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Ohlson (Empirical Accounting Seminars: Elephants in the Room. Accounting, Economics, and Law: A Convivium 15 (1): 1–8) argues that researchers tacitly avoid raising statistics-related 'elephants' that could undermine inferences. We offer a balanced perspective, first applauding the remarkable progress made in deriving testable predictions, leveraging modern statistical techniques, and tapping alternative Big Data sources to address issues relevant to practitioners, regulators and academia. While we concur with Ohlson's elephants, we caution against over-criticism based on statistical design choices, as it risks creating new elephants. Our key lessons: focus on meaningful hypotheses, recognize merits of descriptive studies, balance Type I and II errors in data handling and journal reviewing, employ proper context when interpreting statistical significance and consider economic significance. Overall, though empirical accounting research faces challenges, criticism should not deter innovative research (Type II error in journal reviewing). [ABSTRACT FROM AUTHOR]

This paper develops a new salient factor, STZ, which synthesizes the price-limit-hitting system and the situational dependence of investors on information responses. STZ is capable of predicting stock returns. By analyzing the data of A-shares from May 2000 to April 2023, we employ univariate ranking, conditional bivariate ranking, and Fama-MacBeth regression to investigate the predictive power of salient factors on stock returns. The findings indicate that stocks with a high STZ score exhibit negative returns in the following month, while stocks with a low STZ score demonstrate positive returns. [ABSTRACT FROM AUTHOR]

This study examines the relationship between carbon risk and stock returns for listed firms in Korea, where firms are legally obligated to disclose their carbon emissions. While previous research mostly focuses on major markets like the United States and the European Union, demonstrating the impact of climate change on asset prices, there is a scarcity of studies examining emerging markets. Using data from Korean‐listed firms from 2011 to 2021, we investigate the association between a firm's exposure to carbon risk and cross‐sectional stock returns. We find that stocks with high exposure to carbon risk exhibit higher average returns and the abnormal returns associated with carbon risk are statistically significant and cannot be explained by the Fama‐French three‐ or five‐factor models. Furthermore, this phenomenon is more evident among stocks with high foreign ownership. Finally, the carbon factor commands a significantly positive risk premium, suggesting that carbon risk is an important risk factor even in emerging markets like Korea. [ABSTRACT FROM AUTHOR]

Purpose: The average U.S. farm size has risen dramatically over the last three decades. Motives for this trend are the subject of a large body of literature. This study incorporates farm size risk and return analysis into this research stream. In this paper, cross-sectional and temporal relations between farm size and returns are examined and characterized. Design/methodology/approach: Relying on farm level panel data from Kansas Farm Management Association (KFMA) for 140 farms from 1996 to 2018, this article examines the relationship between farm size and returns and investigates whether farm size is related to risk. Two measures of farm returns are used: excess return on equity and risk-adjusted return on equity. Value of farm production and total farm acres are used as measures of farm size. Findings: Findings suggest a significant and positive relationship between farm size and excess return on equity as well as farm size and risk-adjusted return on equity. However, this return premium associated with farm size is not associated with additional risk. Stated differently, farm size can be viewed as a farm characteristic that is associated with higher return without additional risk. Practical implications: These findings provide further support for ongoing farm consolidation. Originality/value: The results suggest the trend towards consolidation in production agriculture is likely to continue. Larger farms bear less risk. [ABSTRACT FROM AUTHOR]

This paper examines how liquidity risk is priced in the cross‐section of cryptocurrency returns. In doing so, we use the Amihud measure as a liquidity proxy. By employing the univariate portfolio analysis, the bivariate portfolio analysis, and the Fama‐MacBeth regression analysis, we document a negative relationship between liquidity and cryptocurrency returns. Additional tests demonstrate that this finding is robust to alternative liquidity measurement as well as size screens and show no evidence of a significant intertemporal relationship between liquidity and expected returns for three leading cryptocurrencies. Our conclusions add to the understanding of how markets price cryptocurrencies. [ABSTRACT FROM AUTHOR]

By featuring the link of investor heterogeneity to the persistence of the overnight and intraday components of returns, we examine the ESG–overnight (intraday) alpha relation in the Chinese stock market. The empirical results show that ESG score has a significantly negative effect on the expected stock overnight returns in Fama–MacBeth regression. Consistently, given the biggest market capitalization and the least illiquidity subsamples, the trading strategies by going long (short) the top (bottom) ESG quintile would yield negative profits. In addition, we conduct the implication of the ESG pricing by dividing the full sample into green stock subsample and sin stock subsample, and the empirical results present that the ESG pricing is pervasive of the green-type stocks. These conclusions verify the pricing of ESG and support the conjecture that green stocks have lower expected returns because ESG investors value sustainability.

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This paper examines whether momentum drives the disposition effect and vice versa in the US stock market. The results from the analysis of the Fama-Macbethregressions show that the disposition effect drives momentum but not the other way around. Furthermore, we find that this relationship varies over time. Along with the disposition effect, size also has an impact on the momentum. Therefore, the relationship between momentum and disposition effect is examined based on size deciles, and results demonstrate that the relationship does not vary significantly with the size of stocks. However, both the cumulative returns and capital gain varies monotonically with the size of stocks.

This paper examines whether momentum drives the disposition effect and vice versa in the US stock market. The results from the analysis of the Fama-Macbethregressions show that the disposition effect drives momentum but not the other way around. Furthermore, we find that this relationship varies over time. Along with the disposition effect, size also has an impact on the momentum. Therefore, the relationship between momentum and disposition effect is examined based on size deciles, and results demonstrate that the relationship does not vary significantly with the size of stocks. However, both the cumulative returns and capital gain varies monotonically with the size of stocks. [ABSTRACT FROM AUTHOR]

Empirical test of asset-pricing models are typically performed on portfolios based on firm-characteristics such as size and book-to-market ratios etc. However, because of their strong factor structure, the characteristic sorted portfolios do not provide a sufficient test for asset pricing models. In recent, the appropriateness to use characteristics sorted portfolios has been debated. Literature suggests various alternative test portfolios sorted by other attributes to improve the empirical tests. To address this issue, we construct three sets of test portfolios sorted by firm beta, volatility, and clustering method to test various asset pricing models. We examine whether portfolios sorted by the above methods can improve the explanatory power of various alternative asset pricing models. Our test results suggest that for unconditional models, the statistical significance and estimated risk premiums depend on the choice of tests portfolios. The conditional model has more power to explain the variation of average returns than the unconditional model. [ABSTRACT FROM AUTHOR]

We are the first pioneers who evaluate the overall fitness of the two-pass Fama–MacBeth regression and the generalized method of moments (GMM) by comparing the R 2 or mean absolute pricing error (MAE), using a Monte Carlo simulation of different models and portfolios for hundreds of trials and, in particular, focusing on the case that the expected return is always a gross return in both methods. Our findings reveal an innovative finding that both methodologies achieve approximate overall magnitudes of pricing errors. [ABSTRACT FROM AUTHOR]

We propose a weighted Fama-MacBeth (FMB) two-step panel regression procedure and compare the properties of the usual unweighted versus our proposed weighted FMB procedures through a Monte Carlo simulation study. We find evidence that when the cross-sectional regression explanatory power changes over time as well as the standard errors of the coefficient estimates, the proposed weighted FMB procedure produces more efficient coefficient estimators and more powerful tests compared to the usual unweighted FMB procedure across various model specifications in terms of the sampling distribution, sample size, and time-series distribution. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines the sensitivity of commodity price changes to monetary policy uncertainty. We find evidence that the response of commodity price changes hinges on the sign of the monetary policy shock, the level of monetary policy uncertainty as well as a recession dummy. Uncertainty associated with negative monetary policy shocks leads to a decrease in commodity prices and excess speculative activity. The results from estimating an asset pricing model suggest that monetary policy uncertainty appears not to be a priced risk factor in the cross‐section of commodity price changes. [ABSTRACT FROM AUTHOR]

Manuscript type: Research paper. Research aims: This paper aims to examine the role of nonmarketable assets namely, stock index, government bond index, human capital and real estate in a multi-asset proxy for the true market portfolio. It also examines the sensitivity of risk-return tradeoffs with different market portfolio compositions for both the conditional and unconditional versions of the asset pricing model. Design/ Methodology/ Approach: This paper extends on Mayers' (1972) model by building a composite market portfolio which consists of stock, bond, human capital and real estate. The crosssection of asset returns is tested with Fama and Macbeth's (1973) regression method. The capital asset pricing model (CAPM) uses the Kalman filter based approach to estimate the conditional factor loadings. Research findings: This study finds that when per unit of risk premium is equal, the market model standard beta over-estimates the systematic risk that is measured by the composite market portfolio. The effect of bond, human capital and real estate on the complete market portfolio does not have much impact on the empirical testing of the CAPM. However, the conditional model shows a significant and positive risk premium. Theoretical contributions/ Originality: Despite the importance of the non-marketable assets, many studies have rarely integrated or validated these in an asset-pricing framework, within an emerging market. Thus, the present study seeks to advance the theoretical and empirical understanding of the role of non-marketable assets in the composition of market proxies for an asset pricing model, in India. Practitioner/ Policy implications: The validity of the CAPM is insensitive to the inclusion of non-marketable assets in the market portfolio. This implies that investors use prior belief and conditioning variables as predictive variables to determine the cost of capital. The outcome of this study provides academics and practitioners a better understanding of the cross-sectional behaviour of stock returns in the Indian market. Research limitation: The limited work done on the Indian capital market coupled by the limited availability of non-marketable data in this study, may constrain the comparison of the results. In addition, the outcome drawn from this study cannot be generalised on other emerging markets as the focus of this study was only on the Indian capital market. [ABSTRACT FROM AUTHOR]

This paper examines the effects of size, value and momentum on the cross-sectional relation between expected returns and risk in the Indian stock market. We find that the conditional Carhart four-factor model empirically describes the variation of cross-section of return better than the unconditional model. When size, book-to-market and momentum effects are controlled in the conditional model, the positive relation of market beta, book-to-market and momentum with expected returns remains economically and statistically significant. However, this evidence is found to be subject to characteristics of test portfolios. The expected returns are sensitive to changes in predictive macroeconomic variables. [ABSTRACT FROM AUTHOR]

• Time-varying factor exposures and risk premiums are derived at an intra-day level. • Functional Fama-MacBeth approach used to validate the intra-day risk premiums. • Fama-French Carhart risk factors cannot account for systemic comovement over the entire intra-day interval. • Valid risk premiums found over specific intra-day trading hours in Bullish sentiment. This paper investigates the cross-sectional asset pricing for intra-day return curves. By introducing a functional Fama-MacBeth regression approach, the validation of the intra-day risk premium associated with the Fama-French Carhart factors is examined. The empirical evidence reveals that these common risk factors show weak explainability to the entire cross-sectional intra-day returns, despite significant risk premiums that are discovered in specific half-hour time-spans in bullish sentiment. [ABSTRACT FROM AUTHOR]

• A dynamic general equilibrium model with intangible capital (IC) explains the volatility of equity shares. • IC model implies an appreciation in the price of equity shares is accompanied by a decrease in the volatility of equity shares. • Empirical results from the Fama–Macbeth two-step regressions support the idiosyncratic volatility puzzle can be explained by intangible assets • Intangible asset to total asset ratio is a pricing factor to cross-section stock returns. In this paper, we explore whether intangible capital (IC) can help explain idiosyncratic volatility puzzles. The underlying assumption is that firms produce and accumulate IC as part of their normal operations. Investments in IC can either raise a company's future ability to produce or lower its cost of production. The applied model finds empirical support for the hypothesis that IC can help explain idiosyncratic volatility puzzles, especially for firms with higher IC-to-total asset ratios. This paper contributes to existing literature on idiosyncratic volatility puzzles from an IC investment perspective and provides implications for IC on stock markets. [ABSTRACT FROM AUTHOR]