2022, Graef, Frank, Höchle, Daniel, Schmid, Markus

2018, Höchle, Daniel, Schmid, Markus, Zimmermann, Heinz

In empirical asset pricing, it is standard to sort assets into portfolios based on a characteristic, and then compare the top (e.g., decile) portfolio’s risk-adjusted return with that of the bottom portfolio. We show that such an analysis assumes the random effects assumption to hold. Therefore, results from portfolio sorts are valid if and only if firm-specific effects are uncorrelated with the characteristic underlying the portfolio sort. We propose a novel, regression-based approach to analyzing asset returns. Relying on standard econometrics, our technique handles multiple dimensions and continuous firm characteristics. Moreover, it nests all variants of sorting assets into portfolios as a special case, provides a means for testing the random effects assumption, and allows for the inclusion of firm-fixed effects in the analysis. Our empirical results demonstrate that the random effects assumption underlying portfolio sorts is often violated, and that certain characteristics-based factors that are well-known from empirical asset pricing studies do not withstand tests accounting for firm fixed effects.

2021, Höchle, Daniel, Schmid, Markus, Zimmermann, Heinz

We show that portfolio sorts, as widely used in empirical asset pricing, tend to misattribute cross-sectional return predictability to the firm characteristic underlying the sort. Such misattribution arises if the sorting variable correlates with a firm-specific effect capturing unobservable heterogeneity across firms. We propose a new, firm-level regression approach that can reproduce the results from standard portfolio sorts. Besides, our method handles multivariate firm characteristics and, if firm fixed effects are included, is robust to misattributing cross-sectional return predictability. Our empirical results confirm that portfolio sorts have limited power in detecting abnormal returns: Several characteristics-based factors lose their predictive power when we control for unobservable heterogeneity across firms.

2018, Höchle, Daniel, Ruenzi, Stefan, Schaub, Nic, Schmid, Markus

We use a unique data set from a large retail bank containing internal managerial accounting data on revenues and costs per client to analyze how banks and their financial advisors generate profits with customers. We find that advised transactions are associated with higher profits than independently executed trades of the same client. The bank’s own mutual funds and structured products are most profitable for the bank, and profits increase with trade size. We show that advisors recommend exactly those transactions. Furthermore, we find that advised clients achieve a worse performance than independent clients, suggesting that advisors put their employer’s interest first.

2019, Höchle, Daniel, Karthaus, Larissa, Schmid, Markus

We show that a sample of 7,487 U.S. firms going public between 1975 and 2014 significantly underperforms mature firms in the first year after the IPO. Contrary to post-issue horizons of three to five years, the first-year underperformance cannot be explained by Carhart (1997) risk factor exposures. Moreover, this underperformance is robust to the analysis of sub-samples and the consideration of multiple firm characteristics in a statistically robust setting. Further econometric tests reveal that the first-year underperformance is likely due to unobservable heterogeneity across IPO and mature firms. In fact, the first-year underperformance disappears when we control for such unobservable heterogeneity by including firm fixed effects in the analysis. The magnitude of the firm fixed effects is negatively related to IPO firms’ life expectancy. Consistently, there is no significant IPO underperformance, when differences in life expectancy across IPO and mature firms are accounted for.

2018, Höchle, Daniel, Schmid, Markus, Zimmermann, Heinz

We show that portfolio sorts, as commonly employed in empirical asset pricing applications, are at risk of accidentally misattributing parts of the risk-adjusted return (or "alpha") to the firm characteristic underlying the sort. Such misattribution occurs if the firm characteristic is correlated with an unobservable yet time-persistent factor. We propose a novel, regression-based methodology for analyzing asset returns. Our technique can reproduce the alpha and factor exposure estimates from all variants of sorting assets into (e.g., decile) portfolios. In addition, and contrary to standard portfolio sorts, our approach handles multivariate and continuous firm characteristics and, if firm-specific (fixed) effects are included in the analysis, is robust to alpha misattribution. In our empirical analysis, we indeed find alpha misattribution to be an issue in conventional portfolio sorts as several well-known characteristics-based factors lose their predictive power when we account for firm fixed effects.