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!
Tuesday, 13 November 2007
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.
Tuesday, 18 September 2007
Are finance professors overconfident?
I wasn't going to post this week, as I have a couple of other things on. However, this paper arrived in my inbox and I couldn't resist the urge to flag it up to you. Professors Doran, Peterson and Wright survey the academic finance communities attitudes towards market efficiency and investing.
They manage to get around 700 usable responses (a high response rate for such a survey). Doran et al first asked the professors for their beliefs on market efficiency. In academic finance there are 3 levels of efficiency (weak, semi-strong and strong form efficiency). Each corresponds to the amount of information that is incorporated within prices. Weak form efficiency says you can't beat the market using just past prices, semi-strong form says that both past prices and public information are already reflected in the prices. Strong form efficiency says that all information both public and private is reflected in the current price.
The chart below shows the percentage of respondents agreeing (blue bars) or disagreeing (cream bars) with the statements that future returns can be forecast from (i) past returns (weak form), (ii) past returns and public information (semi-strong form) and (iii) past returns and public and private information (strong form).
Nearly 60% of the professors accept that the US market is weak form efficient (i.e. ignoring the enormous literature on momentum as a stock selection tool, incidentally something I am guilty of as well).
One third of the them accept that the US market is semi-strong efficient (i.e. that value strategies don't work since they involve publically available information such as earnings or book value).
Only when it comes to strong form efficiency do we finally see the academics reject the concept on efficiency. Some 57% said it was possible to predict future returns when using private information.
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Two other questions of interest were raised in the survey. The first concerned whether the professors agreed that returns were a compensation for risk (i.e. the multi-factor world of Fama and French - a rejection of behavioural finance). Only a mere 27% of the professors disagreed with this statement. So we behaviouralists are definitely in a distinct minority in academic circles.
The other question concerned whether the professors thought that investment strategies that could consistently beat the market without taking above market risk actually existed. Only 17% of the respondents said yes such strategies existed. So academia seems to remain a bastion of market efficiency and passive investing. When asked, just 18% of the professors said their objective was to outperform the index.
There was one final finding from the survey which made me laugh (I know I'm sad). Doran et al found that finance professors decisions to actively invest were largely independent of their beliefs in market efficiency. Instead they reflected professors confidence in their abilities! As Doran et al note "A professor’s opinion on the general efficiency of US stock markets has little influence on his investment objectives relative to his confidence in his own abilities. Within confidence groupings, there is little dispersion in a professor’s investment goals as his opinion of market efficiency changes. However, within the opinion groupings , there is a substantial amount of near monotonic dispersion in a professor’s investment goals as his confidence in investing abilities changes. This suggests that a respondent’s opinion of market efficiency has little to do with his decision of whether to actively or passively invest. What matters is an investor’s confidence in his own abilities."
Sounds like finance profs are just like the rest of us - human after all!
They manage to get around 700 usable responses (a high response rate for such a survey). Doran et al first asked the professors for their beliefs on market efficiency. In academic finance there are 3 levels of efficiency (weak, semi-strong and strong form efficiency). Each corresponds to the amount of information that is incorporated within prices. Weak form efficiency says you can't beat the market using just past prices, semi-strong form says that both past prices and public information are already reflected in the prices. Strong form efficiency says that all information both public and private is reflected in the current price.
The chart below shows the percentage of respondents agreeing (blue bars) or disagreeing (cream bars) with the statements that future returns can be forecast from (i) past returns (weak form), (ii) past returns and public information (semi-strong form) and (iii) past returns and public and private information (strong form).
Nearly 60% of the professors accept that the US market is weak form efficient (i.e. ignoring the enormous literature on momentum as a stock selection tool, incidentally something I am guilty of as well).
One third of the them accept that the US market is semi-strong efficient (i.e. that value strategies don't work since they involve publically available information such as earnings or book value).
Only when it comes to strong form efficiency do we finally see the academics reject the concept on efficiency. Some 57% said it was possible to predict future returns when using private information.
.jpg)
Two other questions of interest were raised in the survey. The first concerned whether the professors agreed that returns were a compensation for risk (i.e. the multi-factor world of Fama and French - a rejection of behavioural finance). Only a mere 27% of the professors disagreed with this statement. So we behaviouralists are definitely in a distinct minority in academic circles.
The other question concerned whether the professors thought that investment strategies that could consistently beat the market without taking above market risk actually existed. Only 17% of the respondents said yes such strategies existed. So academia seems to remain a bastion of market efficiency and passive investing. When asked, just 18% of the professors said their objective was to outperform the index.
There was one final finding from the survey which made me laugh (I know I'm sad). Doran et al found that finance professors decisions to actively invest were largely independent of their beliefs in market efficiency. Instead they reflected professors confidence in their abilities! As Doran et al note "A professor’s opinion on the general efficiency of US stock markets has little influence on his investment objectives relative to his confidence in his own abilities. Within confidence groupings, there is little dispersion in a professor’s investment goals as his opinion of market efficiency changes. However, within the opinion groupings , there is a substantial amount of near monotonic dispersion in a professor’s investment goals as his confidence in investing abilities changes. This suggests that a respondent’s opinion of market efficiency has little to do with his decision of whether to actively or passively invest. What matters is an investor’s confidence in his own abilities."
Sounds like finance profs are just like the rest of us - human after all!
Monday, 10 September 2007
Yet more evidence on the folly of forecasting, or why we don't need economists!
First a quick comment on the change of colour scheme. Multiple readers have told me that they struggle reading the white font on a background. To make life easier I have switched to this format. Let me know if this is easier on the eye - design never was by strong point!
One of the seven sins of fund management (section III of Behavioural Investing) concerns the folly of forecasting (Chapter 9). This is our obsession with trying to forecast the future. Yet there is an enormous amount of evidence to suggest that we simply can't forecast with any more accuracy than a coin toss (a frequently we perform worse than even chance!).
One of the papers that didn't make it into the Behavioural Investing book (but with hindsight perhaps should have been added in) was on the performance of economists in forecasting recession. In it I pointed that economists are simply hopeless when it comes to forecasting recessions (I could have stopped that sentance before the word recessions).
Their track record is truly appalling. The chart below shows that in recent history (1980 onwards) the consensus of economists has not managed to forecast either of the recessions that have occurred. The data for this charts from the Philly Fed Survey of Professional Forecasters.
In the past I have proposed that simple quant models often have the edge of human judgement (see Chapter 22 of Behavioural Investing). Some new research by the San Francisco Fed shows that my supposition that economists would be no different than many other fields in finding their subjective forecasts outperform by a simple model was correct.
In a new paper Glen Rudebusch and John Williams show that a simple model based on the slope of the yield curve has significantly outperformed economists in forecasting recessions. They show that even if we use the economists own probability of recession estimate (rather than their spot forecast), the simple model wins hands down.
The chart below shows the so called anxious index, which is the economists stated probability of recession over the next four quarters. As Rudebusch and Williams state "Even at a horizon of two quarters, and certainly at three and four quarters ahead, the probability forecasts appear to have little relationship with historical recessions."
Compare these economists probabilities with the probability of a recession from a two variable model (using the level of short rates and the slop of the yield curve). The economists say their is currently a 19% chance of a recession in the next 4 quarters, the simple model says it is closer to a 30% probability.
Of course, economists have been aware of such simple models for many years. But this begs the question why they don't use/follow them? My own answer is overconfidence. This seems to be supported by the Rudebusch and Williams paper which concludes "It is interesting to note that many times during the past twenty years forecasters have acknowledged the formidable past performance of the yield curve in predicting expansions and recessions but argued that this past performance did not apply in the current situation. That is, signals from the yield curve have often been dismissed because of supposed changes in the economy or special factors in‡uencing interest rates. This paper, however, shows that the relative predictive power of the yield curve does not appear to have diminished much, if at all. "
Yet another example of just how poor forecasting really is. We need to find a better way to invest than relying upon our disproven and discredited forecasting abilities.
One of the seven sins of fund management (section III of Behavioural Investing) concerns the folly of forecasting (Chapter 9). This is our obsession with trying to forecast the future. Yet there is an enormous amount of evidence to suggest that we simply can't forecast with any more accuracy than a coin toss (a frequently we perform worse than even chance!).
One of the papers that didn't make it into the Behavioural Investing book (but with hindsight perhaps should have been added in) was on the performance of economists in forecasting recession. In it I pointed that economists are simply hopeless when it comes to forecasting recessions (I could have stopped that sentance before the word recessions).
Their track record is truly appalling. The chart below shows that in recent history (1980 onwards) the consensus of economists has not managed to forecast either of the recessions that have occurred. The data for this charts from the Philly Fed Survey of Professional Forecasters.
In the past I have proposed that simple quant models often have the edge of human judgement (see Chapter 22 of Behavioural Investing). Some new research by the San Francisco Fed shows that my supposition that economists would be no different than many other fields in finding their subjective forecasts outperform by a simple model was correct.
In a new paper Glen Rudebusch and John Williams show that a simple model based on the slope of the yield curve has significantly outperformed economists in forecasting recessions. They show that even if we use the economists own probability of recession estimate (rather than their spot forecast), the simple model wins hands down.
The chart below shows the so called anxious index, which is the economists stated probability of recession over the next four quarters. As Rudebusch and Williams state "Even at a horizon of two quarters, and certainly at three and four quarters ahead, the probability forecasts appear to have little relationship with historical recessions."
Compare these economists probabilities with the probability of a recession from a two variable model (using the level of short rates and the slop of the yield curve). The economists say their is currently a 19% chance of a recession in the next 4 quarters, the simple model says it is closer to a 30% probability.
Of course, economists have been aware of such simple models for many years. But this begs the question why they don't use/follow them? My own answer is overconfidence. This seems to be supported by the Rudebusch and Williams paper which concludes "It is interesting to note that many times during the past twenty years forecasters have acknowledged the formidable past performance of the yield curve in predicting expansions and recessions but argued that this past performance did not apply in the current situation. That is, signals from the yield curve have often been dismissed because of supposed changes in the economy or special factors in‡uencing interest rates. This paper, however, shows that the relative predictive power of the yield curve does not appear to have diminished much, if at all. "
Yet another example of just how poor forecasting really is. We need to find a better way to invest than relying upon our disproven and discredited forecasting abilities.
Monday, 3 September 2007
Something the Boglehead wouldn't want you to know, or index investing isn't passive
Just for the record, Bogleheads are die-hard devotees of index investing. Occassionally someone will mistake my criticisms of much of the active management industry for support of the Bogleheads' position. However, this isn't the case. In fact I reject pretty much all the foundations that index investing is built upon (see Chapter 35 of Behavioural Investing). The only exception is that the Bogleheads are quite right to point out the importance of minimising costs.
I recently came across a paper which I thought deserved some attention. It goes by the title of Index Rebalancing and Long-Term Portfolio Performance by Cai and Houge. It focuses on one of the misnomers of investing, that index investing is passive. This simply isn't true. Many indices are relatively actively managed. In fact most indices are really momentum players effectively adding stocks that have done well and deleting stocks that have done badly. This raises the question as to whether this 'active' element adds or destroys value. That is to say would you be better off if you ignored the index changes made by the index setters?
Cai and Houge take the Russell 2000 index and examine its performance since 1979 and see if the index changes that have occurred managed to add value to the investor over various time horizons. The Russell 2000 index is a small cap index, and makes on average 457 index changes each year (around 10% of market cap).
Cai and Houge show that an average an investor would be 2.2% better off in year one if they ignored the index changes, this rises to 17% in year five! So a buy and hold strategy seems to generate substantially higher returns for investors (yet again evidence of patience being key to investors - see Chapters 30/31 of Behavioural Investing).
Effectively Cai and Houge show that deletions have better future long term returns that additions to the index. In fact they show that deletions outperform non-new issue additions by around 8.9% in year 1 and 28% over five years. If one includes new issues that are added to the index, the situation is even worse since they underperform the deletions by 40% over five years!
Given that they are considering the Russell 2000 (a small cap index remember) , some stocks will leave the index because they become too large. Indeed the returns on these stocks seem particularly important in generating some of the short term outperform of the buy and hold strategy.
However, the results that Cai and Houge uncover are not simply an artifact of the way of the index considered. Siegel and Schwartz (2006) show a similar picture for the S&p500 (where no stock is deleted for being too large!) They track the changes made to the S&P500 from 1957 onwards. Nearly 1000 index additions over the sample period, averaging around 20 a year.
Three portfolios are formed, allowing for different scenarios:
(I) The survivor portfolio consists only of shares of the original S&P 500 firms. Shares of other firms received through mergers are immediately sold and the proceeds invested in the remaining survivor firms in proportion to their market value.
(II)Direct Descendants’ Portfolio (DDP), which consists of the shares of firms in the survivors’ portfolio plus the shares issued by firms acquiring an original S&P 500 firm.
(III)Total Descendants’ Portfolio (TDP) and includes all firms in the DDP plus all the spinoffs and other stock distributions issued by the firms in the Direct Descendants’Portfolio. The only difference between the TDP and the DDP is that the TDP holds all the spin-offs rather than sell them and reinvest in the proceeds in the parent firm.
The returns to the various portfolios are shown below:
All three of the constructed portfolios outperform the index with it's additions and deletions, and they do so with considerably less risk!
The bottom line appears to be that index investing is often very far from passive. The rules of index construction appear to destroy value. Of course, one of the best ways of avoiding this problem is to be a long-term investor (i.e. conduct time arbitrage).
I recently came across a paper which I thought deserved some attention. It goes by the title of Index Rebalancing and Long-Term Portfolio Performance by Cai and Houge. It focuses on one of the misnomers of investing, that index investing is passive. This simply isn't true. Many indices are relatively actively managed. In fact most indices are really momentum players effectively adding stocks that have done well and deleting stocks that have done badly. This raises the question as to whether this 'active' element adds or destroys value. That is to say would you be better off if you ignored the index changes made by the index setters?
Cai and Houge take the Russell 2000 index and examine its performance since 1979 and see if the index changes that have occurred managed to add value to the investor over various time horizons. The Russell 2000 index is a small cap index, and makes on average 457 index changes each year (around 10% of market cap).
Cai and Houge show that an average an investor would be 2.2% better off in year one if they ignored the index changes, this rises to 17% in year five! So a buy and hold strategy seems to generate substantially higher returns for investors (yet again evidence of patience being key to investors - see Chapters 30/31 of Behavioural Investing).
Effectively Cai and Houge show that deletions have better future long term returns that additions to the index. In fact they show that deletions outperform non-new issue additions by around 8.9% in year 1 and 28% over five years. If one includes new issues that are added to the index, the situation is even worse since they underperform the deletions by 40% over five years!
Given that they are considering the Russell 2000 (a small cap index remember) , some stocks will leave the index because they become too large. Indeed the returns on these stocks seem particularly important in generating some of the short term outperform of the buy and hold strategy.
However, the results that Cai and Houge uncover are not simply an artifact of the way of the index considered. Siegel and Schwartz (2006) show a similar picture for the S&p500 (where no stock is deleted for being too large!) They track the changes made to the S&P500 from 1957 onwards. Nearly 1000 index additions over the sample period, averaging around 20 a year.
Three portfolios are formed, allowing for different scenarios:
(I) The survivor portfolio consists only of shares of the original S&P 500 firms. Shares of other firms received through mergers are immediately sold and the proceeds invested in the remaining survivor firms in proportion to their market value.
(II)Direct Descendants’ Portfolio (DDP), which consists of the shares of firms in the survivors’ portfolio plus the shares issued by firms acquiring an original S&P 500 firm.
(III)Total Descendants’ Portfolio (TDP) and includes all firms in the DDP plus all the spinoffs and other stock distributions issued by the firms in the Direct Descendants’Portfolio. The only difference between the TDP and the DDP is that the TDP holds all the spin-offs rather than sell them and reinvest in the proceeds in the parent firm.
The returns to the various portfolios are shown below:
| Geometric return | SD | Sharpe | |||||||
| Survivors Portfolio | 11.31% | 15.72% | 0.4343 | ||||||
| Direct Descendants | 11.35% | 15.93% | 0.4331 | ||||||
| Total Descendants | 11.40% | 16.09% | 0.4337 | ||||||
| S&P 500 | 10.85% | 17.02% | 0.3871 |
All three of the constructed portfolios outperform the index with it's additions and deletions, and they do so with considerably less risk!
The bottom line appears to be that index investing is often very far from passive. The rules of index construction appear to destroy value. Of course, one of the best ways of avoiding this problem is to be a long-term investor (i.e. conduct time arbitrage).
Tuesday, 28 August 2007
Sucker for a story, or, Why you should never buy an IPO
Anyone who has attended one of my behavioural finance speeches will probably have heard me talk about the dangers of stories (Chapter 15 of Behavioural Investing) , and use IPOs as an example. I point out that IPOs have usually got wonderful stories attachted to them, but statistically they are a nightmare investment. In chapter 7 of Behavioural Finance, I present a mass of evidence that shows on average IPOs underperform the market by around 30% over the three years after their listing (after their first day of issue).Generally during the speech I also talk about need to reverse engineer valuation to avoid anchoring on market prices. That is to say, take market prices and back out what they imply for growth, and then assess whether there is any likelihood of that expected growth being delivered (Chapter 2 of Behavioural Investing).
A new paper by Cogliati et al (available here http://papers.ssrn.com/sol3/papers.cfm?abstract_id=965450) combines these two observations. They examine some 168 IPOs that took place in the UK, France, Italy and Germany between 1995 -2001. Because of their methodology Cogliati et al end up with a sample that has actually larger and older IPOs than the average seen across the exchanges that they covered. So their results don't just reflect the explosion of small cap tech stocks seen in the dot.com boom. Additionally, as a result of this the results uncovered are likely to understate the true picture!
Cogliati et al reverse engineer the growth rate in free cash flow implied by the IPO listing price. They use a two stage DCF model with a five year abnormal growth period, before growth settles down to GDP. On average, the IPOs in their sample show an implied growth rate of nearly 20% p.a. !
The chart at the start of this entry shows the implied free cash flow growth rate for years 1,3 and 5 against the actual delivered growth rate. To say that IPOs fall enormously short of investor's expectations would be a gargantuan understatement. Far from delivering 20% growth in FCF in year one, most IPOs continue to have negative FCF. Even by year 5, they are showing a paltry 3.7% FCF growth, a far cry from the 20% expected at listing!
Cogliati et al use the realised cash flows to estimate a true 'fair value' for the IPOs in their sample. They find that the listing price is on average 70% above their intrinsic value calculations!
Focus on the facts, not the stories!
Tuesday, 21 August 2007
Earnings manipulation as source of short ideas
One of the wonderful things about a change of jobs is that it provides a good opportunity to clear out your old stuff. Whilst I was doing this the other day I came across a note I wrote at the end of October 2003, which I called Earnings Junkies. I scanned the note and came across a list of stocks that I had put together which had poor M scores.
The M score was created by Professor Messod Beneish. In many ways it is similar to the Altman Z score, but optimised to detect earnings manipulation rather than bankruptcy. The orginal full paper can be found here www.bauer.uh.edu/~swhisenant/beneish%20earnings%20mgmt%20score.pdf
Beneish used all the companies in the Compustat database between 1982-1992. The M score is based on a combination of the following eight different indices:
DSRI = days' sales in receivables index (measured as the ratio of days' sales in receivables in year t to year t-1). A large increase in DSR could be indicative of revenue inflation.
GMI = gross margin index (measured as the ratio of gross margin in year t-1 to gross margin in year t). Gross margin has deteriorated when this index is above 1. A firm with poorer prospects is more likely to manipulate earnings.
AQI = asset quality index (asset quality is measured as the ratio of noncurrent asses other than plant, property and equipment to total assets). AQI is the ratio of asset quality in year to year t-1.
SGI = sales growth index (ratio of sales in year t to sales in year t-1). Sales growth is not itself a measure of manipulation. However, growth companies are likely to find themselves under pressure to manipulate in order to keep up appearances.
DEPI = depreciation index (measured as the ratio of the rate of depreciation in year t-1 to the corresponding rate in year t). A DEPI>1 indicates that assets are being depreciated at a slower rate. This suggests that the firm might be revising useful asset life assumptions upwards, or adopting a new method that is income friendly.
SGAI = sales, general and administrative expenses index (the ratio of SGA expenses in year t relative to year t -1).
LVGI = leverage index (the ratio of total debt to total assets in year t relative to yeat t-1). An LVGI >1 indicates an increase in leverage
TATA - total accruals to total assets (total accruals calculated as the change in working capital accounts other than cash less depreciation).
These eight variables are then weighted together according to the following:
M = -4.84+0.92*DSRI+0.528*GMI+0.404*AQI+0.892*SGI+0.115*DEPI-0.172*SGAI+4.679*TATA-0.327*LVGI
A score greater than -2.22 indicates a strong likelihood of a firm being a manipulator. In his out of sample tests, Beneish found that he could correctly identify 76% of manipulators, whilst only incorrectly identifying 17.5% of non-manipulators.
When I ran M score on the non-financial companies in the S&P500 at the end of October 2003, some 57 stocks showed a M score greater than this critical threshold. I have recently updated their performance. Since October 2003, the S&P500 is up some 38%. An equally weighted basket of the stocks identified as potential manipulators is up only 21%, a 16% underperformance.
The median return on the identified stocks is just 12% (a 26% underpeformance of the S&P500). Some 73% of the stocks uncovered as potential manipulators had returns below the market, 20% had negative absolute returns!
As I was thinking about this issue a new paper by Prof. Beneish landed in my inbox (
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=100684).
In this new paper Beneish explores the use of the M score as a stock selection technique. Beneish examines portfolio deciles based around his M score over the period 1993-2003 with annual rebalancing done four months after the financial year end.
The results are impressive. When using market and size adjusted returns the M score strategy generates a hedged return of nearly 14% p.a. Using the Fama and French 3 factor model (market, size and style adjusted) , the stocks with the worst M scores show a -12% return, whilst the stocks with the best M scores show a 4% return, generating a long/short return of 16% (of course).
In the last year I have had many conversations with traditional long only fund managers who were setting up internal hedge funds or 130/30 style products. They were often surprised when I suggested that the shorts were not just the opposite of their longs. Shorting requires a different discipline. Perhaps using the M score might offer up a short list of potential candidates.
With earnings at a cyclical peak, finding out who has been fudging their numbers could be a particularly useful pursuit. Sadly, I don't have the data to run this screen from here, but perhaps a call to your favourite quant analyst might be in order (if that happens to be Andy Lapthorne, then you are out of luck as I know he, like me, is enjoying his garden at the moment).
The M score was created by Professor Messod Beneish. In many ways it is similar to the Altman Z score, but optimised to detect earnings manipulation rather than bankruptcy. The orginal full paper can be found here www.bauer.uh.edu/~swhisenant/
Beneish used all the companies in the Compustat database between 1982-1992. The M score is based on a combination of the following eight different indices:
DSRI = days' sales in receivables index (measured as the ratio of days' sales in receivables in year t to year t-1). A large increase in DSR could be indicative of revenue inflation.
GMI = gross margin index (measured as the ratio of gross margin in year t-1 to gross margin in year t). Gross margin has deteriorated when this index is above 1. A firm with poorer prospects is more likely to manipulate earnings.
AQI = asset quality index (asset quality is measured as the ratio of noncurrent asses other than plant, property and equipment to total assets). AQI is the ratio of asset quality in year to year t-1.
SGI = sales growth index (ratio of sales in year t to sales in year t-1). Sales growth is not itself a measure of manipulation. However, growth companies are likely to find themselves under pressure to manipulate in order to keep up appearances.
DEPI = depreciation index (measured as the ratio of the rate of depreciation in year t-1 to the corresponding rate in year t). A DEPI>1 indicates that assets are being depreciated at a slower rate. This suggests that the firm might be revising useful asset life assumptions upwards, or adopting a new method that is income friendly.
SGAI = sales, general and administrative expenses index (the ratio of SGA expenses in year t relative to year t -1).
LVGI = leverage index (the ratio of total debt to total assets in year t relative to yeat t-1). An LVGI >1 indicates an increase in leverage
TATA - total accruals to total assets (total accruals calculated as the change in working capital accounts other than cash less depreciation).
These eight variables are then weighted together according to the following:
M = -4.84+0.92*DSRI+0.528*GMI+0.404*AQI+0.892*SGI+0.115*DEPI-0.172*SGAI+4.679*TATA-0.327*LVGI
A score greater than -2.22 indicates a strong likelihood of a firm being a manipulator. In his out of sample tests, Beneish found that he could correctly identify 76% of manipulators, whilst only incorrectly identifying 17.5% of non-manipulators.
When I ran M score on the non-financial companies in the S&P500 at the end of October 2003, some 57 stocks showed a M score greater than this critical threshold. I have recently updated their performance. Since October 2003, the S&P500 is up some 38%. An equally weighted basket of the stocks identified as potential manipulators is up only 21%, a 16% underperformance.
The median return on the identified stocks is just 12% (a 26% underpeformance of the S&P500). Some 73% of the stocks uncovered as potential manipulators had returns below the market, 20% had negative absolute returns!
As I was thinking about this issue a new paper by Prof. Beneish landed in my inbox (
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=100684).
In this new paper Beneish explores the use of the M score as a stock selection technique. Beneish examines portfolio deciles based around his M score over the period 1993-2003 with annual rebalancing done four months after the financial year end.
The results are impressive. When using market and size adjusted returns the M score strategy generates a hedged return of nearly 14% p.a. Using the Fama and French 3 factor model (market, size and style adjusted) , the stocks with the worst M scores show a -12% return, whilst the stocks with the best M scores show a 4% return, generating a long/short return of 16% (of course).
In the last year I have had many conversations with traditional long only fund managers who were setting up internal hedge funds or 130/30 style products. They were often surprised when I suggested that the shorts were not just the opposite of their longs. Shorting requires a different discipline. Perhaps using the M score might offer up a short list of potential candidates.
With earnings at a cyclical peak, finding out who has been fudging their numbers could be a particularly useful pursuit. Sadly, I don't have the data to run this screen from here, but perhaps a call to your favourite quant analyst might be in order (if that happens to be Andy Lapthorne, then you are out of luck as I know he, like me, is enjoying his garden at the moment).
Friday, 17 August 2007
The Quant meltdown or a tale of too much leverage?
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As markets have declined, I've witnessed a surprising degree of schadenfreude from many commentators (including some of my friends) over the problems suffered by quant funds of late. Bloomberg has been rife with stories of problems at funds such as Goldman's Global Equity Opportunity, AQR and Renaissance.
Some have seized on these problems as 'proof' of the failure of quant investing. This is, of course, utter nonsense. No model works all the time, especially one based on valuation. For those familiar with Joel Greenblatt's Little Book that beats the market, he describes with glee the fact that the strategy fails in four years out of ten. Why? Because it means that those focused on the short term will be unable to follow the strategy in a disciplined fashion. This furthers the opportunity for willing to invest for the longer-term.
We all know that value strategies can have poor years (just recall the problems that value managers suffered in the dot.com bubble). Quant investing is just a method of removing the human from (much of) the decision making process. In the past I've written on the enormous evidence to suggest that quant models are superiour to human judgement in a wide range of fields (Chapter 22 of Behavioural Investing). The recent performance of quant funds does nothing to alter this.
However, this is not to excuse the quant funds. I've been surprised by the scale of the problem they have encountered. When I looked at the performance of a generic value strategy (such as price to book), the last few months have not been pretty. For instance, in the US a strategy of buying the cheapest stocks by P/B and selling the most expensive has generated a 3% negative return in July (and presumably worse in August - although I don't have the data to prove that).
Now a -3% return isn't that bad. It actually isn't anything out of ordinary for such a value strategy. The chart at the start of this entry shows the distribution of returns to the price to book strategy since 1926 (thanks to Ken French for the use of his data). In fact returns of -3% or more per month have been seen 10% of the time.
The mean return of the price to book strategy in the US is 0.4% p.m with a standard deviation of 3.5%. If we assume that the returns to value are normally distributed (obviously not a great assumption) then 95% of monthly returns should lie between 7.5% and -6.7%. So even a cursory glance at the data would reveal that July is nothing out of the ordinary.
How did the quant funds manage to translate this relatively normal occurrence into something so performance destroying?
I can think of three possible routes (not mutually exclusive) :
- They hadn't checked the long term data.
- They were using leverage.
- The trades were overcrowded
The second route involves the use of leverage. Value strategies and leverage don't make easy bedfellows. Indeed John Maynard Keynes observed the dangers of combining a long-term view (which any value strategy must be - Chapter 31 of Behavioural Investing) with leverage. He opined "An investor who proposes to ignore near-term market fluctuations needs greater resources for safety and mist not operate on so large a scale, if at all, with borrowed money."
When I first heard that some of the quant funds were in trouble my first reaction was that they had been deploying leverage. This has recently been confirmed by a conference call between US analysts and the CFO of Goldman Sachs (taken from Street Events).
Susan Katzke, Credit Suisse:
Okay. And just I don't know if you covered this with Roger -- I might have missed it -- but in terms of the leverage in the funds, what are the leverage parameters, and where do you expect them to be going forward? Were they in retrospect a little bit higher than you would have liked them to have been or will be going forward?
David Viniar, Goldman Sachs CFO:
They were higher than we wished they were given how fast the market moved, but they were right in line with what had been expected. And the leverage at GEO as we sit here now is around 3.5 times, which is actually a little bit under where we had told people we would operate but probably around where we will operate going forward.
Susan Katzke, Credit Suisse:
Okay. And the 3.5 times just to clarify is with the $3 billion equity investment?
David Viniar, Goldman Sachs CFO:
With the equity investment.
Susan Katzke, Credit Suisse:
Okay. So closer to 6 times before that.
David Viniar, Goldman Sachs CFO:
That is correct.
So Goldman's Global Equity Opportunity fund was running with leverage of 6 times! This strikes me as madness for a value orientated strategy.
Overleveraged trades often go hand in hand with over-crowded trades. Cliff Asness at AQR wrote the following to his investors "This isn’t about models, this is about a strategy getting too crowded, as other successful strategies both quantitative and non-quantitative have gotten many times in the past, and then suffering when too many try to get out the same door. We knew this was a risk-factor but, like most others, in hindsight, we underestimated the magnitude and the speed with which danger could strike."
If there is something to be learnt from the problems the quant fund find themselves in, it is far less to do with the failure of quant and far more to do with the dangers of leverage.
Thursday, 16 August 2007
Fear and greed
In one of my last notes for Dresdner Kleinwort I stated that perhaps I had lost the plot. When I looked at markets all I could see was irrational complacency, the pricing of assets without any regard to risk, and the dash to trash.
Emerging markets were trading at the same valuation multiple as developed markets. Small caps were on a premium to large caps (ie a negative liquidity premium), and junk equity was the flavour of the day.
Given the unwind we have witnessed in the markets, it is fair to ask how far along are we in the process of risk re-pricing?
The chart above shows my fear and greed index. A simple construct that measures the risk adjusted relative performance of global equities vs global bonds. Whilst it has fallen sharply, it remains a long way from the kinds of panic sentiment that usually characterize buying opportunities.
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