The traditional momentum effect is one of the most well-known and best-documented stock market anomalies. Also, some notable research about the persistency of momentum effect has already taken place at the portfolio level (for example, Chen et al., 2014). However, recent research has taken the issue a bit further, focusing on the performance variation at the firm level rather than on a more general portfolio level. Based on the “top decile long and bottom decile short” approach, the empirical evidence shows, inter alia, that only about 60% of the winner/loser stocks in the sample were consistent winners (losers) – in the top (bottom) decile both during the month of portfolio formation and in the following month – and that at least 1/4 of them even experienced a contrarian effect in the post-formation month. The authors of the recent academic research show that consistent winner stocks outperform inconsistent winner stocks, and conversely, consistent loser stocks underperform inconsistent loser stocks, in the post-formation period. With respect to this result, they suggested a strategy of buying consistent winners while selling consistent losers, which outperformed the traditional and ‘inconsistent’ momentum with 1.25% average monthly return (significantly positive even after adjusting for Fama-French’s three factors and Carhart’s momentum factor, as opposed to the traditional and inconsistent momentum strategies with 1.06% and 0.47% average monthly returns, respectively).
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Momentum anomaly is, in general, related to investors’ irrationality – they underreact to new information as they do not incorporate news in their transaction prices sufficiently. Under the information asymmetry and the heterogeneous beliefs hypotheses, the persistence of momentum effect depends on size, idiosyncratic volatility, % of institutional ownership, and trading volume. According to the former, investors tend to be conservative in the case of stocks with higher idiosyncratic volatilities and a lower percentage of outstanding stocks owned by institutional investors (they become consistent winners/losers due to slow price adjustment). The latter suggests that higher trading volume on stock (a proxy for disagreement among investors) should produce a stronger momentum effect.
Backtest period from source paper
Confidence in anomaly's validity
Notes to Confidence in Anomaly's Validity
Notes to Indicative Performance
per annum, annualized (geometrically) average monthly return of 1,25%, data from table 9
Period of Rebalancing
Notes to Period of Rebalancing
Notes to Estimated Volatility
estimmated from t-statistic, data from table 9
Number of Traded Instruments
Notes to Number of Traded Instruments
more or less, it depends on investor’s need for diversification
Notes to Maximum drawdown
Notes to Complexity Evaluation
Simple trading strategy
The investment universe consists of stocks listed at NYSE, AMEX, and NASDAQ, whose price data (at least for the past seven months) are available at the CRSP database. The investor creates a zero-investment portfolio at the end of the month t, longing stocks that are in the top decile in terms of returns both in the period from t-7 to t-1 and from t-6 to t, while shorting stocks in the bottom decile in both periods (i.e. longing consistent winners and shorting consistent losers). The stocks in the portfolio are weighted equally. The holding period is six months, with no rebalancing during the period. There is a one-month skip between the formation and holding period.
Hedge for stocks during bear markets
Not known - 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