Tag Archives: brian-haskin

Most Factor Anomalies Are Not Persistent

Smart-beta indices are constructed to exploit “anomalies” that reward exposure to risk factors beyond what would be expected as “necessary compensation” under the Capital Asset Pricing Model (“CAPM”). Of course, any factor that results in nominal outperformance must be considered on a risk-adjusted basis, since taking on higher risk should engender a greater reward – and investment researchers at S&P Dow Jones Indices think at least some factor “anomalies” aren’t anomalies at all, but just rewards for greater-than-understood risk-taking. Even still, among the remaining anomalies, the researchers think many are “disappearing,” “statistical,” or “attenuated” – and only a few are truly “persistent.” Writing on behalf of S&P Dow Jones, academic Hamish Preston and S&P Dow Jones Index Investment Strategy professionals Tim Edwards and Craig Lazzara express these views in an October 2015 research paper titled ” The Persistence of Smart Beta .” Disappearing Anomalies Disappearing anomalies don’t last. A great example shared by the paper’s authors is the so-called “Weekend Effect” that was popularized by Frank Cross in 1973. Mr. Cross discovered that if investors had bought stocks at their closing prices each Monday and sold them at their closing prices each Friday – avoiding the weekend and the Monday trading session – they would have dramatically outperformed a “buy and hold” strategy from 1950 to the time of his research. But then, almost immediately after the Weekend Effect became well known, the anomaly didn’t just disappear, it reversed. The Weekend Effect rebounded in 1984, only after another academic research paper called it into question – and then, when a paper called “The Reverse Weekend Effect” was published in 2000, the old Weekend Effect returned. As soon as investors gained knowledge of the Weekend Effect, it reversed. When knowledge of the reversal became widespread, the reversal reversed. Now, it’s taken as a given that the Weekend Effect was a coincidence – hence, it was a disappearing anomaly. Statistical Anomalies Perhaps a better approach is for investors to keep knowledge of anomalies they discover secret – that way, they may be less likely to disappear. This is what David Dolos did when he discovered that applying the price movements of the 1720 South Sea Bubble – second only to Tulip Mania in episodes of old-school irrational exuberance – to the Dow Jones Industrial Average inexplicably produced outsized returns. Mr. Dolos never told anyone about his discovery, and he reaped the rewards in anonymity until 2007, when the system broke down. Why? Well first off, David Dolos didn’t exist. The story is made up, and although the 1720 South Sea Bubble was real, the South Sea Bubble effect was data-mined into existence. As the paper’s authors note, modern computing power can easily produce “false positives” – i.e., anomalies that are purely statistical in nature. In order for an anomaly to be persistent, it must make logical sense. Attenuated Anomalies Momentum is one of the most popular factors. Academic research supports its outperformance, and the concept of momentum stocks – stocks that are going up – outperforming non-momentum stocks makes logical sense. The momentum anomaly is known to anyone who cares to know about it, and yet this knowledge hasn’t caused the anomaly to disappear – instead, it has reinforced it. The downside is that since investors have become aware of the momentum anomaly, its drawdowns have been bigger. This is what the S&P Dow Jones authors mean by an “attenuated anomaly.” In 1997, Mark Carhart published a study that showed adding momentum to the famous Fama-French three-factor model boosted returns. This caused more money to flow into momentum stocks, ultimately leading to bigger drawdowns during crashes. Persistent Anomalies Are there any truly persistent anomalies? The authors say there is at least one: Low volatility. But they conclude with a word of caution: “So far, the investment and attention directed toward low-volatility strategies has not been sufficient to temper their returns or attenuate their risk/return profile.” So far. As the well-known disclaimer goes: ” Past performance does not necessarily predict future results. ” For more information, download a pdf copy of the white paper. Jason Seagraves contributed to this article.

The Challenges And Pitfalls Of Measuring Factor Exposures

Factor-based investing has grown significantly in the years since Eugene Fama and Kenneth French first published (1992) their groundbreaking research on the “three-factor model” to explain the return of stocks. Now, a growing number of investors view their portfolios as “collections of various risk-factor exposures,” including risks to particular asset classes and specific “styles,” such as value, size and momentum. Investors reasonably expect to be rewarded for taking on these various types of risk. Understanding the source of returns has also made it difficult for investment managers to pass off factor-based returns as “alpha” – i.e., something that they (the manager) should be paid for having produced. But in order for investors to be sure they’re not overpaying for factor-based returns falsely portrayed as alpha, they must first be able to measure their exposures to the various risk factors – and this is trickier than one might expect. In a recent white paper from AQR , Ronen Israel and Adrienne Ross consider the challenges associated with measuring factor exposures. The authors draw a distinction between academic and practitioner models, favoring the latter for being more practical to implement. Factor Analysis When conducting factor analysis, investors should ask themselves two questions: Exactly what factors am I using? Are they the same as those I’m getting in my portfolio? The answers to those questions can significantly affect alpha and beta estimates. Factor design is also important and can lead to major discrepancies, too. When comparing alphas and betas across managers, investors should make sure they’re using factors being captured by both portfolios – otherwise, they risk overpaying for inappropriately attributed alpha. For portfolios with more than one risk factor, multivariate statistical models are most appropriate. Mr. Israel and Ms. Ross caution investors to consider t-stats – measurements of statistical significance – and not just betas, especially when comparing portfolios with different volatilities. Decomposing Returns Mr. Israel and Ms. Ross examine a hypothetical long-only stock portfolio designed to capture returns from value, momentum, and size style premia . The portfolio was designed with a 50/50 weight on value (book-to-price) and momentum (12-month trailing returns), entirely within the small-cap universe. From January 1980 through December 2014, the hypothetical portfolio would have returned an annualized 13.8% above the return on cash. Mr. Israel and Ms. Ross start with one factor – equity market risk – and build from there. First, a value factor is added (“HML”), and then momentum (“UMD”) and finally size (“SMB”). The HML, UMD, and SMB abbreviations refer to “common academic” definitions: HML (high-minus low) – Long/short value methodology; long high-value stocks/short low-value stocks; UMD (up minus down) – Long/short momentum methodology; long the stocks up the most/short the stocks down the most; and SMB (small minus big) – Long/short “size” strategy; long small stocks/short big stocks. As you can see, when only considering a single factor (“the market”) in Model 1, it appeared that the portfolio generated nearly half of its returns from manager alpha. But as more factors are accounted for, it became clear that alpha-generation was actually much smaller. As an investor, you shouldn’t have to pay active-manager fees for factor exposures presented as alpha.

Managed Futures Funds: Best And Worst Of November

Managed futures funds performed solidly in November, with the Morningstar category gaining 2.68% in the aggregate – the second best month for 2015 behind January. The month’s gains accounted for more than 100% of the entire year’s gains for the category, as its one-year return improved to +2.60% through November 30. Longer term, the managed futures category has underperformed the private fund index as represented by the Credit Suisse Managed Futures Liquid TR USD Index. This is seen in the negative alpha for the category of 2.13% versus the index. However, the group of mutual funds and ETFs included in the category do behave somewhat differently from a risk perspective given the low beta of 0.57 relative to the Credit Suisse index. In this month’s category review, we look at the three best- and worst-performing managed futures funds in November, in terms of their monthly returns, as well as their long-term term performance. As you will note, only three of the funds have track records of 3 years or more. (click to enlarge) Top Performing Funds The best-performing managed futures funds in November were: Each of these funds posted November gains well in excess of the +2.68% category average, and all three solidly outperformed for the year ending November 30, too. Only one of the funds – the Arrow Managed Futures Strategy Fund – had a three-year track record, with annualized gains of 3.65%, and a Sharpe ratio of 0.43 over that time. Broken down, the fund’s long-term returns consisted of a 0.68 beta and -2.57 alpha versus the Credit Suisse index. At +5.92% in November, it was the third-best managed futures mutual fund to own that month, and at +9.14% for the year ending November 30, it trailed only Salient Trend Fund on that basis. Speaking of which, the Salient Trend Fund was the month’s top-performing managed futures mutual fund, with gains of 7.37%. For the year ending November 30, the fund handily beat the category average of +2.60% with gains of 10.65%. The Equinox BH-DG Strategy Fund was November’s second-best performer among managed futures funds, with gains of 7.06%. For the year ending November 30, the fund returned 7.37%, which while being the weakest of the month’s other top funds, was still well in excess of the category average. (click to enlarge) Worst Performing Funds The worst-performing managed futures funds in November were: At -2.19% for the month, the Equinox IPM Systematic Macro Fund was November’s worst-performing managed futures fund. The fund only debuted in July 2015, and thus it doesn’t have longer-term performance data available, but according to Morningstar, $10,000 invested in the fund at its inception would have turned into $9,810 as of November 30, compared to $10,127 for the category as a whole. The Dunham Alternative Strategy and Altegris Macro Strategy funds were both launched more than three years ago, which gives us more return data to analyze. First, for the month of November, the funds posted respective losses of 1.31% and 0.84%. For the year ending November 30, their respective returns were -3.10% and -0.36%. Longer term, DNASX posted three-year annualized gains of 1.16%, while MCRAX had three-year annualized losses of 3.25% through November 30. In terms of three-year beta, alpha, and Sharpe ratios, DNASX definitely looked more attractive. Through November 30, its three-year beta stood at 0.01 – almost entirely uncorrelated with the broader managed futures market – and its alpha stood at 1.16. MCRAX, by contrast, had a three-year beta of 0.46 and -7.55 alpha. The funds’ respective Sharpe ratios stood at 0.23 and -0.52. (click to enlarge) Conclusion Category-wide gains of 2.68% in November come on top of the 1.82% gains from October, which had reversed the prior month’s 1.21% losses. With the Federal Reserve’s long, and much anticipated interest rate increase now complete, the divergence in global interest rate policy is fully under way. December, and 2016, could prove fruitful for the managed futures category as a whole. Past Performance does not necessarily predict future results. Meili Zeng and Jason Seagraves contributed to this article.