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]]>The results hold for international smart beta tracking error: Though some international smart beta ETFs do provide valuable exposures to idiosyncratic factors, most primarily re-shuffle basic dumb Region and Sector Factors. In fact, dumb beta exposures are even more influential for international smart beta equity ETFs than for their U.S. peers. Most international smart beta equity ETFs are mainly region and sector rotation strategies in disguise. Consequently, investors and allocators must guard against fancy re-packaging of dumb international risk factors as smart beta and should perform rigorous region and sector factor analysis of their smart beta allocations. Further, many international smart beta strategies can be substantially replicated and blended using simple passive Region and Sector Factors.
We used the same dataset of International Smart Beta Equity ETFs as our earlier article on international smart beta ETFs’ dumb factor exposures. We estimated monthly positions of each ETF and then used these positions to calculate portfolio factor exposures to traditional factors such as Regions and Sectors. These ex-ante factor exposures at the end of month m1 were used to predict returns during the subsequent month m2. We then calculated predicted and actual returns relative to the Global Equity Market (defined as the iShares MSCI ACWI Index Fund – ACWI). The correlation between actual and predicted relative returns quantified the influence of dumb beta factors on smart beta tracking error. The higher the correlation, the more similar a smart beta ETF is to a portfolio of traditional, simple, and dumb systematic risk factors.
The simplest systematic risk factor to describe each security is its Region (Region Market Beta). Region Beta measures exposure to one of 10 broad regional equity markets (e.g., North America, Developing Asia). These are the most basic and cheap passive international factors. Even this single-factor model estimated with robust statistical techniques delivered 0.67 mean and 0.68 median correlations between predicted and actual relative returns:
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.2490 0.5662 0.6791 0.6655 0.7805 0.9185
For a quarter of smart beta ETFs, Region Factor alone replicates 61% (0.7805²) of tracking error. For this group of strategies, international smart beta tracking error is largely due to region rotation.
We next extended the model with Sector Factors. Sector Beta measures exposure to one of 10 broad sectors (e.g., Energy, Technology). Region and Sector Betas, estimated with robust methods, delivered 0.75 mean and 0.77 median correlations between predicted and actual relative returns:
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.2604 0.6807 0.7673 0.7461 0.8523 0.9494
For most international smart beta ETFs, Region and Sector Factors replicate 59% (0.7673²) of tracking error. Thus, for most international smart beta equity ETFs, over half of tracking error is due to region and sector rotation.
Rather than measure correlations between relative returns of replicating dumb beta portfolios and ETFs, we can instead measure the fractions of their (relative) variances unexplained by dumb beta exposures. The Dumb Beta Variance (in red below) is the distribution of ETFs’ relative variances due to their dumb Region and Sector Factor exposures. The Smart Beta Variance (in blue below) is the distribution of ETFs’ relative variances unrelated to their dumb beta exposures:
Percentage of international equity smart beta ETFs’ relative variances due to dumb beta exposures:
Min. 1st Qu. Median Mean 3rd Qu. Max. 6.78 46.34 58.88 57.70 72.65 90.13
Percentage of international equity smart beta ETFs’ relative variances unrelated to dumb beta exposures:
Min. 1st Qu. Median Mean 3rd Qu. Max. 9.87 27.35 41.12 42.30 53.66 93.22
For a quarter of strategies, over 2/3 of international smart beta tracking error is due to region and sector rotation, and for some, it reaches 90%.
Our analysis focused on the most basic Region and Sector Factors and excluded Value/Growth and Size Factors, which are decades old and also considered dumb beta by some. If one expands the list of dumb beta factors, smart beta variance shrinks further.
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Original article: What Fraction of International Smart Beta Tracking Error is Dumb Beta?.The post What Fraction of Smart Beta Tracking Error is Dumb Beta? appeared first on AlphaBetaWorks Charts.
]]>This article addresses the above criticism and analyzes smart beta tracking error rather than (absolute) volatility. The results hold: Though some smart beta ETFs do provide valuable exposures to idiosyncratic factors, most primarily re-shuffle basic dumb factors. Whether one considers their absolute or relative performance, most smart beta equity ETFs are largely sector rotation strategies in disguise. Consequently, investors and allocators must guard against elaborate re-packaging of dumb factors as smart beta and perform rigorous sector and industry analysis of their smart beta allocations. Further, dozens of smart beta strategies can be substantially replicated and blended using simple sector factor portfolios.
We used the same U.S. Smart Beta ETF dataset as our earlier study of smart beta ETFs’ dumb factor exposures. For each ETF, we estimated monthly positions and then used these positions to calculate portfolio factor exposures for traditional (dumb beta) factors such as Market and Sectors. The ex-ante factor exposures at the end of each month were used to predict the following month’s returns. The correlation between actual and predicted returns relative to the U.S. Equity Market (defined as the iShares Russell 3000 ETF – IWV) quantified the influence of dumb beta factors on smart beta tracking error. The higher the correlation, the more similar a smart beta ETF is to a portfolio of traditional, simple, and dumb systematic risk factors.
Our simplest test used a single systematic risk factor – Market Beta. Median correlation between predicted and actual relative returns of smart beta ETFs was 0.31:
Min. 1st Qu. Median Mean 3rd Qu. Max. -0.1089 0.2129 0.3050 0.3096 0.4053 0.8899
The influence of Market Beta on tracking error is much lower than its influence on absolute variance since it only measures relative performance due to deviations from Market’s risk.
We next tested a two-factor model that added a Sector Factor. Each security belonged to one of 10 broad sectors (e.g., Energy, Technology). Market and Sector Betas, estimated with robust methods, delivered 0.68 mean and median correlations between predicted and actual relative returns of smart beta ETFs:
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0355 0.5983 0.6821 0.6801 0.7981 0.9630
Put differently: For most broad U.S. equity smart beta ETFs, U.S. Market and Sector Betas alone account for approximately half of tracking error and relative variance.
For the final tests, we added additional dumb factors such as Bonds, Value, and Size. All dumb factor betas delivered 0.74 mean and 0.78 median correlations between predicted and actual relative returns of smart beta ETFs:
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.2094 0.6875 0.7759 0.7447 0.8387 0.9715
Thus, for most broad U.S. equity smart beta ETFs, dumb beta factors account for the majority of tracking error.
Rather than measure correlations between relative returns predicted by dumb beta exposures and actual relative returns, we can instead measure the fraction of relative variance unexplained by dumb beta exposures. This value (in blue below) is the fraction of smart beta tracking error that is unrelated to dumb beta factors:
Percentage of Tracking Error Explained by Dumb Beta Factors
Min. 1st Qu. Median Mean 3rd Qu. Max. 4.384 47.268 60.197 57.141 70.346 94.381
Percentage of Tracking Error Unexplained by Dumb Beta Factors
Min. 1st Qu. Median Mean 3rd Qu. Max. 5.619 29.654 39.803 42.859 52.732 95.616
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Original article: What Fraction of Smart Beta Tracking Error is Dumb Beta?.The post The Predictive Power of Information Ratios appeared first on AlphaBetaWorks Charts.
]]>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.
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):
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.
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:
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 |
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:
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.
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]]>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, low turnover, and broad holdings to be analyzable.
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.
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):
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.
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:
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 |
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:
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.
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]]>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.
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.
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.
Berkshire’s αReturn began to decay in 2014. There is one principal sector responsible for this: Technology.
Berkshire made a significant bet in the technology sector in mid-2011 after having avoided it for a very long time:
From 2006 to 2011, Berkshire’s technology exposure was effectively zero. But starting in 2011 it went up dramatically.
This bet has gone poorly. In fact, this sector is the principal contributor to Berkshire’s recent αReturn decay:
Berkshire’s long equity portfolio suffered over a 12% loss from technology stock selection, peak-to-trough.
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.
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Original article: Berkshire Hathaway’s Alpha Decay.The post Tests of Equity Market Hedging: U.S. Hedge Funds appeared first on AlphaBetaWorks Charts.
]]>We analyze approximately 600 hedge fund long U.S. equity portfolios that are tractable from regulatory filings. Note that roughly half of U.S. hedge fund portfolios are impossible to analyze accurately due to the quarterly data frequency and high turnover. Similarly to our earlier analysis of mutual fund portfolio hedging, we evaluate three approaches to calculating market hedge ratios:
Our study spans 10 years. We calculate hedge ratios at the end of each month and use these to hedge portfolios during the following month. This produces a series of 10-year realized (ex-post) hedged portfolio returns. We further break these series into 12-month intervals and calculate their correlations to the Market. Low average market correlation and low dispersion of correlations indicates that a hedging technique effectively eliminates systematic market exposure of a typical portfolio.
A large return dataset, even when perfectly random, will contain some subsets with high market correlations. To control for this, we generate random return samples (observations) and calculate their market correlations. These results, attainable only with a perfect hedge, are the standard against which we evaluate equity market hedging techniques:
Min. 1st Qu. Median Mean 3rd Qu. Max. -0.9374 -0.2157 -0.0001 0.0022 0.2173 0.9190
As with mutual funds, the assumption that all hedge fund long equity portfolios’ market exposures are 100% (market betas are 1) is flawed:
Min. 1st Qu. Median Mean 3rd Qu. Max. -0.9999 -0.2131 0.0927 0.0578 0.3706 0.9789
A 100% hedge is too small for high-risk portfolios and too large for low-risk ones. Also as seen for mutual funds, some hedged low-exposure portfolios formed a fat tail of nearly -1 realized market correlations.
Most RBSA assumes that portfolio factor exposures are constant over the regression window. Some advanced techniques may allow for random variation in exposures over the window, yet even this relaxed assumption is flawed. Our earlier posts covered the problems that arise when RBSA fails to detect rapid changes in portfolio risk. It turns out that the months or years of delay before RBSA captures changes in factor exposures are especially damaging when analyzing hedge fund portfolios:
Min. 1st Qu. Median Mean 3rd Qu. Max. -0.9998 -0.3321 -0.0503 -0.0527 0.2311 0.9993
RBSA fails more severely for hedge funds than for mutual funds. In fact, RBSA has similar defect as a fixed 100% hedge for some low-exposure portfolios and produces a fat tail of nearly -1 market correlations. Hedge funds’ long equity portfolios can and do cut risk rapidly, so RBSA’s failure to detect these rapid exposure reductions is expected.
The AlphaBetaWorks Statistical Equity Risk Model continues to produce hedges close to the ideal:
Min. 1st Qu. Median Mean 3rd Qu. Max. -0.9913 -0.2683 -0.0304 -0.0273 0.2085 0.9252
The edge comes from the analysis of individual positions that responds rapidly to portfolio changes and the robust regression methods that are resilient to outliers. The result is superior analysis of individual funds.
Whereas tests using hedge fund long equity portfolios accentuate the flaws of simple hedging and returns-based analysis, the AlphaBetaWorks Statistical Equity Risk Model remains close to the baseline of a perfect hedge. Thus, it is even more vital that portfolio managers and investors who analyze or manage hedge fund equity risk rely on robust models and thoroughly tested methods.
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Original article: Tests of Equity Market Hedging: U.S. Hedge Funds.The post Tests of Equity Market Hedging: U.S. Mutual Funds appeared first on AlphaBetaWorks Charts.
]]>We analyze approximately 3,000 non-index U.S. Equity Mutual Funds over 10 years. These provide a broad sample of the real-world long equity portfolios that investors may attempt to hedge. We evaluate the effectiveness of three techniques for calculating hedge ratios:
For each fund and for each month of history we calculate market exposure at the end of the month and then use this estimated (ex-ante) exposure to hedge the fund during the following month. We then analyze realized (ex-post) 12-month hedged portfolio returns and calculate their correlations to the Market. The lower this correlation, the more effective a hedging technique is at eliminating systematic market exposure of a typical U.S. equity mutual fund portfolio.
An effective hedging technique should produce zero mean and median market correlations of hedged portfolio returns. Yet, if sufficiently large, even a set of perfectly random 12-month return series will contain some with large market correlations. Since our study covers over 200,000 12-month samples (observations), some market correlations are close to 1 by mere chance. To account for this and to create a baseline for comparisons, we calculated market correlations for random return series with a Monte Carlo simulation. These results, attainable only with a perfect hedge, are the baseline against which we evaluate equity market hedging techniques:
Min. 1st Qu. Median Mean 3rd Qu. Max. -0.9243 -0.2162 0.0001 -0.0006 0.2146 0.9409
A naive assumption that market exposures of all portfolios are 100% (market betas of all portfolios are 1) is obviously wrong:
Min. 1st Qu. Median Mean 3rd Qu. Max. -0.9956 -0.2927 0.0469 0.0148 0.3527 0.9958
This approach over-hedges some portfolios and under-hedges others. There is a group of low-exposure portfolios for which a fixed 100% market short is too large. These produce a fat tail of negative market correlations of nearly -1 for some hedged portfolios. There is also a group of portfolios for which a fixed 100% market hedge is too small.
Returns-based style analysis with multiple factors suffers from known issues of overfitting and collinearity. Less well-known are the problems that arise from RBSA’s assumption that exposures are constant over the regression window. In practice, portfolio exposures vary over time and can change rapidly as positions change. RBSA will capture these changes months or even years later once they influence portfolio returns, if at all.
RBSA thus fails similarly to the fixed hedging above, if less dramatically: hedges are too large in some cases and too small in others. The exposure estimates are also apparently biased, since they produce hedges that are too large and market correlations that are negative, on average:
Min. 1st Qu. Median Mean 3rd Qu. Max. -0.9946 -0.3229 -0.0518 -0.0459 0.2274 0.9762
The AlphaBetaWorks Statistical Equity Risk Model produces hedges close to the ideal:
Min. 1st Qu. Median Mean 3rd Qu. Max. -0.9565 -0.2230 0.0184 0.0214 0.2669 0.9755
The model estimates security market exposures using robust regression methods to control for outliers. Though robust techniques perform well for most portfolios, they appear to produce hedge ratios that are too low for some high-beta portfolios. This leads to small positive mean and median market correlations of hedged portfolio returns and to the higher probability of positive market correlations compared to random portfolios.
Aside from this under-hedging of a small fraction of portfolios, application of the AlphBetaWorks Statistical Equity risk model to fund holdings comes closest to perfect equity market hedging. Portfolio managers and investors who rely on robust risk models and hedging techniques can thus nearly perfectly hedge the market risk of a typical equity portfolio.
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Original article: Tests of Equity Market Hedging: U.S. Mutual Funds.The post The Persistence of Negative Investment Performance appeared first on AlphaBetaWorks Charts.
]]>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.
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 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.
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:
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 |
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 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.
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):
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 |
This reversion is not present for αReturns. Negative αReturns have similar autocorrelation for the first few years and decay more slowly than overall αReturns:
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.
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Original article: The Persistence of Negative Investment Performance.The post The Decay of Stock Picking Skill appeared first on AlphaBetaWorks Charts.
]]>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.
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:
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.
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:
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 |
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 in the other, but with decay. The following chart shows correlation between 12-month cumulative αReturns calculated with 1-60 month lags:
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 |
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.
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.
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Original article: The Decay of Stock Picking Skill.The post Top Stock Pickers’ Exposure to Valeant appeared first on AlphaBetaWorks Charts.
]]>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.
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:
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 |
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:
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.
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Original article: Top Stock Pickers’ Exposure to Valeant.