Traditional asset pricing theory suggests a positive risk-return relationship, while empirical studies often find a negative association between risk and expected returns. In this paper, we uncover a unique pattern: a negative risk-return relationship among stocks far from their 52-week high prices and a positive relationship among stocks close to their 52-week high prices. We propose that this cross-sectional heterogeneity arises because investors evaluate stocks relative to the 52-week high, becoming risk-seeking when prices are far below this benchmark and risk-averse when prices are near it. We explore various potential explanations for this phenomenon but find no empirical support. Overall, our findings introduce a novel psychological perspective for understanding the risk-return trade-off.

We demonstrate that valuation uncertainty and information arrival are critical stock characteristics determining whether individual factors in leading factor models are influenced by sentiment or limited attention. Therefore, the ability of a factor model to explain cross-sectional stock returns depends on including two distinct types of factors: those that capture sentiment and those that tackle limited attention. Yet, many leading factor models include factors for sentiment but fall short in incorporating factors for limited attention. Our findings are important, guiding future research towards developing new factor models that more effectively capture both sentiment and limited attention compared to existing models. This includes uncovering powerful factors capable of simultaneously capturing sentiment and limited attention.

& mldr;is "should I buy any?". Under Bayesian portfolio theory, ongoing zero weights in cryptocurrency are surprisingly difficult to generate. With 10 years of prior data, equity investors would need very pessimistic priors on mean returns to never buy cryptocurrency: -10.6 percent per month for Bitcoin, and -19.6 percent for a diversified cryptocurrency portfolio. Most priors that involve never purchasing cryptocurrency imply shorting it. Optimal weights are generally small, non-trivial (1-5 percent magnitude), frequently positive, and smooth. The certainty equivalent gains from cryptocurrency are comparable to international diversification and prominent anomaly portfolios. Costs (storage and fees) would need to exceed 21-39 percent annually to deter trading.

This study introduces a media climate change concern index, derived from unexpected climate change coverage across diverse media channels, including print (newspapers), voice (radio), and video (television). The index negatively predicts aggregate stock market returns, both in-sample and out-of-sample, offering potential gains for investors. Our findings emphasize the media's influence on market returns through climate change discussions, mainly via the cash-flow channel. This reveals possible shifts in product demand, while demand for green or brown stocks may not be altered as expected, possibly due to "greenwashing" by institutional investors exaggerating their environmental commitments.

PurposeThe purpose of this paper is to investigate the role of institutional investors in the cost of equity for Chinese firms, especially state-owned enterprises (SOEs).Design/methodology/approachBy using data from Chinese firms with a unique state ownership structure, we provide empirical evidence on whether institutional investors can help reduce the cost of equity for SOEs and non-SOEs, respectively, and if so, identify the underlying channels.FindingsWe find that an increase in the shareholdings of institutions, especially independent institutions, can lead to a reduction in the cost of equity. This effect is particularly prominent in SOEs compared to non-SOEs. Moreover, institutional investors promote corporate social responsibility activities and innovation activities of invested firms, thereby reducing the cost of equity.Originality/valueThis paper contributes to a comprehensive understanding of the effects of institutional shareholdings with heterogeneity on the cost of equity and their influential mechanisms in the process of mixed ownership reform.

We propose that extrapolative beliefs about recent past extreme returns contribute to a better understanding of the source and outcome of extreme positive daily returns. In an extrapolation framework, investors may overestimate the likelihood of future extreme positive daily returns for stocks with such a recent history of salient returns, leading to lower future returns for these stocks than for their counterparts without such a historical return pattern. Moreover, the return predictability is stronger when past extreme positive daily returns occur more recently. Our results are robust to controlling for skewness preferences, investor attention, and firm fundamental shocks and strength.

Using data from Binance, we find strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant predictors of focal cryptocurrencies. The results are robust across various methods, including the adaptive LASSO and principal component analysis. Furthermore, a long-short portfolio formed on the past returns of cryptocurrencies can generate a sizable return out-of-sample after accounting for transaction costs. Overall, our findings corroborate cross-cryptocurrency return predictability and are consistent with the spillover effect mechanism, where common shocks among cryptocurrencies coupled with the limited attention of investors lead to slow information diffusion across coins.

By using the data of Chinese firms with the state ownership structure, we examine whether institutional investors can help reduce the required return of equity for state-owned enterprises (SOEs) and non-SOEs. We find that an increase in the shareholdings of institutions especially independent institutions can lead to a reduction in the required return. This effect is particularly prominent in non-SOEs than in SOEs, indicating that the state ownership may limit the ability of institutional investors in reducing the required return. In addition, institutional investors promote the corporate social responsibility activities of invested firms, thereby reduce the required return of equity.

We present a novel approach that analyzes topics and tones of analyst reports using a deep neural network in a supervised learning approach. By letting trained classifiers evaluate topics and tones of the reports, we find that incorporation of topic tones significantly enhances the accuracy of predicting cumulative abnormal returns, increasing adjusted R-2 from 6.1% without considering textual information to 17.9% with detailed topic tones. This improvement is primarily driven by the inclusion of opinion and corporate fact type of topics. Our findings highlight importance of topic assessment to make the most use of analyst reports for informed investment decisions.

This paper provides a novel perspective to the nexus of oil prices and stock markets by examining the impact of oil price shocks on stock market anomalies. After decomposing oil price shocks into three types , we find that aggregate demand shocks have the strongest influence on stock market anomalies. In contrast, oil supply shocks and oil-specific demand shocks have little impact. Similar results are also found in the industry analysis. Interestingly, the link between aggregate demand shocks and anomalies is the strongest among firms with either small size or high idiosyncratic risks. The documented effects are robust after controlling for investor sentiment as well as several well-known macroeconomic or market factors. Our findings are consistent with but also extend the sentiment-based explanation in that we show that uncertainty also plays a role in explaining stock market anomalies.

We show that ongoing zero portfolio weights in cryptocurrency are surprisingly difficult to generate in a standard Bayesian portfolio theory framework. With ten years of prior data, equity market investors would need very pessimistic priors on mean returns to justify never having bought cryptocurrency: -10.6% per month for Bitcoin, and -19.6% per month for a diversified portfolio of cryptocurrencies. Moreover, most priors that involve never purchasing cryptocurrency imply that investors should short cryptocurrency. Optimal absolute weights are generally small but non-trivial (1-5%), frequently positive, and fairly smooth despite returns being volatile. Under a wide range of priors, the certainty equivalent gains from cryptocurrency are comparable to international diversification and exceed the size anomaly. Trading costs (ambiguity aversion, storage, fees) would need to be enormous to justify non-investment, over 21% per year for Bitcoin and 39% for a diversified cryptocurrency portfolio.

We examine the relationship between two behavioral forces, sentiment and limited attention, and eight prominent factors that have recently been proposed to explain various anomalies. Investor sentiment explains many factors well, such as those related to equity issuance, because short legs are usually more speculative than long legs. Investor sentiment fails, however, to explain certain factors that are equally speculative with respect to the long legs and short legs, especially post-earnings-announcement drift (PEAD). Instead, market-wide attention affects these latter factors significantly but not those explained by investor sentiment. Our evidence illustrates the commonality and differences between recently proposed influential factors.

Taking all A-share listed companies from 2015 to 2019 as the research object, this paper makes an empirical analysis of the relationship between institutional investors and accounting information quality and corporate performance, and further explores the influence of different institutional investors on accounting information quality and corporate performance, and the effect of institutional investor heterogeneity is analyzed from the perspective of enterprise nature. Empirical shows that: institutional investors holdings of listed companies has a positive role in promoting enterprise performance, especially the high independence of institutional investors to promote more obvious, accounting information quality of enterprise performance also has a significant positive effect, but by the influence of institutional investors holdings, accounting information quality positive impact on enterprise performance will change significantly. The research results of this paper have certain reference significance for investors to invest and participate in corporate governance.

Using global cross-firm ownership data, we find that both stock returns and cash-flow news of ownership-linked firms predict focal firm's returns for all types of ownership structures: subsidiary−parent, parent−subsidiary, subsidiary−subsidiary, and parent−parent. This effect, observed only after the establishment of cross-firm ownership, is not subsumed by focal firm or industry momentum, or alternative inter-firm relations, including customer−supplier links and shared analyst coverage. Our findings are explained by mispricing due to internal capital markets – a mechanism unique to complex ownership firms. Higher internal capital market activity among ownership-linked firms also induces larger investments and lower external financing of the focal firm.

Using a novel data set to identify geographic peer firms based on the locations of both firms' headquarters and material subsidiaries, we show that returns on geographical peers have strong predictive power for focal firm returns. A value-weighted long-short strategy that buys stocks with the highest geo-peer returns and shorts stocks with the lowest geo-peer returns generates a Fama and French(2015) five-factor alpha of 0.6% per month. This strategy is distinct from other cross-firm momentum strategies and cannot be explained by local economic conditions. The effect is more pronounced among firms that receive less investor attention and that are more costly to arbitrage, consistent with slow information diffusion in the geographic network into stock prices.

This study investigates the impact of investor sentiment on excess equity return forecasting. A high (low) investor sentiment may weaken the connection between fundamental economic (behavioral-based nonfundamental) predictors and market returns. We find that although fundamental variables can be strong predictors when sentiment is low, they tend to lose their predictive power when investor sentiment is high. Nonfundamental predictors perform well during high-sentiment periods while their predictive ability deteriorates when investor sentiment is low. These paradigm shifts in equity return forecasting provide a key to understanding and resolving the lack of predictive power for both fundamental and nonfundamental variables debated in recent studies. This paper was accepted by David Simchi-Levi, finance.

The failure probability and economic losses are astonishingly high in the cryptocurrency market. We perform a comparative analysis of a dynamic logit model and machine learning methods for the predictors for cryptocurrency failure and the pricing of crypto failure risk. We document different significant market- and characteristic-based predictors for coin and token failures. Moreover, we document a significantly positive relation between failure risk and returns, which cannot be explained by the common pricing factors and arbitrage costs in the cryptocurrency market. The high failure risk premium suggests that investors require extra returns for bearing high failure risk of crypto assets.