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Momentum is a simple well-known trading strategy that buys stocks with the best returns over the past three to 12 months and sells stocks with the worst returns over the same horizon. Many tweaks to the basic momentum strategy have been published in academic papers.
Momentum combined with a volatility effect is one such useful trick as research shows that momentum returns could be enhanced by using the most volatile stocks. An additional advantage of this approach is that it works very well within large-cap stocks (it is well-known that momentum works better in a small-cap universe; therefore, any trick which works within large caps is helpful).
Fundamental reason
Academic research postulates that the medium-term momentum is rationalized largely along the behavioral avenue. Gradual information diffusion and/or investor under-reaction leads to momentum (Chan, Jegadeesh and Lakonishok, 1996; and Hong, Lim and Stein, 2000). Some researchers show that information uncertainty can intensify return continuations under the postulation that investors under-react more (due to overconfidence) when presented with vague information. Following this line of thinking, investors should see stronger momentum in securities with greater information uncertainty, such as in smaller stocks and stocks with higher volatility.
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Keywords
Market Factors
Confidence in Anomaly's Validity
Period of Rebalancing
Number of Traded Instruments
Notes to Number of Traded Instruments
Complexity Evaluation
Financial instruments
Backtest period from source paper
Indicative Performance
Notes to Indicative Performance
Estimated Volatility
Notes to Estimated Volatility
Maximum Drawdown
Notes to Maximum drawdown
Sharpe Ratio
Regions
Simple trading strategy
The investment universe consists of NYSE, AMEX, and NASDAQ stocks with prices higher than $5 per share. At the beginning of each month, the sample is divided into equal halves, at the size median, and only larger stocks are used. Then each month, realized returns and realized (annualized) volatilities are calculated for each stock for the past six months. One week (seven calendar days) prior to the beginning of each month is skipped to avoid biases due to microstructures. Stocks are then sorted into quintiles based on their realized past returns and past volatility. The investor goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest-performing quintile from the highest volatility group. Stocks are equally weighted and held for six months (therefore, 1/6 of the portfolio is rebalanced every month).
Hedge for stocks during bear markets
Unknown – Source and related research papers don't offer insight into the correlation structure of the proposed trading strategy to equity market risk; therefore, we do not know if this strategy can be used as a hedge/diversification during the time of market crisis. The strategy is built as a long-short, but it can be split into two parts. The long leg of the strategy is surely strongly correlated to the equity market; however, the short-only leg might be used as a hedge during bad times. Rigorous backtest is, however, needed to determine return/risk characteristics and correlation.
Out-of-sample strategy's implementation/validation in QuantConnect's framework(chart, statistics & code)
Related picture
Source paper
Wei: Do Momentum and Reversals Coexist?
Abstract: The answer to the title question is "Yes." Examining stocks traded on the NYSE, AMEX and NASDAQ for the period of 1964 to 2009, this study discovers that, while momentum prevails among small stocks, momentum and reversals coexist among large stocks for a holding period of up to six months. The momentum/reversal divide is along the volatility dimension: Large-cap/low-volatility stocks exhibit reversals while large-cap/high-volatility stocks experience momentum. This new discovery cannot be fully rationalized with either risk-based or behavioral-based explanations.
Other papers
Wei, Yang: Short-Term Momentum and Reversals in Large Stocks
Abstract: Using stocks traded on the NYSE, AMEX and NASDAQ for the period of 1964 to 2009, this study demonstrates that, while momentum prevails among small stocks, momentum and reversals coexist among large stocks for a holding period of up to six months. The momentum/reversal divide is along the volatility dimension: Large-cap/low-volatility stocks exhibit reversals while large-cap/high-volatility stocks experience momentum. Our finding is in sharp contrast with those in the existing literature which mostly documents and explains momentum and reversals for different horizons. As such, our study not only offers fresh, new empirical findings on cross-section return predictability but also poses a challenge to the existing theoretical paradigms that are tailored to sequential occurrence of momentum and reversals. Specifically, we contribute to the literature by 1) uncovering a new empirical regularity which explains why large stocks are generally associated with no or weak momentum in the short-term, and 2) advancing a theoretical model based on "moderated confidence" which can rationalize empirical findings such as the one in the current paper where underreaction and overreaction can occur simultaneously with the same investor.
Chiang, Kirby, Nie: Nonlinearity, Return Reversals, Information Flow, and the Idiosyncratic Volatility Puzzle
Abstract: Stock returns display a robust cross-sectional relation with prior idiosyncratic volatility (IVOL). However, the relation is both nonlinear and non-monotonic. Because the relation is concave in nature, it is consistent with a positive price of volatility risk combined with a behavioral preference for high-volatility stocks on the part of some investors. The effect of prior IVOL is also heavily influenced by prior stock returns. It is negative for stocks that are big losers and positive for stocks that are big winners. Replacing IVOL with dollar trading volume produces similar findings. The strong similarities between the results for IVOL and those for trading volume suggests that IVOL acts as a proxy for the arrival rate of information that spurs speculative trading, and that the likelihood of return reversals falls as the relative importance of speculative trading increases.