New strategy:
#373 – Earnings Acceleration and Stock Returns
Period of rebalancing: Quarterly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1997 – 2015
Indicative performance: 9.82%
Estimated volatility: 12.26%
Source paper:
Hardouvelis, Gikas A. and Karalas, Georgios I.: Style Concentration in Ownership and Expected Stock Returns
https://ssrn.com/abstract=3065191
Abstract:
We examine the relation of expected stock returns with fund style concentration in stock ownership over the period 1997-2015. Concentration is measured by the Herfindahl index H of the shares of different investment styles in the ownership of stocks and represents a measure of investor inattention in stocks. Decile portfolios on H reveal a strong positive association of H with future returns, with the long-short portfolio on H having significant alphas after passing through the five-factor Fama-French (2015) model. The econometric results confirm the positive association and are robust to the inclusion of known risk-factors as determinants of expected stock returns, the returns of the investment styles themselves, plus a set of style-related control variables and other liquidity, size, or volatility characteristics of stocks. The relation coexists with short-run price and style momentum and long-run style and price reversals of Barberis and Shleifer (2003) and remains present over multi-year horizons of stock returns, being both economically and statistically significant. The results are consistent with the model of Merton (1987), which claims a stock’s excess risk premium over the CAPM premium, is the product of investor participation (which is proxied by H in our framework), idiosyncratic volatility and size. These results also shed light on the small firm effect.
New research papers related to existing strategies:
#33 – Post-Earnings Announcement Effect
#208 – Share Issuance Effect
Daniel, Hirschleifer, Sun: Short and Long Horizon Behavioral Factors
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3086063
Abstract:
Recent theories suggest that both risk and mispricing are associated with commonality in security returns, and that the loadings on characteristic-based factors can be used to predict future returns. We offer a parsimonious model which features: (1) a factor motivated by limited attention that is dominant in explaining short-horizon anomalies, and (2) a factor motivated by overconfidence that is dominant in explaining long-horizon anomalies. Our three-factor risk-and-behavioral composite model outperforms both standard models and recent prominent factor models in explaining a large set of robust return anomalies.
#118 – Time Series Momentum Effect
#253 – Momentum in Futures
Fan, Li, Liu: Risk Adjusted Momentum Strategies: A Comparison between Constant and Dynamic Volatility Scaling Approaches
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3076715
Abstract:
We compare the performance of two volatility scaling methods in momentum strategies: (i) the constant volatility scaling approach of Barroso and Santa-Clara (2015), and (ii) the dynamic volatility scaling method of Daniel and Moskowitz (2016). We perform momentum strategies based on these two approaches in an asset pool consisting of 55 global liquid futures contracts, and further compare these results to the time series momentum and buy-and-hold strategies. We find that the momentum strategy based on the constant volatility scaling method is the most efficient approach with an annual return of 15.3%.
Two additional related research papers have been included into existing free strategy reviews during last 2 weeks:
Deep learning is a very popular area of research and is used in a lot of industries. We link to a new paper which gives interesting insights about equity factor investing:
Messmer: Deep Learning and the Cross-Section of Expected Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3081555
Abstract:
Deep learning is an active area of research in machine learning. I train deep feedforward neural networks (DFN) based on a set of 68 firm characteristics (FC) to predict the US cross-section of stock returns. After applying a network optimization strategy, I find that DFN long-short portfolios can generate attractive risk-adjusted returns compared to a linear benchmark. These findings underscore the importance of non-linear relationships among FC and expected returns. The results are robust to size, weighting schemes and portfolio cutoff points. Moreover, I show that price related FC, namely, short-term reversal and the twelve-months momentum, are among the main drivers of the return predictions. The majority of FC play a minor role in the variation of these predictions.
Related to all momentum based strategies:
Roncalli: Keep Up the Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3083921
Abstract:
The momentum risk premium is one of the most important alternative risk premia alongside the carry risk premium. However, it appears that it is not always well understood. For example, is it an alpha or a beta exposure? Is it a skewness risk premium or a market anomaly? Does it pursue a performance objective or a hedging objective? What are the differences between time-series and cross-section momentum? What are the main drivers of momentum returns? What does it mean when we say that it is a convex and not a concave strategy? Why is the momentum risk premium a diversifying engine, and not an absolute return strategy?
The goal of this paper is to provide specific and relevant answers to all these questions. The answers can already be found in the technical paper "Understanding the Momentum Risk Premium" published recently by Jusselin et al. (2017). However, the underlying mathematics can be daunting to readers. Therefore, this discussion paper presents the key messages and the associated financial insights behind these results.
Among the main findings, one result is of the most importance. To trend is to diversify in bad times. In good times, trend-following strategies offer no significant diversification power. Indeed, they are beta strategies. This is not a problem, since investors do not need to be diversified at all times. In particular, they do not need diversification in good times, because they do not want that the positive returns generated by some assets to be cancelled out by negative returns on other assets. This is why diversification may destroy portfolio performance in good times. Investors only need diversification in bad economic times and stressed markets.
This diversification asymmetry is essential when investing in beta strategies like alternative risk premia. On the contrary, this diversification asymmetry is irrelevant when investing in absolute return strategies. However, we know that generating performance with alpha strategies is much more difficult than generating performance with beta strategies. Therefore, beta is beautiful, but convex beta is precious and scarce. Among risk premia, momentum is one of the few strategies to offer this diversification asymmetry. This is why investing in momentum is a decision of portfolio construction, and not a search for alpha.
