New research papers related to existing strategies:
#6 – Volatility Effect in Stocks – Long-Short Version
#7 – Volatility Effect in Stocks – Long-Only Version
Feijoo, Kochard, Sullivan, Wang: Low-Volatility Cycles: The Influence of Valuation and Momentum on Low-Volatility Portfolios
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2310353
Abstract:
Research showing that the lowest risk stocks tend to outperform the highest risk stocks over time has led to rapid growth in so-called low-risk equity investing in recent years. We provide evidence that both extends and contrasts with existing research on low-risk investing. First, we demonstrate that the low-risk anomaly might more accurately be referred to as the high-risk anomaly due to the fact that the anomalous returns are found primarily among those stocks in the highest risk quintile. Next, we demonstrate that the historical performance of low risk investing is strikingly cyclical and driven to a large degree by swings in the relative valuation levels of low risk versus high risk stocks and also by varying appetite for momentum driven investing. Furthermore, the current valuation cycle nears historically high levels, which, combined with high exposure to momentum, indicates greater uncertainty in low-risk investing future outcomes.
#6 – Volatility Effect in Stocks – Long-Short Version
#7 – Volatility Effect in Stocks – Long-Only Version
#77 – Beta Factor in Stocks
Chow, Hsu, Kuo, Li: A Survey of Low Volatility Strategies
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2298117
Abstract:
This paper replicates various low volatility strategies and examines their historical performance using U.S., global developed markets, and emerging markets data. In our sample, low volatility strategies outperformed their corresponding cap-weighted market indexes due to exposure to the value, betting against beta (BAB), and duration factors. The reduction in volatility is driven by a substantial reduction in the portfolios' market beta. Different approaches to constructing low volatility portfolios, whether optimization or heuristic based, result in similar factor exposures and therefore similar long-term risk-return performance. For long-term investors, low volatility strategies can contribute to a considerably more diversified equity portfolio which earns equity returns from multiple premium sources instead of market beta alone. While the lower risk and higher return seem persistent and robust across geographies and over time, we identify flaws with naïve constructions of low volatility portfolios. First, naïve low volatility strategies tend to have very high turnover and low liquidity, which can erode returns significantly. They also have very concentrated country/industry allocations, which neither provide sensible economic exposures nor find theoretical support in the more recent literature on the within-country/industry low volatility effect. Additionally, there is concern that low volatility stocks could become expensive, a development which would eliminate their performance advantage. This highlights the potential danger of a portfolio construction methodology that is unaware of the fundamentals of the constituent stocks — after all, low volatility investing is useful only if it comes with superior risk-adjusted performance. That many naïve low volatility portfolios are no longer value portfolios today bodes poorly for their prospective returns. More thoughtful portfolio construction research is necessary to produce low volatility portfolios that are more likely to repeat the historical outperformance with reasonable economic exposure and adequate investability.
#64 – Statistical Arbitrage with ETFs
Fulkerson, Jordan, Riley: Predictability in Bond ETF Returns
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2273930
Abstract:
We study the persistence of bond ETF premiums and discounts. Following a day of high or low premiums or discounts over NAV, ETFs tend to maintain a premium or discount for up to 30 days. Premiums and discounts also predict distinct patterns of returns after daily closing. Overnight returns are negative following a high premium, while ETFs with large discounts are followed by positive overnight returns. The large discount ETFs have substantially higher returns than high premium ETFs over the subsequent thirty days. We find that traditional liquidity measures, along with prior deviations from NAV, are significant in explaining a fund’s premiums/discounts. Finally, we examine a long-short portfolio strategy to exploit the observed deviations from NAV, and find it generates an alpha of .96% per month or about 11.5% per year.
#118 – Time Series Momentum Effect
Hurst, Ooi, Pedersen: Demystifying Managed Future
http://pages.stern.nyu.edu/~lpederse/papers/DemystifyingManagedFutures.pdf
Abstract:
We show that the returns of Managed Futures funds and CTAs can be explained by simple trend-following strategies, specifically time series momentum strategies. We discuss the economic intuition behind these st rategies, including the potential sources of profit due to initial under-reaction and delayed over-reaction to news. We show empirically that these trend-following strategies explain Managed Futures returns. Indeed, time series momentum strategies produce large correlations and high R-squares with Managed Futures indices and individual manager returns, including the largest and most successful managers. While the largest Managed Futures managers have realized significant alphas to traditional long-only benchmarks, controlling for time series momentum strategies drives their alphas to zero. Finally, we consider a number of implementation issues relevant to time series momentum strategies, including risk management, risk allocation across asset classes and trend horizons, portfolio rebalancing frequency, transaction costs, and fees.
#229 – Earnings Quality Factor
Assness, Frazzini, Pedersen: Quality Minus Junk
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2312432
Abstract:
We define a quality security as one that has characteristics that, all-else-equal, an investor should be willing to pay a higher price for: stocks that are safe, profitable, growing, and well managed. High-quality stocks do have higher prices on average, but not by a very large margin. Perhaps because of this puzzlingly modest impact of quality on price, high-quality stocks have high risk-adjusted returns. Indeed, a quality-minus-junk (QMJ) factor that goes long high-quality stocks and shorts low-quality stocks earns significant risk-adjusted returns in the U.S. and globally across 24 countries. The price of quality – i.e., how much investors pay extra for higher quality stocks – varies over time, reaching a low during the internet bubble. Further, a low price of quality predicts a high future return of QMJ.
