This paper considers the impact of heterogeneous gain learning in an asset pricing model. A relatively stylized model is shown to generate persistent swings of asset prices from their fundamental values which replicates long range samples of U.S financial data. The detailed mechanisms of the...

The literature usually ascribes time-variation in discount rates to time-variation in the risk premium. This is probably also true over the short- and medium term. This paper set out to explore time-variation in investors required return over the longer term and the resulting impact on equity...

The use of fundamentalist traders in the stock market models is problematic since fundamental values in the real world are unknown. Yet, in the literature to date, fundamentalists are often required to replicate key stylized facts. The authors present an agent-based model of the stock market in...

Stock market fundamentals would not seem to meaningfully predict returns over a shorter-term horizon - instead, I shift focus to severe downside risk (i.e., crashes). I use the cointegrating relationship between the log S&P Composite Index and log earnings over 1871 to 2015, combined with...

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...

According to no-arbitrage, risk-adjusted returns should be unpredictable. Using several prominent factor models and a large cross-section of anomalies, we find that past pricing errors predict future risk-adjusted anomaly returns. We show that past pricing errors can be interpreted as deviations...

In this paper, we confirm cross-sectional reversals in intraday returns in China's A-share market. Intraday reversals are shown to be robust with respect to seasonality, alternative samples, and the daily price-limit rule. To investigate the potential drivers, trade volumes and order imbalances...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage...

In a complete market for short-lived assets, we investigate long run wealth-driven selection on a general class of investment rules that depend on endogenously determined current and past prices. We find that market instability, leading to asset mis-pricing and informational efficiencies, is a...