New strategies:
#338 – Timing of Option Returns
Period of rebalancing: daily
Markets traded: equities
Instruments used for trading: options
Complexity: Simple strategy
Bactest period: 1996-2015
Indicative performance: 8.69%
Estimated volatility: 3.11%
Source paper:
Tosi, Ziegler: The Timing of Option Returns
https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2909163
Abstract:
We document empirically that the returns from shorting out-of-the-money S&P 500 put options are concentrated in the few days preceding their expiration. Back-month options generate almost no returns, and front-month options do so only towards the end of the option cycle. The concentration of the option premium at the end of the cycle re ects changes in options' risk characteristics. Speci cally, options' convexity risk increases sharply close to maturity, making them more sensitive to jumps in the underlying price. By contrast, volatility risk plays a smaller role close to maturity. Our results imply that speculators wishing to harvest the put option premium should short front-month options only during the last days of the cycle, while investors wishing to protect against downside risk should use back-month options to reduce hedging costs.
#339 – Expected Investment Growth within the Cross-section of Stocks Returns
Period of rebalancing: monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1974-2015
Indicative performance: 6.28%
Estimated volatility: 22.44%
Source paper:
Li, Wang: Expected Investment Growth and the Cross Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2882025
Abstract:
Expected investment growth (EIG) is a strong predictor for cross-sectional stock returns. Between July 1953 and December 2015 in the US, an investment strategy that takes a long position in rms with high EIG and a short position in rms with low EIG generates an average annual return of more than 20%, with a Sharpe ratio of 1.01. This return predictability holds both in subperiods and in di erent subsamples of rms, as well as in all other G7 countries. Leading empirical factor models including CAPM, Fama-French three-factor model, Carhart four-factor model, and the recent Hou, Xue, and Zhang four-factor model and Fama and French ve-factor model all fail to fully capture the pro tability of this investment strategy. Further analyses suggest that EIG is closely related to nancial distress risk, especially at a short horizon up to one year, and is a better predictor of stock returns than failure probability from Campbell, Hilscher, and Szilagyi (2008). We provide supporting evidence for both risk-based and behavioral explanations for this large EIG premium.
New research paper related to existing strategy:
#324 – Risk-Managed Industry Momentum
Grobys: Risk-Managed 52-Week High Industry Momentum, Momentum Crashes, and Hedging Macroeconomic Risk
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2903989
Abstract:
This is the first study that investigates the profitability of Barroso and Santa-Clara’s (2015) risk managing approach for George and Hwang’s (2004) 52-week high momentum strategy in an industrial portfolio setting. The findings indicate that risk-managing adds value as the Sharpe ratio increases, and the downside risk remarkably decreases. Even after controlling for the spread of the traditional 52-week high industry momentum strategy in association with standard risk-factors, the risk-managed version generates economically and statistically significant payoffs. Notably, the risk-managed strategy is partially explained by changes in cross-sectional return dispersion, whereas the traditional strategy does not appear to be exposed to such economic risks.
Two additional related research papers have been included into existing free strategy reviews during last 2 week:
Nice academic paper. What's the return premium of short-sale restrictions:
Jiang, Li: The No-Short Return Premium
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2903517
Abstract:
Theory predicts that securities with greater limits to arbitrage are more subject to mispricing and thus should command a higher return premium. We test this prediction using the unique regulatory setting from the Hong Kong stock market, in which some stocks can be sold short and others cannot. We show that no-short stocks on average earn significantly higher returns than shortable stocks and the two groups of stocks tend to comove negatively. Moreover, stocks that comove more with the portfolio of no-short stocks on average earn higher subsequent abnormal returns while those comoving more with the shortable stocks earn lower subsequent abnormal returns. New additions to and deletions from the shorting list only partially contribute to the no-short return premium.
Plus an interesting academic paper related to multiple equity factors:
Briere, Szafarz: Factor Investing: The Rocky Road from Long Only to Long Short
https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2908491
Abstract:
The performances of factor investing rely heavily on short sales, not only for building the initial long-short strategy, but also for regularly rebalancing the positions. Since short selling is subject to both legal restrictions and substantial costs, this paper examines how severely restrictions on short positions affect the financial attractiveness of factor investing. To fill the gap between unconstrained long-short allocations and restricted long-only portfolios, we consider two in-between strategies: the first imposes that only the market can be shorted, and the second is the so-called “130/30” scenario that caps total short exposure at 30%. The takeaways are twofold. First, any infringement to the long-short strategy can harm significantly the mean-variance performances of efficient factor-based portfolios. This is linked to the fact that the total short exposure of optimal long-short portfolios can reach figures around 400% and above. Second, the factor portfolios built originally by Fama and French (1992) with the purpose of developing asset pricing are impressively clear-sighted when it comes to portfolio management. Indeed, combining these portfolios generates mean-variance performances similar to those of optimized long-short portfolios, except for low levels of volatility.
