Hamburg Financial Research Center

Hubert Dichtl

Research Fellow
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Curriculum Vitae

Hubert Dichtl studierte Betriebswirtschaftslehre an der Ludwig-Maximilians-Universität München und Informatik an der Technischen Hochschule Rosenheim. Er promovierte im Jahr 2000 an der Universität Bremen und habilitierte sich 2017 an der Universität Hamburg. Ein Jahr später folgte die Ernennung zum Privatdozenten. Seitdem lehrt Herr Dichtl an der University of Hamburg Business School jedes Sommersemester die Master-Veranstaltung „Asset Management II“. Am 23. Januar 2025 wurde ihm von der University of Hamburg Business School die akademische Bezeichnung „Professor“ (gem. §17(1) HmbHG) verliehen. Ab dem Wintersemester 2025/2026 wird er zudem die Bachelor-Vorlesung „Derivative Financial Instruments“ anbieten.

Hubert Dichtl forscht zu methodischen und praktischen Fragestellungen des Asset Managements. Die Ergebnisse seiner Arbeiten wurden in renommierten wissenschaftlichen Fachzeitschriften veröffentlicht, darunter Journal of Banking & Finance, Financial Analysts Journal, Journal of Portfolio Management, International Review of Financial Analysis, Journal of Behavioral Finance, Review of Financial Economics und Finance Research Letters.

Neben seiner akademischen Tätigkeit ist Hubert Dichtl Geschäftsführer der 2017 gegründeten dichtl research & consulting GmbH, deren Fokus auf der Beratung institutioneller Kapitalanleger liegt.

Ausgewählte Publikationen

Alle Publikationen anzeigen

A bootstrap-based comparison of portfolio insurance strategies

Hubert Dichtl, Wolfgang Drobetz, Martin Wambach
European Journal of Finance | 01/2017
This study presents a systematic comparison of portfolio insurance strategies. We implement a bootstrap-based hypothesis test to assess statistical significance of the differences in a variety of downside-oriented risk and performance measures for pairs of portfolio insurance strategies. Our comparison of different strategies considers the following distinguishing characteristics: static versus dynamic protection; initial wealth versus cumulated wealth protection; model-based versus model-free protection; and strong floor compliance versus probabilistic floor compliance. Our results indicate that the classical portfolio insurance strategies synthetic put and constant proportion portfolio insurance (CPPI) provide superior downside protection compared to a simple stop-loss trading rule and also exhibit a higher risk-adjusted performance in many cases (dependent on the applied performance measure). Analyzing recently developed strategies, neither the TIPP strategy (as an ‘improved’ CPPI strategy) nor the dynamic VaR-strategy provides significant improvements over the more traditional portfolio insurance strategies.

Active factor completion strategies

Hubert Dichtl, Wolfgang Drobetz, Harald Lohre, Carsten Rother
Journal of Portfolio Management | 02/2020 | Forthcoming
Embracing the concept of factor investing, we design a flexible framework for building out different factor completion strategies for traditional multi-asset allocations. Our notion of factor completion comprises a maximally diversified reference portfolio anchored in a multi-asset multi-factor risk model that acknowledges market factors such as equity, duration, and commodity, as well as style factors such as carry, value, momentum, and quality. The specific nature of a given factor completion strategy varies with investor preferences and constraints. We tailor a select set of factor completion strategies that include factor-based tail hedging, constrained factor completion, and a fully diversified multi-asset multi-factor proposition. Our framework is able to organically exploit tactical asset allocation signals while not sacrificing the notion of maximum diversification. To illustrate, we additionally embed the common trend style that permeates many asset classes, and we also include the notion of style factor momentum.

Are stock markets really so inefficient? The case of the “Halloween Indicator”

Hubert Dichtl, Wolfgang Drobetz
Finance Research Letters | 06/2014
The old and simple investment strategy “Sell in May and Go Away” (also referred to as the “Halloween effect”) enjoys an unbroken popularity. Recent studies suggest that the Halloween effect even strengthened rather than weakened since its first publication by Bouman and Jacobsen (2002). We implement regression models as well as Hansen’s (2005) “Superior Predictive Ability” test to analyze whether stock markets are really so inefficient. In line with the predictions of market efficiency, our results reject the hypothesis that a trading strategy based on the Halloween effect significantly outperforms.

Bootstrapping and bias: The economic costs of misjudging downside risk

Hubert Dichtl, Wolfgang Drobetz, Tizian Otto, Tatjana Xenia Puhan
HFRC Working Paper Series | Version 03/2025
The maximum drawdown (MDD), the maximum peak-to-trough loss associated with a series of returns, is a simple but highly important measure for investors with a downside risk budget. This paper compares the performance of three bootstrap simulation methods to estimate the entire distribution of MDDs from various global stock-bond allocations, quantifying the economic costs of biased estimates for three realistic decision-making scenarios. Compared to its benchmarks, the stationary bootstrap of Politis and Romano (1994) leads to the most precise estimates for the MDD which, in turn, helps avoid costly investment errors in portfolio construction and dynamic risk control strategies.

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.

Does tactical asset allocation work? Another look at the fundamental law of active management

Hubert Dichtl, Wolfgang Drobetz
Journal of Asset Management | 09/2009
The performance potential of forecasting-based tactical asset allocation strategies is difficult to assess. The fundamental law of active management suggests that the value added through active investment decisions depends on the forecasting quality and the number of independent forecasts. Although easy to use, the law depends on several specific assumptions that are not fulfilled in practice. Therefore, it is not clear ex ante whether the actual performance of tactical asset allocation is close to what the fundamental law predicts. Using a simulation approach, we quantify the entire distribution of information ratios, active returns and tracking errors under realistic conditions (for example, with transaction costs and tactical bounds rather than a simple mean-variance optimisation). Our results reveal that the fundamental law systematically underestimates the required forecasting quality to reach a very good information ratio. While all other assumptions of the law seem innocuous, transaction costs are responsible for most of the wedge between the law's prediction and the performance of tactical asset allocation in a realistic setup. Our results are robust for stock and bond market data from different countries.

Dollar-cost averaging and prospect theory investors: An explanation for a popular investment strategy

Hubert Dichtl, Wolfgang Drobetz
Journal of Behavioral Finance | 03/2011
Dollar-cost averaging requires investing equal amounts of an investment sum step-by-step in regular time intervals. Previous studies that assume expected utility investors were unable to explain the popularity of dollar-cost averaging. Statman [1995] argues that dollar-cost averaging is consistent with the positive framework of behavioral finance. We assume a prospect theory investor who implements a strategic asset allocation plan and has the choice to shift the portfolio immediately (comparable to a lump sum) or on a step-by-step basis (dollar-cost averaging). Our simulation results support Statman's [1995] notion that dollar-cost averaging may not be rational but a perfectly normal behavior.

Don’t draw the downs apart – How to best simulate asset price drawdowns

Hubert Dichtl, Wolfgang Drobetz, Tizian Otto, Tatjana Xenia Puhan
HFRC Working Paper Series | Version 08/2025
This paper evaluates bootstrap simulation techniques for calculating the distribution of the maximum drawdown (MDD), an important risk indicator in stock and cryptocurrency markets. Using stochastic dominance tests, we assess the full distributional properties of MDD under different methods. Our findings reveal that the standard Efron (1979) bootstrap, which assumes independence and identically distributed random variables, systematically underestimates the true MDD. While the moving block bootstrap provides reasonable estimates, it is subject to non-stationarity bias, particularly when large drawdowns occur at the boundaries of a return series. Alternative procedures, such as the block-block bootstrap and the tapered bootstrap, do not lead to better results. Of all the methods studied, the stationary bootstrap of Politis and Romano (1994) produces the most accurate and robust results, particularly with longer block lengths. We recommend this method as the preferred choice for researchers and practitioners modelling drawdown risk.