Large-Scale Asset Purchases can impact the price of securities directly, when securities are targeted by the central bank, or indirectly through portfolio re-balancing of private investors. We quantify both the direct and the portfolio re-balancing impact, emphasizing the role of investor...

We merge the literature on downside return risk and liquidity risk and introduce the concept of extreme downside liquidity (EDL) risks. The cross-section of stock returns reflects a premium if a stock's return (liquidity) is lowest at the same time when the market liquidity (return) is lowest....

We propose a heuristic switching model of an asset market where the agents' choice of heuristic is consistent with their individual risk aversion. They choose between a fundamentalist and a trend-following rule to form expectations about the price of a risky asset. Given their risk aversion,...

We develop a four-factor model intended to capture size, value, and credit rating transition patterns in excess returns for a panel of predominantly mid- and large-cap entities. Using credit transition matrices and rating histories from 48 US issuers, we provide evidence to support a...

This paper studies the evolution of the greenium, i.e. a risk premium linked to firms' greenness and environmental transparency, based on individual stock returns. We estimate an asset pricing model with time-varying risk premia, where the greenium is associated to a priced 'greenness and...

We analyze how a benevolent, privately-informed government agency would optimally release information about the economy's growth rate when the agents hold heterogeneous beliefs. We model two types of agents: "trusting" and "distrustful." The former has a prior that is identical to that of the...

This paper disentangles the complexity of the distress risk premium in stock returns using the risk-neutral measure of credit risk (valued by CDS spread) and investigates the relationship between credit risk and the market , size, value, and momentum effects. Consistent with the argument for a...

We provide the first comprehensive analysis of option information for pricing the cross-section of stock returns by jointly examining extensive sets of firm and option characteristics. Using portfolio sorts and high-dimensional methods, we show that certain option measures have significant...

Using Indian bank-level data, we examine the cross-sectional returns predictability for banking stocks in view of the distinct industry parameters prevalent in the financial services space. We find the existence of abnormal returns in banking stocks. We also observe that the celebrated...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...