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Value And Momentum In Sports Betting

By Jack Vogel As noted through our previous posts, we are big proponents of Value investing and Momentum investing strategies. We even highlight the best way to combine value and momentum . However, there is a new paper by Toby Moskowitz, titled “ Asset Pricing and Sports Betting ,” which examines how size, value and momentum affect sports betting contracts: I use sports betting markets as a laboratory to test behavioral theories of cross-sectional asset pricing anomalies. Two unique features of these markets provide a distinguishing test of behavioral theories: 1) the bets are completely idiosyncratic and therefore not confounded by rational theories; 2) the contracts have a known and short termination date where uncertainty is resolved that allows any mispricing to be detected. Analyzing more than a hundred thousand contracts spanning two decades across four major professional sports (NBA, NFL, MLB, and NHL), I find momentum and value effects that move betting prices from the open to the close of betting, that are then completely reversed by the game outcome. These findings are consistent with delayed overreaction theories of asset pricing. In addition, a novel implication of overreaction uncovered in sports betting markets is shown to also predict momentum and value returns in financial markets. Finally, momentum and value effects in betting markets appear smaller than in financial markets and are not large enough to overcome trading costs, limiting the ability to arbitrage them away. Some Interesting Points The figure below explains the different price movements which are studied in the paper: The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Here are the T-stats for the momentum betas in the figure below: (click to enlarge) The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Analysis from the paper: A consistent pattern emerges for the Spread and Over/under contracts in every sport, where the momentum betas exhibit a tent-like shape over the three horizons—near zero from open-to-end, significantly positive from open-to-close, and significantly negative from close-to-end, with the initial price movement from open-to-close related to momentum being fully reversed by the game outcome. The patterns for the Moneyline contracts exhibit the same tent-like shape, but are less pronounced, consistent with the Moneyline perhaps being less affected by “dumb” money and more dominated by “smart” money. Then the paper shows the T-stats for the value betas in the figure below: (click to enlarge) The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Analysis from the paper: A consistent pattern is evident from the plots: a value contract’s betting line declines between the open and close and then rebounds between the close and game end, reaching the same level it started at the open. These patterns are consistent with an overreaction story for value, where value contracts, which measure “cheapness”, continue to get cheaper between the open and the close, becoming too cheap and thus rebounding positively when the game ends. This picture is the mirror image of momentum, where value or cheapness is negatively related to past performance, and hence the pictures for momentum and value tell the same story. (Though, recall the measures for value and momentum were only mildly negatively correlated.) Conclusion from the paper: Examining momentum, value, and size characteristics of these contracts, analogous to those used to predict financial market security returns, I find that momentum exhibits significant predictability for returns, value exhibits significant but weaker predictability, and size exhibits no return predictability. The patterns of return predictability over the life of the betting contracts—from opening to closing prices to game outcomes—matches those from models of investor overreaction. The results suggest that at least part of the momentum and value patterns observed in capital markets could be related to similar investor behavior. The magnitude of return predictability in the sports betting market is about one-fifth that found in financial markets, where trading costs associated with sports betting contracts are too large to generate profitable trading strategies, possibly preventing arbitrage from eliminating the mispricing. Our Thoughts: An interesting paper, showing that Value and Momentum work within the sports betting market, but the cost of trading on the signals is too large for profitable trades. This is probably why the “house always wins.” It’s a good thing I watch countless hours of sports to form my own “expert” opinions! Original post

The Active Share Debate: AQR Versus The Academics

By Jack Vogel, Ph.D. There is an interesting discussion in the geeky world of academic finance literature between the intellectual muscle at AQR and academia. The discussion revolves around the following question: ” Does Active Share matter? ” This is an important topic for active ETFs and Mutual Funds in the marketplace. The original paper on this measure was written by Cremers and Petajisto and was published in the Review of Financial Studies in 2009 (top finance journal). Links to the paper can be found here and here . The abstract of the paper is the following: We introduce a new measure of active portfolio management, Active Share, which represents the share of portfolio holdings that differ from the benchmark index holdings. We compute Active Share for domestic equity mutual funds from 1980 to 2003. We relate Active Share to fund characteristics such as size, expenses, and turnover in the cross-section, and we also examine its evolution over time. Active Share predicts fund performance : funds with the highest Active Share significantly outperform their benchmarks, both before and after expenses, and they exhibit strong performance persistence. Nonindex funds with the lowest Active Share underperform their benchmarks. Main Finding of the paper: For non-index funds, the higher the active share, the better the performance. We tend to agree, as we have talked about diworsification in the past. However, just because a manager creates a more active portfolio (a necessary condition for outperformance ), this doesn’t imply an active manager will actually have outperformance. The team at AQR (Frazzini, Friedman, and Pomorski), in a forthcoming article in the Financial Analyst Journal (link to the paper is here ), address this question. The abstract is the following: We investigate Active Share, a measure meant to determine the level of active management in investment portfolios. Using the same sample as Cremers and Petajisto (2009) and Petajisto (2013) we find that Active Share correlates with benchmark returns, but does not predict actual fund returns ; within individual benchmarks, it is as likely to correlate positively with performance as it is to correlate negatively. Our findings do not support an emphasis on Active Share as a manager selection tool or an appropriate guideline for institutional portfolios. Main point of the paper: Active share should not be used as a manager selection tool. Basically, for a given index, they find that active share cannot be used as a reliable tool to identify out-performance. So is Active Share a waste of time? As Lee Corso says every Saturday morning during College Gameday, “Not so fast!” The two authors of the original paper, Martijn Cremers and Antti Petajisto were quick to shoot down the AQR findings. Here is the executive summary from Antti Petajisto: All of the key claims of AQR’s paper were already addressed in the two cited Active Share papers: Petajisto (2013) and Cremers and Petajisto (2009). 1) The fact about the level of Active Share varying across benchmarks has been widely known for many years. Its performance impact was explicitly studied and discussed in the first drafts of Petajisto (2013) back in 2010, and the performance results remained broadly similar. The reason for the apparent discrepancy is AQR’s choice of summarizing results by benchmark, which effectively gives the same weight to the most popular index (S&P 500, assigned to 870 funds) and the least popular index (Russell 3000 Growth, assigned to 24 funds), which is not sensible as a statistical approach. 2) The issue about four-factor alphas varying across benchmark indices does nothing to change the fact that higher Active Share managers have been able to beat their benchmark indices. However, it does raise an interesting point about the four-factor approach to measuring performance, and in fact my coauthors and I wrote a long and detailed paper about this exact issue first in 2007 (published later as Cremers, Petajisto, and Zitzewitz (2013)). 3) AQR’s researchers argue that there is no theory behind Active Share and they remain mystified by the differences between Active Share and tracking error. It is unfortunate that they have entirely missed the lengthy sections of both Active Share papers that discuss this exact topic: pages 74-77 in Petajisto (2013) and sections 1.3, 3.1, and 4.1 in Cremers and Petajisto (2009). The short answer is that Active Share is more about stock selection, whereas tracking error is more about exposure to systematic risk factors. So clearly ignoring large and essential parts of the original Active Share papers is simply not the way to conduct impartial scientific inquiry. If that executive summary wasn’t scathing enough, Martijn Cremers actually wrote a paper titled ” AQR in Wonderland: Down the Rabbit Hole of ‘Deactivating Active Share’ (and Back Out Again?) ” Here is the abstract: The April 2015 paper “Deactivating Active Share”, released by AQR Capital Management, aims to debunk the claim that Active Share (a measure of active management) predicts investment performance. The claim of the AQR paper is that “neither theory nor data justify the expectation that Active Share might help investors improve their returns,” arguing that previous results are “entirely driven by the strong correlation between Active Share and the benchmark type.” This paper’s first and main aim is to establish that the AQR paper should not be interpreted using typical academic standards. Instead, our conjecture is that this AQR paper falls into a wonderfully creative but altogether different genre, which we label the Wonderland Genre, as its main characteristic seems to be “Sentence First, Verdict Later.” For example, the results in the AQR-WP cannot be taken at face value, as the information that is not shared reverses their main conclusion. Secondarily, we consider the plausible claim that benchmark styles matter and find that controlling for the main benchmark style, the predictability of Active Share is robust. While Active Share is only one tool among many to analyze investment funds and needs to be carefully interpreted for each fund individually, Active Share may indeed plausibly help investors improve their returns. Thirdly and finally, we impolitely consider why AQR may not be a big fan of Active Share by taking a look at the AQR mutual funds offered to retail investors. We find that these tend to have relatively low Active Shares, have shown little outperformance to date (with performance data ending in 2014) and thus seem fairly expensive given the amount of differentiation they offer. So who is the winner in the debate? The answer is both are probably correct at some level. More concentration (less diworsification) probably has higher active share and in the past had higher returns. However, one cannot just take any random selection of stocks and expect to outperform, the style of the investment matters, which was AQR’s argument (we prefer Value and Momentum ). Let us know what you think! Link to the original post on Alpha Architect

Visualizing Stock Market Risk: 7/1926 To 6/2015

Summary How crazy is current market action? Not that crazy. Seeing a -3%+ or a +3% observation is roughly a 1/100 event, or ~ 2.5 times a year. Obviously, return events are not independent and volatility tends to cluster, but the numbers above establish a basic starting point for discussions about daily return action. Clearly, if you can’t handle volatility, you shouldn’t be in the stock market. By Wesley R. Gray How crazy is current market action? Not that crazy. …and if you lived through 2008, definitely not that crazy. Seeing a -3%+ or a +3% observation is roughly a 1/100 event, or ~ 2.5 times a year. Obviously, return events are not independent and volatility tends to cluster, but the numbers above establish a basic starting point for discussions about daily return action. Here we present daily total return data from the Ken French library : Value-weight return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ that have a CRSP share code of 10 or 11 (essentially ordinary common shares). There are 23,509 daily return in total. Daily Return Distribution: (click to enlarge) Here are the specific stats: Bucket Observations Frequency Cumulative -5.00% 59 0.25% 0.25% -4.50% 20 0.09% 0.34% -4.00% 31 0.13% 0.47% -3.50% 46 0.20% 0.66% -3.00% 85 0.36% 1.03% -2.50% 164 0.70% 1.72% -2.00% 289 1.23% 2.95% -1.50% 547 2.33% 5.28% -1.00% 1154 4.91% 10.19% -0.50% 2566 10.91% 21.10% 0.00% 5599 23.82% 44.92% 0.50% 7048 29.98% 74.90% 1.00% 3416 14.53% 89.43% 1.50% 1331 5.66% 95.09% 2.00% 563 2.39% 97.49% 2.50% 237 1.01% 98.49% 3.00% 115 0.49% 98.98% 3.50% 69 0.29% 99.28% 4.00% 61 0.26% 99.54% 4.50% 37 0.16% 99.69% 5.00% 19 0.08% 99.77% More 53 0.23% 100.00% How about drawdowns? Daily returns are one thing – let’s review the top 30 stock market drawdowns over the past 90+ years. Rank Date Start Date End VW_CRSP 1 8/30/1929 6/30/1932 -83.67% 2 10/31/2007 2/28/2009 -50.37% 3 2/27/1937 3/31/1938 -49.18% 4 12/31/1972 9/30/1974 -46.46% 5 8/31/2000 9/30/2002 -45.09% 6 11/30/1968 6/30/1970 -33.56% 7 8/31/1987 11/30/1987 -29.91% 8 8/31/1932 2/28/1933 -28.47% 9 5/31/1946 5/29/1947 -23.85% 10 12/31/1961 6/30/1962 -23.06% 11 1/31/1934 7/31/1934 -18.34% 12 8/31/1933 10/31/1933 -17.95% 13 4/30/2011 9/30/2011 -17.71% 14 6/30/1998 8/31/1998 -17.39% 15 5/31/1990 10/31/1990 -16.97% 16 11/30/1980 7/31/1982 -16.62% 17 1/31/1966 9/30/1966 -15.45% 18 7/31/1957 12/31/1957 -14.95% 19 4/30/2010 6/30/2010 -12.99% 20 1/31/1980 3/31/1980 -11.98% 21 8/31/1978 10/31/1978 -11.95% 22 6/30/1983 5/31/1984 -10.83% 23 3/31/2000 5/31/2000 -9.64% 24 12/31/1976 2/28/1978 -9.33% 25 7/31/1956 2/28/1957 -8.37% 26 8/31/1986 9/30/1986 -8.15% 27 3/31/1936 4/30/1936 -8.02% 28 12/31/1959 10/31/1960 -7.97% 29 6/30/1943 11/30/1943 -7.76% 30 1/31/1994 6/30/1994 -7.60% And here are the numbers outlined on a chart: (click to enlarge) Clearly, if you can’t handle volatility , you shouldn’t be in the stock market. Original Post Share this article with a colleague