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.
Saturday, 29 September 2007
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).
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