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The paper aims to investigate whether or not a firm’s capital structure can interact with past stock returns to affect future stock returns. Specifically, the authors examine whether or not capital structure can help improve momentum profit.

The authors use the US common stocks data from 1965 to 2022 to empirically examine the impact of capital structure on momentum profit.

When capital structure is measured either as the ratio of debt to asset or the ratio of liability to asset, we all find out that momentum strategies tend to be more profitable for stocks with large capital structure.

Besides documenting the empirical evidence of the impact of capital structure on momentum profit, the authors also present a simple explanation for their empirical results and show that their finding is consistent with the behavioral finance theory that characterizes investors’ increased psychological bias and the more limited arbitrage opportunity when the estimation of firm value becomes more difficult or less accurate.

The purpose of this paper is to study the US stock market and try to explain why short-term contrarian profits have largely disappeared in the past two decades.

In this work, the authors decompose the short-term contrarian profits into cross-sectional variations, firm-level overreactions and lead-lag effects to study the changes in their shares. Then, the authors study the behavior of the subgroups in the winner and loser subportfolios of contrarian investment strategies.

The authors find that short-term contrarian profits have largely vanished since 2000. Changes in the shares of the three components of contrarian profits, which are cross-sectional variations, firm-level overreactions and lead-lag effects, are not the main reason for the disappearance of contrarian profits in the past two decades. Instead, the disappearance of short-term contrarian profits is primarily due to the heterogeneous evolution of subgroups in the portfolio, which leads to a decrease in the overall level of overreactions that drive the contrarian profit.

The work explains the disappearance of short-term contrarian profits in the US stock market.

The authors believe the COVID-19 pandemic has an impact on supply and demand. The potential decline in real sector performance leads to lower expectations of securities performance. The uncertainty of future performance can change investor behaviour. This study tried to gain insight into stock investor behaviour during the COVID-19 pandemic. The results showed that the majority of the investor realized and believed the pandemic would affect the stock market performance. Hence, they did not show herding behaviour and were very confident during the COVID-19 pandemic. The survey also indicates that investors tend to avoid risk rather than take the opportunity to buy at a lower price. Moreover, investors believe that the COVID-19 vaccine will soon be found, and the economy will return to normal. Government and self-regulated organizations (SRO) are responsible for making effective policies to convince the investors about the future prospect.

As 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.

Firstly, 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.

The 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.

Prior 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.

This 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.

We form portfolios based on return and liquidity and examine the effects of liquidity and other risk factors on asset pricing in the Chinese stock market. Our results show that the past loser-and-illiquid stock portfolios tend to outperform the past winner-and-liquid stock portfolios in the 1–12 months holding period. The excess return is significantly associated with the market-wide liquidity factor even when we control the three Fama-French and momentum factors. Cross-sectionally, the liquidity beta significantly affects the excess return even with control of other risk betas and other traditional liquidity proxies.

Investors are inattentive to continuous information as opposed to discrete information, resulting in underreaction to continuous information. This paper aims to examine if the well-documented return predictability of the strategies based on the ratio of short-term to long-term moving averages can be enhanced by conditioning on information discreteness. Anchoring bias has been the popular explanation for the source of underreaction in the context of moving averages-based strategies. This paper proposes and studies another possible source based on investor inattention that can potentially result in superior performance of these strategies.

The paper uses portfolio sorting as well as Fama-MacBeth cross-sectional regressions. For examining the role of information discreteness in the return predictability of the moving average ratio, the sample stocks are double-sorted based on the moving average ratio and information discreteness measure. The returns to these portfolios are computed using standard approaches in the literature. The regression approach controls for various well-known return predictors.

This study finds that the equally-weighted monthly returns to the long-short moving average ratio quintile portfolios increase monotonically from 0.54% for the discrete information portfolio to 1.37% for the continuous information portfolio over the 3-month holding period. This study observes a similar pattern in risk-adjusted returns, value-weighted portfolios, non-January returns, large and small stocks, for alternative holding periods and the ratio of 50-day to 200-day moving average. The results are robust to control for well-known return predictors in cross-sectional regressions.

To the best of the authors’ knowledge, this is the first paper to document the significant role of investor inattention to continuous information in the return predictability of strategies based on the moving average ratios. There are many underreaction anomalies that have been reported in the literature, and the paper's results can be extended to those anomalies in subsequent research.

The findings of this paper have important practical implications. Strategies based on moving averages are an extremely popular component of a technical analyst's toolkit. Their profitability has been well-documented in the prior literature that attributes the performance to investors' anchoring bias. This paper offers a readily implementable approach to enhancing the performance of these strategies by conditioning on a straightforward measure of information discreteness. In doing so, this study extends the literature on the role of investor inattention to continuous information in anomaly profits.

While there is considerable literature on technical analysis, and especially on the performance of moving averages-based strategies, the novelty of this paper is the analysis of the role of information discreteness in strategy performance. Not only does the paper document robust evidence, but the findings suggest that the investor’s inattention to continuous information is a more dominant source of underreaction compared to anchoring. This is an important result, given that anchoring has so far been considered the source of return predictability in the literature.

This paper applies a volume-price probability wave differential equation to propose a conceptual theory and has innovative behavioral interpretations of intraday dynamic market equilibrium price, in which traders' momentum, reversal and interactive behaviors play roles.

The authors select intraday cumulative trading volume distribution over price as revealed preferences. An equilibrium price is a price at which the corresponding cumulative trading volume achieves the maximum value. Based on the existence of the equilibrium in social finance, the authors propose a testable interacting traders' preference hypothesis without imposing the invariance criterion of rational choices. Interactively coherent preferences signify the choices subject to interactive invariance over price.

The authors find that interactive trading choices generate a constant frequency over price and intraday dynamic market equilibrium in a tug-of-war between momentum and reversal traders. The authors explain the market equilibrium through interactive, momentum and reversal traders. The intelligent interactive trading preferences are coherent and account for local dynamic market equilibrium, holistic dynamic market disequilibrium and the nonlinear and non-monotone V-shaped probability of selling over profit (BH curves).

The authors will understand investors' behaviors and dynamic markets through more empirical execution in the future, suggesting a unified theory available in social finance.

The authors can apply the subjects' intelligent behaviors to artificial intelligence (AI), deep learning and financial technology.

Understanding the behavior of interacting individuals or units will help social risk management beyond the frontiers of the financial market, such as governance in an organization, social violence in a country and COVID-19 pandemics worldwide.

It uncovers subjects' intelligent interactively trading behaviors.

In the post-capture stage, the tumbling target rotates the combined spacecraft system, and the detumbling operation performed by the space robot is required. To save the costly onboard fuel of the space robot, this paper aims to present a novel post-capture detumbling strategy.

Actuated by the joint rotations of the manipulator, the combined system is driven from three-axis tumbling state to uniaxial rotation about its maximum principal axis. Only unidirectional thrust perpendicular to the axis is needed to slow down the uniaxial rotation, thus saving the thruster fuel. The optimization problem of the collision-free detumbling trajectory of the space robot is described, and it is optimized by the particle swarm optimization algorithm.

The numerical simulation results show that along the trajectory planned by the detumbling strategy, the maneuver of the manipulator can precisely drive the combined system to rotate around its maximum principal axis, and the final kinetic energy of the combined system is smaller than the initial. The unidirectional thrust and the lower kinetic energy can ensure the fuel-saving in the subsequent detumbling stage.

This paper presents a post-capture detumbling strategy to drive the combined system from three-axis tumbling state to uniaxial rotation about its maximum principal axis by redistributing the angular momentum of the parts of the combined system. The strategy reduces the thrust torque for detumbling to effectively save the thruster fuel.

The intention of the empirics is to contribute to the general understanding of investor responses to market price shocks. The authors review assumptions about investor behavior in response to price shocks and investigate alternative rebalancing heuristics.

The authors use market data over 40 years to define market shocks. Portfolio rebalancing implements constrained Markowitz mean-variance (MV) heuristics.

Momentum rebalancing in portfolio management outperforms contrarian rebalancing in the study interval. Sensitivity analysis by decade, sector constraints and proportion of security holdings bought or sold continue to support momentum rebalancing.

The results are consistent with under-responding to price shocks at consensus levels in financial markets. The theoretical background provides a basis for experimental lab studies of shocks of different magnitudes under conditions in which participants have information on the levels of other participants and a condition in which they can only observe their previous estimates.

Managing portfolios in the face of price disturbances of different magnitudes is informed by empirical studies and their implications for investor behavior.

This is the first study the authors can locate that uses market data with alternative rebalancing heuristics to estimate price returns from the respective heuristics over a time interval of 40 years. The authors support the results with sensitivity estimates and consider implications for the underlying agent heuristics in light of background studies.

The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.