Momentum Strategies Research Articles

Momentum Investment Strategies across Time and Trends: A Review and Preview

Background: This study provides a bibliometric analysis of momentum investment strategies, examining their historical evolution, behavioral dynamics, and the challenges faced by professionals in equity markets. It aims to explore the persistent profitability of momentum strategies amidst market fluctuations and the significant influence of behavioral biases on trading decisions. Methods: Employing a Bibliometric Analytical framework, the study systematically maps the knowledge structure of momentum investment strategies. It utilizes the Dimensions database for scholarly journal searches and examines peer-reviewed research publications spanning from 1993 to 2024. This method enables the identification of key research works, emerging trends, and an assessment of the literature's impact. Results: The research reveals the persistent profitability of momentum strategies despite market changes, highlighting the significant role of behavioral biases in trading decisions. This work contributes to both academic discourse and practical investment strategy development, offering insights into exploiting momentum's transformative potential in dynamic equity markets. Conclusion: This study contributes to both academic discourse and practical investment strategy development by offering evidence-based insights into the effectiveness of momentum strategies. It identifies research gaps, suggests new directions for future studies, and analyzes collaboration networks to enhance interdisciplinary research efforts. Novelty: The utilization of a Bibliometric Analytical framework in studying momentum investment strategies brings a novel approach to the field. This method not only offers a systematic mapping of knowledge but also facilitates the identification of influential research works and emerging trends, guiding both academic research and practical applications.

Gradual information diffusion across commonly owned firms

This paper studies how common institutional ownership (CIO) affects information diffusion in the stock market. My findings suggest that CIO can exacerbate the slow spread of information across firms. With over 50% of institutional investors holding concentrated stock portfolios, I infer a fundamental connection among firms with CIO. These firms exhibit cross-predictability in monthly stock returns, leading to a CIO-based peer momentum strategy that outperforms Ali and Hirshleifer's (2020) shared-analyst momentum strategy. This anomaly stems primarily from institutional investors with fewer stock holdings, who employ passive asset management characterized by lower portfolio turnover and more delegated investment.

Combining low-volatility and momentum: recent evidence from the Nordic equities

ABSTRACT This paper investigates the profitability of combined low-volatility and momentum investment strategies in the Nordic stock markets from January 1999 to September 2022. Confirming earlier studies, our results first indicate that both the volatility and momentum effects persist as pure-play strategies. Further, we explore combined strategies using 50/50, double screening, and ranking strategies. Among the long-only portfolios, the momentum-first strategy generates the best Sharpe ratio using the double screening method−slightly outperforming the ranking method. Additionally, all long-only combination portfolios outperform the market in terms of risk-adjusted returns. Combination long-short strategies produce significantly higher risk-adjusted returns than pure-play strategies. Surprisingly, novel evidence suggests that none of the combination long-short strategies outperforms the pure momentum strategy after risk-adjusting the returns using the Fama and French five-factor model, implying that while momentum may enhance the returns from the low-volatility strategy, the reverse is not true for the Nordic stock markets.

Analyzing the Efficacy of the Relative Strength Indicator of Capital Inflows and Outflows Based on Big Data Analysis in Achieving Abnormal Returns Evidence from the Chinese Stock Market

This study originates from an analysis of market microstructure and introduces a novel statistical indicator of relative strength in capital flows through the application of big data analytics. This indicator effectively captures the impact of capital movements on future stock prices by integrating variations in stock prices with the volume of transactions within a corresponding timeframe. Building upon this foundation, the research develops an innovative momentum investment strategy based on the relative strength indicator of capital inflows and outflows, extending beyond the traditional fixed holding period momentum strategy framework. Empirical analysis reveals that this new indicator significantly demonstrates momentum effects in the Chinese A-share market; notably, in portfolios of stocks with smaller market capitalizations, the momentum strategy grounded on the new indicator achieves higher excess returns compared to traditional momentum strategies. These findings not only validate the efficacy of the new indicator but also underscore the critical role of big data analytics in enhancing the analysis of financial markets and the efficiency of investment decision-making.

Comomentum in China: Inferring arbitrage activity from return correlation

This paper investigates whether arbitrageurs can have a destabilizing effect on the Chinese stock market. We use comomentum defined as high-frequency abnormal return correlation among stocks to measure arbitrage activity. In contrast to the findings of Lou and Polk (2022), we find that the returns on momentum stocks exhibit long-term stabilization in China, regardless of whether comomentum is higher or lower. The results suggest that fluctuations in arbitrage activity do not serve as predictive indicators for changes in long-term momentum returns in the Chinese stock market. These results further emphasize the missing effect of the momentum strategy in the Chinese market.

How is Trading Behavior in Commodity Futures Affected by the 52-Week High and Low?

We use the Commodity Futures Trading Commission’s Commitment of Traders report to investigate the trading behavior of traders in 12 commodity futures markets. Commercial traders are contrarian, consistent with anchoring and the prevalence of short hedgers. Noncommercial traders follow momentum strategies, reflecting the presence of CTAs trading in attention grabbing futures. Our key contribution is in understanding the effect of trading at the 52-week high and low – technical levels that are known to influence investor behavior – where we find trading is moderated at the 52-week high (anchor reversion) and accentuated at the 52-week low. Our results are consistent regardless of whether the futures market is in backwardation or contango. We find little support for evidence of market timing ability for either trader type.

Zoom in on momentum

Portfolios sorted by momentum show stronger return monotonicity than those formed using other anomalies. Compared with other strategies, the performance of such a momentum strategy improves monotonically with the number of portfolios. These improvements are significant beyond the influences of the usual pricing factors. Momentum factors based on more portfolios span those based on fewer portfolios, whereas the opposite effects do not hold. The evidence reported in this study suggests that a momentum factor formed on more than 10 portfolios sharpens the factor and its stylized facts.

시계열 모멘텀과 기술적 분석에 관한 연구

This study comprehensively analyzes time series momentum and moving average strategies for the Korean stock market. The results of the empirical analysis are as follows. The correlation coefficients between the returns of time series momentum and moving average strategies are on average above 0.8, indicating that the two strategies are closely related. When calculating risk-adjusted returns and abnormal returns, Sharpe ratios and Jensen alphas were generally higher for the moving average strategy compared to the time series momentum strategy. Decomposing the difference in returns between time-series momentum and moving average strategies, we found that moving average strategies tend to run earlier and time-series momentum later, with earlier strategies generating positive returns and later strategies generating negative returns. When analyzing stock price crash risk, we found that unlike cross-sectional momentum, neither time series momentum nor moving average strategies were sensitive to stock price crash risk.

Industry momentum and trading volume: evidence from China

PurposeAs the world's largest emerging market, the evidence of momentum effect in China is also mixed. Meanwhile, prior studies mainly examined individual stock momentum in China, with little concern for industry momentum and its relationship with trading volume. The motivation of this study is to investigate industry momentum in China and examine whether trading volume can enhance its profitability.Design/methodology/approachFirstly, the authors test the existence of industry momentum in China; secondly, the authors test the correlation between trading volume and momentum returns using the double ranking method; finally, the authors test whether trading volume enhances the momentum returns using Fama–French five-factor model.FindingsThe authors find that there is a significant industry momentum effect in China, and the momentum returns jointly come from winner and loser portfolios. The intervals between the formation and holding periods have an impact on the performance of momentum portfolios. In terms of trading volume, the authors find that high-volume industries have industry momentum effects while low-volume industries do not. The industry momentum strategies achieve higher excess returns in high-volume industries.Practical implicationsPrior literature found higher momentum returns in low-volume stocks in China, but the research in this study suggests that implementing an industry momentum strategy in low-volume industries will miss out on higher returns or even bring losses, and instead the investors should invest in high-volume industries to get the best performance.Originality/valueThis study extends existing research by focusing on industry momentum and its relationship with trading volume in the Chinese stock market and finds an interesting relationship between industry momentum returns and trading volume, which is different from related studies.

A STUDY OF FRACTAL DUAL MOMENTUM INVESTMENT STRATEGY UNDER THE CONSTRAINT OF MULTI-FRACTAL CHARACTERISTICS OF STOCK MARKET

Since the discovery of momentum effect, people have started the journey of using the momentum effect to construct momentum strategies. As a result of coupling cross-sectional and time-series momentum strategy, dual momentum strategy (DM strategy) has been widely used in practice and closely followed by academics. To address the shortcoming of the classical DM strategy that has not considered the multi-fractal characteristics of the stock market, we construct the fractal dual momentum strategy (FDM strategy) from the two aspects of optimizing the ranking index of the cross-sectional momentum strategy by using fractal statistical measures and improving the timing selection of the time-series momentum strategy by using the trend entropy dimension. The empirical results show that the FDM strategy outperforms the DM strategy. Both in terms of the size and stability of the strategy returns, the FDM strategy shows an optimization effect compared with the DM strategy, which is beneficial to provide investors with better decision-making references.

Momentum Origin and Investor Behaviors on Wall Street

The Efficient Market Hypothesis (EMH) asserts that consistently outperforming the market is implausible because market prices promptly incorporate all available information. However, the momentum strategy has demonstrated consistent profitability, which poses a significant challenge to the EMH and suggests the presence of factors that extend beyond existing theoretical frameworks in influencing price movements. Here we present a model attributing changes in asset return to behavior interactions between liquidity takers and providers that directly impact price formation and discovery processes. The analysis of the measured behavior of liquidity takers and inferred market microstructure due to behaviors of liquidity providers generates signals relating to the forming, terminating, and reversal of price momentums. Applying these signals to the 1987 market crash yields warning flags about the looming crisis weeks before the sudden market plunge on Black Monday. As market crises always correspond with substantial negative price momentum, our model can serve to mitigate or avoid catastrophic investment losses during such events.

Research on momentum strategy and contrarian strategy in AI stock prediction

The emergence of ChatGPT has significantly enhanced the recognition and acceptance of artificial intelligence concept stocks within the Chinese stock market. Nevertheless, the short- and long-term fluctuations in the prices of AI companies remain uncertain. Therefore, the purpose of this research is to determine optimal strategy for evaluating the suitability of the contrarian strategy versus the momentum strategy in predicting the stock prices of AI concept stocks in the Chinese stock market. Based on a cross-comparison of the Chinese financial data sources iFinD and Wind Economic Database (EDB), this study collects the price data of AI concept stocks over the past six months, starting from the date of ChatGPT's publication. This study employ Python to model stock price movements for both the momentum and reversal strategies. The goodness of fit is evaluated by comparing the modeled stock prices with the actual stock prices. This study demonstrates that the momentum strategy exhibits greater explanatory power than the contrarian strategy, accurately predicting 84.21% of artificial intelligence concept stocks. However, other studies suggest that while AI concept stocks continue to rise, momentum strategies remain effective, whereas when market sentiment cools down, contrarian strategies become more suitable for Chinese AI concept stocks. Hence, in China, the effectiveness of these strategies may vary depending on the prevailing market conditions.

Research on Forecasting the Yield of China's Commodity Futures Market Based on the Intraday Momentum Effect

By exploring the intraday momentum effect of the Chinese commodity futures market, it was found that there is a significant intraday momentum effect in the Chinese commodity futures market. The first half hour return of night opening and the first half hour return of day opening can significantly predict the first half hour return of closing within and outside the sample. Furthermore, it was found that the momentum effect of commodity futures is more significant at high trading volume and high volatility levels. The momentum strategy constructed based on the intraday momentum effect can significantly outperform the benchmark strategy, providing investors with a robust investment strategy.

Factor momentum in the Chinese stock market

Based on 10 commonly used factors, we construct a novel factor momentum strategy in the Chinese stock market, which earns an annualized return of 9.91 %, with a Sharpe ratio of 1.15. Factor momentum subsumes stock momentum, high-priced momentum, and industry momentum, digests its component factors and a variety of anomalies, and represents the momentum anomaly in China. Furthermore, mispricing correction helps explain factor momentum, which produces stronger returns during higher aggregate idiosyncratic volatility periods as well as among stocks with higher information asymmetry and short-sale constraints. Exposure to factor premiums and the manifestation of predictability determine factor momentum in China.

Return prediction: A tree-based conditional sort approach with firm characteristics

This paper proposes the Augmented Tree-Based Conditional Sort approach to investigate the return prediction problems. We find a momentum effect in the Chinese stock market using monthly data over 5000 stocks from January 2005 to December 2022. The corresponding momentum strategy robustly outperforms the market portfolio and earns an excess return of 10.60 % annually. Besides a full-period momentum effect, we find a sub-period reversal effect before year 2018 and locate year 2018 as a turning point from the reversal effect to the momentum effect. We attribute this phenomenon to the rapid increase of institutional investors since then.

Post-Earnings Announcement Drift, Momentum, and Contrarian Strategies in the Saudi Stock Market: Risk Explanation vs. Behavioral Explanation

Post-Earnings Announcement Drift, Momentum, and Contrarian Strategies in the Saudi Stock Market: Risk Explanation vs. Behavioral Explanation

Application of Momentum Strategy to S&P 500: A Five-Year Retrospective Analysis

Momentum strategy, a popular approach since the 1990s, has seen various successful iterations over the years. However, with the market environment evolving considerably in recent times, this study seeks to explore the efficacy of a specific momentum strategy on the S&P 500 in the past five years. The hypothesis centers on the correlation between returns and the moving average, serving as a momentum indicator. A simple linear model relating the two is trained using historical data, and the subsequent strategy is formed based on the model's parameters. Different lookback periods are considered, leading to an evaluation of diverse strategies. Despite the strategy's simplicity, findings suggest that it might struggle to thrive in real market conditions. There are some strategies that beat the benchmark under ideal conditions, but all of them loss when the transection fees are taken into account. The analysis uncovers situations where momentum strategies can indeed be effective. This underlines the potential for further research and the development of a more sophisticated, precise strategy that mirrors real market dynamics with greater accuracy.

Investment Strategy Analysis of Internet and Technology Companies Taking Apple Inc as an Example

With the continuous development of the internet and its related industries, the internet is constantly popularized in residents' daily life. Therefore, the development and progressive prospects of internet technology companies are significant for investors. However, little research is concentrating on the strategy of investing in the technology sector and its corresponding development. Therefore, the research theme of this paper is to take Apple Inc. as an example to analyze the development trend and corresponding investment strategies of internet technology companies. This paper uses the stock data of the top 30 companies ranked in market value in the technology sector in the past eight years and analyzes the data with a momentum strategy model. This paper finds that the internet technology company and its corresponding technology field will continue to develop and grow. Apple Inc. has gained a leading position in this field through its innovative products and technologies. Additionally, the momentum strategy has higher returns after improvement and application in science and technology. In the future, with continuous development in science and technology, the corresponding investment in this field will be further profitable.

Performance of Time-series Momentum Strategy: US Evidence

This paper examines the effectiveness of the time series momentum strategy in generating positive returns in the US stock market, with a focus on exploring its dynamics and performance using different moving average methods. The author conducted an empirical analysis of the time series momentum strategy using S&P500 data from 2000 to 2022. A regression model was applied to estimate the expected returns and volatility of each as-set, and then an evaluation of momentum trading strategy based on different moving average methods was developed. The author evaluates the performance of the strategy with and without transaction costs. The study contributes to the literature by providing empirical evidence on the effectiveness of the time series momentum strategy in the US stock market and by exploring the performance of different moving average methods on the strategy. The findings of this study can provide insights for investors and portfolio managers interested in implementing momentum strategies in their investment portfolios.

Momentum: Evidence and insights 30 years later

Since Jegadeesh and Titman (1993) documented the momentum effect in the US 30 years back, the literature has grown substantially. This paper evaluates various explanations for the phenomenon, which include the view that such evidence of market inefficiencies likely results from data mining, rational theories that suggest that past winners have greater exposure to systematic risk than past losers, and behavioral theories that provide rationales for why returns may underreact to information. We also review more recent literature, including several papers that appear in this issue, and provide an analysis of the post-2000 performance of momentum strategies in both the Pacific Basin and developed Western markets.