New strategies:
#252 – Closed-End Fund Mean Reversion Trading
Period of rebalancing: monthly
Markets traded: equities, bonds
Instruments used for trading: funds
Complexity: Complex strategy
Bactest period: 1998 – 2011
Indicative performance: 18.20%
Estimated volatility: 9.49%
Source paper:
Patro, Piccotti, Wu: Exploiting Closed-End Fund Discounts: The Market May Be Much More Inefficient than You Thought
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2468061
Abstract:
We find significant evidence of mean reversion in closed-end fund premiums. Previous studies substantially understate the magnitudes of arbitrage profits in the closed-end fund market. Capitalizing on the property of mean reversion, we devise a parametric model to estimate expected fund returns by incorporating the information content of a fund’s premium innovation history. Our strategy of buying the quintile of funds with the highest expected returns and selling the quintile of funds with the lowest expected returns yields an annualized arbitrage return of 18.2 percent and a Sharpe ratio of 1.918, which are substantially higher than the corresponding figures produced using the extant methods. The results are robust to a wide range of tests. They greatly deepen the closed-end fund discount puzzle and pose a challenge to the market efficiency in these products.
New research papers related to existing strategies:
#205 – Switching between Value and Momentum in Stocks
Oversby: Exploiting Factor Autocorrelation to Improve Risk Adjusted Returns
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2456543
Abstract:
The Fama-French three factor model is ubiquitous in modern finance. Returns are modeled as a linear combination of a market factor, a size factor and a book-to-market equity ratio (or “value”) factor. The success of this approach, since its introduction in 1992, has resulted in widespread adoption and a large body of related academic literature. The risk factors exhibit serial correlation at a monthly timeframe. This property is strongest in the value factor, perhaps due to its association with global funding liquidity risk. Using thirty years of Fama-French portfolio data, I show that autocorrelation of the value factor may be exploited to efficiently allocate capital into segments of the US stock market. The strategy outperforms the underlying portfolios on an absolute and risk adjusted basis. Annual returns are 5% greater than the components and Sharpe Ratio is increased by 86%. The results are robust to different time periods and varying composition of underlying portfolios. Finally, I show that implementation costs are much smaller than the excess return and that the strategy is accessible to the individual investor.
#54 – Momentum and State of Market (Sentiment) Filters
#171 – Market Timing Filter Applied to a Classical Stock Anomalies
#205 – Switching between Value and Momentum in Stocks
Januario: A Comprehensive Look at Size, Value and Momentum Return Predictability
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2456850
Abstract:
This paper reexamines the performance of variables that have been suggested in the literature to be good predictors of the return of size, value and momentum equity investment strategies. I find that, using simple linear regression models at a monthly frequency the predictors perform poorly both in-sample and out-of-sample, and are unstable within subsamples (with the exception of lagged returns). At annual frequency, results are more in line with the literature: value is predicted by a generalization of the value spread of Cohen et al. (2003) and Asness et al. (2000); momentum is predicted by stock variance (Barroso and Santa-Clara (2013)) and co-momentum (Lou and Polk (2013)), although with out-of-sample R-squares robust to different sub-samples only at 10% significance level and not robust to leverage constraints. These results suggest that the variety of predictors proposed by the literature are of little value for the predictability of size, value and momentum returns.
