All too often, measuring statistical dependencies between financial time series is reduced to a linear correlation coefficient. However, this may not capture all facets of reality. This paper studies empirical dependencies of daily stock returns by their pairwise copulas. We investigate in...

This paper evaluates a specification for conditional beta models following Fama and French (2019). In this paper, I reject the Fama and French model that assumes characteristics are conditional betas in favor of a linear conditional beta model following Shanken (1990). Model-implied zero-beta...

We give explicit algorithms and source code for extracting factors underlying Treasury yields using (unsupervised) machine learning (ML) techniques, such as nonnegative matrix factorization (NMF) and (statistically deterministic) clustering. NMF is a popular ML algorithm (used in computer...

We apply machine learning techniques and use stock characteristics to predict the cross-section of stock returns in 33 international markets. We conduct a stringent out-of-sample test to examine concerns about overfitting: the models are trained with past U.S. data and used to predict...

Expected idiosyncratic volatility and its positive relation to expected returns of Fu (2009) can be closely replicated, but only when we include information up to time t to estimate the idiosyncratic volatility at time t. Since this involves look-ahead bias, we re-estimate expected idiosyncratic...

We investigate the impact of potential information hiding or disclosure delay originated from private subsidiaries on the future returns of their public parent firms. We find a significantly positive link between private subsidiaries' information disclosure (PSID) and the cross-section of future...

A unified explanation of risk and mispricing in stock returns underpinned by their aggregate liquidity risk is presented. We argue alternating liquidity exposures depict two distinct investment preferences-hedging against aggregate liquidity risk or betting on it. A three-factor model capturing...

This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates...

According to the financial press, firms with low leverage have lower distress risk due to their reduced exposure to the credit market, especially during credit crises. Compared to their conventional and socially responsible (SRI) counterparts, sharia compliant (SC) stocks are low-leverage...

The market value of the Chinese stock market is considerably large given its short history. However, unlike stock market in other countries, we notice a unique phenomenon in China that investors usually chase after hot topics wherever aroused by policies, technologies or many other issues. The...