New strategies:
#386 – Enhanced Betting Against Beta Strategy in Equities
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1995 – 2009
Indicative performance: 24.15%
Estimated volatility: 27.61%
Source paper:
De, Koustav: Losers Buy Beta
https://ssrn.com/abstract=3134118
Abstract:
I empirically show that investors tend to buy higher beta stocks following realized losses. This behavior is observed in institutional as well as individual investors, but is more pronounced among individual investors with lower expertise, who on an average buy a new stock with up to 15% higher beta than that of the old stock they were holding. For an agent with utility consistent with prospect theory, this behavior emerges as the optimal response to her problem of maximizing utility within a mental account. Furthermore, this behavior can aggregate up during market downturns and cause pricing distortions in a direction similar to the beta anomaly. With this insight, I suggest a modification to the betting against beta trading strategy that can improve the Sharpe ratio more than twofold.
#387 – Moving Averages Distance Strategy in Equities
Period of rebalancing: Daily
Markets traded: equties
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1977 – 2015
Indicative performance: 10.12%
Estimated volatility: 14.15%
Source paper:
Avramov, Doron and Kaplanski, Guy and Subrahmanyam, Avanidhar: The Predictability of Equity Returns from Past Returns: A New Moving Average-Based Perspective
https://ssrn.com/abstract=3111334
Abstract:
The distance between the short- and long-run moving averages of prices is a potent predictor of stock returns in the cross-section. The greater the positive (negative) distance between the short run average and the long-run one, the greater (lower) is the expected return. The corresponding strategy yields reliable profits that do not decay even after several months and that survive modern factor models and reasonable transaction costs. The distance also reliably predicts returns at the market and industry levels, as well as in international settings. We propose and provide supporting evidence for the notion that large deviations of prices from their long-run moving averages represent surprises relative to prevailing anchors to which investors react insufficiently.
New research papers related to existing strategies:
#123 – Options Skewness Predicts Consecutive Stocks Returns
#223 – Realized Skewness Predicts Equity Returns
#268 – Expected Skewness and Momentum in Stocks
Langlois: Measuring Skewness Premia
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3141416
Abstract:
We provide a new methodology to empirically investigate the respective roles of systematic and idiosyncratic skewness in explaining expected stock returns. Forming a risk factor that captures systematic skewness risk and forming idiosyncratic skewness sorted portfolios only require the ordering of stocks with respect to each skewness measure. Accordingly, we use a large number of predictors to forecast the cross-sectional ranks of systematic and idiosyncratic skewness which are considerably easier to predict than their actual values. Compared to other measures of ex ante systematic skewness, our forecasts create a significant spread in ex post systematic skewness. A predicted systematic skewness risk factor carries a significant risk premium that ranges from 7% to 12% per year, adds information beyond other leading factor models, and subsumes the size factor. In contrast to systematic skewness, the role of idiosyncratic skewness in pricing stocks is less robust. Finally, we document how the determinants of systematic skewness differ from those of idiosyncratic skewness.
#363 – Technology Momentum
Khimich, Bekkerman: Technological Similarity and Stock Return Cross-Predictability: Evidence from Patents’ Big Data
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3147218
Abstract:
We investigate asset pricing implications of innovation links between firms. We identify technologically similar patents and firms via textual analysis of 7.7 million patents. We find that information about technological similarity is impounded into stock prices, although not immediately and fully; stocks of technologically similar firms cross-predict one another’s returns. This effect is distinct from the industry and supply-chain momentum and predictable lead–lag relations between large and small firms. The magnitude of predictability increases with information opacity and investors’ inattention to links as proxied by common analyst coverage of technologically similar pairs, suggesting attention constraints as a potential explanation.
Two additional related research papers have been included into existing free strategy reviews during last 2 weeks:
There are a lot of media articles showing how "expensive" the current stock market (or some equity factor) is. However, these articles can be based on a weak statistical analysis:
Boudoukh, Israel, Richardson: Long Horizon Predictability: A Cautionary Tale
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3142575
Abstract:
Long-horizon return regressions have effectively small sample sizes. Using overlapping long-horizon returns provides only marginal benefit. Adjustments for overlapping observations have greatly overstated t-statistics. The evidence from regressions at multiple horizons is often misinterpreted. As a result, there is much less statistical evidence of long-horizon return predictability than implied by existing research, casting doubt over claims about forecasts based on stock market valuations and factor timing.
And an interesting paper, an analysis of a day of the week effect in the crypto currency market … :
Caporale, Plastun: The Day of the Week Effect in the Crypto Currency Market
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3082117
Abstract:
This paper examines the day of the week effect in the crypto currency market using a variety of statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal-Wallis test, and regression analysis with dummy variables) as well as a trading simulation approach. Most crypto currencies (LiteCoin, Ripple, Dash) are found not to exhibit this anomaly. The only exception is BitCoin, for which returns on Mondays are significantly higher than those on the other days of the week. In this case the trading simulation analysis shows that there exist exploitable profit opportunities that can be interpreted as evidence against efficiency of the crypto currency market.
