Publications

Deep Parametric Portfolio Policies

with Frederik Simon & Tom Zimmermann

Management Science, forthcoming

We consider parametric portfolio policies of any complexity using deep neural networks to optimize investor utility. Risk aversion acts as an economic regularization mechanism, with higher risk aversion constraining model complexity. Empirically, Deep Parametric Portfolio Policies generate 43-102 basis points higher monthly certainty equivalent returns compared to linear policies. Looking beyond expected returns, non-linear portfolio policies better capture the complex relationship between investor preferences and firm characteristics but the benefits of using complex models vary with investor preferences. Results hold across different utility functions and remain robust to transaction costs and short-selling restrictions. Overall, economic regularization constrains model complexity much like statistical regularization but emerges endogenously from investor preferences. 

Working Papers

Hard to Process: Atypical Firms and the Cross-Section of Expected Stock Returns

Job Market Paper

Theories of limited attention predict that investors rely on typical patterns to navigate high-dimensional firm characteristics, making atypical firms hard to process. To quantify this difficulty, we propose a data-driven measure of firm atypicality (ATYP) using an autoencoder. The model learns typical patterns that describe most firms, and our measure aggregates the deviations those patterns cannot explain. Unlike return- or belief-based proxies of this friction, ATYP isolates the ex-ante processing difficulty in the underlying characteristics. Empirically, we document that atypicality strongly predicts future returns. A decile portfolio that sells high-ATYP firms and buys low-ATYP firms earns 1.47% per month (equal-weighted) and 0.82% (value-weighted). The effect strengthens where investor attention is low and arbitrage is limited, suggesting mispricing as the explanation.

Interpretable Machine Learning for Earnings Forecasts: Leveraging High-Dimensional Financial Statement Data 

with Dieter Hess & Frederik Simon

Journal of Accounting Research, Major Revision

Using the full set of Compustat financial statement variables, we find that flexible machine learning models significantly increase the out-of-sample accuracy of street earnings forecasts for horizons of up to five years, yielding economically meaningful improvements for valuation and asset pricing. Compared to traditional linear models, our approach reduces the mean squared forecast error one year ahead by 28% and generates annualized long-short return spreads of approximately 18% in implied cost of capital portfolios. Moreover, the disagreement between machine learning and traditional forecasts predicts both future earnings and abnormal returns, indicating that the machine learning model captures information not yet reflected in prices. Although income statement variables are the most important predictors at short horizons, balance sheet fundamentals gain substantial relevance as the forecast horizon extends. Finally, a novel decomposition reveals that these predictive improvements stem primarily from modeling non-linear relationships rather than from merely expanding the input set.

Work in Progress

Parametric Portfolio Strategies with Transaction Costs 

with Justus Boeker & Tom Zimmermann

This paper develops a framework for nonlinear parametric portfolio strategies that explicitly incorporates transaction costs. Building on recent advances in machine learning–based asset pricing, we propose a one-step approach that directly optimizes investor utility rather than relying on expected return forecasts as an intermediate target. We analyze both ex-post and ex-ante methods for reducing trading costs and show that simple ex-post adjustments can substantially improve net performance with only minimal efficiency losses.  

How Do Banks Adjust to Windfall Taxation?

with Shafik Hebous & Tom Zimmermann

This paper examines the impact of windfall taxation on banks’ loan and deposit rate setting behavior. We analyze the asymmetric interest-rate pass-through following tax policy changes, employing a comprehensive dataset of banking institutions to identify differential effects across bank types. Our findings provide policy-relevant insights into unintended consequences of bank taxation on credit provision, highlighting potential trade-offs between fiscal objectives and the effectiveness of monetary policy transmission. By illuminating the mechanisms through which windfall taxes influence banks’ pricing decisions, this study contributes to the ongoing debate on optimal bank taxation and its implications for monetary policy efficacy.