We employ instrumented principal component analysis (IPCA) to provide a new factor model for the cross-section of country equity risk premia. Using data from 71 equity markets, we identify latent factors and condition betas on a comprehensive set of accounting and market characteristics from the finance literature. A four-factor conditional asset pricing model best captures the variation in country returns, beating prominent factor models. IPCA’s superior performance stems primarily from its enhanced ability to predict emerging market returns while also generalizing well to developed markets. Among the global “signal zoo”, size, momentum, volatility, political risk, and valuation are the most important predictors of return differences.

Abstract We propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across different time horizons. Using data on more than 3,000 coins, we employ machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts cryptocurrency returns. The effect cannot be subsumed by known factors and remains robust across different subperiods, market states, and alternative research designs. Moreover, it survives the impact of transaction costs and persists in big and liquid coins. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns.