a valuable conclusion if that’s where this leads.

Global Macro and Fund of Funds

I received an email from one reader the other day asking me about setting up a fund-of-funds approach to global macro investing. It wasn’t clear to me at the time whether the author was asking about setting up 1) a fund of global macro hedge funds, or 2) a fund of hedge funds to implement global macro strategies. Both of these are potentially mechanisms for global investing, but they present different investment challenges and require different management skills to execute correctly.

With these questions, one needs to consider what the advantage of a fund-of-funds approach is in the first place. I see four basic ways in which fund-of-funds approach can add value: it provides diversification benefits, it can allow a degree of tactical or cyclical strategy allocation, a publicly-traded fund of funds might allow ordinary (i.e. non-sophisticated) investors to have access to hedge fund returns through equity returns, and finally, it may simply be the appropriate business structure for managing an individual investor’s wealth, or a family office. I discuss some of these benefits below.

Diversification

First, a fund-of-funds provides some diversification against manager risk. This is the risk that an individual manager will make a major mistake, turn out to be crooked, be hit by a bus, leave the firm, or some other unexpected event that impinges on a fund’s key productive asset. To be truly diversified against this risk might require some 20 funds or so, which is probably larger than most fund-of-funds prefer to allocate. If all the funds revolve around the same basic strategy, the diversification against manager risk is largely achieved.

In addition, one could diversify amongst multiple strategies, which would help protect against specific hedge fund strategies moving in and out of favor. Optimizing the allocations to specific hedge funds in a diversified fund approach may be a challenge, because of short price histories and hidden risks may introduce substantial estimation error and result in underestimation of portfolio risks. In addition, optimization would likely be constrained to long-only portfolios of hedge funds, as there are few ways for a hedge fund to directly short another hedge fund (but see point three, and there might be).

Strategic, tactical, and cyclical asset allocation

Secondly, a fund of hedge funds approach might offer an opportunity to engage in tactical or cyclical asset allocation by systematically adjusting the weights given to different funds in response to changing short or medium-term market expectations. For example, if a convertible arbitrage strategy does well in a higher volatility environment, then one may want to overweight the allocation to these strategies relative to the strategic allocation if the forward economic environment looks choppier. To succeed here requires a very careful understanding of manager strengths and weaknesses, a reliable feel for the economic environment, and knowledge of how the two will interact. It is also complicated by liquidity constraints, given that hedge funds may involve lockup periods that interfere with the ability to rebalance on a tactical basis. Indeed rebalancing allocations even to the strategic targets can run into liquidity difficulties.

Hedge Fund Accessibility

It is conceivable that a fund of hedge funds could be a public company, and if there were a policy of not offering dividends, then the book value of assets should grow more or less in line with fund performance and offer ordinary non-sophisticated investors an opportunity to receive hedge-fund-type returns by purchasing the fund’s stock. Moreover, if the fund stocks are available and representative of net asset values, investors could take short positions in these stocks to implement views of negative fund performance. One might conceivably create indices of hedge fund strategies: merger-arb, convertible-arb, distressed debt, etc. type equities could proliferate much in the manner that exchange-traded-funds do today. Relative value and special situations strategies lend themselves most readily to this type of securitization, but there is no reason that one could not attempt to try this with more directional funds as well.

Part of the challenge of this structure is that the market price of a fund’s stock will reflect not only the net asset value, but also discounted expectations for future fund growth. Thus the stock price will likely have greater volatility than the underlying NAV, reflecting changes in public sentiment and growth expectations.

Logical Business Structure

For high net worth clients and family offices, a fund-of-funds structure may simply be the best business structure to take advantage of the opportunity for hedge-fund style returns without committing to a single fund or strategy, and without requiring the institutional and personnel overhead necessary for creating and managing one’s own hedged strategies on a day-to-day basis. On the surface, this is simply a reiteration of the diversification argument, but while the diversification argument might be made by an existing fund to obtain an external investor’s money, the business structure argument is for a person who already possesses substantial funds and effectively needs to create a business structure for the purpose of managing their own money. In this context, diversification is required practically by definition. That a family office would be a fund of funds, but the funds invested in might not be individually diversified is similar to the argument against conglomerates: the idea here is that investors should decide how to diversify in by choosing what companies to invest in, and therefore do not need companies with diversified businesses lines to provide diversification for them within a single stock.

Funds of Global Macro Funds

What does this mean about global macro funds of funds? I am eager to discuss how to think about the challenges of managing of a fund of global macro funds, but that will have to wait until the next installment.

Global Macro and Fund of Funds

I received an email from one reader the other day asking me about setting up a fund-of-funds approach to global macro investing. It wasn’t clear to me at the time whether the author was asking about setting up 1) a fund of global macro hedge funds, or 2) a fund of hedge funds to implement global macro strategies. Both of these are potentially mechanisms for global investing, but they present different investment challenges and require different management skills to execute correctly.

With these questions, one needs to consider what the advantage of a fund-of-funds approach is in the first place. I see four basic ways in which fund-of-funds approach can add value: it provides diversification benefits, it can allow a degree of tactical or cyclical strategy allocation, a publicly-traded fund of funds might allow ordinary (i.e. non-sophisticated) investors to have access to hedge fund returns through equity returns, and finally, it may simply be the appropriate business structure for managing an individual investor’s wealth, or a family office. I discuss some of these benefits below.

Diversification

First, a fund-of-funds provides some diversification against manager risk. This is the risk that an individual manager will make a major mistake, turn out to be crooked, be hit by a bus, leave the firm, or some other unexpected event that impinges on a fund’s key productive asset. To be truly diversified against this risk might require some 20 funds or so, which is probably larger than most fund-of-funds prefer to allocate. If all the funds revolve around the same basic strategy, the diversification against manager risk is largely achieved.

In addition, one could diversify amongst multiple strategies, which would help protect against specific hedge fund strategies moving in and out of favor. Optimizing the allocations to specific hedge funds in a diversified fund approach may be a challenge, because of short price histories and hidden risks may introduce substantial estimation error and result in underestimation of portfolio risks. In addition, optimization would likely be constrained to long-only portfolios of hedge funds, as there are few ways for a hedge fund to directly short another hedge fund (but see point three, and there might be).

Strategic, tactical, and cyclical asset allocation

Secondly, a fund of hedge funds approach might offer an opportunity to engage in tactical or cyclical asset allocation by systematically adjusting the weights given to different funds in response to changing short or medium-term market expectations. For example, if a convertible arbitrage strategy does well in a higher volatility environment, then one may want to overweight the allocation to these strategies relative to the strategic allocation if the forward economic environment looks choppier. To succeed here requires a very careful understanding of manager strengths and weaknesses, a reliable feel for the economic environment, and knowledge of how the two will interact. It is also complicated by liquidity constraints, given that hedge funds may involve lockup periods that interfere with the ability to rebalance on a tactical basis. Indeed rebalancing allocations even to the strategic targets can run into liquidity difficulties.

Hedge Fund Accessibility

It is conceivable that a fund of hedge funds could be a public company, and if there were a policy of not offering dividends, then the book value of assets should grow more or less in line with fund performance and offer ordinary non-sophisticated investors an opportunity to receive hedge-fund-type returns by purchasing the fund’s stock. Moreover, if the fund stocks are available and representative of net asset values, investors could take short positions in these stocks to implement views of negative fund performance. One might conceivably create indices of hedge fund strategies: merger-arb, convertible-arb, distressed debt, etc. type equities could proliferate much in the manner that exchange-traded-funds do today. Relative value and special situations strategies lend themselves most readily to this type of securitization, but there is no reason that one could not attempt to try this with more directional funds as well.

Part of the challenge of this structure is that the market price of a fund’s stock will reflect not only the net asset value, but also discounted expectations for future fund growth. Thus the stock price will likely have greater volatility than the underlying NAV, reflecting changes in public sentiment and growth expectations.

Logical Business Structure

For high net worth clients and family offices, a fund-of-funds structure may simply be the best business structure to take advantage of the opportunity for hedge-fund style returns without committing to a single fund or strategy, and without requiring the institutional and personnel overhead necessary for creating and managing one’s own hedged strategies on a day-to-day basis. On the surface, this is simply a reiteration of the diversification argument, but while the diversification argument might be made by an existing fund to obtain an external investor’s money, the business structure argument is for a person who already possesses substantial funds and effectively needs to create a business structure for the purpose of managing their own money. In this context, diversification is required practically by definition. That a family office would be a fund of funds, but the funds invested in might not be individually diversified is similar to the argument against conglomerates: the idea here is that investors should decide how to diversify in by choosing what companies to invest in, and therefore do not need companies with diversified businesses lines to provide diversification for them within a single stock.

Funds of Global Macro Funds

What does this mean about global macro funds of funds? I am eager to discuss how to think about the challenges of managing of a fund of global macro funds, but that will have to wait until the next installment.

The Deleveraging and Credit Hangover

It has been a while since my last post, in part because August is the month where many families (including mine) take holidays, and in part because I too have been digesting what to make of the market’s recent turmoil. I was both surprised and flattered by the response to my last posting and want to thank readers for your comments and reactions. Although I’d read to expect this, the Blogosphere is indeed bigger and farther reaching than I thought.

Today’s post mostly lists a number of questions going through my mind about how to interpret recent events. As a result it is a little less organized than usual. Still, I think the answers to these issues will tell us a lot about what to expect moving forward. I don’t actually have answers to these questions here, but I’m sure that knowing the answers will help to define strategies moving forward.

Question #1: is the worst over, or is there another shoe about to drop?

Question #2: although volatility has increased substantially, overall market prices have not suffered a large correction in (recent) historical terms; why is this?

Question #3: quantitatively driven funds were squeezed hard in the August turmoil; have fundamental funds been similarly affected, and if not, why?

Question #4: to what extent is the fund-of-funds community affected?

To keep the posting lengths manageable, I will address questions will stem over several posts. Here I address question #1.

1. Is the worst over, or is there another shoe about to drop?

My piano teacher used to point out that musical errors tend to come in pairs. First comes the initial, often unexpected mistake, and then comes a more predictable choppiness as one attempt to recover one’s place in the rhythm and melody. Because most classical music is set to measures of a fixed number of beats, the two-error pattern makes some sense. As a player unbalances one part of the measure, the line must be rebalanced somewhere else, precisely - and also differently from originally written - in order to continue with the piece in time.

Similarly, as a financial pressure comes from one unexpected event, accounts, portfolios, and trading strategies need to adjust to absorb them. If that adjustment is feasible, some return to normality is possible, even if a financial shock still has a noticeable effect.

Although my teacher did not elaborate further, I noticed that the real test is whether one truly recovers the musical line following that second glitch. After error two, the piece might continue, albeit with shaken confidence, but a third error would typically result in a harmonic death-spiral requiring one to stop and start over from scratch.

Technical traders often comment that markets have a kind of musical rhythm of their own, and I believe the three-error test - which I pose entirely as a hypothesis - has valid roots in human psychology. We are human beings and know that we can make mistakes and errors, sometimes big ones. When these mistakes happen, we enter a kind of “emergency mode” to try to resolve them, and these can lead to over-corrections that then themselves get more fine tuned corrections. If we are successful with this correction and the smaller fine-tuning that follows, we feel confident that we have been able largely to manage the situation and can therefore continue ahead. However, if a third large-scale calamity happens, many of us suddenly doubt whether we can truly understand or manage the situation and panic, flee, or massively overreact.

In 1929, this 3 pointed panic was clearly evident. Before the famous “Black Tuesday,” there was also a “Black Thursday” and a “Black Monday.” Black Thursday, October 24th 1929, was the first sudden drop in the US Equities market, which shocked observers so certain that prices could go nowhere but up. This drop produced stunned surprise, but the tide was turned on Thursday when Richard Whitney, vice president of the NYSE, ran across the floor, using funds his personal account to buy shares, push up prices, and turn the mood more positive. The technique worked that day: prices began to rise, and they rose further Friday as investors began to worry that they had missed a buying opportunity at the dip.

By the weekend, many thought that the trouble was over and clear skies lay ahead, but on Monday, October 28th, stocks fell again as investors reduced their exposure “just in case” and through the accumulated leverage, drove prices down again. No one stepped in to save the mood this time, and the result was the second worst percentage drop in US Stock history (the first was 1987’s Black Monday). This second event underscored that the previous intervention did not successfully salve the market, and people prepared to run for the doors. By Tuesday, word had spread and people began mass selling (the harmonic death-spiral) and a generation of Americans concluded to stay far far away from the stock market. As a result, US businesses found it extremely difficult to acquire capital and the following recession was exceedingly prolonged.

In 1987, however, we did not see this three-beat rhythm, and although traders were greatly hurt, the sudden crash did not result in an enormous crisis of confidence. The Fed stepped in the very next day, lowering interest rates substantially, which helped to provide a liquidity cushion and to increase the value of assets. This seemed to work, and prices picked up. Indeed, from a long term perspective, the overall market trend appeared to pick up right where it had left off, albeit at a lower level. On long term graphs of the Dow Jones Industrial Average (see a DJIA log graph and linear graph ), one has to look carefully even to locate the 1987 event. In this case, there was a shock and a recovery (though not a bounceback). If the proposal I am making is correct, a second shock in 1987 would have made a recovery substantially more difficult, because it would challenge the belief that the system could withstand a single shock.

So what does this mean for today? As funds have deleveraged, it appears that markets as a whole have returned to some kind of normal behavior. Every day that passes suggests that the system has absorbed this risk and found a way to pass around the pain of liquidity losses. Unfortunately for quantitative funds, even though markets have roughly returned to their absolute performance levels, what appears to have happened is that quantitative funds deleveraged on the way down to control risk, but then were insufficiently leveraged on the way back up to recover their losses in line with the larger market. The trading community would probably recognize this as one of the ironic behavioral quirks that all floor traders must come to grips with.

The fixed income market may have more adjusting to do than the equity markets. Fixed income portfolios actually have assets that have been written down, and trading strategies that depended on holding MBSs, CDOs, and CDSs for short periods may find that these items do have value, but only when they are held to maturity. Therefore, this might be a good time for buy-and-hold strategies in the fixed income to take advantage of low prices created by panic from higher frequency trading strategies.

The Deleveraging and Credit Hangover

It has been a while since my last post, in part because August is the month where many families (including mine) take holidays, and in part because I too have been digesting what to make of the market’s recent turmoil. I was both surprised and flattered by the response to my last posting and want to thank readers for your comments and reactions. Although I’d read to expect this, the Blogosphere is indeed bigger and farther reaching than I thought.

Today’s post mostly lists a number of questions going through my mind about how to interpret recent events. As a result it is a little less organized than usual. Still, I think the answers to these issues will tell us a lot about what to expect moving forward. I don’t actually have answers to these questions here, but I’m sure that knowing the answers will help to define strategies moving forward.

Question #1: is the worst over, or is there another shoe about to drop?

Question #2: although volatility has increased substantially, overall market prices have not suffered a large correction in (recent) historical terms; why is this?

Question #3: quantitatively driven funds were squeezed hard in the August turmoil; have fundamental funds been similarly affected, and if not, why?

Question #4: to what extent is the fund-of-funds community affected?

To keep the posting lengths manageable, I will address questions will stem over several posts. Here I address question #1.

1. Is the worst over, or is there another shoe about to drop?

My piano teacher used to point out that musical errors tend to come in pairs. First comes the initial, often unexpected mistake, and then comes a more predictable choppiness as one attempt to recover one’s place in the rhythm and melody. Because most classical music is set to measures of a fixed number of beats, the two-error pattern makes some sense. As a player unbalances one part of the measure, the line must be rebalanced somewhere else, precisely - and also differently from originally written - in order to continue with the piece in time.

Similarly, as a financial pressure comes from one unexpected event, accounts, portfolios, and trading strategies need to adjust to absorb them. If that adjustment is feasible, some return to normality is possible, even if a financial shock still has a noticeable effect.

Although my teacher did not elaborate further, I noticed that the real test is whether one truly recovers the musical line following that second glitch. After error two, the piece might continue, albeit with shaken confidence, but a third error would typically result in a harmonic death-spiral requiring one to stop and start over from scratch.

Technical traders often comment that markets have a kind of musical rhythm of their own, and I believe the three-error test - which I pose entirely as a hypothesis - has valid roots in human psychology. We are human beings and know that we can make mistakes and errors, sometimes big ones. When these mistakes happen, we enter a kind of “emergency mode” to try to resolve them, and these can lead to over-corrections that then themselves get more fine tuned corrections. If we are successful with this correction and the smaller fine-tuning that follows, we feel confident that we have been able largely to manage the situation and can therefore continue ahead. However, if a third large-scale calamity happens, many of us suddenly doubt whether we can truly understand or manage the situation and panic, flee, or massively overreact.

In 1929, this 3 pointed panic was clearly evident. Before the famous “Black Tuesday,” there was also a “Black Thursday” and a “Black Monday.” Black Thursday, October 24th 1929, was the first sudden drop in the US Equities market, which shocked observers so certain that prices could go nowhere but up. This drop produced stunned surprise, but the tide was turned on Thursday when Richard Whitney, vice president of the NYSE, ran across the floor, using funds his personal account to buy shares, push up prices, and turn the mood more positive. The technique worked that day: prices began to rise, and they rose further Friday as investors began to worry that they had missed a buying opportunity at the dip.

By the weekend, many thought that the trouble was over and clear skies lay ahead, but on Monday, October 28th, stocks fell again as investors reduced their exposure “just in case” and through the accumulated leverage, drove prices down again. No one stepped in to save the mood this time, and the result was the second worst percentage drop in US Stock history (the first was 1987’s Black Monday). This second event underscored that the previous intervention did not successfully salve the market, and people prepared to run for the doors. By Tuesday, word had spread and people began mass selling (the harmonic death-spiral) and a generation of Americans concluded to stay far far away from the stock market. As a result, US businesses found it extremely difficult to acquire capital and the following recession was exceedingly prolonged.

In 1987, however, we did not see this three-beat rhythm, and although traders were greatly hurt, the sudden crash did not result in an enormous crisis of confidence. The Fed stepped in the very next day, lowering interest rates substantially, which helped to provide a liquidity cushion and to increase the value of assets. This seemed to work, and prices picked up. Indeed, from a long term perspective, the overall market trend appeared to pick up right where it had left off, albeit at a lower level. On long term graphs of the Dow Jones Industrial Average (see a DJIA log graph and linear graph ), one has to look carefully even to locate the 1987 event. In this case, there was a shock and a recovery (though not a bounceback). If the proposal I am making is correct, a second shock in 1987 would have made a recovery substantially more difficult, because it would challenge the belief that the system could withstand a single shock.

So what does this mean for today? As funds have deleveraged, it appears that markets as a whole have returned to some kind of normal behavior. Every day that passes suggests that the system has absorbed this risk and found a way to pass around the pain of liquidity losses. Unfortunately for quantitative funds, even though markets have roughly returned to their absolute performance levels, what appears to have happened is that quantitative funds deleveraged on the way down to control risk, but then were insufficiently leveraged on the way back up to recover their losses in line with the larger market. The trading community would probably recognize this as one of the ironic behavioral quirks that all floor traders must come to grips with.

The fixed income market may have more adjusting to do than the equity markets. Fixed income portfolios actually have assets that have been written down, and trading strategies that depended on holding MBSs, CDOs, and CDSs for short periods may find that these items do have value, but only when they are held to maturity. Therefore, this might be a good time for buy-and-hold strategies in the fixed income to take advantage of low prices created by panic from higher frequency trading strategies.