Category Archives: Skill

How Much Time is Needed to Detect Evidence of Investment Skill?

A recent MarketWatch piece cited a talk in Hong Kong by Economics Nobel Prize winner Professor Robert Merton wherein he discussed the challenges of evaluating investment managers. The following article assumes that the above summary of Professor Merton’s talk is accurate. The piece, and assumedly the talk, argued that, given typical nominal portfolio returns and volatilities, it takes impractically long to detect evidence of investment skill. The argument claimed to prove that all manager selection is futile. Instead, it proved that naïve nominal performance metrics are of little use.

Any test of the effectiveness of manager selection is also a test of the analytical process that distills skill. That nominal investment performance is primarily due to factor (systematic, market) noise and thus reverts is well-known. It is thus unsurprising to find flaws in an approach to manager selection that is as antiquated as Ptolemaic Astronomy.

In this article, we will illustrate the difference between a naïve attempt to detect evidence of investment skill using nominal returns and a more productive effort relying on alphas (residual, security selection, stock picking returns) isolated using a capable modern multi-factor equity risk model. Whereas the former approach is futile at best, the latter approach is successful. In fact, rather than taking decades, a capable modern system can identify skill with high confidence in months.

Detecting Evidence of Investment Skill Using Nominal Returns

Consider nominal returns of a Portfolio and a Benchmark. The Portfolio is a live long-only fund implementing a Smart Beta active investment strategy:

Chart of the absolute cumulative returns for the Portfolio and the Benchmark as well as Portfolio’s cumulative return relative to the Benchmark

Portfolio’s and Benchmark’s Cumulative Returns

                           Portfolio Benchmark
 Annualized Return            0.1336    0.1433
 Annualized Std Dev           0.0879    0.1093
 Annualized Sharpe (Rf=0%)    1.5194    1.3115

With a heroic assumption that log returns follow a normal distribution, a t-test appears to confirm Professor Merton’s argument. Even with over six years of data, the returns are too noisy for a statistical inference:

Distribution of Portfolio’s Returns Relative to the Benchmark

    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 -6.1441 -1.2186 -0.0201 -0.1149  1.2481  5.4068 
 
      One Sample t-test
 t = -0.4607, df = 78, p-value = 0.6768
 alternative hypothesis: true mean is greater than 0
 95 percent confidence interval:
  -0.5300        Inf

Detecting Evidence of Investment Skill Using Alphas/Residuals

By comparison, consider the same Portfolio’s residual returns, or alphas, for the same period, isolated with the AlphaBetaWorks’ standard Long-Horizon Statistical U.S. Equity Risk Model. These are also the returns Portfolio would have generated if its factor exposures had been fully hedged (its returns factor-neutralized, or residualized) using the Model:

Chart of the absolute cumulative residual (alpha, security selection, stock picking returns) for the Portfolio

Portfolio’s Cumulative Residual/Alpha

With an equally questionable assumption that log residuals follow a normal distribution, a t-test is now highly statistically significant:

Chart of the distribution of Portfolio’s residual returns used to detect evidence of investment skill

Distribution of the Portfolio’s Residuals/Alphas

    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 -1.5300 -0.2064  0.2643  0.2620  0.7289  2.3663 
 
      One Sample t-test
 t = 3.3126, df = 78, p-value = 0.0007
 alternative hypothesis: true mean is greater than 0
 95 percent confidence interval:
  0.1303         Inf

Whereas Professor Merton’s argument does indeed apply to nominal returns, it does not apply to their residuals. A critical difference is the lower dispersion of residual returns. Over 90% of the variance of a typical active equity portfolio is due to factor exposures rather than to stock picking. Therefore, using nominal returns to measure skill is like trying to take a baby’s temperature by examining her bath water, rather than the baby herself.

Whereas at least 67 out of 100 monkeys picking stocks at random are expected to outperform the Portfolio, less than 1 out of 1,000 is expected to generate higher residuals – a highly statistically significant result. Thus, with the help of a capable equity risk model, strong evidence of skill can be identified in months rather than in decades.

Converting Residuals into Nominal Outperformance

Assuming the equity risk model uses investable factors, as AlphaBetaWorks’s models do, the residual return stream above is investable. In fact, in the idealized case of costless leverage, positive residual returns can be turned into outperformance relative to any benchmark. Below is the performance of Portfolio after it is hedged to match the factor exposures of the Benchmark. The evidence of skill is now plainly visible in the naïve absolute and relative nominal return metrics:

Chart of the absolute cumulative returns for the Portfolio hedged to match the factor exposures of the Benchmark, the Benchmark, as well as Portfolio’s cumulative return relative to the Benchmark

Cumulative Returns for the Portfolio Hedged to Match the Benchmark and the Benchmark

                          Portfolio with Benchmark Risk  Benchmark
 Annualized Return                                0.1784    0.1433
 Annualized Std Dev                               0.1168    0.1093
 Annualized Sharpe (Rf=0%)                        1.5276    1.3115

Conclusions

Since factor noise dominates nominal returns, the use of nominal returns to detect evidence of investment skill takes far too long to be practical.
After distilling stock picking performance (alphas, residual returns) from factor noise, statistically significant evidence of investment skill can become evident in months, rather than in decades.
Hedging makes it possible to turn positive stock picking returns into nominal outperformance with respect to any benchmark.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2018, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

The Predictive Power of Information Ratios

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Our earlier work showed that simple performance metrics, such as nominal returns and Sharpe Ratios, revert. Because of this reversion, above-average past performers tend to become below-average and vice versa. This reversion is primarily due to systematic (factor) noise. Consequently, metrics that remove factor effects from performance reveal persistent stock picking skill. Prompted by readers’ questions, we have investigated the predictive power of popular performance metrics. This article reviews the predictive power of information ratios. They offer a large improvement over simple nominal returns, naive alphas, and Sharpe ratios, but still fall short of the most predictive metrics. Over a 3-year window, the predictive power of information ratios for skill evaluation and manager selection is approximately half that of security selection distilled with a statistical equity risk model.

Measuring the Predictive Power of Information Ratios

We analyze portfolios of all institutions that have filed Forms 13F in the past 15 years. This survivorship-free portfolio dataset covers firms that have held as least $100 million in long U.S. assets. Approximately 5,000 portfolios had sufficiently long histories and low turnover to be analyzable.

To measure the persistence of performance metrics over time, we compare metrics measured in two 12-month periods separated by variable delay. One example is the 24-month delay that separates metrics for 1/31/2010-1/31/2011 and 1/31/2013-1/31/2014. A 24-month delay of 12-month metrics thus covers a 48-month time window. We use Spearman’s rank correlation coefficient to calculate statistically robust correlations.

Serial Correlation of Information Ratios

The information ratio is similar to the Sharpe ratio, but with a key upgrade: Sharpe ratio evaluates returns relative to the risk-free rate. Information ratio evaluates returns relative to a (presumably appropriate) benchmark. We use the S&P 500 Index as the benchmark, following a common practice. As a benchmark increasingly matches the factor exposures of a portfolio, information ratios converge to the standard score (z-score) of active returns estimated with a capable equity risk model. Due to the more effective handling of systematic risk, the predictive power of information ratios receives a boost.

The chart below shows correlation between 12-month Information Ratios calculated with lags of one to sixty months (1-60 month delay):

Chart of the predictive power of information ratios as measured by their autocorrelation (the correlation between Information Ratios for one 12-month period and a different 12-month period separated by a given lag) for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of Information Ratios

Delay (months)	Serial Correlation
1	0.06
6	0.05
12	0.03
18	0.05
24	0.06
30	0.06
36	0.02
42	-0.02
48	-0.06
54	-0.04
60	0.02

Over the 3-year window, the serial correlation (autocorrelation) of Information Ratios is approximately half of the serial correlation of security selection returns provided in the following section. Unlike simple nominal returns and Sharpe ratios, information ratios do not suffer from short-term reversion.

Serial Correlation of Nominal Returns

For comparison, the following chart shows serial correlation of 12-month cumulative nominal returns calculated with 1-60 month lags. As we discussed in prior articles, these revert with an approximately 18-month cycle – so strong past nominal returns are actually predictive of poor short-term future nominal returns:

Chart of the correlation between returns for one 12-month period and a different 12-month period separated by a given lag for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of nominal returns

Delay (months)	Serial Correlation
1	-0.14
6	-0.24
12	-0.33
18	-0.06
24	0.16
30	0.23
36	0.08
42	-0.26
48	-0.44
54	-0.23
60	0.13

Serial Correlation of Security Selection Returns

As we mentioned above, when a benchmark’s factor exposures match those of the portfolio, information ratio is equivalent to the standard score (z-score) of active returns estimated with a capable equity risk model. In practice, however, information ratio is typically calculated relative to a broad benchmark, such as the S&P 500 Index for equity portfolios. Consequently, one would expect the predictive power of information ratios to be lower than the predictive power of security selection returns, properly estimated. For comparison, we provide serial correlation of a security selection metric that uses an equity risk model to control for factor exposures.

To eliminate the disruptive factor effects responsible for performance reversion, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection net of factor effects. αReturn is the return a portfolio would have generated if all factor returns had been zero. The following chart shows correlation between 12-month cumulative αReturns calculated with 1-60 month lags:

Chart of the correlation between αReturns (risk-adjusted returns from security selection) for one 12-month period and a different 12-month period separated by a given lag for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of αReturns (risk-adjusted returns from security selection)

Delay (months)	Serial Correlation
1	0.08
6	0.10
12	0.08
18	0.05
24	0.06
30	0.04
36	-0.02
42	-0.04
48	-0.05
54	-0.04
60	-0.02

The predictive power of αReturns, as measured by their serial correlation of 12-month performance metrics, is approximately twice that of information ratios over a 3-year window (12-month delay between 12-month performance metrics), but the two begin to converge after three years.

For all performance metrics, the above data is aggregate, spanning thousands of portfolios and return windows. Individual firms can overcome the averages; however, the exceptions require especially careful monitoring.

Summary

The predictive power of information ratios is significantly higher than that of nominal returns and Sharpe ratios.
As a benchmark converges to the factor exposures of a portfolio, information ratios converge to the standard score (z-score) of active returns estimated with a capable risk model.
Over a 3-year window, the predictive power of information ratios, as commonly calculated, is approximately half that of the security selection return calculated with a predictive equity risk model.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending

The Predictive Power of Sharpe Ratios

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Our earlier work showed that performance metrics dominated by market noise, such as simple nominal returns, revert. Because of this reversion, above-average performers of the past tend to become below-average performers and vice versa. Since the reversion is primarily due to systematic (factor) noise, metrics that control for factor exposures reveal persistent stock picking skill. Prompted by readers’ questions, this series of articles will measure the predictive power of popular performance metrics. We first consider the predictive power of Sharpe Ratios. For the universe of all institutional U.S. long equity portfolios, the use of Sharpe Ratios for skill evaluation and manager selection is almost as damaging as the use of simple nominal returns.

Measuring the Predictive Power of Sharpe Ratios

To measure the decay of performance metrics over time, we compare metrics measured in two 12-month periods separated by variable delay. One example of 24-month delay is metrics for 1/31/2010-1/31/2011 and 1/31/2013-1/31/2014. We use Spearman’s rank correlation coefficient to calculate statistically robust correlations.

Serial Correlation of Sharpe Ratios

Sharpe Ratio is perhaps the most common performance metric. Since it does not directly control for systematic (factor) portfolio exposures, one would expect this approach to suffer from similar reversion as nominal returns. Indeed, tests reveal that Sharpe Ratios fail to isolate security selection performance: Sharpe Ratios of portfolios revert when factor regimes change. Thus, former leaders tend to become laggards, and former laggards tend to become leaders.

The serial correlation (autocorrelation) of Sharpe Ratios is similar to the serial correlation of nominal returns in the next section. The following chart shows correlation between 12-month Sharpe Ratios calculated with lags of one to sixty months (1-60 month lag):

The predictive power of Sharpe Ratios: Chart of the correlation between Sharpe Ratios for one 12-month period and a different 12-month period separated by a given lag for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of Sharpe Ratios

Delay (months)	Serial Correlation
1	-0.09
6	-0.20
12	-0.28
18	-0.06
24	0.12
30	0.15
36	-0.08
42	-0.40
48	-0.49
54	-0.24
60	0.12

Sharpe Ratios revert with an approximately 18-month cycle. Historical Sharpe Ratios thus have some predictive value, but a negative one. There is a narrow window at 2-3 year lag when past Sharpe Ratios are positively predictive of the future Sharpe Ratios. This is due to the approximately 18-month cycle of reversion.

Serial Correlation of Nominal Returns

For comparison, the following chart shows serial correlations between 12-month cumulative nominal returns calculated with 1-60 month lags. The relationship is similar to that of the Sharpe Ratios. Strong past (nominal) returns are predictive of poor short-term future returns:

13F Equity Portfolios: Serial correlation of nominal returns

Delay (months)	Serial Correlation
1	-0.14
6	-0.24
12	-0.33
18	-0.06
24	0.16
30	0.23
36	0.08
42	-0.26
48	-0.44
54	-0.23
60	0.13

Serial Correlation of Security Selection Returns

For additional comparison, we provide serial correlation of a security selection metric that adjusts for factor exposures. To eliminate the disruptive factor effects responsible for performance reversion, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection net of factor effects. αReturn is the return a portfolio would have generated if all factor returns had been flat. Firms with above-average αReturns in one period are likely to maintain them, though with a decay. The following chart shows correlation between 12-month cumulative αReturns calculated with 1-60 month lags:

13F Equity Portfolios: Serial correlation of αReturns (risk-adjusted returns from security selection)

Delay (months)	Serial Correlation
1	0.08
6	0.10
12	0.08
18	0.05
24	0.06
30	0.04
36	-0.02
42	-0.04
48	-0.05
54	-0.04
60	-0.02

Though the above serial correlations of αReturn may appear low, they are amplified and compounded in practical portfolios of multiple funds. A hedged portfolio of the net consensus longs (relative overweights) of the top 5% long U.S. equity stock pickers delivered approximately 8% return independently of the market. The above data is aggregate. Specific outstanding disciplined firms can overcome performance reversion, but they are the exceptions that require careful monitoring.

Summary

Sharpe Ratios revert rapidly and are not significantly better predictors of future performance than nominal returns.
Once performance is controlled for systematic (factor) exposures, security selection returns persist for approximately 5 years.
Selection of superior future performers is possible, but it requires abandoning popular non-predictive metrics and spotting skill long before it is plainly visible and arbitraged away.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending

Berkshire Hathaway’s Alpha Decay

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Berkshire’s Recent Exposure and Performance Raise Questions

There is a good reason many investors consider Berkshire Hathaway the paragon of investment success. For over 20 years, Berkshire’s equity portfolio has outperformed the general market on a risk-adjusted basis. But, in the past two years Berkshire’s alpha has turned negative. We identify the principal culprit and provide evidence of style drift at Berkshire – either by Warren Buffett or one of his possible successors.

Berkshire Hathaway Security Selection Return

Below is a chart showing Berkshire’s long equity portfolio performance since 2006, during which time it gained 108%. Many investors and guru-followers will dig no further. But simplistic analysis produces dangerous conclusions. Much of Berkshire’s performance—and that of most stocks—can be attributed to systematic sources, or factors. Berkshire’s nominal performance is influenced by the Market, bond rates, and FX rates. Since factor returns revert, and nominal returns are a contrarian indicator of future performance, it is important to separate Berkshire’s factor performance from its stock-picking skill.

We use the AlphaBetaWorks Statistical Equity Risk Model to analyze Berkshire’s equity portfolio since 2006. The chart below shows Berkshire returns due to systematic (factor) and idiosyncratic (stock selection, alpha) components.

Chart of the Factor and Stock Selection Components of Berkshire Hathaway's Long Equity Portfolio Returns

Berkshire Hathaway Long Equity Portfolio Performance – Factor and Stock Selection Components

In the chart above, the black line represents Berkshire’s long equity portfolio total return. Within this, the gray line is performance due to factors, and the blue area reflects Berkshire’s positive returns from stock selection, or αReturn. Since “alpha” and security selection performance are widely and often inconsistently used, AlphaBetaWorks defines a rigorous metric of security selection performance as αReturn, which is performance net of all factor effects, or the return a portfolio would have generated if markets were flat.

From 2006 to 2013 Berkshire’s return from stock selection alone was +29.9%. But in 2014 Berkshire’s αReturn started to decline (thinning blue area). Between 2014 and 2015, Berkshire’s return from stock selection was -16.3%.

Whereas the chart above depicts Berkshire αReturn including factor returns, the chart below depicts Berkshire’s ten-year αReturn in isolation.

Chart of the cumulative Return from Stock Selection (αReturn) of Berkshire Hathaway

Berkshire Hathaway Return from Stock Selection (αReturn)

Berkshire’s αReturn began to decay in 2014. There is one principal sector responsible for this: Technology.

Berkshire Hathaway Technology Exposure

Berkshire made a significant bet in the technology sector in mid-2011 after having avoided it for a very long time:

Chart of the historical technology factor exposure of Berkshire Hathaway Long Equity portfolio

Berkshire Hathaway Long Equity Factor Exposure – Technology

From 2006 to 2011, Berkshire’s technology exposure was effectively zero. But starting in 2011 it went up dramatically.

Berkshire Hathaway Technology Security Selection

This bet has gone poorly. In fact, this sector is the principal contributor to Berkshire’s recent αReturn decay:

Chart of the cumulative Return from Stock Selection (αReturn) of Berkshire Hathaway technology portfolio

Berkshire Hathaway Return from Stock Selection (αReturn) – Technology Sector

Berkshire’s long equity portfolio suffered over a 12% loss from technology stock selection, peak-to-trough.

Style Drift, Succession Issues?

Warren Buffett has long been very public about his avoidance of technology investments, citing reluctance to allocate capital to a business he cannot understand. So why was a manager with no record in, and known skepticism towards, the technology industry making large bets in it? In this light, the above data indicates worrying style drift. Perhaps this large technology sector bet was made by one of his likely successors, which raises an equally important question about manager succession and the style drift risk therein: does a new portfolio manager demonstrate security selection skill (positive αReturn) in general, and in the areas of growing allocation like technology? And what other changes should investors expect of new leadership?

Whether Berkshire’s recent negative αReturn is Buffett’s own error or commission or a byproduct of portfolio manager transition is beyond the scope of this piece. Our focus is identifying active return, its sources (factor timing, stock selection), and their predictive value. All of the above lead to improved performance. In Berkshire’s case, our approach has identified that an established star manager shifted focus to an area previously avoided (technology), and that this shift reduced stock selection performance. Whatever the reason, the results are troublesome. Their early warnings are critical to investors’ and managers’ performance.

Such analysis of risk and performance using holdings data and a predictive equity risk model provides indicators that enhance manager/fund selection and future performance. It can also highlight emerging issues, such as style drift.

Summary

After a long, positive run, Berkshire Hathaway’s stock selection returns (alpha) turned negative in 2014.
Berkshire’s allocation to the technology sector was the principal driver behind its recent negative alpha.
Whether by Buffett or someone new, style drift is a cause of Berkshire’s increased allocation to technology and the resulting negative alpha.
Even when investing in or alongside star managers, it is critical that investors and allocators rely on robust and predictive tools to detect early signs of style drift and stock selection decay.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

The Persistence of Negative Investment Performance

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And why Poor Nominal Returns are a Reason to Hire Rather than Fire a Manager

Our earlier pieces discussed how nominal investment performance reverts. Since returns are dominated by systematic risk factors (primarily the Market), they are subject to reversal when investment regimes change. In the simplest terms, high risk funds do well in bull markets, and low risk funds do well in bear markets, irrespectively of stock picking skill. When the tide turns, so does the funds’ relative performance. The persistence of stock picking skill becomes evident once systematic effects are removed.

This piece focuses on the persistence of negative investment performance. Negative investment performance exacerbates the losses due to simplistic performance metrics and sharpens the edge of predictive skill analytics: Negative nominal returns revert more sharply than overall nominal returns; negative security selection returns persist longer.

Measuring Persistence of Investment Performance

As our prior performance persistence work, this study analyzes portfolios of all institutions that have filed Form 13F during the past 15 years. This survivorship-free portfolio database covers thousands of firms that have held at least $100 million in U.S. long assets during this period.

The relationship between performance metrics of a portfolio calculated at different points in time captures their persistence. To measure the persistence of nominal returns, we analyze nominal returns during two 12-month periods separated by variable delay. For example, analysis of 24-month delay includes periods 1/31/2010-1/31/2011 and 1/31/2013-1/31/2014. We use the Spearman’s rank correlation coefficient to calculate statistically robust correlations between metrics. Technically speaking, we are studying the metrics’ serial correlation or autocorrelation.

The Persistence of Investment Performance

The following charts of autocorrelation have been updated with data through 5/31/2016 and remain virtually unchanged from our earlier work on the decay of stock picking skill.

Serial Correlation of Nominal Returns

Portfolios with above-average nominal returns for prior 12 months tend to underperform for approximately the following two years; similarly, those with below average nominal returns tend to then outperform:

Chart of the persistence of stock picking performance as measured by the correlation between returns for one 12-month period and a different 12-month period separated by a given lag for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of nominal returns

Delay (months)	Serial Correlation
1	-0.11
6	-0.26
12	-0.36
18	-0.09
24	0.15
30	0.22
36	0.08
42	-0.26
48	-0.42
54	-0.22
60	0.17

Serial Correlation of Security Selection Returns

To eliminate the disruptive factor effects responsible for the above reversion, the AlphaBetaWorks Performance Analytics Platform calculates return from security selection after controlling for the factor exposures. The resulting metric, αReturn, is the return a portfolio would have generated if all factor returns had been flat. Above-average and below-average 12-month αReturns tend to persist for approximately four years:

Delay (months)	Serial Correlation
1	0.08
6	0.10
12	0.08
18	0.05
24	0.05
30	0.04
36	-0.01
42	-0.03
48	-0.04
54	-0.03
60	-0.01

The Persistence of Negative Investment Performance

The autocorrelation of overall nominal returns and αReturns captures the persistence of both negative and positive investment performance, but positive and negative metrics need not have similar persistence. In fact, the problems with nominal returns and simplistic performance metrics derived from them are accentuated when the nominal returns are negative.

Serial Correlation of Negative Nominal Returns

Negative 12-month nominal returns revert even more rapidly and more strongly than overall returns. Rank correlation coefficient for 12-month nominal returns separated by 6 months is approximately -0.5 for negative nominal returns and -0.2 for overall nominal returns. Poor recent nominal returns are a reason to hire rather than fire a manager, at least in the short term (the subsequent 12-18 months):

Chart of the persistence of negative investment performance as measured by the correlation between negative returns for one 12-month period and a different 12-month period separated by a given lag for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of negative nominal returns

Delay (months)	Serial Correlation
1	-0.57
6	-0.23
12	-0.01
18	-0.14
24	0.52
30	0.18
36	-0.27
42	-0.27
48	-0.24
54	-0.12
60	0.07

Serial Correlation of Negative Security Selection Returns

This reversion is not present for αReturns. Negative αReturns have similar autocorrelation for the first few years and decay more slowly than overall αReturns:

Chart of the persistence of negative investment performance as measured by the correlation between negative αReturns (risk-adjusted returns from security selection) for one 12-month period and a different 12-month period separated by a given lag for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of αReturns (risk-adjusted returns from security selection)

Delay (months)	Serial Correlation
1	0.08
6	0.09
12	0.08
18	0.08
24	0.08
30	0.06
36	0.03
42	0.03
48	0.02
54	0.01
60	0.00

The decay in security selection performance is typically due to such things as talent turnover, style drift, management distraction, and asset growth. Since these are more likely to affect the top-performing funds, negative αReturn remains predictive for longer. The above data is aggregate and specific firms can and do overcome the average fate. Though the above serial correlations may appear low, they are amplified and compounded in portfolios of multiple funds.

Cheerful consensus is usually a recipe for mediocrity, whether investing in a stock or in a fund. Fear and panic in the face of nominal underperformance are more dangerous still. Just as it pays to be a contrarian stock picker, it pays to be a contrarian fund investor or allocator.

Summary

Nominal returns and related simplistic metrics of investment skill revert rapidly.
Negative nominal returns revert more strongly than overall nominal returns.
Negative security selection performance persists longer than overall security selection performance.
When negative investment performance is merely nominal, it is a contrarian indicator.
When negative investment performance is due to poor security selection net of factor effects, it is a persistent and predictive indicator.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending

The Decay of Stock Picking Skill

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Our earlier work showed that nominal returns and related simplistic performance metrics are dominated by market noise and hence revert. The reversion means that yesterday’s best-performing managers tend to be tomorrow’s worst. Yet, once distilled from systematic noise, stock picking skill is evident. This piece measures the decay of stock picking performance over time and identifies the historical window most predictive of future performance. We demonstrate that superior manager selection requires spotting skill well before the crowd arbitrages it away.

Measuring the Decay of Stock Picking Skill

This study analyzes portfolios of all institutions that have filed Form 13F. This is the broadest and most representative survivorship-free portfolio database covering thousands of firms that hold at least $100 million or more in U.S. long assets. Approximately 5,000 firms had sufficiently long histories, low turnover, and broad portfolios suitable for skill evaluation.

To measure the decay of stock picking performance over time, we compare metrics measured in two 12-month periods separated by variable delay. One example of 24-month delay is metrics for 1/31/2010-1/31/2011 and 1/31/2013-1/31/2014. We use Spearman’s rank correlation coefficient to calculate statistically robust correlations.

Serial Correlation of Nominal Returns

The following chart shows serial correlation (autocorrelation) between 12-month cumulative nominal returns calculated with lags of one to sixty months (1-60 month lag). The relationship is generally negative. This illustrates that strong past (nominal) returns are predictive of future returns, albeit poor in the short-term:

Chart of the decay of stock picking performance as measured by the correlation between returns for one 12-month period and a different 12-month period separated by a given lag for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of nominal returns

Delay (months)	Serial Correlation
1	-0.11
6	-0.26
12	-0.36
18	-0.09
24	0.15
30	0.22
36	0.08
42	-0.26
48	-0.42
54	-0.22
60	0.17

There is a narrow window at 2-3 year lag when past returns are predictive of the future results. This appears to be due to the approximately 18-month cycle of reversion in 12-month nominal performance.

Serial Correlation of Naive Alphas

It is common to measure alpha simply as outperformance relative to a benchmark. We will call this approach “naive alpha.” Since it ignores portfolio risk, this approach does not eliminate systematic (factor) effects and fails to isolate security selection performance: The top nominal performers who took the most systematic risk in a bullish regime remain the top performers after a benchmark return is subtracted. When regimes change, these former leaders tend to become the laggards, and vice versa.

Indeed, the serial correlation of naive alphas is similar to the serial correlation of nominal returns. The following chart shows correlation between 12-month cumulative naive alphas calculated with 1-60 month lags:

Chart of the decay of stock picking performance as measured by the correlation between naïve alphas (returns over S&P 500) for one 12-month period and a different 12-month period separated by a given lag for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of naive alphas (returns relative to the S&P 500 index)

Delay (months)	Serial Correlation
1	0.00
6	-0.02
12	-0.02
18	0.03
24	0.04
30	0.05
36	0.01
42	-0.02
48	-0.05
54	-0.02
60	0.04

Serial Correlation of Security Selection Returns

Firms with above-average αReturns in one period are likely to maintain them in the other, but with decay. The following chart shows correlation between 12-month cumulative αReturns calculated with 1-60 month lags:

Chart of the decay of stock picking performance as measured by the correlation between αReturns (risk-adjusted returns from security selection) for one 12-month period and a different 12-month period separated by a given lag for all U.S. equity 13F portfolios

13F Equity Portfolios: Serial correlation of αReturns (risk-adjusted returns from security selection)

Delay (months)	Serial Correlation
1	0.08
6	0.10
12	0.08
18	0.05
24	0.05
30	0.04
36	-0.01
42	-0.03
48	-0.04
54	-0.03
60	-0.01

For approximately 3 years, strong security selection performance, as measured by the 12-month αReturn, is predictive of the future 12-month results. Returns due to security selection thus persist for approximately 5 years. This means that as little as 12 month of consistently positive αReturns are a positive indicator for the following four years. Skilled stock pickers can be spotted years before their skill is plainly visible and broadly exploited.

The decay in security selection performance is typically due to the following sources: talent turnover, style drift, management distraction, and asset growth. It is not a coincidence that the conventional requirement for large institutional allocation is 3-5 year track record.

The above data is aggregate. Specific outstanding disciplined firms can overcome this reversion, but they are the exceptions that require careful monitoring. Spotting skilled managers before their skill is visible to all is a sounder path to superior selection. In this respect, investing with managers is very similar to investing in stocks. Manager skill is arbitraged away – analytical advantage over the crowd is key. Cheerful consensus is usually a recipe for mediocrity whether investing in a stock or in a fund.

Summary

Nominal returns and related simplistic metrics of investment skill revert rapidly.
Security selection returns persist for approximately 5 years.
Selection of superior future performers is possible, but it requires spotting skill long before it is plainly visible and arbitraged away.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.

Top Stock Pickers’ Exposure to Valeant

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In Early – Out Just in Time

Our recent article established that a portfolio of skilled stock pickers’ ideas generates consistent alpha. This brief analyzes what the AlphaBetaWorks Expert Aggregate had to say about one controversial name, Valeant Pharmaceuticals (VRX). In short, the smart money was in early and left almost a year before Valeant’s recent headaches.

Performance of the Top U.S. Stock Pickers

Since genuine investment skill persists, top U.S. stock pickers tend to generate persistently positive returns from security selection (idiosyncratic, residual returns). This strong performance derives from the top stock pickers’ individual positions. Their consensus exposures thus cut through the fog of panic and confusion, such as that surrounding VRX.

We track institutional ownership of Valeant by the AlphaBetaWorks Expert Aggregate (ABW Expert Aggregate). The ABW Expert Aggregate is sourced from all institutions that have filed Form 13F.

Nominal returns and related simplistic metrics of investment skill (Sharpe Ratio, Win/Loss Ratio, etc.) are dominated by systematic factors and thus revert. So for an accurate assessment of manager skill, we must eliminate the systematic effects and estimate residual performance due to stock picking. The AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection – αReturn. αReturn is the performance a portfolio would have generated if all factor returns had been flat. Each month we identify the five percent of 13F-filers with the most consistently positive αReturns over the prior 36 months. This expert panel of the top stock pickers typically consists of 100-150 firms.

A hedged portfolio that combines the top U.S. stock pickers’ net consensus longs (relative overweights) – lagged 2 months to account for filing delay (the ABW Expert Aggregate) – delivers consistent positive returns as illustrated below:

Chart of the cumulative return of the Market (Russell 3000) and the cumulative return of the hedged portfolio that combines the to 5% U.S. long stock pickers net consensus long (relative overweight) exposures

Cumulative Hedged Portfolio Return: Top U.S. Stock Pickers’ Net Consensus Longs

	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
ABW Expert Aggregate	4.56	14.43	12.74	5.95	-1.25	15.35	2.20	2.24	15.47	8.81	13.16	1.70
iShares Russell 3000 ETF	9.04	15.65	4.57	-37.16	28.21	16.81	0.78	16.43	32.97	12.41	0.34	-5.72

	ABW Expert Aggregate	iShares Russell 3000 ETF
Annualized Return	8.36	6.40
Annualized Standard Deviation	5.25	15.50
Annualized Sharpe Ratio (Rf=0%)	1.59	0.41

Top Stock Pickers’ Exposure to Valeant Pharmaceuticals (VRX)

The positive idiosyncratic returns of the ABW Expert Aggregate come from its individual positions: Its longs (experts’ relative overweights) tend to generate positive future αReturns. Its shorts (experts’ relative underweights) tend to generate negative future αReturns. The ABW Expert Aggregate thus measures the top stock pickers’ positioning which is predictive of the stocks’ future performance.

The top panel on the following chart shows the performance of VRX. Nominal returns are in black and cumulative residual returns (αReturns) are in blue. αReturn is the performance VRX would have generated if all systematic factor returns had been flat. The bottom panel shows exposure to VRX within the ABW Expert Aggregate:

Chart of the cumulative αReturn (residual return) of Valeant Pharmaceuticals (VRX) and exposure to VRX within the hedged portfolio that combines the to 5% U.S. long stock pickers net consensus long (relative overweight) exposures

VRX: Cumulative αReturns and Exposure within the ABW Expert Aggregate

Top stock pickers had negligible exposure to VRX until 2011. In early-2011 smart-money exposure to VRX grew rapidly, peaking between 2012 and 2014. Throughout 2014, as the shares appreciated, the top U.S. stock pickers sharply cut their exposure to VRX.

The panic hit a year after the top stock pickers dramatically cut their exposure. By 2015 smart-money exposure to VRX was a mixed signal as it tracked the stock’s volatility. Ironically, this is when VRX became one of the most crowded hedge fund bets. Though VRX had a history of smart-money ownership, this vote of confidence was withdrawn by 2015. Future stock-specific returns to VRX, as predicted by the ABW Expert Aggregate, increased rapidly in 2011 and dropped rapidly by 2015.

Investors, allocators, and fund-followers, armed with our analytics, would have taken note. Those looking for the smart money to back their long or short views of VRX must now look elsewhere.

Conclusions

Top stock pickers’ ownership is predictive of future stock performance, since their net consensus longs (relative overweights) tend to generate positive future αReturns and net consensus shorts (relative underweights) tend to generate negative future αReturns.
Valeant Pharmaceuticals (VRX) was a large overweight of the top stock pickers between 2011 and 2014, but these positions were mostly liquidated by 2015.
Top stock pickers’ current ownership of VRX is not a predictive indicator, but their rapid liquidation in 2014 was a visible warning.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.

Do Equity Risk Models Need a Quality Factor?

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It is common to augment risk models with numerous interrelated factors. This causes problems: Size, Value, Quality, Volatility, and their kin have much in common. At best, overzealous addition of related factors leads to unnecessarily bloated models. At worst, it leads to overfitting, multicollinearity, and questionable statistical analysis.

Fortunately, most complex factors derive virtually all of their volatility and performance from more basic ones such as Market, Sectors, and Size. Therefore, simple statistical equity risk models that capture a few intuitive investable factors with robust statistics usually suffice to describe and predict the performance of investable portfolios of more complex factors. We illustrate this with a popular Quality Factor ETF.

Attributing the Performance of a Quality ETF to Simpler Factors

We analyzed a popular Quality ETF using the AlphaBetaWorks Statistical Equity Risk Model – a proven tool for forecasting portfolio risk and performance. We estimated monthly positions from regulatory filings and aggregated positions’ factor (systematic) exposures. This produced a series of monthly portfolio exposures to simple investable risk factors such as Market, Sector, and Size. The factor exposures and subsequent factor returns were used to calculate future residual (security-selection, idiosyncratic, stock-specific) returns un-attributable to these simple investable factors.

iShares MSCI USA Quality Factor (QUAL): Performance Attribution

We used iShares MSCI USA Quality Factor (QUAL) as an example of a practical implementation of a quality factor portfolio. QUAL is a $1.7bil ETF that seeks to track an index of U.S. large- and mid-cap stocks with high return on equity, high earnings variability, and low debt-to-equity ratio.

iShares MSCI USA Quality Factor (QUAL): Factor Exposures

The following non-Quality factors are responsible for most of the historical returns and variance of QUAL within the parsimonious statistical equity risk model used:

Chart of exposures to the risk factors contributing most to the historical performance of iShares MSCI USA Quality Factor (QUAL)ETF

iShares MSCI USA Quality Factor (QUAL): Significant Historical Factor Exposures

	Latest	Mean	Min.	Max.
Market	88.99	85.98	81.07	89.44
Technology	19.93	33.39	19.93	37.35
Health	15.56	17.44	12.34	20.54
Consumer	35.08	33.41	28.70	35.50
Industrial	13.60	11.40	8.86	13.92
Energy	4.14	5.76	3.05	12.02
Size	9.11	6.27	2.91	9.11
Value	-0.93	-0.78	-1.54	0.10
Oil Price	-1.83	-0.09	-1.83	1.17
Finance	4.95	-1.22	-2.57	4.95

For instance, since Quality companies tend to be larger, some of QUAL’s performance is due to its long exposure to the Size Factor (overweighting of stocks that behave like large-capitalization companies):

Chart of the historical exposures to the Size Factor of iShares MSCI USA Quality Factor (QUAL)ETF

iShares MSCI USA Quality Factor (QUAL): Historical Size Factor Exposures

iShares MSCI USA Quality Factor (QUAL): Active Return

To replicate QUAL with simple non-momentum factors, one can use a passive portfolio of these simple non-momentum factors with QUAL’s mean exposures to them as weights. This portfolio defined the Passive Return in the following chart. Active return, or αβReturn, is the performance in excess of this passive replicating portfolio. It in turn is the sum of active return from residual stock-specific performance (αReturn) and active return from variation in factor exposures, or factor timing (βReturn):

Chart of the cumulative historical active return from security selection and factor timing of iShares MSCI USA Quality Factor (QUAL)ETF

iShares MSCI USA Quality Factor (QUAL): Cumulative Passive and Active Returns

QUAL’s performance closely tracks the passive replicating portfolio. Pearson’s correlation between Total Return and Passive Return is 0.97 – 94% of the variance of monthly returns is attributable to passive factor exposures, primarily to Market, Sector, and Size factors. Active return – performance due to idiosyncratic Quality effects rather than simpler factors – is negligible. Even without a factor to identify quality, the model comprehensively captures the risk and performance of QUAL.

QUAL offers convenient and cheap exposure to quality companies. We cite it here as an example of the reduction of the Quality Factor to simpler non-Quality factors. More elaborate, non-transparent, and expensive smart beta strategies can be hazardous. Many “smart beta” funds are merely high-beta and offer no value over portfolios of conventional dumb-beta funds. It is thus vital to test any new resident of the Factor Zoo to determine whether they are merely exotic breeds of its more boring residents.

Conclusion

Investable portfolios based on complex factors such as Quality, tend to derive virtually all of their volatility and performance from more basic factors, such as Market, Sectors, and Size.
A popular Quality ETF, iShares MSCI USA Quality Factor (QUAL), has had 0.97 correlation with a passive replicating portfolio of basic non-quality factors.
Even simple statistical equity risk models capturing a few intuitive and investable factors with robust statistics may adequately describe and predict the performance of Quality portfolios.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

Hedge Fund Industrials Factor Timing

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In an earlier post, we discussed the largest bets hedge fund long portfolios were making in Q1 2015. The third largest was on the Industrials Factor. This is the risk specific to the industrials sector after controlling for market exposure. It captures capital allocation to industrials and sensitivity (beta) to the sector. There is weak statistical evidence of poor industrials factor timing by hedge funds – investors who follow hedge funds should either ignore this bet or treat it as a negative indicator.

At the end of Q1 2015, high industrials sector factor exposure was the third largest source of U.S. hedge funds’ long portfolio crowding. HF Aggregate, a portfolio consisting of popular long U.S. equity holdings of all hedge funds tractable from quarterly filings, had over 25% industrials factor exposure – a 9% overweight relative to Market. This exposure was at an all-time high:

Chart of the historical industrials factor exposure of U.S. Hedge Fund Aggregate

U.S. Hedge Fund Aggregate’s Industrials Sector Factor Exposure History

This industrials factor exposure captures sector risk after controlling for market exposure. For example, a fund with 10% allocated to a broad industrials index will have approximately 10% industrials factor exposure. A fund with 10% allocated to a 2x-levered broad industrials ETF will have approximately 20% industrials factor exposure.

Here we analyze the hedge fund industry’s skill in timing the U.S. Industrials Factor by varying this exposure. The AlphaBetaWorks Performance Analytics Platform evaluates market timing skills and performance using two related tests:

Statistical test for the relationship between factor exposure and subsequent factor returns,
Statistical test for the size and consistency of returns generated by varying factor exposures.

Hedge Fund Industrials Factor Exposure and Industrials Factor Return

We calculated the Spearman’s rank correlation coefficient of HF Aggregate’s industrials factor exposure and subsequent industrials factor return for the past 10 years and tested it for significance. The chart below illustrates the correlation between the two series and the test results:

Chart of the correlation between historical industrials factor exposure of U.S. Hedge Fund Aggregate and subsequent factor return

U.S. Hedge Fund Aggregate’s Industrials Factor Exposure and Return

There is a statistically weak negative relationship between HF Aggregate’s industrials factor exposure and subsequent factor performance. Hedge fund industrials factor exposure is a weak predictor of future industrials returns.

Hedge Fund Industrials Factor Timing Returns

Over the past 10 years, HF Aggregate (USHFS in red) made approximately 0.9% less than it would have with constant industrials factor exposure, as illustrated below. The performance of HF Aggregate is compared to all tractable 13F filers (Group in gray). The AlphaBetaWorks Performance Analytics Platform identifies this performance due to industrials factor timing as industrials βReturn:

Chart of the cumulative historical contribution of variation in industrials factor exposure of U.S. Hedge Fund Aggregate to the Aggregate’s performance

U.S. Hedge Fund Aggregate’s Industrials Factor Timing Return

The weak evidence of poor industrials factor timing by the industry, combined with the high recent industrials factor exposure, is a weak bearish indicator for the sector.

Summary

The industrials factor exposure of U.S. hedge funds’ long portfolios is weakly predictive of subsequent sector performance.
Current hedge fund industrials factor exposure, at 10-year highs, is a weak bearish indicator for the Industrials Sector.
Investors who track hedge fund holdings should either ignore this bet or treat is as a negative indicator.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

Hedge Fund Oil Factor Timing

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In an earlier post, we discussed the largest bets hedge fund long portfolios were making in Q1 2015. The second largest was on the oil price. This exposure is the residual oil price risk after controlling for market and sector exposures. It captures overweighting within various sectors of companies that out- or underperform under rising oil price. There is weak statistical evidence of skilled oil factor timing by hedge funds – the current bet is a weak bullish indicator for oil prices.

At the end of Q1 2015, high oil price factor exposure was the second largest source of U.S. hedge funds’ long portfolio crowding. HF Aggregate, a portfolio consisting of popular long U.S. equity holdings of all hedge funds tractable from quarterly filings, had approximately 2.5% oil price factor exposure. This exposure was approaching the 10-year highs reached in 2007-2009:

Chart of the exposure of oil price factor of the U.S. Hedge Fund Aggregate

U.S. Hedge Fund Aggregate’s Oil Factor Exposure History

This oil price exposure captures residual oil risk after controlling for sector exposures. For examples, airlines with higher operational or financial leverage than peers have negative oil price factor exposure – they will underperform peers when oil price increases; airlines with lower operational or financial leverage than peers have positive oil price factor exposure –they will outperform peers when oil price increases.

Here we analyze the hedge fund industry’s skill in timing the oil price by varying this intra-sector oil price risk. The AlphaBetaWorks Performance Analytics Platform evaluates market timing skills and performance using two related tests:

Statistical test for the relationship between factor exposure and subsequent factor returns,
Statistical test for the size and consistency of returns generated by varying factor exposures.

Hedge Fund Oil Factor Exposure and Oil Return

We calculated the Spearman’s rank correlation coefficient of HF Aggregate’s oil price factor exposure and subsequent oil price return and tested it for significance. The chart below illustrates the correlation between the two series and the test results:

Chart of the correlation between oil price factor exposure of the U.S. Hedge Fund Aggregate and subsequent oil price return

U.S. Hedge Fund Aggregate’s Oil Factor Exposure and Return

There is a statistically weak positive relationship between HF Aggregate’s oil factor exposure and subsequent oil performance. Hedge fund oil factor exposure is a weak indicator of future oil price direction.

Hedge Fund Oil Factor Timing Returns

Over the past 10 years, HF Aggregate (USHFS in red) made approximately 1.5% more than it would have with constant oil factor exposure, as illustrated below. The performance of HF Aggregate is compared to all tractable 13F filers (Group in gray). The AlphaBetaWorks Performance Analytics Platform identifies this performance due to oil price factor timing as oil price βReturn:

Chart of the return due to variation in oil price factor exposure of the U.S. Hedge Fund Aggregate

U.S. Hedge Fund Aggregate’s Oil Factor Timing Return

The weak evidence of oil factor timing skill by the industry, combined with the high recent oil factor exposure, is a weak bullish indicator for oil prices.

Summary

The oil price factor exposure of U.S. hedge funds’ long portfolios is weakly predictive of subsequent oil performance.
Current hedge fund oil factor exposure, near 10-year highs, is a weak bullish indicator for oil prices.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.