Asset Management

Unsere Forschungsgruppe Asset Management befasst sich vorwiegend mit Fragen der quantitativen Vermögensverwaltung. Die Publikationen der Forschungsgruppe Asset Management fallen in die Themenbereiche:

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Ausgewählte Publikationen

Foreign bias in institutional portfolio allocation: The role of social trust

Wolfgang Drobetz, Marwin Mönkemeyer, Ignacio Requejo, Henning Schröder
Journal of Economic Behavior & Organization | 10/2023
We study the role of social trust in the equity allocation decisions of global investors using a large sample of institutionally managed portfolios of 8,088 investors from 33 countries over the 2000 through 2017 period. The negative relationship between social trust and foreign bias suggests that institutional investors from high-social trust countries are less prone to underinvesting in foreign equity. Our results provide credence to an information-based explanation, indicating that social trust reduces foreign bias by compensating for the lack of information about foreign stock markets. Moreover, the effect of social trust on foreign bias is stronger if host-country institutions are weak, while it vanishes when the host country is characterized by strong institutions. The informal institution of social trust compensates for the lack of well-functioning formal country-level institutions in international portfolio decisions. Finally, the allocation effect resulting from social trust is different from “blind” trust. The portfolios of high-trust investors exhibit higher cross-country diversification and an enhanced portfolio risk-return trade-off.

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.

Data snooping in equity premium prediction

Hubert Dichtl, Wolfgang Drobetz, Andreas Neuhierl, Viktoria-Sophie Wendt
International Journal of Forecasting | 05/2020 | Forthcoming
We analyze the performance of a comprehensive set of equity premium forecasting strategies. All strategies were found to outperform the mean in previous academic publications. However, using a multiple testing framework to account for data snooping, our findings support Welch and Goyal (2008) in that almost all equity premium forecasts fail to beat the mean out-of-sample. Only few forecasting strategies that are based on Ferreira and Santa-Clara’s (2011) sum-of-the-parts approach generate robust and statistically significant economic gains relative to the historical mean even after controlling for data snooping and accounting for transaction costs.

Factor-based asset allocation: Is there a superior strategy?

Hubert Dichtl, Wolfgang Drobetz, Viktoria-Sophie Wendt
European Financial Management | 04/2020 | Forthcoming
Factor‐based allocation embraces the idea of factors, as opposed to asset classes, as the ultimate building blocks of investment portfolios. We examine whether there is a superior way of combining factors in a portfolio and provide a comparison of factor‐based allocation strategies within a multiple testing framework. Factor‐based allocation is profitable beyond exploiting genuine risk premia, even when applying multiple testing corrections. Investment portfolios can be efficiently diversified using factor‐based allocation strategies, as demonstrated by robust economic performance over various economic scenarios. The naïve equally weighted factor portfolio, albeit simple and cost‐efficient, cannot be outperformed by more sophisticated allocation strategies.