Many thanks for visiting my blog. I have now returned to work full time with Soc Gen. I'm afraid I will not have the time to update this blog. My thoughts on behavioural finance and value investing will now be published by Soc Gen. In fact they have already published three - on simplicity, underperformance and action bias. If you are an institutional investor who would like access to my work please let me know.
Wishing you all the very best
James
Saturday, 12 January 2008
Tuesday, 13 November 2007
The little book that makes you rich: what is really going on?
In my last post I questioned the long term relevance of some of the fundamental factors outlined in the Navellier's Little Book that makes you rich. However, I also noted that all eight of these factors only add up to a 30% weight in his final analysis. The other 70% is given to what Navellier calls his quantitative stock grade. He describes this as a measure of buying pressure amongst institutional investors.
However, he also tells us exactly what this measure actually is. On page 88 he says "In basic form, we divide a stock's alpha (the return independent of the stock market that typically comes from buying pressure) by its standard deviation. We measure this over a 52-week period".
The 'alpha' Navellier calculates comes from a simple CAPM model. However, as we show in Chapter 35 of Behavioural Investing, the simple CAPM model is deeply flawed. It just doesn't work. In fact in general there seems to be a negative relationship between beta and return, rather than a positive one.
Of course, Fama and French suggested a revised multifactor model of asset pricing - based on size, and price to book as well as the normal market factor. To help reduce the pricing errors in this model a momentum term was introduced by Cahart. A very recent modification has been proposed by Hirshleifer
. This adds a new factor to the equation of repurchases minus issuers (labeled UMO). This again reduces the significance of the alphas calculated from the FF4 model. In general the alphas become statistically insignificant under this five factor model.
So effectively, Navellier is running a semi reduced form model, not specifying which factors matter, but rather taking the alpha as a catch all term (which could be broken down into more understandable elements - such as size, value, issuance and momentum). However, Navellier demonstrates that these alphas have persistence. He estimates them over the past 52 weeks, and then uses them going forward.
To my mind this is consistent with recent work on style momentum. Chen and De Bondt have shown that style categories have a degree of persistence. They show that if you buy styles that have done well in the last year (in terms of size, price to book and dividend yield) they continue to do well over the next 12 months (but not beyond). The return achieved from a long short position based around this style momentum is around 7% p.a. using a 12 month holding period, and style past returns calculated over 12 months. In long only space a style momentum strategy generates a return of around 17% p.a. over the period 63-97. So Navellier's idea of alpha persistence certainly gets some support from this viewpoint.
Some final thoughts
I found much to agree with in Navellier's Little Book such as the over-reliance on stories, and the meeting with company management being a waste of time. His reliance on numbers based analysis echoes very much my own views on evidence based investing. However, ultimately I found the book couldn't stick with its own discipline. For instance, Navellier can't help but eulogize over the wonderful outlook for stocks that deliver out future. Despite his pronouncements that his eight factors and his quantitative grading system are really all you need to invest, he spends a considerable amount of time telling you to read the newspapers, whilst simultaneously ignoring the noise. Such overtly contradictory advice can do little but confuse the reader.
Personally I am not convinced that Navellier puts together a coherent defense of growth investing. But then again that won't surprise those of you who know me!
However, he also tells us exactly what this measure actually is. On page 88 he says "In basic form, we divide a stock's alpha (the return independent of the stock market that typically comes from buying pressure) by its standard deviation. We measure this over a 52-week period".
The 'alpha' Navellier calculates comes from a simple CAPM model. However, as we show in Chapter 35 of Behavioural Investing, the simple CAPM model is deeply flawed. It just doesn't work. In fact in general there seems to be a negative relationship between beta and return, rather than a positive one.
Of course, Fama and French suggested a revised multifactor model of asset pricing - based on size, and price to book as well as the normal market factor. To help reduce the pricing errors in this model a momentum term was introduced by Cahart. A very recent modification has been proposed by Hirshleifer
. This adds a new factor to the equation of repurchases minus issuers (labeled UMO). This again reduces the significance of the alphas calculated from the FF4 model. In general the alphas become statistically insignificant under this five factor model.
So effectively, Navellier is running a semi reduced form model, not specifying which factors matter, but rather taking the alpha as a catch all term (which could be broken down into more understandable elements - such as size, value, issuance and momentum). However, Navellier demonstrates that these alphas have persistence. He estimates them over the past 52 weeks, and then uses them going forward.
To my mind this is consistent with recent work on style momentum. Chen and De Bondt have shown that style categories have a degree of persistence. They show that if you buy styles that have done well in the last year (in terms of size, price to book and dividend yield) they continue to do well over the next 12 months (but not beyond). The return achieved from a long short position based around this style momentum is around 7% p.a. using a 12 month holding period, and style past returns calculated over 12 months. In long only space a style momentum strategy generates a return of around 17% p.a. over the period 63-97. So Navellier's idea of alpha persistence certainly gets some support from this viewpoint.
Some final thoughts
I found much to agree with in Navellier's Little Book such as the over-reliance on stories, and the meeting with company management being a waste of time. His reliance on numbers based analysis echoes very much my own views on evidence based investing. However, ultimately I found the book couldn't stick with its own discipline. For instance, Navellier can't help but eulogize over the wonderful outlook for stocks that deliver out future. Despite his pronouncements that his eight factors and his quantitative grading system are really all you need to invest, he spends a considerable amount of time telling you to read the newspapers, whilst simultaneously ignoring the noise. Such overtly contradictory advice can do little but confuse the reader.
Personally I am not convinced that Navellier puts together a coherent defense of growth investing. But then again that won't surprise those of you who know me!
Wednesday, 7 November 2007
The little book that makes you rich: A critical analysis of the fundamental factors
Firstly apologies for the recent lack of posts, I've been enjoying a sojourn visiting some of my family in New Zealand. I've recently been reading Louis Navellier's 'Little Book that makes you rich' subtitled "a proven market beating formula for growth investing".
I'm generally skeptical of the benefits of growth investing. All too often growth investing simply seems to be a cover for buying the latest fad or fashion in the investing world. So when I saw a book purporting to offer a numbers based approach (what I have called evidence based investing) to growth investing I was intrigued.
Navellier starts out by listing out his eight criteria for fundamental investing.
1. Earnings revisions
2. Earnings surprise
3. Sales growth
4. Operating margin growth
5. Cash flow to MV
6. Earnings growth
7. Earnings momentum
8. ROE
When I looked at this list I was somewhat surprised. Many of these factors struck me as odd. For instance, I have never come across a single paper claiming that sales growth had any kind of positive relationship with returns, nor ROE. Others such as earnings revisions and surprises were less shocking.
I decided to run a quick check on each of these factors, using a variety of sources ( I will run a full set of tests once I'm back at work, but for now I'll rely on others results). For each factor I tried to find the study with the longest history. The table below presents the results showing how much each factor added to a long only portfolio vs the market.
1. Earnings revisions 4.8% p.a
2. Earnings surprises 2.7% p.a.
3. Sales growth -13% p.a
4. Operating margin growth N/A
5. Cash flow to MV 4% p.a.
6. Earnings growth -2% p.a.
7. Earnings momentum 0% p.a.
8. ROE 0.8% p.a
In fairness to Navellier, he does note that the importance of each of these factors waxes and wanes over time. However, with a number of his factors appearing to add no value over a consistent time horizon, one must wonder what these fundamental variables bring to the party?
Interestingly, one of the best fundamental factors turns out to be a value factor! Although Navellier dresses up his use of cash flow as a growth variable, nothing can alter the fact that it is really a value variable. This is consistent with work that I have done which showed that value strategies did well within a growth universe (see Chapter 31 of Behavioural Investing).
It is also noteworthy that despite spending around two thirds of the little book of these variables, they only get a 30% weight in the final system....so what is the this little book really doing? I'll examine this in my next post.
I'm generally skeptical of the benefits of growth investing. All too often growth investing simply seems to be a cover for buying the latest fad or fashion in the investing world. So when I saw a book purporting to offer a numbers based approach (what I have called evidence based investing) to growth investing I was intrigued.
Navellier starts out by listing out his eight criteria for fundamental investing.
1. Earnings revisions
2. Earnings surprise
3. Sales growth
4. Operating margin growth
5. Cash flow to MV
6. Earnings growth
7. Earnings momentum
8. ROE
When I looked at this list I was somewhat surprised. Many of these factors struck me as odd. For instance, I have never come across a single paper claiming that sales growth had any kind of positive relationship with returns, nor ROE. Others such as earnings revisions and surprises were less shocking.
I decided to run a quick check on each of these factors, using a variety of sources ( I will run a full set of tests once I'm back at work, but for now I'll rely on others results). For each factor I tried to find the study with the longest history. The table below presents the results showing how much each factor added to a long only portfolio vs the market.
1. Earnings revisions 4.8% p.a
2. Earnings surprises 2.7% p.a.
3. Sales growth -13% p.a
4. Operating margin growth N/A
5. Cash flow to MV 4% p.a.
6. Earnings growth -2% p.a.
7. Earnings momentum 0% p.a.
8. ROE 0.8% p.a
In fairness to Navellier, he does note that the importance of each of these factors waxes and wanes over time. However, with a number of his factors appearing to add no value over a consistent time horizon, one must wonder what these fundamental variables bring to the party?
Interestingly, one of the best fundamental factors turns out to be a value factor! Although Navellier dresses up his use of cash flow as a growth variable, nothing can alter the fact that it is really a value variable. This is consistent with work that I have done which showed that value strategies did well within a growth universe (see Chapter 31 of Behavioural Investing).
It is also noteworthy that despite spending around two thirds of the little book of these variables, they only get a 30% weight in the final system....so what is the this little book really doing? I'll examine this in my next post.
Wednesday, 10 October 2007
The Sources of Value
It is one of the established 'facts' of finance that value outperforms growth over reasonable time horizons (see Chapters 22-34 of Behavioural Investing). However, which of the component sources of return generate this performance?
In a new paper Fama and French explore the composition of returns for value and growth stocks. They start by decomposing returns into dividends and capital gains. The chart below shows the results they uncovered using US data since 1963 with value and growth defined by price to book/size quintile intersections. It is worth noting that Fama and French perform their analysis in nominal terms (so the capital gains they show include the effects of inflation - around 4.5% over the their sample period). The chart shows that importance of dividend returns to value investors. The dividend yield on big value stocks is 50% higher than that of the market, and twice that seen by growth investors.
Fama and French then go onto to decompose the capital gain term into several sub-components. They show that the capital gain term can be broken down into an element due to growth in book value (effectively the investment carried out by the firm), and the change in the valuation ratio (price to book in this case).
They also observe that that the change in valuation can be decomposed into an element they call drift associated with the general upward trend in valuations over the sample, and an component called convergence which is due to a rise in profitability and a reversion to the mean in valuation. Drift is measured by comparing the price to book of the original portfolio with its new counterpart when the data are resorted each year. Convergence is measured as the price to book on the original portfolio at the formation date and the price to book on the same portfolio one year later.
The results of this further decomposition are shown in the chart below again for the period 1963-2006. The picture reveals show huge differences between value and growth stocks. Value stocks see hardly any growth in book value - not hugely surprising, they don't tend to invest large sums, in general they are more interested in cost cutting than investment. However, their is a very strong tendancy for convergence in price to book terms - that is to say their valuation rebound - although the decomposition is silent on whether this is the result of a bounce back in profitability or not.
The same can not be said of growth stocks. They see an enormous amount of growth in book value - as they engage in large cap ex and M&A. However, they convergence is negative, they witness declines in price to book as their profitability erodes and valuations return to 'normal' levels.
From a behavioural standpoint this is exactly what we would expect to see, if investors over pay for growth. Of course, Fama and French prefer a rational explanation which I find far from convincing, but the bottom line is that the return decomposition can't help us distinguish between the rational and behavioural explanations - we have observational equivalence.
In Chapter 43 of Behavioural investing I show a decomposition of returns for the US market. I argue that returns can be decomposed into the dividend yield, growth in real dividends, the change in valuation and inflation. I usually do my analysis in real terms so I can dispense with the last term. I have recently completed a similar exercise in terms of value and growth stocks. The results can be seen below.
The returns are lower than the Fama and French numbers because I remove the effects of inflation. The importance of the dividend yield is once again revealed, it contributes 53% of the real return to value stocks, real dividend growth accounts for a further 30% of the return to value stocks. It is noticeable than the real dividend growth of value stocks is faster than the equivalent rate for 'growth' stocks. So by buying value you get both value and growth.
Investors of all kinds ignore dividends at their peril.
In a new paper Fama and French explore the composition of returns for value and growth stocks. They start by decomposing returns into dividends and capital gains. The chart below shows the results they uncovered using US data since 1963 with value and growth defined by price to book/size quintile intersections. It is worth noting that Fama and French perform their analysis in nominal terms (so the capital gains they show include the effects of inflation - around 4.5% over the their sample period). The chart shows that importance of dividend returns to value investors. The dividend yield on big value stocks is 50% higher than that of the market, and twice that seen by growth investors.
Fama and French then go onto to decompose the capital gain term into several sub-components. They show that the capital gain term can be broken down into an element due to growth in book value (effectively the investment carried out by the firm), and the change in the valuation ratio (price to book in this case).
They also observe that that the change in valuation can be decomposed into an element they call drift associated with the general upward trend in valuations over the sample, and an component called convergence which is due to a rise in profitability and a reversion to the mean in valuation. Drift is measured by comparing the price to book of the original portfolio with its new counterpart when the data are resorted each year. Convergence is measured as the price to book on the original portfolio at the formation date and the price to book on the same portfolio one year later.
The results of this further decomposition are shown in the chart below again for the period 1963-2006. The picture reveals show huge differences between value and growth stocks. Value stocks see hardly any growth in book value - not hugely surprising, they don't tend to invest large sums, in general they are more interested in cost cutting than investment. However, their is a very strong tendancy for convergence in price to book terms - that is to say their valuation rebound - although the decomposition is silent on whether this is the result of a bounce back in profitability or not.
The same can not be said of growth stocks. They see an enormous amount of growth in book value - as they engage in large cap ex and M&A. However, they convergence is negative, they witness declines in price to book as their profitability erodes and valuations return to 'normal' levels.
From a behavioural standpoint this is exactly what we would expect to see, if investors over pay for growth. Of course, Fama and French prefer a rational explanation which I find far from convincing, but the bottom line is that the return decomposition can't help us distinguish between the rational and behavioural explanations - we have observational equivalence.
In Chapter 43 of Behavioural investing I show a decomposition of returns for the US market. I argue that returns can be decomposed into the dividend yield, growth in real dividends, the change in valuation and inflation. I usually do my analysis in real terms so I can dispense with the last term. I have recently completed a similar exercise in terms of value and growth stocks. The results can be seen below.
The returns are lower than the Fama and French numbers because I remove the effects of inflation. The importance of the dividend yield is once again revealed, it contributes 53% of the real return to value stocks, real dividend growth accounts for a further 30% of the return to value stocks. It is noticeable than the real dividend growth of value stocks is faster than the equivalent rate for 'growth' stocks. So by buying value you get both value and growth.
Investors of all kinds ignore dividends at their peril.
Wednesday, 3 October 2007
Sector rotation: an investment dead end?
Two chapters in Behavioural Investing suggest that investors focusing on sectors rather than stocks are barking up the wrong tree. Chapter 32 outlines the evidence showing that value and momentum effects are much greater at the stock (and even country level) than they are at the sector level. It also cautions that sectors are rarely stable entities in terms of their investment characteristics. Pretty much every sector has been 'vale' and 'growth' or it's lifespan. So ruling out sectors because they are growth or value is a big mistake.
Chapter 19 also touches on sectors. This presents some of the work of Cremers and Petajisto who show that those fund managers with low active share, but high tracking error (those taking sector bets) manage to destroy value for clients (having a negative gross and net alpha). Such managers account for around 35% of the US market! Whilst this isn't proof that sector rotation strategies are hopeless (that would be to confuse the absence of evidence with evidence of the absence), it does at least make one stop and think about the role of sectors.
A new paper sheds further light on the fruitlessness of trying to rotate sectors. The paper provides evidence of the absence! Stangl, Jacobsen and Visaltanachoti explore the possibility of timing sector rotation across the stages of the business cycle. They identify five stages of the cycle shown in the diagram below.
They follow a rotation strategy that seems to me to capture the conventional wisedom regarding sector rotation, as set out below.
They investigate the US market from 1948 to 2006. In a heroic leap of faith, they assume perfect foresight on the part of investors. That is to say, they assume that investors know with absolute certainty which phase of the business cycle they are in.
Even assuming such prescient powers, the sector rotation strategy only outperforms by around 2% p.a. If one were to include transaction costs, and drop the perfect foresight assumption then this would quickly become a zero, or even negative, alpha.
When one examines the detail of the sector rotation strategy, some further issues are created. For instance, those sectors favoured by the conventional wisdom in early and middle stages of expansion actually have negative alpha over those phases!
Those sectors favoured in the late expansion did outperform, but were beaten by sectors whose attractions are usually assocaited with late stage contraction! In fact, it was only really in the late stage contraction where the conventional wisdom over sector selection was the best strategy.
Stangl et al show that even if you can forecast the business cycle with complete accuracy (see my earlier post on why we don't need economists for my thoughts on this) then following the conventional wisdom with regard to sector selection is an suboptimal investment decision. You would be better off following a simple market timing model which stayed long equities apart from during the early recession period.
So sector rotation (at least as represented by the conventional wisdom viewpoint) is not a good source of outperformance. This certainly calls into question the raison d'ete of many strategists!
In fact, all of the evidence mentioned in this post raises challenges to the way in which investment is done. Not only is sector rotation highly dubious, the fact that useful investment characteristics such as value and momentum are better defined at the stock level rather than the industry level brings the role of sector specialists into doubt. I have long argued that what we need is a few analysts with good investment skills, rather an armies of industry 'experts'.
Chapter 19 also touches on sectors. This presents some of the work of Cremers and Petajisto who show that those fund managers with low active share, but high tracking error (those taking sector bets) manage to destroy value for clients (having a negative gross and net alpha). Such managers account for around 35% of the US market! Whilst this isn't proof that sector rotation strategies are hopeless (that would be to confuse the absence of evidence with evidence of the absence), it does at least make one stop and think about the role of sectors.
A new paper sheds further light on the fruitlessness of trying to rotate sectors. The paper provides evidence of the absence! Stangl, Jacobsen and Visaltanachoti explore the possibility of timing sector rotation across the stages of the business cycle. They identify five stages of the cycle shown in the diagram below.
They follow a rotation strategy that seems to me to capture the conventional wisedom regarding sector rotation, as set out below.
They investigate the US market from 1948 to 2006. In a heroic leap of faith, they assume perfect foresight on the part of investors. That is to say, they assume that investors know with absolute certainty which phase of the business cycle they are in.
Even assuming such prescient powers, the sector rotation strategy only outperforms by around 2% p.a. If one were to include transaction costs, and drop the perfect foresight assumption then this would quickly become a zero, or even negative, alpha.
When one examines the detail of the sector rotation strategy, some further issues are created. For instance, those sectors favoured by the conventional wisdom in early and middle stages of expansion actually have negative alpha over those phases!
Those sectors favoured in the late expansion did outperform, but were beaten by sectors whose attractions are usually assocaited with late stage contraction! In fact, it was only really in the late stage contraction where the conventional wisdom over sector selection was the best strategy.
Stangl et al show that even if you can forecast the business cycle with complete accuracy (see my earlier post on why we don't need economists for my thoughts on this) then following the conventional wisdom with regard to sector selection is an suboptimal investment decision. You would be better off following a simple market timing model which stayed long equities apart from during the early recession period.
So sector rotation (at least as represented by the conventional wisdom viewpoint) is not a good source of outperformance. This certainly calls into question the raison d'ete of many strategists!
In fact, all of the evidence mentioned in this post raises challenges to the way in which investment is done. Not only is sector rotation highly dubious, the fact that useful investment characteristics such as value and momentum are better defined at the stock level rather than the industry level brings the role of sector specialists into doubt. I have long argued that what we need is a few analysts with good investment skills, rather an armies of industry 'experts'.
Saturday, 29 September 2007
The book is out
I'm delighted to say that Behavioural Investing was published on Friday. It is available from amazon at a £20 discount to the RRP. It is a slighter longer than I had envisioned, weighing in at 705 pages! However, the good news that it is designed to be dipped into, and not necessarily read sequentially.
I'm afraid those in the US will have to wait a little longer. The books are printed here in the UK, and sent over to the US. I believe it should be available in the US at the end of Nov.
Let me know what you think of it.
I'm afraid those in the US will have to wait a little longer. The books are printed here in the UK, and sent over to the US. I believe it should be available in the US at the end of Nov.
Let me know what you think of it.
Monday, 24 September 2007
The myth of exogenous risk and the recent quant problems
Regular readers of my work will know that I am deeply skeptical over the idea of exogenous risk (like a gambler playing roulette, where the behaviour of other players is irrelevant). In Chapter 36 of Behavioural Investing I argue that many aspect of risk are endogenous ( like a gambler playing poker, where the actions of the other plays are integral to the game) to the way in which we invest. The problems experienced by the quant funds in August may help highlight some of these issues.
Now as my post on the 17 August detailed I suspect that leverage had much to do with the problems experienced by the quant funds. As one observation reader commented the hubristic use of excessive leverage is an all too human failing, and has nothing do to with the 'quant' process as such.
Andrew Lo (of MIT) and Amir Khandani have written a fascinating paper on the problems of August (available here ). They use indirect evidence to establish the scale of the issue and the kind of problems to be encountered. They use a very simple contrarian strategy of simply buying yesterdays losers, and selling yesterday's winners on a daily basis. They ignore all the issues surrounding transaction costs and turnover, as this is only an example.
They document several interesting features using this simple strategy. The first is alpha cannibalisation (or alpha decay as they call it). As more and more funds have set up in the quant arena so the return to this strategy has declined from an average daily return of 1.38% in 1995, to a mere 0.13% in 2007.
They show that in order to increase the return back to the level seen in 1998, leverage of around 8-times would have been required. As noted in my original post Goldmans Global Equity Opportunity fund was running 6 times! They also show that this strategy performed exceptionally badly in August. It witnessed several consecutive days in August (nearly 7% over 3 days, a 12 standard deviation event).
In a comparsion between 1998 and 2007, Lo and Khandani show that the big difference between 1998 and 2007 is the lack of spillover in the LTCM crisis. Whilst markets had a turblent time as LTCM problems became widely known in the wake of the Asian and Russian crises, simple quant strategies continued to work.
This hints at one possible (perhaps even probable) cause of August's events. A multi-strategy funds (or prop desk) took a big hit in the mortgage/CDO space, and as a result was forced to scale back on operations across the board, terminating their positions in equity space.
This in turn probably caused other quant funds to hit their limits, and unleashed a vicious spiral of selling, and further selling. The more crowded a trade is, the more likely this outcome becomes.
A survey for the the CFA publication Trends in Quantitative Finance (April 2006) showed that out of 21 firms using quant, 18 used them for return forecasting (with around $2 trillion invested in equities). The most popular factors uncovered were 'reversal' with 86% of those questioned citing it's use. In second place came 'momentum' with 81%, and in third was exogenous factors (such as valuations, earnings quality, capex ect) with 62% of respondents using such methods.
The recent so called 'quant problems' are a timely reminder of the endogenous nature of risk in our markets.
Now as my post on the 17 August detailed I suspect that leverage had much to do with the problems experienced by the quant funds. As one observation reader commented the hubristic use of excessive leverage is an all too human failing, and has nothing do to with the 'quant' process as such.
Andrew Lo (of MIT) and Amir Khandani have written a fascinating paper on the problems of August (available here ). They use indirect evidence to establish the scale of the issue and the kind of problems to be encountered. They use a very simple contrarian strategy of simply buying yesterdays losers, and selling yesterday's winners on a daily basis. They ignore all the issues surrounding transaction costs and turnover, as this is only an example.
They document several interesting features using this simple strategy. The first is alpha cannibalisation (or alpha decay as they call it). As more and more funds have set up in the quant arena so the return to this strategy has declined from an average daily return of 1.38% in 1995, to a mere 0.13% in 2007.
They show that in order to increase the return back to the level seen in 1998, leverage of around 8-times would have been required. As noted in my original post Goldmans Global Equity Opportunity fund was running 6 times! They also show that this strategy performed exceptionally badly in August. It witnessed several consecutive days in August (nearly 7% over 3 days, a 12 standard deviation event).
In a comparsion between 1998 and 2007, Lo and Khandani show that the big difference between 1998 and 2007 is the lack of spillover in the LTCM crisis. Whilst markets had a turblent time as LTCM problems became widely known in the wake of the Asian and Russian crises, simple quant strategies continued to work.
This hints at one possible (perhaps even probable) cause of August's events. A multi-strategy funds (or prop desk) took a big hit in the mortgage/CDO space, and as a result was forced to scale back on operations across the board, terminating their positions in equity space.
This in turn probably caused other quant funds to hit their limits, and unleashed a vicious spiral of selling, and further selling. The more crowded a trade is, the more likely this outcome becomes.
A survey for the the CFA publication Trends in Quantitative Finance (April 2006) showed that out of 21 firms using quant, 18 used them for return forecasting (with around $2 trillion invested in equities). The most popular factors uncovered were 'reversal' with 86% of those questioned citing it's use. In second place came 'momentum' with 81%, and in third was exogenous factors (such as valuations, earnings quality, capex ect) with 62% of respondents using such methods.
The recent so called 'quant problems' are a timely reminder of the endogenous nature of risk in our markets.
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