Joint trend information in cross-sectional stock returns: combining technical and fundamental trends

This study explores the joint forecasting effect of comprehensive technical trend information and comprehensive fundamental trend information from a dynamic perspective, and further investigates the pricing power of technical trend and fundamental trend factor. We find that the combination of two different types of trend information has a better prediction performance on the cross-sectional stock returns. The joint trend information strategy delivers significant excess returns that persist even after accounting for transaction costs. Further analysis suggests that this predictive power is stronger under stocks with greater limits to arbitrage and lower investor attention, weaker for lottery-type stocks, and stronger under stocks with higher heterogeneous beliefs, consistent with a behavioural and frictions-based interpretation. Moreover, we show the explanatory power of technical trend factor and fundamental trend factor on cross-sectional stock returns and compare the difference in the pricing ability between the pricing model with trend factors and the classical asset pricing model, and provide some support that the pricing models including these two trend factors exhibit improved pricing performance relative to classical benchmarks.