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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.

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.

Prior research on meeting or beating earnings thresholds documents that firms with earnings momentum are awarded with valuation premiums. However, it is unclear from this strand of literature why this is the case. Therefore, this study aims to investigate the effects of time-varying earnings persistence on earnings momentum and their pricing effects.

This study exploits a firm that reports earnings momentum as research setting to examine whether earnings persistence is significantly higher for firms with consecutive earnings increases. In addition, it investigates a relation between earnings momentum and fundamentals-driven earnings persistence and estimates return associations of earnings momentum conditional on economic-based persistence of earnings.

The empirical evidence suggests that firms with earnings momentum reflect higher time-varying earnings persistence. It further reveals that longer duration of earnings momentum is associated with higher fundamentals-driven earnings persistence. More importantly, valuation premiums are exclusively assigned to earnings momentum determined by strong firm fundamentals, not momentum itself.

This study provides new empirical evidence that valuation premiums accrued to firms with earnings momentum are conditional on time-varying earnings persistence. The research implications are relevant to investors, regulators and auditors, as the results bring conclusions that earnings momentum reflects successful business models not poor accounting quality. This leads to a more complete view of earnings momentum and helps allocate resources when evaluating earnings-momentum firms.

The purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.

Given the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.

The authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.

The authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.

This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics.

The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks.

Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets.

This research applies to markets that require some home equity contributions from buyers of housing services.

Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices.

Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.

This study investigates whether the skilled minority of active equity managers in emerging markets can be identified using a machine learning (ML) framework that incorporates a large set of performance characteristics.

The study uses a cross-section of South African active equity managers from January 2002 to December 2021. The performance characteristics are analysed using ML models, with a particular focus on gradient boosters, and naïve selection techniques such as momentum and style alpha. The out-of-sample nominal, excess and risk-adjusted returns are evaluated, and precision tests are conducted to assess the accuracy of the performance predictions.

A minority of active managers exhibit skill that results in generating alpha, even after accounting for fees, and show that ML models, particularly gradient boosters, are superior at identifying non-linearities. LightGBM (LG) achieves the highest out-of-sample nominal, excess and risk-adjusted return and proves to be the most accurate predictor of performance in precision tests. Naïve selection techniques, such as momentum and style alpha, outperform most ML models in forecasting emerging market active manager performance.

The authors contribute to the literature by demonstrating that a ML approach that incorporates a large set of performance characteristics can be used to identify skilled active equity managers in emerging markets. The findings suggest that both ML models and naïve selection techniques can be used to predict performance, but the former is more accurate in predicting ex ante performance. This study has practical implications for investment practitioners and academics interested in active asset manager performance in emerging markets.

This paper jointly examines how firm size and idiosyncratic risk impact momentum returns.

Using 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.

This 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.

This 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.

This 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.

This 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.

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.

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.