Short-term Reversal Strategies Research Articles

Identifying inconsistency in short-term reversal: evidence from the Chinese stock market

ABSTRACT We study the inconsistency between past return and profitability growth to explore the mispricing explanation for short-term reversal. Our empirical results indicate that reversal profits are more concentrated in stocks where past returns are inconsistent with profitability growth. Based on our finding, we develop a growth-anchored reversal (GAR) strategy defined as buying past losers with high profitability growth and selling past winners with low growth. This refined short-term reversal strategy turns out to be more profitable than conventional reversal strategy in the Chinese stock market. Our results remain robust under different sample periods, different market situations and alternative profitability measures.

Trading volume and liquidity provision in cryptocurrency markets

We provide empirical evidence within the context of cryptocurrency markets that the returns from liquidity provision, proxied by the returns of a short-term reversal strategy, are primarily concentrated in trading pairs with lower levels of market activity. Empirically, we focus on a moderately large cross section of cryptocurrency pairs traded against the U.S. Dollar from March 1, 2017 to March 1, 2022 on multiple centralised exchanges. Our findings suggest that expected returns from liquidity provision are amplified in smaller, more volatile, and less liquid cryptocurrency pairs where fear of adverse selection might be higher. A panel regression analysis confirms that the interaction between lagged returns and trading volume contains significant predictive information for the dynamics of cryptocurrency returns. This is consistent with theories that highlight the role of inventory risk and adverse selection for liquidity provision.

Portfolio Selection Using Multiple Factors: A Machine Learning Approach

In this paper we present a novel framework for portfolio selection using deep neural networks and elastic net regularization: At the beginning of each month T, we follow a three-step methodology. First, for each stock, we use the previous seven years of data in order to compute over 36 firm-specific factors. Second, we perform features selection using elastic net regularization. Finally, we train a deep neural network in order to learn portfolio weights and hold this portfolio until the end of the month T. Compared with momentum, long-term reversal, and short-term reversal strategies, our approach demonstrates a superior performance in terms of the monthly rate of return (2% versus 1.22% for long-term reversal, 1.15% for momentum, and only 0.68% for short-term reversal), Sharpe ratio (21.67% versus 19.31% for momentum, 15.51% for long-term reversal, and 8.69% for short-term reversal), and the monthly risk-adjusted return (1.85% versus 0.74% for momentum, 0.72% for long-term reversal, and 0.31% for shortterm reversal). The results of our approach are all statistically significant at 1% level.

A Machine Learning Approach for the Short-term Reversal Strategy

The short-term reversal effect is a pervasive and persistent phenomenon in worldwide financial markets that has been found to generate abnormal returns not explainable by traditional asset pricing models. In contrast to the linear model employed in most studies on the short-term reversal, this article aims to establish a nonlinear framework to study the reversal anomaly, by using machine learning approaches. Machine learning methods including Random Forest, Adaptive Boosting, Gradient Boosted Decision Trees and extreme Gradient Boosting, are employed to test the profitability of the short-term strategy in the US and Chinese stock markets. Significant outperformances with extremely high Sharpe ratio, moderate kurtosis, and positive skewness are found, showing remarkable classification efficiency of the machine learning models and their applicability to various markets. Further studies reveal that the strategy returns can be weakened with the extension of the holding period. Notably, by comparing the performances of machine learning with our newly developed linear reversal strategy, the nonlinear methods are proved to be capable of providing a diversified model predictability with improved classification accuracy. Our research indicates the significant potential of machine learning in resolving the stock return and feature relationship, which can be helpful for quantitative traders to make profitable investment decisions.

Reversal returns and expected returns from liquidity provision: Evidence from emerging markets

In this study, we document, for a number of emerging markets, that positive returns can be obtained using a short-term reversal strategy. These returns are higher for small and illiquid firms, and highest for more volatile firms. Overall, the reversal strategy-based alphas are significant when accessed through different asset pricing models. Our results provide, however, an important unexplored explanation; the reversal return is higher, irrespective of firm characteristics, when market volatility is high, and pronounced for the stocks that witness higher active investor exits. These findings reconcile with the notion that the reversal returns proxy the expected returns from liquidity provision in adverse times.

Overnight stock returns, intraday returns, and firm-specific investor sentiment

In this study, we propose a new index for measuring firm-specific investor sentiment using overnight and intraday stock returns. We use actual equity data to construct the firm-level investor sentiment index and find that the new index has characteristics expected of a sentiment measure. In addition, we propose a novel sentiment-weighted trading strategy and apply it to momentum and short-term reversal strategies. We find that the sentiment-weighted trading strategy generates better performance in momentum and short-term reversal strategies. The sentiment-weighted trading strategy’s superior performance is evidence that our firm-level investor sentiment index possesses predictive powers with regard to future returns.

Fundamental strength and short-term return reversal

Abstract We document that the fundamental strength (FSCORE) of a firm exerts a significant influence on the performance of short-term reversal strategies. Past losers with strong fundamentals significantly outperform past winners with weak fundamentals. Our FSCORE approach is complementary to Da et al. (2014) cash flow news metrics based on analysts’ forecast revisions in that many firms do not have analyst following. Our approach also seems capable of capturing the lagged effects from past fundamental news shocks. After controlling for fundamental strength, we find that investor sentiment plays a more dominant role than do liquidity shocks in explaining return reversal.

On the Realized Performance of Market Timing Strategies

The Information Ratio IR is the conventional metric to gauge the ex post risk-adjusted performance of a market timing strategy. A deficiency of this metric is that it does not account for an average “long bias”, which can confound the timing ability of the evaluated strategy. In this paper, we evaluate two alternative metrics, the Appraisal Ratio and the “Timing IR”, which intent to purge the IR from a non-zero average or systematic exposure to the underlying market. We compare both alternative metrics to the IR, both analytically and in an empirical setting where we evaluate a short-term reversal strategy on the US equity market over the period 1998-2018. We conclude that the Appraisal Ratio is the preferred metric.

Noise Trading, Slow Diffusion of Information, and Short-Term Reversals: A Fundamental Analysis Approach

We document that the fundamental strength (FSCORE) of a firm exerts a significant influence on the performance of short-term reversal strategies. Past losers with strong fundamentals significantly outperform past winners with weak fundamentals. Our FSCORE approach is complementary to Da et al. (2014) cash flow news metrics based on analysts’ forecast revisions in that many firms do not have analyst following. Our approach also seems capable of capturing the lagged effects from past fundamental news shocks. After controlling for fundamental strength, we find that investor sentiment plays a more dominant role than do liquidity shocks in explaining return reversal.

The Reversal Strategy: A Study Across All Emerging Markets

In this study, we document, for a number of emerging markets, that positive returns can be obtained using a short-term reversal strategy. Further, as expected, these returns are higher for small and illiquid firms, and the highest for more volatile firms. Overall, the reversal strategy-based alphas are significant when accessed through different asset pricing models. Our results provide, however, an important unexplored explanation; that the reversal return is higher when the market volatility is high and pronounced for the stocks that witness higher active investor exits. These findings reconcile with the notion that the reversal returns proxy the expected returns from liquidity provision in adverse times.

Deep learning with long short-term memory networks for financial market predictions

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the constituent stocks of the S&P 500 from 1992 until 2015. With daily returns of 0.46 percent and a Sharpe ratio of 5.8 prior to transaction costs, we find LSTM networks to outperform memory-free classification methods, i.e., a random forest (RAF), a deep neural net (DNN), and a logistic regression classifier (LOG). The outperformance relative to the general market is very clear from 1992 to 2009, but as of 2010, excess returns seem to have been arbitraged away with LSTM profitability fluctuating around zero after transaction costs. We further unveil sources of profitability, thereby shedding light into the black box of artificial neural networks. Specifically, we find one common pattern among the stocks selected for trading – they exhibit high volatility and a short-term reversal return profile. Leveraging these findings, we are able to formalize a rules-based short-term reversal strategy that yields 0.23 percent prior to transaction costs. Further regression analysis unveils low exposure of the LSTM returns to common sources of systematic risk – also compared to the three benchmark models.

Profitability of reversal strategies: A modified version of the Carhart model in China

This paper shows that buying stocks that have performed poorly and selling stocks that have performed well in the past generates positive returns in the Chinese stock market. Profits from reversal strategies cannot be explained by systematic risk and fundamental factors. Testing the Fama-French three-factor model and reversal strategies in multiple formation periods, we find that the price-to-earnings ratio explains the stock returns better than book-to-market ratio does, and investors benefit more from short-term reversal strategies than medium-term momentum strategies. Substituting the book-to-market factor and momentum factor for the price-to-earnings ratio factor and reversal factor, we propose a modified Carhart model. Our findings deepen understanding on the link between Chinese stock returns and their historical performance.

Liquidity Provision and the Cross Section of Hedge Fund Returns

I investigate whether hedge funds that supply liquidity earn superior returns. Using transaction data, I find that hedge funds following short-term contrarian strategies (i.e., liquidity suppliers) earn significantly higher returns on their equity trades and holdings. Similarly, using commercial databases, I find that hedge funds with greater exposure to a liquidity provision factor earn significantly higher excess returns and Sharpe ratios. The superior performance of liquidity-supplying hedge funds arises from strategies that are more complex than mechanical short-term reversal strategies. For example, among stocks with similar past returns, liquidity-supplying funds are more likely to trade against stocks heavily traded by constrained mutual funds and less likely to trade against stocks heavily traded by unconstrained mutual funds. The outperformance of liquidity-supplying funds is also concentrated in periods of low funding liquidity, suggesting that less-binding financial constraints contribute to their superior returns. The Internet appendix is available at https://doi.org/10.1287/mnsc.2016.2687 This paper was accepted by Lauren Cohen, finance.

The Interaction of Short-Term Reversal and Momentum Strategies

This article investigates the interaction between short-term reversal and momentum strategies. The authors find that the magnitude of price reversals of short-term winners and losers is significantly related to past medium-term performance. Both past medium-term winners and losers with the best short-term performance experience the strongest price continuation. Short-term reversal strategies perform best in the momentum-loser quintile, and momentum strategies perform best in the short-term-winner quintile. The authors’ results imply that investors could achieve higher momentum profits by also considering short-term performance and vice versa. The results also suggest that investors adhere to prior dominant beliefs in the face of new contradictory information. Short squeezes and fire sales (self-attribution bias) may explain the continued underperformance (outperformance) of momentum losers (winners) with good short-term performance.

Market Closure and Short-Term Reversal

A strategy that holds daily long and short positions, respectively, in assets with low and high past overnight returns - overnight-intraday reversal strategy - generates for the US stock market an average excess return that is five times larger when compared to a conventional short-term reversal strategy. Our results remain robust to using international stocks as well as equity index, interest rate, commodity, and currency futures. We find that overnight-intraday reversals are consistent with the simulated patterns generated by the continuous-time model with periodic market closures of Hong and Wang (2000). Finally, we demonstrate that only intraday returns matter for the reversal-based liquidity measure of Pastor and Stambaugh (2003).

Profitability of Short Term Reversal Strategies on the Johannesburg Stock Exchange

Short term reversal strategies involve the frequent buying (or selling) of l oser (or winner) stocks in order to profit in the short run if prices revert. Several studies report profitability of these strategies while others reject profitability due to trading costs. This study reports profitability of a short term reversal rule when trading in large capitalization Johannesburg Stock Exchange listed stocks, while allowing for trading costs and taking trade execution skills into account. Evaluation of the performance of the rule is done in terms of newly introduced reward and risk measures based on daily cash flows and market exposures respectively.

Practical Applications of Academic Knowledge Dissemination in the Mutual Fund Industry: Can Mutual Funds Successfully Adopt Factor Investing Strategies?

Practical Applications <b>The net effect.</b> Factor investing outperformed, even after taxes and fees. <b>Loud and clear.</b> Low-volatility, value and small cap strategies strongly outperformed in the study. <b>Mixed results.</b> The performance of momentum and long-term reversal strategies, compared to the control group, was mixed. Short-term reversal strategies underperformed. Financial researchers traditionally have taken a theoretical approach. Compared to medical research, there is much less focus on evidence and the effects of an intervention. Dutch-based practitioner-researchers <b>Eduard van Gelderen</b> and <b>Joop Huij</b> are trying to change this, by bringing evidence-based practice to finance. In this report, the authors describe their research method and demonstrate that academic theories can indeed work well in practice, at least for the specific factors tested.

Did the Profitability of Momentum and Reversal Strategies Decline with Arbitrage Costs After the Turn of the Millennium?

This article investigates the issue of whether major developments in U.S. stock markets around the turn of the millennium reduced arbitrage costs, and consequently the profitability of momentum, long-term reversal, and short-term reversal strategies. Evidence for the years 1990 to 2013 is generally consistent with these predictions, though effects of the financial crisis of 2008 to 2009 are also evident.

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 shortterm 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.

How Do Hedge Fund 'Stars' Create Value? Evidence from Their Daily Trades

I investigate whether hedge funds that supply liquidity earn superior returns. Using transaction data, I find that hedge funds following short-term contrarian strategies (i.e., liquidity-suppliers) earn significantly higher returns on their equity trades and holdings. Similarly, using commercial databases, I find that hedge funds with greater exposure to a liquidity-provision factor earn significantly higher excess returns and Sharpe ratios. The superior performance of liquidity-supplying hedge funds arises from strategies that are more complex than mechanical short-term reversal strategies. For example, among stocks with similar past returns, liquidity-supplying funds are more likely to trade against stocks heavily traded by constrained mutual funds and less likely to trade against stocks heavily traded from unconstrained mutual funds. The outperformance of liquidity-supplying funds is also concentrated in periods of low funding liquidity, suggesting that less-binding financial constraints contribute to their superior returns.