Stock Momentum Research Articles

A four-factor model based on factor momentum

By focusing on the momentum effect in China, we introduce a novel four-factor model grounded by factor momentum. We address this by progressively exploring three key questions. Firstly, we identify a significant momentum premium. In contrast to the US, where momentum forms over a period of 2–12 months, the formation period for momentum in China spans 7–12 months. Moreover, among various momentum factors, factor momentum stands out as the most significant, effectively explaining stock momentum, industry momentum, and regional momentum, whereas the reverse is not true. Secondly, we find that there exists a notable orthogonality between factor momentum and non-momentum factors in China, implying that factor momentum can hardly be explained by non-factor momentum, and vice versa. Thirdly, leveraging this orthogonality, we establish a four-factor model. Expanding upon the MKT and SMB factors, the model additionally includes a mispricing factor and a momentum factor. GRS tests demonstrate that this model outperforms traditional Fama-French three-factor model, Carhart four-factor model, Q-4 factor model, and the three-factor model proposed by Liu et al. (2019).

Nominal price illusion, return skewness, and momentum

We document a significantly negative association between the nominal price measure of Birru and Wang (2015) and the profit of the stock momentum strategy. The negative relationship is driven by the underperformance of loser stocks with high nominal price illusions. Further analysis shows that the previously documented positive correlations between return skewness measures and momentum profit are subsumed by the effect of nominal price on momentum profit.

On the linkage of momentum and reversal – evidence from the G7 stock markets

This paper studies the linkage of momentum and reversal in the G7 stock markets. We confirm Conrad and Yavuz’s (Rev Financ 21(2):555–581, 2017) finding that momentum is not linked to subsequent return reversal in the US stock market. In the stock markets of the remaining G7 countries, our results object the decoupling of momentum and return reversal. In these stock markets, the two return anomalies are linked to each other. In particular, momentum is followed by return reversal in the stock markets of Germany, the UK, Japan, Canada, France, and Italy. These observations obtain for momentum portfolios which are made up of different risk profiles with respect to size and book-to-market ratio. Our results hold true both in raw returns and in risk-adjusted returns.

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.

Can factor momentum beat momentum factor? Evidence from China

While existing studies have not detected a significant standard momentum in the A-share market, recent literature has documented several modified momentum factors. Echoing the findings of Ehsani and Linnainmaa (2022), our study identifies strong factor momentum in the A-share market. Factor momentum accounts for all modified momentum factors except for high-priced stock momentum. Interestingly, none of the momentum factors can explain factor momentum. Moreover, combining the high-priced stock momentum with the Chinese three-factor model outperforms other models in explaining 38 of 43 anomalies. Our study suggests that individual stock momentum relates to factor momentum but may differ from it.

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.

LONG TERM INVESTMENT: CRITICALLY EVALUATING THE SPECULATIVE NATURE OF INVESTING IN HIGH PE RATIO STOCKS

Price to Earnings ratio is one of the most significant and top used financial metric when it comes to fundamental analysis and investment decision making. Even the market moves based on the expected PE ratio and adjusts itself from time to time accordingly. This study aims at critical evaluation by comparing a stock purchase at a historic date at a higher PE multiple with a current date and calculating returns on both stock price levels as well as dividend yield level over the entire period in absolute terms. It wants to highlight that a purchase at higher PE falls out of the definition of investing and should be categorized and treated as speculative for very clear reasons that are critically evaluated in this study. It is noticed that unless the company grows top line and bottom-line exponentially, a stock purchase at higher PE delivers very dismal dividend yield if any and mostly negative return on account of stock price depreciation rather than appreciation. Hence, such purchase can be said to be speculative as it relies solely on momentum stock price rise for gains.

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.

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.

Option Momentum

ABSTRACTThis paper investigates the performance of option investments across different stocks by computing monthly returns on at‐the‐money straddles on individual equities. We find that options with high historical returns continue to significantly outperform options with low historical returns over horizons ranging from 6 to 36 months. This phenomenon is robust to including out‐of‐the‐money options or delta‐hedging the returns. Unlike stock momentum, option return continuation is not followed by long‐run reversal. Significant returns remain after factor risk adjustment and after controlling for implied volatility and other characteristics. Across stocks, trading costs are unrelated to the magnitude of momentum profits.

The Influence of Institutional Investors' Position on Stock Momentum Phenomenon

Momentum phenomenon is one of the most classic anomalies in the field of securities investment. This paper investigates the influence of institutional investors' opening behavior on stock return momentum phenomenon by using the annual investor shareholding data publicly disclosed by listed companies. The findings are as follows: First, there is a positive correlation between institutional investors' opening behavior and stock excess return in one year; Second, if there is an obvious momentum phenomenon in the stock return in the first two years, then the stock return in the next year still depends on the institutional investor's position behavior, and has nothing to do with the investor's momentum strategy. Under the condition that other conditions remain unchanged, for the positive momentum group, if institutional investors buy portfolios substantially in the next year, the stock momentum phenomenon will continue; On the contrary, stock reversal occurs. A similar conclusion was found for the negative momentum group. In addition, the study also found that the momentum phenomenon is statistically significant, but because stock returns cannot be predicted in advance, it is not meaningful in the actual investment process. Therefore, this paper supports and complements the efficient market hypothesis to some extent.

Overlapping momentum portfolios

Different momentum investors use different formation periods to evaluate past stock performance. We explore the consequences of this diversity by studying the stocks that are the likely constituents of the portfolios of the heterogeneous momentum investors that coexist in practice. We show that stocks in the intersection of the 6 and 12-month momentum portfolios—“overlapping” momentum stocks—display enhanced medium-term return momentum. This finding is robust to considering a broad set of risk factors, transaction costs, and alternative explanations. Focusing on overlapping momentum stocks improves the returns of several momentum-based strategies proposed in the literature. The results are in line with the concurrence of trades by heterogeneous momentum investors exacerbating return continuation.

Recent evidence on the short-term and long-term performance persistence of emerging-market mutual fund returns

This paper analyzes the performance persistence of US-based emerging-market mutual funds. We use a sample of 275 actively managed funds between July 1989 and December 2020 and regress their returns on emerging-market benchmark portfolios. On average, the funds had a significant negative alpha. Contrary to some earlier evidence, we document that the short-term consistency is entirely driven by losses of underperforming funds. The return spread between the short-term winners and losers generates a significant positive alpha that can be fully explained by the momentum in emerging-market stocks. We find no evidence of any long-term regularities. Our findings show that the observed funds exhibit very similar behavior to their developed-market counterparts and may contribute to resolving some inconsistencies in the earlier results.

Momentum: what do we know 30 years after Jegadeesh and Titman’s seminal paper?

For over 30 years, extensive research has found corroborating evidence that past winners continue to yield higher returns than past losers. This momentum effect is robust across various asset classes and across the globe and presents perhaps the most pervasive contradiction of the efficient market hypothesis. This article reviews three strands of literature on momentum. First, I outline the construction of momentum strategies, emphasizing improvements and alternatives such as time-series momentum, residual momentum, and risk-managed momentum. Second, I summarize the most prominent behavioral-based and risk-based explanations for the origin of momentum. Finally, I present in detail the findings on commonality in stock momentum, namely on industry and factor momentum.

Dispotition Effect And Momentum

Background - An important challenge for behavioral finance is to find a direct relationship between the behavior of individual investors and asset price dynamics. One of irrational investors are most conspicuous in the financial markets is the tendency of some investors to sell winner stocks too early and keep losers stocks too long, called the disposition effect. Disposition effect can trigger a momentum in the stock price. Aim - To examine the past returns and disposition effect in predicting the momentum of stock returns. Desaign / methodology / approach - The data used in this study is weekly data. This study uses a variable capital gain overhang as a proxy disposition effect. Other variables used in this study is the stock return short, medium, and long period, turnover short, medium, and long period, and market capitalization. The analytical method used is regression and different test. Results and Discussion - Simultaneous test results also show that the variables of short period return, medium period return, long period return, short period turnover, medium period turnover, long period turnover, and market capitalization together have an effect on the capital gain overhang variable. The adjusted R2 value in this model shows that the independent variable in this equation is able to explain the dependent variable of 35.1%. Stocks with a low capital gain overhang (G1), have a low return value as well. Likewise, stocks with high capital gain overhangs (G4), have high return values. The disposition effect is an important cause of momentum, stocks with high capital gain overhangs will have positive returns. From these results, it can be concluded that hypothesis 2 is supported, namely the disposition effect can predict the presence of momentum in stock returns. Conclussion - The results showed past returns can predict the momentum in stock returns and disposition effect could predict the momentum of stock return. Research Implication –This research can provide an overview of the anomalies that often occur in the capital market, particularly the existence of the disposition effect and momentum in the Indonesian capital market. Limitations – This research uses only one proxy, namely the capital gain overhang as a proxy for the disposition effect which was first developed by the research of Grinblatt and Han (2005) and there are no other proxies that can be used to compare research results

A reexamination of factor momentum: How strong is it?

AbstractRecent studies show that most financial market anomalies exhibit a momentum effect. Based on two datasets, (i) an original 22‐factor sample and (ii) a more comprehensive 187‐factor sample, we find that factor momentum effect is weak at the individual factor level. In both samples, only about 22%– 27% of the factors exhibit strong return continuation and dominate the factor momentum portfolio while the remaining factors do not. The factor momentum strategies do not outperform the corresponding long‐only strategies in either sample. The choice of factors affects the ability of factor momentum to explain individual stock momentum.

Governance quality and momentum returns: international evidence

PurposeMomentum returns are considered an anomaly in the finance literature as their existence cannot be fully explained under the asset pricing paradigm. This study attempts to shed more light on this anomaly by investigating the determinants of momentum returns.Design/methodology/approachThe panel data technique is applied to the sample of 40 countries worldwide from 1996 to 2018. The authors use the panel-corrected standard error (PCSE) model to estimate the coefficient of World Governance Indicators (WGI), whereas the fixed effect model is used to determine the coefficient for corporate governance indicators (CGIs). The choice of PCSE estimation methods is guided by the fact that WGI variables are subjected to serial correlation, heteroskedasticity and cross-sectional dependence problems while CGI variables are not. Furthermore, a composite WGI index is constructed using principal component analysis (PCA).FindingsRegression analysis shows a negative and significant relationship between WGI index and momentum returns. The negative coefficient value of WGI supports the prediction of the overreaction hypothesis, which postulates a lower behavioral bias in the market with high governance quality. Breaking down of the WGI by their six indicators reveals that four of the indicators (control over corruption, government effectiveness, stability and avoidance of violence) are negative statistically significant with momentum returns while two indicators are not significant. As for CGIs, only one (strength of investor protection) of the four tested indicators is negative and significantly related to momentum returns.Originality/valueThe study fills the gap in economic literature by highlighting the association between governance quality at the country (WGI) and firm level (CGI) on stock momentum returns.

Factor Momentum and the Momentum Factor

ABSTRACTMomentum in individual stock returns relates to momentum in factor returns. Most factors are positively autocorrelated: the average factor earns a monthly return of six basis points following a year of losses and 51 basis points following a positive year. We find that factor momentum concentrates in factors that explain more of the cross section of returns and that it is not incidental to individual stock momentum: momentum‐neutral factors display more momentum. Momentum found in high‐eigenvalue principal component factors subsumes most forms of individual stock momentum. Our results suggest that momentum is not a distinct risk factor—it times other factors.

Is factor momentum greater than stock momentum?

Is factor momentum greater than stock momentum?

Forecasting Stock Market Momentum in Nepal: Application of Fuzzy Logic Model

Background: Stock market experts analyse various indicators to estimate the stock market, including historical prices, economic analysis, industry analysis and company analysis, but this study uses historical prices for the NEPSE index, making forecasting more precise. Purpose: The purpose of this study is to explore short-term stock market momentum using fuzzy logic. The study also aims to establish a suitable fuzzy model to predict stock momentum, reduce the risk, and make the right investment decision. Methodology/Design: This study employed exploratory research design to understand the problem of chaotic decision making in the stock market. The mathematical method employed in this study is membership functions, which are part of fuzzy logic. This includes only the commercial banks, as it has the highest market capitalization, 53.11% of total market capitalization. Using 14-day past data as a base, the suggested fuzzy model determines the stock index’s momentum over the next 5 days. Findings: The forecasted trend value for the Nabil, Civil, and Prime Commercial bank is 0.92, 0.92, and 0.80, which shows a bullish trend. Compared to previously collected data, the findings closely reflect the expected real-world values.