Jiaqi Wang is a PhD candidate in Finance at Hanken School of Economics, Finland, where he began his doctoral studies in 2024. He holds bachelor's and master's degrees in Finance from Zhejiang University, China.
His research focuses on empirical asset pricing, with particular interests in stock return predictability, cross-sectional asset pricing, and the evaluation and economic interpretation of factor models. His broader research interests include macro-finance, consumption-based asset pricing, and present-value relations.
His doctoral supervisor is Paulo F. Maio.
Email: jiaqi.wang@hanken.fi
I revisit the expected growth factor of Hou et al. (2021). Alternative information sets improve forecasts of investment growth but lower the return spread, eliminate significant q-factor alphas, and weaken cross-sectional pricing performance, all driven by information outside Hou et al. (2021)'s predictor set. Thus, the performance of the expected growth factor is predictor specific rather than a general property of expected investment growth, casting doubt on the interpretation that its pricing success reflects expected investment growth.
I examine whether return seasonality in anomaly portfolios reflects seasonal factor risk premia or stock-level seasonality carried through portfolio construction. A stock-level seasonality factor spans anomaly seasonality strategies, but not vice versa. Seasonality is stronger among anomaly portfolios with more stable constituents, and anomaly deciles are most informative for pricing seasonality-sorted stock portfolios when their lagged returns closely proxy those of their current constituents. These findings show that anomaly seasonality is a projection of stock-level seasonality through portfolio construction, not evidence of distinct seasonal factor risk premia. They caution against interpreting predictability in characteristic-sorted portfolios as evidence of factor premia.