Publikationen

Unsere Forschungergebnisse unterstützen die gesellschaftliche Debatte rund um aktuelle finanzökonomische Fragestellungen. Durch die Veröffentlichung der Arbeiten in internationalen Fachzeitschriften und unserer Working Paper Series sollen diese für einen möglichst breiten Adressatenkreis zugänglich werden.

HFRC Working Paper Series

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Alle Working Papers

Publikationen von Tizian Otto

Estimating stock market betas via machine learning

Wolfgang Drobetz, Fabian Hollstein, Tizian Otto, Marcel Prokopczuk
HFRC Working Paper Series | Version 08/2023
Machine learning-based market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we show that machine learning-based estimators produce the lowest forecasting and hedging errors. They also help create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform best overall. Model complexity is highly time-varying. Historical betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves the predictive performance.

Predicting corporate bond illiquidity via machine learning

Axel Cabrol, Wolfgang Drobetz, Tizian Otto, Tatjana Xenia Puhan
HFRC Working Paper Series | Version 06/2023
This paper examines the predictive performance of machine learning methods in estimating the illiquidity of U.S. corporate bonds. We compare the predictive performance of machine learning-based estimators (linear regressions, tree-based models, and neural networks) to that of the most commonly used benchmark model based on historical illiquidity. Machine learning techniques outperform the historical illiquidity-based approach from both a statistical and an economic perspective. Moreover, tree-based models and neural networks outperform linear regressions, which incorporate the exact same set of covariates. Gradient boosted regression trees perform particularly well. While historical illiquidity, due to its high persistence, is the most important single predictor variable, several fundamental, risk-, and return-based covariates also possess notable predictive power. Capturing interactions and nonlinear effects among these predictors further enhances predictive performance.

Forecasting stock market crashes via machine learning

Hubert Dichtl, Wolfgang Drobetz, Tizian Otto
Journal of Financial Stability | 04/2023
This paper uses a comprehensive set of predictor variables from the five largest Eurozone countries to compare the performance of simple univariate and machine learning-based multivariate models in forecasting stock market crashes. In terms of statistical predictive performance, a support vector machine-based crash prediction model outperforms a random classifier and is superior to the average univariate benchmark as well as a multivariate logistic regression model. Incorporating nonlinear and interactive effects is both imperative and foundation for the outperformance of support vector machines. Their ability to forecast stock market crashes out-of-sample translates into substantial value-added to active investors. From a policy perspective, the use of machine learning-based crash prediction models can help activate macroprudential tools in time.

Empirical asset pricing via machine learning: Evidence from the European stock market

Wolfgang Drobetz, Tizian Otto
Journal of Asset Management | 11/2021 | Forthcoming
This paper evaluates the predictive performance of machine learning methods in forecasting European stock returns. Compared to a linear benchmark model, interactions and nonlinear effects help improve the predictive performance. But machine learning models must be adequately trained and tuned to overcome the high dimensionality problem and to avoid overfitting. Across all machine learning methods, the most important predictors are based on price trends and fundamental signals from valuation ratios. However, the models exhibit substantial variation in statistical predictive performance that translate into pronounced differences in economic profitability. The return and risk measures of long-only trading strategies indicate that machine learning models produce sizeable gains relative to our benchmark. Neural networks perform best, also after accounting for transaction costs. A classification-based portfolio formation, utilizing a support vector machine that avoids estimating stock-level expected returns, performs even better than the neural network architecture.

Estimating security betas via machine learning

Wolfgang Drobetz, Fabian Hollstein, Tizian Otto, Marcel Prokopczuk
HFRC Working Paper Series | Version 10/2021
This paper evaluates the predictive performance of machine learning techniques in estimating time-varying betas of US stocks. Compared to established estimators, tree-based models and neural networks outperform from both a statistical and an economic perspective. Random forests perform the best overall. Machine learning-based estimators provide the lowest forecast errors. Moreover, unlike traditional approaches, they lead to truly ex-post market-neutral portfolios. The inherent model complexity is strongly time-varying. The most important predictors are various historical betas as well as fundamental turnover and size signals. Compared to linear regressions, interactions and nonlinear effects enhance the predictive performance substantially.

Predictability and the cross section of expected returns: Evidence from the European stock market

Wolfgang Drobetz, Rebekka Haller, Christian Jasperneite, Tizian Otto
Journal of Asset Management | 11/2019
This paper examines the cross-sectional properties of stock return forecasts based on Fama–MacBeth regressions using all firms contained in the STOXX Europe 600 index during the September 1999–December 2018 period. Our estimation approach is strictly out of sample, mimicking an investor who exploits both historical and real-time information on multiple firm characteristics to predict returns. The models capture a substantial amount of the cross-sectional variation in true expected returns and generate predictive slopes close to one, i.e., the forecast dispersion mostly reflects cross-sectional variation in true expected returns. The return predictions translate into high value added for investors. For an active trading strategy, we find strong market outperformance net of transaction costs based on a variety of performance measures.