Publikationen

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.

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 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.

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.

Aiming to optimally harvest global equity factor premiums, we investigated the benefits of parametric portfolio policies for timing factors conditioned on time-series predictors and tilting factors based on cross-sectional factor characteristics. We discovered that equity factors are predictably related to fundamental and technical time-series indicators and to such characteristics as factor momentum and crowding. We found that such predictability is hard to benefit from after transaction costs. Advancing the timing and tilting policies to smooth factor allocation turnover slightly improved the evidence for factor timing but not for factor tilting, which renders our analysis a cautionary tale on dynamic factor allocation.

The literature on gold is dominated by empirical studies on its diversification, hedging, and safe haven properties. In contrast, the question “When to invest in gold?” is generally not analyzed in much detail. We test more than 4000 seasonal, technical, and fundamental timing strategies for gold and find evidence for some market timing ability and economic gains relative to a passive buy-and-hold benchmark. However, since the results are not robust to data-snooping biases and limited to specific evaluation periods, we conclude that our findings support the efficiency of the gold market.

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.

Many private and institutional investors attempt to time the market and generate abnormal returns by periodically switching their portfolio allocations between the stock market and the cash market based on their return predictions. However, most academic studies emphasize that a successful market timing strategy requires a prediction accuracy that is usually not observable in reality. While prior studies evaluate the outcomes based on traditional return and risk measures, we adopt both expected and non-expected utility models to compare market timing with common benchmarks. Our analyses are based on a “simulated market timer” that does not require a specific forecast model. Bootstrap-based simulations show that even with low hit ratios, investors with non-expected utility preferences can consider market timing as highly desirable. The attractiveness of market timing is also partly attributable to short-termism in performance evaluation.

We compare the risk-adjusted performance of stock–bond portfolios between rebalancing and buy-and-hold across different asset allocations by reporting statistical significance levels. Our investigation is based on a 30-year dataset and incorporates the financial markets of the United States, the United Kingdom and Germany. To draw useful recommendations to investment management, we implement a history-based simulation approach which enables us to mimic realistic market conditions. Even if the portfolio weight of stocks is very low, our empirical results show that a frequent rebalancing significantly enhances risk-adjusted portfolio performance for all analysed countries and all risk-adjusted performance measures.

This study examines whether the “Sell in May and Go Away” (or Halloween) trading strategy still offers an opportunity to earn abnormal returns. In contrast to prior studies, we consider sample periods during which adequate investment instruments were available for an effective implementation of the Halloween strategy. In addition, we account for when the first study confirming the Halloween effect was published in a top academic journal. To use the limited data in the most efficient way, and to avoid possible data-snooping biases, we implement a bootstrap simulation approach. We find that the Halloween effect strongly weakened or even disappeared in recent years. Our results are robust across different markets and against various parameter variations. Overall, our findings support the theory of efficient capital markets.

This study compares the performance of different rebalancing strategies under realistic market conditions by reporting statistical significance levels. Our analysis is based on historical data from the United States, the United Kingdom, and Germany and comprises three different classes of rebalancing (periodic, threshold, and range rebalancing). Despite cross-country differences, our history-based simulation results show that all rebalancing strategies outperform a buy-and-hold strategy in terms of Sharpe ratios, Sortino ratios, and Omega measures. The differences in risk-adjusted performance are not only statistically significant, but also economically relevant. However, the choice of a particular rebalancing strategy is of only minor economic importance.

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.

Portfolio insurance strategies are used on both the institutional and the retail side of the asset management industry. While standard utility theory struggles to provide an explanation, this study justifies the popularity of portfolio insurance strategies in a behavioral finance context. We run Monte Carlo simulations as well as historical simulations for popular portfolio insurance strategies and benchmark strategies in order to evaluate the outcomes using cumulative prospect theory. Our simulation results indicate that most portfolio insurance strategies are the preferred investment strategy for a prospect theory investor. Moreover, the analysis provides insights into how portfolio insurance products should be designed and structured to meet the preferences of prospect theory investors as accurately as possible.

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.

The constant proportion portfolio insurance (CPPI) strategy is frequently used on both the institutional and the retail sides of the asset management industry. While standard finance theory struggles to justify its popularity, this article attempts to explain the widespread use of the CPPI strategy by referring to elements of behavioral finance. We run bootstrap as well as Monte Carlo simulations for the CPPI strategy and for simple benchmark strategies in order to evaluate the outcomes using cumulative prospect theory. Our simulation results indicate that the CPPI strategy is the preferred strategy for a prospect theory investor. The analysis provides hints at how a CPPI-based investment product should be designed in order to meet the preferences of a prospect theory investor as well as possible.

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.