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Promotion: ​Daniel Hofmann

Foto_Hofmann ©​Daniel Hofmann

​Daniel Hofmann: Essays on Empirical Finance.

This thesis investigates the predictive power of past stock returns. Jointly with Karl Ludwig Keiber, we introduce an innovative auto-correlation framework that generalizes momentum investment strategies. In the German stock market, our strategies earn up to 233 basis points per month on average from 1998 to 2017. The same strategies generate average monthly raw returns of up to 125 basis points in the US stock market for the period 1984 to 2018. The results in both markets are robust to risk-adjusting, value-weighting, and January-effect-type anomalies. We show that medium-risk profile stocks do not contribute to investment performance in either of the markets. In another study, collaboratively with Karl Ludwig Keiber and Adalbert Luczak, we examine the relationship between short-term momentum and long-term reversal strategies in the German stock market. Unlike Conrad and Yavuz’s (2017) finding in the US stock market, we find the two phenomena to be inseparable. Put differently, in the German stock market, reversal likely follows momentum. We document that low-risk profile stocks are prone to short-term momentum, whereas high-risk profile stocks drive long-term strategies.