Deep learning and the international capital asset pricing model

This is the authors' abstract. We don't add key points for this paper.

We revisit the international conditional Capital Asset Pricing Model (ICCAPM) using a Long Short-Term Memory (LSTM) model to estimate high-dimensional, time-varying covariances with fewer parametric restrictions than standard covariance estimators. Using international equity data, we find a positive and significant price of world covariance risk, in contrast to conventional covariance estimators. The LSTM approach delivers superior out-of-sample covariance forecasts and improves portfolio performance, yielding higher Sharpe ratios. Results also indicate increasing global market integration over time. These findings highlight the importance of flexible covariance estimation for empirical tests of international asset-pricing models.