Momentum Trading Strategies Research Articles

Trading behavior by foreign and domestic investors

Purpose- Momentum, a well-known anomaly in financial markets, has garnered considerable attention from both academicians and market participants due to its implications for the market efficiency hypothesis and investment plans of investors. While momentum trading can yield excess returns, it is not a possible strategy for all investors because if one segment of investors employs a momentum trading strategy to profit from their trades, it requires another type of investor to take the opposite position to clear the market. Methodology- I investigate the momentum trading behavior of investors in different segments of Borsa İstanbul. Specifically, I focus on the momentum trading behavior among local and foreign investors and private and institutional investors. Findings- The results show that foreign investors’ stock holdings are positively associated with past returns inferring momentum trading while domestic investors are contrarians. Further analysis of domestic investors’ investing strategies reveals that the contrarian trading of domestic investors is mainly driven by individual investors. Conclusion- The results show that foreign investors’ stock holdings are positively associated with past returns inferring momentum trading while domestic investors are contrarians. Also, the contrarian trading of domestic investors is mainly driven by indvidual investors. Keywords: Momentum trading, investor behavior, foreign investor, domestic investor, panel data JEL Codes: G40, G11, G14

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.

A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment

Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges from various aspects because of the complexity of the stock market. Existing research has inadequately utilized stock news information, overlooking significant details within news content. By constructing a deep hybrid model for comprehensive analysis of historical trading data and news information, complemented by momentum trading strategies, this paper introduces a novel quantitative investment approach. For the first time, we fully consider two dimensions of news, including headlines and contents, and further explore their combined impact on modeling stock price. Our approach initially employs fundamental analysis to screen valuable stocks. Subsequently, we built technical factors based on historical trading data. We then integrated news headlines and content summarized through language models to extract semantic information and representations. Lastly, we constructed a deep neural model to capture global features by combining technical factors with semantic representations, enabling stock prediction and trading decisions. Empirical results conducted on over 4000 stocks from the Chinese stock market demonstrated that incorporating news content enriched semantic information and enhanced objectivity in sentiment analysis. Our proposed method achieved an annualized return rate of 32.06% with a maximum drawdown rate of 5.14%. It significantly outperformed the CSI 300 index, indicating its applicability to guiding investors in making more effective investment strategies and realizing considerable returns.

A Time-Varying Network for Cryptocurrencies

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.

Maximally predictable currency portfolios

We investigate the predictability of the G10 currencies with respect to lagged currency returns from the perspective of a U.S. investor, using the maximally predictable portfolio (MPP) approach of Lo and MacKinlay (1997). We show that, out-of-sample, the MPP yields a higher Sharpe ratio, higher cumulative return and lower maximum drawdown than both a naïve equal-weighted portfolio of the currencies and an equal-weighted portfolio of momentum trading strategies, and that a mean–variance investor would be willing to pay a performance fee to switch from the naïve and momentum portfolios to the MPP. The MPP has performed particularly well since the 2008 financial crisis, in contrast with the momentum portfolio, the value of which declined significantly over this period. Our results are robust to the estimation window length, the type and level of portfolio weight constraints and transaction costs.

Free Cash Flows and Price Momentum

This study investigates the role of free cash flows and (cross-sectional and time-series) price momentum in predicting future stock returns. Past returns and free cash flows each positively predict future stock returns after controlling for the other, suggesting that cash flows and momentum both contain valuable and distinctive information about future stock returns. A strategy of buying past winners with high free cash flows and shorting past losers with low free cash flows significantly outperforms the traditional momentum trading strategy. The enhanced performance is not sensitive to investor sentiment, time variations, or transaction costs. Further analysis shows that the incremental cash flow effects are largely attributable to net distributions to equity/debt holders. Overall, our findings shed light on the role of corporate fundamentals in technical trading strategies.

Corporate Investment and Stock Return Momentum

We investigate the link between corporate investment and the momentum effect in stock returns. We argue that the momentum effect in a firm’s stock returns tends to be generated as a result of a series of information exchanges between stock market investors and firm insiders regarding the firm’s investment opportunities. Our theoretical setup predicts that past winners (losers) are likely to increase (decrease) their net investment, and the more they invest (disinvest) the more likely they are to realize positive (negative) returns in subsequent periods. Our empirical tests using a large sample of firms in 1984–2017 provide support for our hypotheses. We further design a momentum trading strategy, which buys past winners with positive predicted changes in net investment and sells past losers with negative predicted changes in net investment, and demonstrate that this trading strategy generates superior returns compared to a simple momentum trading strategy.

The role of financial Reporting Quality on the Tendency of Transient Institutional Investors in Adopting Value and Momentum Trading Strategies

در سال‌های اخیر، مفهوم راهبردهای معاملاتی ارزشی و حرکتی اهمیت یافته است. در این پژوهش، نقش کیفیت گزارشگری مالی بر تمایل سرمایه‌گذاران نهادی کوتاه‌مدت به اتخاذ راهبردهای حرکتی یا ارزشی و همچنین زمان‌بندی دستیابی به بازده هر یک از دو راهبرد گفته شده مورد بررسی قرار می­گیرد. بررسی داده‌های مربوط به شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران از سال‌های 1392 تا 1398 نشان داد که میزان گرایش سرمایه‌گذاران نهادی گذرا برای سرمایه‌گذاری در شرکت‌های با کیفیت ضعیف گزارشگری مالی، در زمان اتخاذ هر دو راهبرد ارزشی و حرکتی کاهش می­یابد. همچنین سرمایه‌گذاران نهادی در زمان اتخاذ راهبرد حرکتی نسبت به زمان اتخاذ راهبرد حرکتی زودتر به بازده‌های غیرعادی دست می‌یابند.

Partial moment momentum

While momentum benefits from persistent trends of the market, such strategies are unable to distinguish between upside and downside risk and suffer consequently. We propose a Partial Moment Momentum (PMM) trading strategy that is sensitive to the sign of risk and show risk-adjusted outperformance compared to plain momentum and volatility-adjusted momentum strategies. The outperformance is robust across multiple time periods and in particular during market downturns. Further analysis based on conventional linear factor models shows negligible exposure to factor risk for our PMM portfolio. Finally, the performance of our proposed strategy appears to be enhanced when time series momentum is present and allows for improved risk management by distinguishing between upside and downside risks.

Inferring financial bubbles from option data

SummaryFinancial bubbles arise when the underlying asset's market price deviates from its fundamental value. Unlike other bubble tests that use time series data and assume a reduced‐form price process, we infer the existence of bubbles nonparametrically using option price data. Under no‐arbitrage and acknowledging data constraints, we can partially identify asset price bubbles using a cross section of European option prices. In the empirical analysis, we obtain interval estimates of price bubbles embedded in the S&P 500 Index. The estimated index bubbles are then used to construct profitable momentum trading strategies that consistently outperform a buy‐and‐hold trading strategy.

Momentum investing: evidence from the US tourism and hospitality

PurposeThe present research aims to examine a range of momentum trading strategies for the tourism and hospitality sector.Design/methodology/approachThe paper followed the methodology of Jegadeesh and Titman (1993) to construct the portfolios. In this methodology, all portfolios were formed and evaluated by their cumulative stock returns over the past J periods and holding the position for the next K periods. In total, nine formation and holding periods were used, represented by 3, 6 and 12. For example, strategy 3–3 (that is, strategy with J = 3 and K = 3) refers to the strategy that stocks are ranked based on their previous three months and then held for the next three months.FindingsThe findings demonstrated that none of these momentum investing strategies was profitable. Most of the results, however, show positive, but insignificant momentum returns. This finding can be interpreted as price reversal over a horizon of three to twelve months in the US hospitality and tourism sector. These results are robust to size, different formation and holding combinations, beta and turnover.Research limitations/implicationsRegarding the research limitations, this paper only considers the US tourism and hospitality sector. Therefore, the extension of results to other developed and developing markets should be taken carefully. Also, this paper relies only on the methodology of Jegadeesh and Titman (1993). Other methodologies could be suitable avenues for future research.Practical implicationsInvestors and portfolio managers who seek for earning abnormal returns by investing in the US HT stocks can attain their hopes by constructing portfolios based on existing guidelines in the literature and adopting a short-term reversal trading strategy or by buying past losers and selling past winners of the US tourism and hospitality stocks.Originality/valueThis research contributes to the hospitality finance literature by offering the investors who are interested in the US hospitality and tourism sector an uncomplicated trading rule that uses real return data and is expected to generate actual returns. Moreover, the momentum strategy of Jegadeesh and Titman (1993) is never used in the hospitality finance literature.

Can Economic Factors Improve Momentum Trading Strategies? The Case of Managed Futures during the COVID-19 Pandemic

Systematic momentum trading is a prevalent risk premium strategy in different portfolios. This paper focuses on the performance of the managed futures strategy based on the momentum signal across different economic regimes, focusing on the COVID-19 pandemic period. COVID-19 had a solid but short-lived impact on financial markets, and therefore gives a unique insight into momentum strategies’ performance during such critical moments of market stress. We offer a new approach to implementing momentum strategies by adding macroeconomic variables to the model. We test a managed futures strategy’s performance with a well-diversified futures portfolio across different asset classes. The research concludes that constructing a portfolio based on academically/economically sound momentum signals with its allocation timing based on broader economic factors significantly improves managed futures strategies and adds significant diversification benefits to the investors’ portfolios.

Dynamic time series momentum of cryptocurrencies

This paper examines the momentum effect for twenty cryptocurrencies compared to the US stock market. For this purpose, we implement a dynamic modeling approach to define and test momentum periods that follow a formation period for interday and various intraday price levels. We find evidence that large proportions of the asset classes’ formation periods are followed by momentum periods, strongly supporting the momentum effect. In particular cryptocurrencies have significantly larger and longer momentum periods in all frequencies which we attribute to the lower derivability of their intrinsic value leading to a higher degree of noise traders in the market. A momentum trading strategy based on the identical approach outperforms a buy-hold strategy for both asset classes, while only cryptocurrencies have higher risk-adjusted returns and lower downside risks than a passive investment. We also find critical price levels during structural elements of the momentum period where the volatility shortly but intensively increases and consequently initiates a price impulse in the direction of the momentum.

Heterogeneity in Retail Investors: Evidence from Comprehensive Account-Level Trading and Holdings Data

Retail investors are heterogeneous, with vast differences in wealth, skills and demographics. Using comprehensive proprietary account-level data on trading and holdings from the Shanghai Stock Exchange from 2016 to 2019, we separate tens of millions of retail investors into five groups by their account sizes as well as other demographic variables, and we examine their trading behavior and return performance in Chinese equities. Retail investors with account sizes less than three million CNY follow momentum trading strategies, and the prices of stocks they buy experience negative returns next day, while the ones they sell experience positive returns. In contrast, retail investors with larger account balances follow contrarian strategies, and they buy and sell stocks in directions consistent with future price movements. In addition, retail investors with smaller account sizes fail to process public news and they incur losses from trading, while retail investors with larger account sizes incorporate public news in their trading and experience trading gains. These patterns are stronger for young male retail investors.

Maximally Predictable Currency Portfolios

We investigate the predictability of the G10 currencies with respect to lagged currency returns from the perspective of a U.S. investor, using the maximally predictable portfolio (MPP) approach of Lo and MacKinlay (1997). Out-of-sample, the MPP yields a higher Sharpe ratio, higher cumulative return and lower maximum drawdown than both an equal-weighted portfolio of the currencies and an equal-weighted portfolio of momentum trading strategies. The MPP has performed particularly well since the 2008 financial crisis, in contrast with the momentum portfolio, the value of which declined significantly over this period. Our results are robust to the estimation window length, the type and level of portfolio weight constraints, and transaction costs.

Asymmetrical impacts from overnight returns on stock returns

This paper documents significant relationship between overnight returns and future stock returns in the long-term where high averages of overnight returns lead to low future stock returns, with formation periods ranging from 1 month to 1 year. On the other hand, variations in overnight returns lead to different reactions of future stock returns, depending on the levels of past return performances and stabilities of momentum effects. Return reversals are strongest for stocks with extreme past returns. When momentum effects are volatile, higher variations of overnight returns lead to higher future stock returns. When momentum effects are stable, lower variations of overnight returns lead to higher future stock returns for stocks with extreme positive past returns; for stocks that perform worst in the past few months, the two variables have a non-linear relationship. A set of sample sorting criteria according to above relationship are found to significantly enhance the profitability of momentum trading strategy.

Empirical evidence on the profitability of momentum trading strategies using ETFs

PurposeWe extend empirical evidence on the profitability of momentum trading to the realm of plain-equity ETFs.Design/methodology/approachWe employ several ranking measures used in prior research, and for each we apply a traditional ranking based on total return, and a variation based only on the capital gain/loss portion of return.FindingsWhile we find that past momentum is not a strong predictor of future performance in our overall sample period, 2007 to June 2018, we find that the percent off 52-week high price results in positive performance in the recovery years following the financial crisis of 2008–2009.Research limitations/implicationsOur study is limited by the availability of ETF experience and data, and our test period covers just 2007 through June 2018. This period includes the financial crisis of 2008–2009, which previous research finding is associated with the momentum strategy's loss of profitability. When we exclude that period, we find evidence of a profitable momentum strategy based on the measure of percent off 52-week high price, enabling us to reject the null hypothesis that the momentum trading strategy is no longer profitable.Practical implicationsIt is profitable based on both return measures used in the rankings. Our finding of a profitable momentum trading strategy suggests that the null hypothesis that the momentum strategy is no longer profitable can be rejected.Originality/valueWhile perhaps not so strong as to reject the efficient markets hypothesis fully, our empirical findings are more consistent with a behavioral explanation and a market inefficiency. In view of the relative ease and low transactional costs of trading in ETFs, the markets have yet another opportunity to recognize an apparent mispricing and employ arbitrage based on it. To the extent that the relative ease of trading in ETFs makes momentum strategies easier to employ, the momentum anomaly might still be expected to disappear in an efficient market.

A Dynamic Network Perspective on the Latent Group Structure of Cryptocurrencies

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.

Are intraday reversal and momentum trading strategies feasible? An analysis for German blue chip stocks

The success of trading strategies that lead to abnormal excess returns based on annual/monthly investment periods has recently declined significantly. We adopt the original frameworks of De Bondt and Thaler (J Finance 40(3):793–808, 1985) and Jegadeesh and Titman (J Finance 48(1):65–91, 1993) to an intraday reversal as well as momentum strategy scheme based on 5-min stock returns. We analyze 16 reversal and momentum strategies each with ranking and holding periods of 60, 120, 180 or 300 min (reversal strategies) and 15, 30, 45 or 60 min (momentum strategies) from a retail investor’s perspective. We find no indications for momentum in stock prices but strong indications for reversals. Our results are furtherly robust regarding to market adjustment, portfolio sizes and skipping periods between ranking and holding periods. Our results show that the returns of the reversal strategies are statistically significant, however, yet too small to be economically significant. Our results also confirm the efficiency on the stock markets.

Bitcoin: Speculative asset or innovative technology?

We unite investment by speculators and tech-savvy investors with a heterogeneous agent model. While speculators seek to profit from extrapolating the price trends, tech-savvy investors trade based on the prospective value of Bitcoin, which is a function of factors that capture the market demand and technical supply of Bitcoin. Estimating the structural model, we find the coexistence of speculators and tech-savvy investors in the Bitcoin market. We further show that, regardless of the market states, tech-savvy investors consistently buy (sell) Bitcoin when its price goes below (above) the prospective value. However, speculators follow a momentum trading strategy in the high-market-volatility regime and switch to a contrarian strategy in the low-market-volatility regime. Our finding suggests that a significant fraction of tech-savvy investors value the potential of Bitcoin as an innovative technology. Incorporating heterogeneous investors yields better in-sample estimation efficiency and out-of-sample forecasting precision than models that consider only speculators or tech-savvy investors.