Price Momentum Research Articles

Impact of Herding Behavior on Mutual Fund Performance

n this study, we look at how herd mentality affects the efficiency of mutual funds. It is well-documented that investors often engage in herding behaviour in the financial markets, when they blindly follow the actions of others rather than evaluating information independently. Yet, there has been a dearth of empirical research into its particular consequences for mutual funds. To analyse the impact of herding behaviour on fund performance, this research uses a thorough dataset of mutual fund returns and investor flows. The first step of the research is to use literature-established metrics to determine whether mutual fund investors herd. After that, we use risk-adjusted returns, volatility, and alpha to evaluate the herding behaviour vs. fund performance connection. The research further delves into the ways in which herding affects the performance of funds by looking at things like trading fees, liquidity restrictions, and market circumstances. Herding behaviour has a substantial impact on mutual fund performance, according to preliminary data. However, the exact nature of this impact varies across fund kinds and market conditions. Although herding may provide short-term gains in price momentum and liquidity-driven returns, it eventually causes mispricing and reversals if it continues. In addition, less liquid asset classes may have their long-term performance eroded by herding-induced trading costs and portfolio churn. These results have consequences for fund managers and investors alike. By learning the ins and outs of herd mentality, investors may make better investing choices that stay true to their long-term value drivers and avoid the traps of herd mentality. To lessen the impact of market emotion on fund performance, fund managers should be aware of investors' herding tendencies so they can create portfolios and use risk management methods. Keywords – Herding Behavior, Mutual Funds, Investor Behavior, Market Sentiment, Performance Measurement

Investor heterogeneity and anchoring-induced momentum

This paper investigates how anchoring-induced investors’ trading behavior drives momentum anomaly. The results show that price momentum does not retain its ability to predict future returns after considering the stock’s nearness to its 52-week high. The stock price’s nearness to the 52-week high is a stronger return predictor for stocks with a higher retail trading proportion. This suggests an anchoring-induced momentum pattern, which is affected by investor heterogeneity. Our trading flow analysis reveals that retail investors are subject to anchoring bias. Their trading behavior causes price underreaction to good (bad) information for stocks traded near (far from) their 52-week high.

Seasonality, Monetary Supply and Taiwanese Momentum

Abstract For the Taiwanese stock market, evidence from the present study documents significant reversal in January-February, but strong momentum in March-December when there are increases of lagged M1B. Moreover, the M1B-induced momentum manifests only over economic expansion, rather than economic recession. Both the reversal and the momentum can be partly explained by unrealized capital gains, implying the disposition effect to some extent driving both phenomena since Grinblatt and Han (2005) used unrealized capital gains as a proxy for the disposition effect. We further find the reversal primarily occurring in January, implying reverse disposition trading occurring before the beginning of a year. As there are no capital gain taxes levied in Taiwan, the reverse disposition trading cannot be related to tax-loss selling as in U.S. Furthermore, time-varying market risk exposure cannot explain the reversal in most cases. For the March-December momentum, apart from unrealized capital gains, the CAPM and the Fama-French 3-factor models can each to some extent explain the momentum. JEL classification numbers: G10, G11, G19. Keywords: Price momentum; Reversal; Disposition effect; Emerging stock market.

Speculative Trading and Price Momentum

Speculative Trading and Price Momentum

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.

Momentum and individual investor trades: Evidence from Singapore

This paper examines the role of retail investor trading activity on stock price momentum. We find that there is little evidence of momentum for stocks traded on the Singapore Exchange (SGX) unconditionally and momentum is concentrated in stocks with high market capitalization and high nominal prices. While these stocks are likely to be the trading habitat of institutional investors, they exhibit substantially greater price momentum when they are accompanied by heavy trading by retail investors. Moreover, contrarian trading by retail investors on momentum stocks increases stock price underreaction to information and generates momentum of above 2% per month.

Momentum, reversals and liquidity: Indian evidence

This paper addresses three interesting questions. First, we explore whether price momentum exists for equities listed on the Bombay Stock Exchange (BSE). Second, whether liquidity (measured by turnover ratio) enhances the momentum effect. Third, whether momentum profits exhibit reversals for illiquid stocks. Using a large sample of 3956 stocks for the period 2000 to 2021, we establish three key results. First, we find evidence of significant price momentum (intermediate and long-term) for equities listed on the BSE. Second, when conditioned on liquidity, we find that the momentum returns (short, intermediate, and long-term) are more significant for the most liquid portfolio and exhibit persistence for the next 12 months. Third, although we document evidence of short and intermediate-term reversals among the most illiquid portfolios, we fail to find evidence of persistence. Our results continue to hold after controlling for risk factors related to market premium, firm size, value, and a host of macroeconomic indicators. Taken together, our paper highlights the role of liquidity in amplifying the momentum effect in the Indian market.

Who's afraid of a Texas hedge?

There is anecdotal evidence of hedgers taking speculative positions using futures contracts—so-called “Texas hedges”—but the literature provides little guidance on this phenomenon. The paper explores the conditions under which “Texas hedges” may be optimal within the standard risk management framework. Using an empirical setting of multi-commodity hedging, we find that (1) “Texas hedges” do not occur when hedges are optimized individually (single-commodity hedging), (2) neither do they occur when the hedger's objective is to minimize variance, but (3) they do occur 18% of the time in a multi-commodity framework under a downside risk minimization objective (i.e., LPM2), which is closer to what hedgers actually do. Furthermore, (4) “Texas hedges” arise following periods of strong price momentum. This can be explained by downside risk-averse hedgers being concerned only about one side of the distribution. Therefore, expected favorable price movements are not treated as risk, but rather as a profit-enhancing opportunity.

Stock price momentum modeling using social media data

Alternative data has rapidly gained popularity as a means of observing investor reactions to current stock market events, analyzing sector movements, and developing investment strategies. Signals observed from alternative data may add valuable information to conventional market analysis, especially when there exists some information asymmetry in the market, to which small-cap stocks are particularly sensitive. Our research uses alternative textual data obtained from social media to predict the price momentum of small-cap stocks, which are known to be sensitive to market events and retail investors’ reactions. We directly address the challenges in the analysis of texts, especially in the Korean language, such as the use of jargon, acronyms, and typos, by taking a two-fold approach to preprocessing. Then, we extract the social keywords and label them by two standards: word momentum and correlation with stock price movement. Using these social keywords as social indicators derived from the retail investors trading small-cap stocks, in addition to the conventional technical indicators, we propose a price momentum forecast model which classifies whether the closing price of a given small-cap stock will increase by more than 10% in the next 5 business days. Experimental results show that our approach successfully extracts social keywords that play a significant role in determining the price momentum in the market, and our selected social indicators are intuitive to human understanding. Furthermore, we consider example investment scenarios and run tests by training various classifiers with the social indicators and computing cumulative returns. Our results are very promising and show that the investment scenarios with social indicators outperform the benchmark considerably. We anticipate that our work will expand the scope of using alternative data for stock market analysis and price momentum forecasts by incorporating social indicators extracted from social media.

Capital Market Volatility During Crises: Oil Price Insights, VIX Index, and Gold Price Analysis

Abstract The study aims to investigate capital market volatility during crises, exploring the relationships between three key financial indicators: oil prices, the VIX index, and gold prices, using monthly data covering the period from January 2013 to May 2023, based on the Granger causality approach and the impulse response function testing empirically the existence of the long-run relationship using Johansen multivariate approach and the estimation of the VAR/VECM model. By analysing their interdependencies, the research sheds light on how these indicators respond to economic turbulence. The study employs robust econometric methods to investigate causal relationships and predictive patterns, providing valuable insights for investors, policymakers, and analysts navigating uncertain financial landscapes. The findings reveal nuanced dynamics, such as the momentum in oil prices, the inverse relationship between oil prices and the VIX index, and a significant Granger causality relationship running from the VIX index to oil and gold prices. Furthermore, based on the impulse response patterns, the shock in the VIX index caused a notable oil price decrease in the second quarter after the shock, followed by oscillations. Gold prices exhibit a minor initial decline after the VIX shock, with no lasting effects.

An Empirical Analysis of Price Momentum Effect in A-share Market

This article sorts the returns of listed companies in the A-share market from May 1991 to April 2022 over the past J months, selects winners and losers, constructs a zero investment portfolio, and holds it for K months. The results showed that there is a reversal effect in the short term and a momentum effect in the medium to long term. Only when the holding period is one month, no obvious pattern can be observed. This article is interested in the combination of winners and losers with a holding period of one month. Next, Fama-MacBeth regression is used to further verify whether there is momentum effect in the A-share market when the holding period is one month. It was found that when the holding period is one month, there is no momentum effect in the A-share market, and there is a significant reversal effect in the short to medium term. In addition to the reversal effect in the short to medium term, winners and losers also experience a reversal in the long term. Taking the split share structure reform as the dividing point, winners and losers only have mid-term and long-term reversals before the reform, and there will also be short-term reversals after the reform. In addition, winners and losers have short-term and long-term reversals during bull markets, while bear markets have neither momentum nor reversal effects, while volatile markets only have short-term reversals.

Understanding Momentum and Reversal Investing Strategies

Momentum and reversals are two phenomena to explain the past return trend. Originally introduced by Jegadeesh and Titman in 1993, momentum is now a common investment strategy when investors are trading securities. It points out the stock price may have a relationship with their past performance. A large number of researchers have been trying to find out the momentum investment effect based on empirical evidence in different markets in different investment periods, which include short term, medium term and long term. Moreover, a series of research concludes that the momentum investment strategy tends to help investors to get a higher return. In recent years, a large number of researchers have focused more on analysing financial markets in China, and they have paid more attention to improving the traditional Jegadeesh and Titman models. In addition, an increasing number of researchers also point out that noise trading is quite important in the security investment strategy, and the investors who are using a momentum investment strategy to trade their portfolios are supposed to take the noise trading strategy into consideration. Then a series of papers have been tried to explain the sources of the momentum effect, either risk-based or behavioural-based. When it assumes that the market is efficient, the past market prices could be reflected, and it is hard to get excess returns by observing the stock's past prices performance. Nevertheless, the momentum effect is likely to examine that the market is inefficient. When it tries to explain the momentum effect in risk-based sources, the abnormal price return may be derived from a risk that is undiversifiable. As for the behaviour-based explanation, several behavioural biases applied by different researchers could be used to study the momentum effect, such as cognitive errors, including the conservatism bias, the representative bias, and the emotional bias, including the loss aversion bias, the overconfidence bias and the self-attribution bias. Another possible explanation within this behavioural source is that overreaction to the news leads to the existence of price momentum.

Behavioral asset pricing under expected feedback mode

We develop an enhanced DSSW model of behavior asset pricing by introducing the expected feedback mode. Through numerical simulation, it has been theoretically proved that risky asset price is jointly determined by trend extrapolated effect of expected feedback traders and the creating space effect of noise traders, and that price fluctuation depends on the expected feedback coefficient. As expected feedback traders' expectation return has been realized and the above two effects are obviously imbalance, their confidence will deteriorate prior to price collapse, and eventually achieve self-fulfilling expectation of price reversal in the process of price momentum. This model sheds lights on financial anomalies of trade size clustering and the formation of stock bubble, besides it provides a new perspective for avoiding the “bewitching” trading and reducing market volatility.

Size, conditional idiosyncratic risk and price momentum: an international study

PurposeThis paper jointly examines how firm size and idiosyncratic risk impact momentum returns.Design/methodology/approachUsing regression analysis, the authors investigate how firm size and idiosyncratic risk impact price momentum. The authors review firm price data in 25 country markets in the Thomson Financial Datastream database from 1979 to 2009.FindingsThis study’s findings suggest price momentum is more significant among stocks with smaller size and higher idiosyncratic risk. The authors find that winner and loser portfolios have significantly smaller size and higher idiosyncratic risk than portfolios in the middle quintiles.Research limitations/implicationsThis study’s results are consistent with the notion that firm size matters in price momentum and mispricing is greatest for small firms because of the greater risk potential to arbitrageurs. In addition, this finding that firms with higher idiosyncratic risk have greater price momentum supports the idea that investors underreact to firm-specific information.Practical implicationsThis work finds evidence that investors underreact to firm-specific information. As such, these findings are of particular interest for investors looking to exploit opportunities for abnormal returns through price momentum trading.Originality/valueThis paper jointly examines the effects of firm size and idiosyncratic risk on momentum returns. This investigation considers these effects in the global markets. This work adds to the research base by illustrating that both winner and loser portfolios have significantly smaller size and higher idiosyncratic risk than portfolios in the middle quintiles. Also unique to this study, the authors capture the time-variation of expected IdioRisk and the asymmetric effects of volatility by using an exponential general autoregressive conditional heteroskedastic (EGARCH) model to calculate conditional idiosyncratic risk.

Noxtrader: Lstm-Based Stock Return Momentum Prediction For Quantitative Trading

We introduce NoxTrader, a sophisticated system designed for portfolio construction and trading execution with the primary objective of achieving profitable outcomes in the stock market, specifically aiming to generate moderate to long-term profits. The underlying learning process of NoxTrader is rooted in the assimilation of valuable insights derived from historical trading data, particularly focusing on time-series analysis due to the nature of the dataset employed. In our approach, we utilize price and volume data of US stock market for feature engineering to generate effective features, including Return Momentum, Week Price Momentum, and Month Price Momentum. We choose the Long Short-Term Memory (LSTM) model to capture continuous price trends and implement dynamic model updates during the trading execution process, enabling the model to continuously adapt to the current market trends. Notably, we have developed a comprehensive trading backtesting system — NoxTrader, which allows us to manage portfolios based on predictive scores and utilize custom evaluation metrics to conduct a thorough assessment of our trading performance. Our rigorous feature engineering and careful selection of prediction targets enable us to generate prediction data with an impressive correlation range between 0.65 and 0.75. Finally, we monitor the dispersion of our prediction data and perform a comparative analysis against actual market data. Through the use of filtering techniques, we improved the initial -60% investment return to 325%.

Profits from Underreactions and Overreactions: Price Momentum and Reversal in International Stock Markets

Profits from Underreactions and Overreactions: Price Momentum and Reversal in International Stock Markets

Profitability of momentum investing strategies in an emerging market

This paper examines the profitability of momentum strategies on the Nigerian stock market over a 20-year period, from 1996 to 2016, using all listed equities on the Nigeria Exchange Limited (NGX) All Share Index. It also evaluates whether or not the profitability of momentum strategies is conditional upon the state of the market. A momentum strategy creates and buys a portfolio of past winners and short-sells a portfolio of past losers to generate excess profit. Our result shows that the Nigerian stock market exhibits medium-term price momentum, with eight out of sixteen strategies recording statistically significant excess momentum returns. The main contribution to the profit is from investing in winner portfolios, all of which recorded positive returns, and mostly significant. Evidence also shows that momentum profits depend on the state of the market. The momentum effect follows the UP market with a mean return of 0.2938%, while it is insignificant in the DOWN market with a mean return of -0.0237%. This paper offers valuable insight into market behavior, highlights the opportunities for alpha generation, and contributes to our understanding of market efficiency in emerging markets.

CEO incentive compensation and stock price momentum

AbstractWe document strong evidence that CEO incentive compensation can predict the significance of stock price momentum through discretionary accrual and real activities manipulation. The profit of momentum strategy increases with CEO pay‐for‐performance incentive, but decreases with CEO risk‐taking incentive. It also evaluates the effects of information uncertainty on such relationship. The evidence is more significant for firms with older and longer tenured CEOs and firms with more informed traders. The relationship between the profit of momentum strategy and CEO pay‐for‐performance incentive is stronger among CEOs without the risk‐taking incentive. Our results are robust for different sub‐samples based on before and after Reg FD and Sarbanes–Oxley Act, even after controlling for the potential endogeneity. Further, our findings are consistent with the information diffusion explanation of momentum and the agency theory that incentivised CEOs tend to manipulate information by smoothing good news, concealing mildly bad news and accelerating the disclosure of extremely bad news.

Overreaction and momentum in the Vietnamese stock market

PurposeAlthough the solid empirical proof of momentum is documented in various stock markets, there are many debates among academics with respect to the source of momentum profit. The first aim of this paper is intensively re-examine the momentum profit in Vietnam, an important emerging market. Secondly, the authors study the return predictability of a measure of investors’ overreaction, then examine whether the momentum effect in Vietnam is explained by overreaction.Design/methodology/approachUsing the weekly data of more than 300 non-financial Vietnamese stocks during 2009–2019, the authors build a measure of investors’ overreaction, which is based on trading volume and the sign of stock returns. Consequently, to investigate whether momentum exits after controlling for overreaction, the authors carefully compare trading strategies based on overreaction with price momentum strategies using adjusted returns and double sorts on past returns and levels of overreaction.FindingsThe article makes three main findings. Firstly, the authors discover the empirical evidence of momentum in the Vietnamese equity market. Secondly, the measure of overreaction could be a predictor of Vietnamese stock returns. Stocks that have experienced a stronger upward overreaction provide a higher average return. Finally, returns on trading strategies based on overreaction are robust after adjusting for momentum, while returns on momentum portfolios become insignificant after adjusting for overreaction. By double-sorting, the authors document that holding past returns constant, the average returns of portfolios rise monotonically with their measure of overreaction. Hence, the momentum profit in Vietnam arises from investors’ overreaction.Originality/valueThe paper extends previous research on the behavioral explanation of momentum in emerging stock markets, which has not been fully exploited in the literature.

Infrequent Random Portfolio Decisions in an Open Economy Model

Abstract We introduce a portfolio friction in a two-country DSGE model where investors face a constant probability to make new portfolio decisions. The friction leads to a more gradual portfolio adjustment to shocks and a weaker portfolio response to changes in expected excess returns. We apply the model to monthly data for the US and the rest of the world for equity portfolios. We show that the model is consistent with a broad set of evidence related to portfolios, equity prices, and excess returns for an intermediate level of friction. The evidence includes portfolio inertia, limited sensitivity to expected excess returns, a significant impact of financial shocks, excess return predictability, and asset price momentum and reversal.