The short-term reversal anomaly, the phenomenon that stocks with relatively low returns over the past month or week earn positive abnormal returns in the following month or week, and stocks with high returns earn negative abnormal returns, is well-researched, where a lot of research has been made about this particular anomaly. However, the researchers hypothesize that reversal strategies require frequent trading and rebalancing in disproportionately high-cost securities, and this would lead to a situation where trading costs prevent profitable strategy execution. The results might be interpreted in a way that the abnormal returns of reversal investment strategies, which were documented in studies create an illusion of profitable investment strategies, but due to the transaction costs, the strategies are not applicable and the profitable strategies simply do not exist.
However, research has found that the impact of trading costs on reversal profits can largely be attributed to an excessively trading in small-cap stocks and therefore there exists a solution how to make a reversal strategy profitable, even when the transaction costs are included, but the potential investor must modify the basic strategy. Simply said, the turnover of standard reversal strategies is excessively high. The solution to this problem is simple; the strategy has to be traded on stocks with a larger market capitalization. The aforementioned lead to a profitable and both economically, and statistically significant strategy, which sells past winners, and buys past losers, but is slightly modified in terms of the size of the stocks. Moreover, the paper also presented research about European stocks and found that if the investor invests in the stocks with larger market capitalization, the reversal strategy will work. Last but not least, the ability of the reversal anomaly to survive trading costs was also studied in the paper Frazzini, Israel and Moskowitz: Trading Costs of Asset Pricing Anomalies.
Past research speculates that the fundamental reason why does the reversal anomaly work, is the investor’s overreaction to the past information and a correction of that reaction after a short time horizon. The work of Stefan Nagel in the paper Evaporating Liquidity claims that returns of short-term reversal strategies in equity markets can be interpreted as a proxy for the returns from liquidity provision and shows that reversal anomaly returns closely track the returns earned by liquidity providers.
Overall, there is a mutual consent that theoretically, reversal strategy does work, however, there is a problem with the transaction costs, that make the strategy inapplicable. This paper and also the work of de Groot, Wilma and Huij, Joop and Zhou, Weili: Another Look at Trading Costs and Short-Term Reversal Profits proved that the strategy works if the investor focus purely on the larger stocks, what reduces the transaction costs. Moreover, research of this paper has used the Nomura cost estimates instead of the trading cost estimates resulting from the Keim and Madhavan model, because trading costs of the model are substantially lower than the Nomura cost estimates and the trading costs of Keim and Madhavan model can even be negative in many cases. Therefore, the results from the Keim and Madhavan model should be interpreted with caution and probably should not even be used. Moreover, additional attractive feature of the trading cost model which was obtained from the Nomura Securities is that it has also been calibrated using European trade data, which eases the European stocks analysis. Lastly, the study has found that net reversal profits are large and positive among large cap stocks over the most recent decade in the sample, which is characterized by the dramatically increased market liquidity. This rules out the explanation that reversals are induced by inventory imbalances by market makers and that the contrarian profits are a compensation for bearing inventory risks.
Confidence in anomaly's validity
Backtest period from source paper
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
Notes to Indicative Performance
per annum, net return for strategy with 100 largest stocks and weekly rebalancing, annualized (geometrically) net weekly return 0,29% from Table 7 Panel C
Notes to Period of Rebalancing
Number of Traded Instruments
Notes to Estimated Volatility
estimated from t-statistic from Table 7 Panel C
Notes to Number of Traded Instruments
Notes to Maximum drawdown
Notes to Complexity Evaluation
Simple trading strategy
The investment universe consists of the 100 biggest companies by market capitalization. The investor goes long on the 10 stocks with the lowest performance in the previous month and goes short on the 10 stocks with the greatest performance from the previous month. The portfolio is rebalanced weekly.
Hedge for stocks during bear markets
Yes - Equity reversal strategies are usually type of “liquidity providing” strategies and as such they usually perform well during market crises. However, reversal strategies are also naturally a “short volatility” strategies, their returns increase mainly in the weeks following large stock market market declines. Traders must be catious during crisis during days with a high volatility as reversal strategies usually force traders to buy stocks which performed especially bad (and to sell short stocks with an extremelly positive short term performance). This position is emotionally hard to open and risk management of reversal strategies must be also very strict during these days. We recommend reading research paper by Nagel: “Evaporating Liquidity” to gain insight into behaviour of reversal strategies during crises.
Groot, Huij, Zhou: Another Look at Trading Costs and Short-Term Reversal Profits
Several studies report that abnormal returns associated with short-term reversal investment strategies diminish once transaction costs are taken into account. We show that the impact of transaction costs on the strategies’ profitability can largely be attributed to excessively trading in small cap stocks. Limiting the stock universe to large cap stocks significantly reduces trading costs. Applying a more sophisticated portfolio construction algorithm to lower turnover reduces trading costs even further. Our finding that reversal strategies can generate 30 to 50 basis points per week net of transaction costs poses a serious challenge to standard rational asset pricing models. Our findings also have important implications for the understanding and practical implementation of reversal strategies.
Strategy's implementation in QuantConnect's framework
Frazzini, Israel, Moskowitz: Trading Costs of Asset Pricing Anomalies
Using nearly a trillion dollars of live trading data from a large institutional money manager across 19 developed equity markets over the period 1998 to 2011, we measure the real-world transactions costs and price impact function facing an arbitrageur and apply them to size, value, momentum, and short-term reversal strategies. We find that actual trading costs are less than a tenth as large as, and therefore the potential scale of these strategies is more than an order of magnitude larger than, previous studies suggest. Furthermore, strategies designed to reduce transactions costs can increase net returns and capacity substantially, without incurring significant style drift. Results vary across styles, with value and momentum being more scalable than size, and short-term reversals being the most constrained by trading costs. We conclude that the main anomalies to standard asset pricing models are robust, implementable, and sizeable.
Nagel: Evaporating Liquidity
The returns of short-term reversal strategies in equity markets can be interpreted as a proxy for the returns from liquidity provision. Using this a pproach, this article shows that the return from liquidity provision is highly predictable with the VIX index. Expected returns and conditional Sharpe ratios from liquidity provision spike during periods of financial market turmoil. The results point to withdrawal of liquidity supply, and an associated increase in the expected returns from liquidity provision, as a main driver behind the evaporation of liquidity during times of financial market turmoil, consistent with theories of liquidity provision by financially constrained intermediaries.
Messmer: Deep Learning and the Cross-Section of Expected Returns
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.
Miwa: Short-Term Return Reversals and Intraday Transactions
I examine whether a short-term reversal is attributed to past intraday or overnight price movements. The results show that intraday returns significantly reverse in the following week, while overnight returns do not, indicating that the short-term reversal is attributed to past intraday price movements. In addition, the reversal of intraday returns is stronger for more illiquid stocks and during more volatile market conditions, while the reversal is unaffected by fundamental news. This result supports the view that short-term reversals are attributable mainly to price concessions for liquidity providers to absorb intraday uninformed transactions, rather than intraday price reactions to fundamental information.