We examine subjective risk premia implied by return expectations of individual investors and professionals for aggregate portfolios of stocks, bonds, currencies, and commodity futures. While in-sample predictive regressions with realized excess returns suggest that objective risk premia vary...

This study examines the effect of the COVID-19 pandemic on the relationship between idiosyncratic volatility and expected stock returns. Using daily stock return data in the US market from the Center for Research in Security Prices (CRSP), we estimate monthly idiosyncratic volatility and...

This study analyzes the volatility spillover effects in the US stock market (S&P500) and cryptocurrency market (BGCI) using intraday data during the COVID-19 pandemic. As the potential drivers of portfolio diversification, we measure the asymmetric volatility transmission on both markets. We...

Being the health pandemic with the highest impact on the global financial market, the recent COVID-19 pandemic has led to significant risk transmissions across stock markets. Although an increasing number of studies have examined the effects of the pandemic on financial markets, we provide novel...

We examine the effects of limited investor attention on stock returns by using Google search volume index to measure investor attention. We also investigate whether national culture and market development have any role in this relationship. We find that the impact of investor attention on stock...

Energy policy uncertainty - as measured by uncertainty about a U.S. President signing an energy related executive order in the future - covaries positively with corporate investment and aggregate consumption growth, and its innovations carry a negative price of risk. I propose and test a...

We study the problem of detecting structural instability of factor strength in asset pricing models for financial returns. We allow for strong and weaker factors, in which the sum of squared betas grows at a rate equal to and slower than the number of test assets, respectively: this growth rate...

Stock returns predictability has been a long-standing topic in the literature on financial economics. Developments in prediction technology have facilitated the wide use of machine learning techniques, which motivates our study of whether stock returns predictability can be improved using...

We investigate the impact of financial news on equity returns and introduce a non-parametric model to generate a sentiment signal, which is then used as a predictor for short-term, single-stock equity return forecasts.We build on Google's BERT model (for Bidirectional Encoder Representations for...

We analyze the sentiment of emojis separately to the plain text-expressed sentiment in Reddit posts about meme stocks such as Gamestop during the Covid-19 pandemic. We document that a one-standard deviation change in emoji sentiment magnitude measured as the quantity of positive emoji sentiment...

We investigate the use of the normalized imbalance between option volumes corresponding to positive and negative market views, as a predictor for directional price movements in the spot market. Via a nonlinear analysis, and using a decomposition of aggregated volumes into five distinct market...

We examine the cross-section of international equity risk premia with machine learning methods. We identify, classify, and calculate 88 market characteristics and use them to forecast country returns with various machine learning techniques. While all algorithms produce substantial economic...

This paper explores the effect of oil price fluctuations on the stock returns of U.S. oil firms using a strategy of identification through heteroskedasticity exploiting the 2020 oil crash. Results are twofold. First, we find that a decline in oil prices statistically significantly reduces stock...

We investigate the effect of COVID-19 on the investors’ awareness about climate risk. The COVID-19 have led investors to draw a parallel between pandemic and climate risks, renewing investors’ attention to climate change. Using a novel dataset of trading by each investor types for Korean...

Previous studies find corporate investments negatively predict firm performance and stock returns. Using data from the Chinese A-share stock market, we find firms that substantially increase their investments have higher, rather than lower, subsequent stock returns. This effect persists after...

We infer the forward-looking Bitcoin risk premium from options contracts. Using data from 2018 to 2020, we show that the expected excess returns for Bitcoin are time-varying and significantly higher than in equities or gold, averaging almost 80% per annum. A temporal analysis of the term...

We propose a belief-generating model from which we build a statistical measure of investor disagreement. We simulate differences in beliefs across investors by endowing them with different machine learning models for forecasting returns from the same set of inputs. We measure disagreement as the...

Purpose: Previous research on the relationship between a firm’s distress risk and future stock returns produces inconsistent results. This study attempts to explain the conflicting results of earlier studies by showing that systematic distress risk leads to positive rewards, while unsystematic...

We study the transmission of sovereign debt inflow shocks on domestic firms. We exploit episodes of large sovereign debt inflows in six emerging countries that are due to the announcements of these countries' inclusion in two major local-currency sovereign debt indexes. We show that these...

The equity risk premium is generally considered to be a reward that investors earn on top of the prevailing risk-free return, implying that, all else equal, total expected stock returns should increase with the level of the risk-free return. We examine whether this notion is true using long-term...

Momentum is a pervasive characteristic of financial markets that lacks a broadly accepted explanation. In addition to its longstanding challenge to asset pricing theory, recent work finds that momentum poses a challenge for expected utility (EU) theory, opening an avenue for new decision...

Existing research has documented cross-sectional seasonality of stock returns—the periodic outperformance of certain stocks during the same calendar months or weekdays. A model in which assets differ in their sensitivities to investor mood explains these effects and implies other seasonal...

This paper uses a comprehensive set of variables from the five largest Eurozone countries to compare the performance of simple univariate and machine learning-based multivariate models in predicting stock market crashes. The statistical predictive performance of a support vector machine-based...

We provide the first comprehensive study of examining the predictive power of characteristic-based factors on market excess return. We find that characteristic-based factors negatively predict market excess return regardless of whether they are originated from investment, mispricing or...

This study cross-checks the symmetric and asymmetric effects of investor’s optimistic and pessimistic sentiments of Bitcoin on 23 sectoral stock return indices (10 Islamic stock sectoral indices and one composite Islamic market index by Dow Jones Islamic market and 12 industrial indices by...

This paper uses a multifactor model to examine the role of crude oil as a pricing factor in Australian excess industry returns over the period January 1980 to August 2006. A dynamic model is also specified to provide insights into the relationship between the stock market and past oil price...

We investigate the value-growth premium puzzle by merging insights from urban economics and finance that relate firm location to its stock performance. The value-growth premium in locations with high historical house price appreciation is 3.6% larger per year than the premium in areas that...

This paper studies the trading behavior of U.S. actively managed equity mutual funds during the COVID-19 market crash. We show that Environmental, Social, and Governance (ESG) funds helped to stabilize the market by contributing to the resiliency of ESG stocks, but interestingly non-ESG funds...

This paper documents a positive cross-sectional relation between returns and lagged idiosyncratic volatility (IVOL) in the corporate bond market. The relation is stronger following periods of low funding liquidity due to a funding liquidity driven decrease in returns and its subsequent reversal....

The existing literature on developed and advanced emerging markets documents that the expected stock returns exhibit a positive-, negative-, and no-relationship with both idiosyncratic volatility (IVOL) and extreme daily returns (MAX or MIN). Different from developed and advanced emerging...

We study the predictability of cross-sectional uncertainty (CSU) proposed by Dew-Becker and Giglio (2021) for stock returns. We find that CSU exhibits significant power for predicting monthly stock returns with annual out-of-sample R^2 of 6.34%, greater than popular predictors. CSU generates...

We propose a new measure of firm-level climate regulatory exposure based on 10-K filings. Using the 2016 Trump election as an exogenous shock to perceived climate regulatory risks, we identify a positive effect on stock returns for firms with higher climate regulatory exposures; they experience...

Operating leverage (OL) and profitability are interrelated determinants of stock returns. We show that the outperformance of firms with high OL is driven by periods of unconstrained aggregate funding conditions. Firms with high OL are more risky in general, but when the Fed eases funding...

Society needs financial intermediaries to create orderly efficient markets, to have informative prices, and to best allocate resources. However, when trust is eroded with high volatility and unpredictable events, financial crises are amplified and prices are distorted as financial intermediaries...

Green assets delivered high returns in recent years. This performance reflects unexpectedly strong increases in environmental concerns, not high expected returns. German green bonds outperformed their higher-yielding non-green twins as the ``greenium'' widened, and U.S. green stocks outperformed...

Using 278 economic and financial variables we study the power of machine learning (ML) in predicting the daily CBOE implied volatility index (VIX). Designing and applying an automated three-step ML framework with a large number of algorithms we identify Adaptive Boosting as the best...

Empirical evidence indicates that environmental social and governance (ESG) practices are associated with firm financial performance, but little is covered about investors’ attention towards stock performance. In this paper, we conduct a test of the relationship between ESG performance...

The low volatility factor in conjunction with the style factors Quality, Value and Momentum, has empirically proven to be able to moderate market risks and improve a portfolio’s overall risk-return profile. By integrating ESG into such a factor portfolio, future risks may be mitigated. We...