Raw Returns Research Articles

Stock price delay and the cross-section of expected returns: A story of night and day

We examine how individual stock price reacts to intraday and overnight market information. By examining stock betas, we document a stark gap between day beta and night beta and nontrivial asynchronous beta, which captures the slow diffusion of information. We provide evidence that this information delay can predict future stock returns over a one-month period. Long-short portfolios sorted on the gap between day beta and night beta and asynchronous beta generate raw returns of 0.8% and 0.39% and risk-adjusted alphas of 0.77% and 0.28% per month. These results are robust to alternative asset pricing models and when controlling for firm characteristics, such as size, book-to-market ratios, liquidity, investor recognition and limits-to-arbitrage characteristics. We also explore the heterogeneity of the information delay premium and find that the predictive power of information delay for future return is stronger among small, value firms, less visible firms, illiquid firms and firms with higher limit-to-arbitrage. We further provide evidence that our measure of price delay indeed offers incremental information for cross-section return prediction after we explicitly control for the conventional measure of Hou and Moskowitz (2005)'s price delay.

Economic policy uncertainty and the predictability of stock returns: impact of China in Asia

PurposeMotivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.Design/methodology/approachWe estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.FindingsApplying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.Research limitations/implicationsOur analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.Originality/valueThe findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

How Political Connections and Financial Constraints Affect Total Asset Growth Anomaly Before and During the Covid-19 Pandemic?

Our research addresses the gap in investigating how political connections and financial constraints affect asset growth anomalies before and during the Covid-19 pandemic. Our sample includes listed non-financial firms in the Vietnam stock market from 2008 to December 2020. We employ Fama and MacBeth regressions and portfolio sorting methodology to analyze a sample with 34,441 monthly observations, including 478 firms. Our findings indicate a negative relationship between asset growth and subsequent stock returns in all periods. While the asset growth anomaly existed in the Vietnam stock market before the pandemic, it disappeared during the pandemic. The average raw return difference between stocks in the highest and lowest asset growth terciles was −0.26% per month before the pandemic. Our robustness tests show that asset growth anomaly exists in firms with political connections during the pandemic. In addition, the asset growth anomaly only persists in larger firms before the pandemic. Our findings support agency and pecking order theory, behavior, and rational explanations. Finally, this study contributes practical implications for managers and individual investors to prevent adverse impacts of asset growth anomalies.

Rank is all you need: development and analysis of robust causal networks

Financial networks can be constructed to model the intertemporal price dependencies within an asset market, giving rise to a causal network. These networks are traditionally inferred through multivariate predictive modelling. However, the application of these techniques to financial data is highly challenged. The interplay of social and economic factors produces unstable price behaviour that violates most conventional modelling assumptions, limiting the informational content of networks derived from standard inference tools. Despite these limitations, it remains unclear whether the improved accuracy of robustly estimated networks translates into qualitatively unique insight. This study provides an extended analysis of our recently introduced Rank-Vector-Autoregression model, demonstrating its capacity to identify properties that are undetected with standard methodology. We initially validate the superior accuracy of Rank-VAR through a simulation study on processes that contain adversarial abnormalities. When applied to a dataset of 261 cryptocurrencies, our rank network uniquely displays capitalisation-dependent hierarchical ordering, with outgoing influence being positively, and incoming influence negatively correlated to total coin valuation. Applying our method to the squared deviations verifies that even under robust estimation, volatility networks display fundamentally differing dynamics to raw returns, with more connections, clustering, and causal cycles. Our results demonstrate the use of Rank-VAR to identify and verify unique properties in the causal structures of cryptocurrency markets.

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.

A Study on the Performance of Japanese ETFs

The current study examines the performance of 76 Japanese equity Exchange Traded Funds (ETFs) over the period 1/1/2018-12/31/2022. Performance is estimated in several ways, that is, raw returns, alphas from single- and multi-factor regression models, and risk-adjusted returns. The market timing skills of ETF managers are examined too. The results reveal that, on average, the examined ETFs do not produce any material alpha. The results also indicate that the risk factors suggested by Fama and French (1993 & 2015) are more or less capable of explaining the performance of the Japanese ETFs. Finally, the findings show that 21% of ETF managers possess some sort of market timing skills. However, the managers fail to time the volatility of the stock market.

Impact of Underpricing Determinants on Market Return of S&P BSE SME IPO in India

Primary capital market i.e. Initial Public Offering (IPO) market forms a significant part of it. India, an emerging market, has seen tremendous growth in the BSE SME IPO market over the past few years. In light of this, the Underpricing of BSE SME IPOs is being examined. This study aims to investigate and Analyze the determinants of Underpricing of BSE SME IPOs in the India. This study examines the cause-and-effect relationship and Impact of Raw Return and Market Adjusted Excess Return between Issue Price, Issue Size, Lot Size , Market Return and Volatility of BSE SME IPOs of Underpricing in India. There is a significant relationship between Raw Return and Market Adjusted Excess Return between lot size, volatility, market return, issue size, issue price of S&P BSE SME IPO.The null hypothesis is rejected at 5% level of significant for the Specific variables among Raw Return and Market Adjusted Excess Return in the Study Period.While investing in an S&P BSE SME IPO the investor should be careful and they may choose long-term investment because there is a low risk and they can earn a return at longtime.The Present Study Provides a detailed analysis of S&P BSE SME IPO in India and Underpricing of Raw Return and Market Adjusted Excess Return relationship and impact for 10 years January 2013 to December 2022 by taking a yearly observation. At the same time,they have a Strong Relationship between the issue-specific and market-specific variables like volatility and market return and integrated with each other

Tug of war with noise traders? Evidence from the G7 stock markets

This paper studies the tug of war between overnight noise trading and daytime arbitrage in the G7 stock markets. We confirm Akbas, Boehmer, Jiang, and Koch (2022) reporting that this tug of war predicts future returns in the US stock market. We verify this result also in the Canadian stock market. In contrast, for the stock markets of France, Germany, Italy, the UK, and Japan, this tug of war is not predictive for future returns. These country-wise findings are documented in average raw returns and prevail upon risk adjustment along both Carhart (1997) four factors and Fama and French (2018) six factors. A microstructure perspective on the tug of war suggests that the split evidence between the US and Canadian stock markets and the remaining G7 stock markets is due to institutional and regulatory differences which restrict daytime institutional arbitrage in the European stock markets and Japan.

The highest-lowest price range and the cross-sectional returns predictability

ABSTRACT Motivated by literature on heterogeneous investors, we use the difference between the highest and lowest prices to proxy the intensity of competition among bullish-bearish investors and investigate its significance in the cross-sectional pricing of stocks. Portfolio-level analyses and cross-sectional regressions indicate a negative and significant relation between the intensity of competition among investors and stock returns in the next month. Average raw returns differences between stocks in the lowest and highest deciles are 0.86% per month, and risk-adjusted returns differences are nearly 1% per month. These results are robust to size and value effects which are important two characteristics related to future returns. To explain it, we link this anomaly with risk compensation and investor mispricing. The results show that risk compensation explanation is excluded. We find that this anomaly is stronger for stocks with lower shareholding of institutional investors, higher market volatility, higher investor sentiment, and larger retail investors. Therefore, we conclude that this anomaly is caused by mispricing.

Replicating and Digesting Anomalies in the Chinese A-Share Market

We replicate 469 anomaly variables similar to those studied by Hou et al. (2020) using Chinese A-share data and a reliable testing procedure with mainboard breakpoints and value-weighted returns. We find that 83.37% of the anomaly variables do not generate significant high-minus-low quintile raw return spreads. Further adjusting risk increases the failure rate slightly to 84.22% based on CAPM alphas and 86.99% based on Fama–French three-factor alphas. We show that the conventional procedure using all A-share breakpoints with equal-weighted returns for the anomaly test is indeed problematic as it assigns too much weight to microcaps and has a very limited investment capacity. The CH3-factor, CH4-factor, and q-factor models show the best performance over the whole sample period. The q-factor model is the best performer in the post-2007 subsample period after significant improvements occurred in China’s financial market environment, such as the completion of the split-share structure reform and the implementation of new accounting standards conforming to the International Financial Reporting Standards. The non–state-owned enterprise subsample in the post-2007 period is a cleaner sample in which the CH4-factor and q-factor models are the best performers. This paper was accepted by Lukas Schmid, finance. Funding: Z. Li acknowledges financial support from the National Natural Science Foundation of China [Grant 72103043] and the Fundamental Research Funds for the Central Universities in UIBE [Grants 19QN01 and 22PY053-72103043]. L. X. Liu acknowledges financial support from the National Natural Science Foundation of China [Grants 71872006 and 72273006]. L. X. Liu and X. Liu acknowledge financial support from the Guanghua Thought Leadership Platform of Peking University. K. C. J. Wei acknowledges partial financial support from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant 15507320]. The authors acknowledge financial support from the Guanghua School of Management, Peking University; the School of Banking and Finance, University of International Business and Economics; and the Non-PAIR Research Centre “Research Centre for Quantitative Finance” at Hong Kong Polytechnic University. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4904 .

CEO performance impact on medical leave outcomes

Using a comprehensive CEO medical leave dataset, we find that prior firm performance is an important determinant of CEOs’ return to their position at the firm. Firms continue to experience superior performance measured by raw returns, market-adjusted returns, and industry-adjusted returns after the return of outperforming CEOs, reaffirming the performance-driven career outcome for medical leave CEOs. We also find that shareholders generally respond negatively to medical leave news while CEO age and prior performance significantly affect stock price reactions to the news. Shareholders respond more negatively to the return of older CEOs and CEOs with extended medical leave. Shareholders react more positively to the return of outperforming CEOs. Investors respond more negatively to CEO exit news if preceding medical leave news indicated an expected CEO return. We also find that the return of CEOs and duration of medical leave are negatively associated with ex-post abnormal returns or alphas using the CAPM model.

Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity

AbstractThis study designs a methodological framework to forecasting allocation weights and evaluating multiobjective portfolios considering the investment horizon heterogeneity. The investment horizon heterogeneity may induce the terrible data loss problem under traditional asset return definition, while in the real world, there are many portfolio makers whose investment horizons are not consistent. The methodological framework has three parts. First, this paper gives a novel multitimescale analysis (MTA) tool as a computation procedure to decompose the raw return series, and the decomposed subreturn series could represent the information for a portfolio maker with specific investment horizon and has the same data length as the raw return series. Second, proposed methodological framework uses a time‐varying parameter GAS‐D‐Vine‐Copula model to construct the joint distribution of subreturn series of multiassets in a portfolio. Third, due to the stochastic dominance consistency issue, this paper applies three different utility functions as the outputs of a portfolio strategy and two cost functions as the inputs of a portfolio strategy in an efficiency evaluation model. The empirical example of US aviation stock market data from 2013 to 2021 reveals that the Mean‐Skewness‐Volatility‐HMCR‐LPM multiobjective has the greatest numbers of optimal strategy timings for portfolio makers with 2‐, 3–5‐, and 10–50‐day‐length investment horizons. The investment horizon of 1‐day length is the least efficient, and the investment horizon between 10‐ and 50‐day length is the most efficient. The proposed methodology indicates that more multiobjectives in a portfolio strategy are not necessarily better, and there is an optimal range of investment horizons for portfolio makers.

Dependence structure among rare earth and financial markets: A multiscale-vine copula approach

This paper examines the dynamic upper and lower tail dependence across rare earth metals, clean energy, gold, world equity, base metals, and crude oil markets at various time scales. Firstly, raw return series are decomposed into various time scales using the maximum overlapping discrete wavelet transform method, then the time-varying pairwise dependencies, accounting for the impact of the covariate (in our case, the rare earth stock index), are analysed using vine-copula. This so called multiscale-vine copula approach is applied to daily data from June 25, 2009 to October 7, 2022, covering the Covid-19 outbreak. The results show that, for raw returns, the rare earth market moderates the positive dependence between world equity and clean energy markets. At the short-term time scale, unlike other pairwise dependencies, rare earth eases the dependency between clean energies. During the Covid-19 pandemic period, the rare earth stock index significantly affects the correlation of the gold and oil markets and makes them more resilient to global health shocks. At the mid-term time scale, the impact of the rare earth index is more pronounced, for both the entire sample and during the Covid-19 outbreak, as the dynamic dependencies of most indices, such as clean energy-world equity, base metals-world equity, and crude oil-clean energy, significantly decline after accounting for the influence of rare earth metals. The main result at the long-term time scale is that the Covid-19 pandemic moderates the dependency of clean energy-gold even further when considering the impact of the rare earth stock index. In general, the rare earth stock index plays a significant role in easing the extent of dependency in the medium term during the entire sample and the pandemic. These findings provide some useful implications for heterogeneous investors and market participants operating at various time scales.

Assessing the impacts of Covid-19 on stock exchange, gold prices, and financial markets: Fresh evidences from econometric analysis

This research analyses the relative efficacy of gold price, financial market, and stock exchange hedging against sectoral and industry-level global stock market returns. Incorporating Gold into equity-based asset allocation techniques and assessing the stock market and financial sector during the COVID-19 epidemic is one way to diversify your portfolio and reduce risk. After orthogonalizing raw returns concerning a robust collection of relevant universal variables, we conduct our analysis inside a bivariate GARCH(p, q) framework. To further assess ideal portfolio proportions and the efficacy of hedging methods, we expand the volatility spillovers study by calculating the optimal weights for a minimal risk portfolio and determining the hedge ratio. In high-volatility environments, our results show which financial market and stock exchange sectors and industries investors should prioritize to minimize the risk and maximize reward. Use of country-specific macroeconomic variables indices to supplement the worldwide index, (3) separate analysis for the COVID-19 first wave due to the existing argument that the pandemic raises unexpected market events and our early data showing co-movement among the three unpredictability metrics during the pandemic. These findings have important implications for portfolio entrepreneurs and business investors looking to buy international equities.

Performance and Performance Persistence of Europe-Focused ETFs in the United States

This study examines the performance and performance persistence of US-listed exchange-traded funds (ETFs) that are focused on the European capital markets. Performance is estimated in several ways—raw returns, alphas from single-factor and multifactor regression models, and risk-adjusted returns. The sample includes 43 ETFs, and the study period spans from January 1, 2015–December 31, 2022. The results reveal that, on average, the examined ETFs cannot beat the S&P 500 Index and STOXX Europe 600 Index, which serve as proxies for the US and European stock markets, respectively. In addition, the results show that the performance of the Europe-focused ETFs does not persist.

The market reaction to COVID-19: European Evidence

The aim of this study is to examine the market reaction to COVID- 19 on European capital markets and its long-run performance. Using a dataset of 3,181 firms over the period 2019-2021 results show that the COVID-19 effect differs by region, country and sector. The average cumulative abnormal returns (CARs) for the European countries under investigation are -12.32%, with Austria (-19.24%), Germany (-16.31%) and Ireland (-16.63%) being the most affected countries from the pandemic over the 11-day window around the event. Sectors were affected differently, with Energy (-15.74%), as expected, being the most negatively affected in the short run. Regarding the long-term effects of the pandemic, evidence based on the 18-month buy and hold raw returns (BHR) shows increase of 41.6%, with the Utilities sector being the best performer in the Southeastern EU with BHR returns of 124.6%. Interestingly, our evidence suggests that larger, more profitable, more efficient firms with greater operating cash flow ability were those that yield the greatest long run market return performance after the pandemic.

The Financial Performance of Newly Launched Chinese Infrastructure REITs

This paper explores the financial performance of the first group of nine infrastructure real estate investment trusts (REITs) that went public in mainland China in June 2021. Under the short-term event study framework, this research compares the abnormal returns and correlations of nine Chinese REITs with three groups of competitors (i.e., 49 quasi-REITs, 54 infrastructure asset-backed securities (ABSs), and 132 Wind utility firms). Three approaches are used to evaluate the performance of REITs: Risk-return indicators (e.g., Sharpe ratios and Jensen’s alphas) and raw returns measure the preliminary results, and panel data analyses capture the relative advantages of REITs over their competitors within the infrastructure industry. REITs are small-cap firms with the largest Sharpe ratios and greater diversification potential than the ABSs. Despite beating the market, they did not start to trade with a premium until the first month after their initial public offerings (IPOs). Post-event annualized abnormal returns are more than 15% against the ABSs and close to zero against Wind utility firms after controlling for key financial variables, and three mechanisms behind the REIT IPOs (i.e., asset type, taxation, and liquidity) are verified. Therefore, REITs outperform quasi-REITs and infrastructure ABSs as well as the market proxies, and their launch brought out favorable outcomes.

The green and Brown performances of mutual fund portfolios

The past decade has seen an increasing interest in socially responsible investing (SRI) in the mutual fund industry. Central to this development is whether SRI funds underperform conventional funds. Using a novel approach and a sample of 2,255 mutual funds, we decompose fund portfolios into socially responsible (green) and non-socially responsible (brown) components. We find that, in comparison to the non-socially responsible component, the socially responsible portion exhibits a lower raw return, lower risk-adjusted return (alpha), lower Sharpe ratio, and similar degree of performance reversal. The magnitude of this underperformance is, however, relatively small. The results align with SRI having a limited negative impact on fund performance while potentially offering some diversification benefits.

DETERMINANTS OF IPO OVERSUBSCRIPTION ON ISLAMIC STOCKS: EVIDENCE FROM INDONESIA

This study aims to investigate factors that affect IPO oversubscription on Islamic stocks. Using data of 202 IPOs indexed from Indonesia Sharia Stock Index, this study uses ordinary least squaresand quantile regression to test the formulated hypothesis from 2011 to 2020. This study finds that issue price and issue size negatively affect IPO oversubscription. Meanwhile, firm size and raw return positively affect IPO oversubscription. The findings of this study offer implications forcompanies especially related to go public subscription. This study can be reference for investors as well when engage in IPO related activities for Islamic stocks.

Profitability and low-risk anomalies reexamined

This study re-examines the low-risk and profitability anomalies, which are often seen as closely related phenomena. The study uses a global dataset and cash flow return on investment (CFROI) as a profitability metric that allows comparison across sectors, regions, and time. The results confirm the low-risk anomaly and reveal that more profitable firms outperform over time when it comes to raw returns. When applying common risk factor models, the low-risk anomaly vanishes. However, the profitability effect is only subsumed by the Fama–French five-factor model that itself accounts for operating profitability. This study demonstrates that both anomalies represent distinct phenomena, although both exhibit some overlap in terms of underlying portfolio holdings.