Stock return prediction has been in the spotlight because it involves numerous factors. Improving the accuracy of stock return prediction and quantifying the impact of individual factors on forecasting remain challenging tasks. Motivated by these challenges, we propose a novel forecasting method...

Decision-makers usually have an aspiration level, a target, or a benchmark they aim to achieve. This behavior can be rationalized within the expected utility framework, which incorporates the probability of success (achieving the aspiration level) as an important aspect of decision-making....

This paper examines how a change in health policy uncertainty affects US industry returns using monthly data from January 1985 to September 2020. We employ in-sample and out-of-sample analyses, and we find evidence that 25 out of 49 considered industries are predictable during the health crisis...

This study assessed the connectedness between oil shocks and industry stock indexes in the United States (US). We consider the normal and extreme conditions across different frequency horizons, and the quantile time-frequency connectedness method is used to determine the tail risk contagion...

As investors increasingly use Environmental, Social, and Governance (ESG) criteria, a key challenge remains: ESG data is typically reported annually, while financial markets move much faster. This study investigates whether incorporating annual ESG scores can improve monthly stock return...

If intraday price data are unavailable, then using daily returns to construct realized measures of the variances of lower-frequency returns is a natural substitute for using high-frequency returns in this context. Notably, a suitable application of this approach yields realized measures that are...

The growing emphasis on sustainability within the business community has led to a progressive integration of Environmental, Social, and Governance values (ESG) into the investment process. This study aims to identify those key fund characteristics that best predict financial performance, with a...

Recent studies highlight the superior performance of non-linear machine learning models, such as neural networks, over traditional linear models in predicting cross-sectional stock returns. These models are capable of capturing complex non-linear interactions between predictive signals and...

We study a direct, firm-level measure of investor sentiment derived from social media (BTSS sentiment). While related to firm fundamentals, BTSS sentiment contains a substantial non-fundamental component. We decompose sentiment into fundamental and pure sentiment and show that return...

This study examines the impact of China's "Zero-COVID" policies on stock returns in the healthcare sector from January 2020 to December 2022. Using panel regression analysis, we find that increases in the Stringency Index increased healthcare stock returns. In contrast, vaccination rates are...

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor...

We propose a methodology to construct tests for the null hypothesis that the pricing errors of a panel of asset returns are jointly equal to zero in a linear factor asset pric- ing model - that is, the null of "zero alpha". We consider, as a leading example, a model with observable, tradable...

Are carbon emissions priced in equity markets? The literature is split with different approaches yielding conflicting results. We develop a stylized model showing that, if emissions are priced, stock returns depend on expected emissions and the product of the innovation in emissions and the...

Purpose - Contrary to the developed markets, where ESG (environmental, social and governance) investing has received considerable attention, the extant literature in the context of emerging markets remains fragmented and scarce. To fill this gap, the study examines the financial performance of...

We studied decades-long (1980 to 2024) historic distributions of accumulated S&P500 returns, from daily returns to those over several weeks. The time series of the returns emphasize major upheavals in the markets - Black Monday, Tech Bubble, Financial Crisis, and the COVID pandemic - which are...

This study employs asymmetric quantile regression to investigate the asymmetric impact of WTI crude oil prices and economic policy uncertainty (EPU) on stock market returns from May 2014 to December 2024 in oil-importing (China, India, Germany, Italy, Japan, USA, and South Korea) and...