Publikationen

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.

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.

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.

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.

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.

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.

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.

We examine whether catastrophe bonds can serve as a hedge or a safe haven for global stock, bond, real estate, commodity, private equity, and infrastructure markets. Our findings indicate that catastrophe bonds are a poor hedge, but they act as an effective diversifier against other asset classes. Furthermore, catastrophe bonds serve as a strong safe haven against extreme price drops of the stock market only during the post-crisis period.

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.

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.

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 authors study the out-of-sample predictability of the returns of pan-European harmonized mutual funds that follow hedge fund–like investment strategies (“alternative UCITS”) and allow retail investors to gain access to nontraditional investment strategies. Given these funds’ higher liquidity compared with hedge funds, investors could exploit relevant information more easily and use it for their asset allocation and risk management decisions. Using a large set of fundamental and technical variables, the authors estimate single predictor models, combination forecasts, and multivariate regression models. Forming hypothetical funds-of-funds portfolios based on predicted returns generates economic gains for investors, especially during crisis times. Combination approaches and multivariate models reduce estimation uncertainty and lead to economic gains across different market environments.

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.

We study the performance persistence of alternative UCITS funds, which are a hybrid between mutual funds and hedge funds. Persistence is gauged by alternative measures of performance and risk. Based on contingency tables, we find that performance persists for up to 2 years following ranking. However, persistence is stronger in the short run, and ranked portfolio tests indicate that investors can benefit from persistence for only up to 1 year. The evidence for persistence in risk is ambiguous. We link fund characteristics to performance persistence and find that offshore hedge fund experience enhances persistence. Our results are robust against survivorship bias and other potential database biases.

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.

We study the performance of alternative UCITS funds and account for potential survivorship biases in our sample in the best possible manner. Alternative UCITS funds offer similar raw returns but a lower volatility compared to offshore hedge funds. Single-index models show that alternative UCITS funds provide only marginal exposure to variations in hedge fund returns. Multifactor models indicate that the most important risk factors for both alternative UCITS funds and their matched hedge funds strategies are related to stock market risks, but alternative UCITS funds exhibit a lower exposure to these factors than hedge funds. Moreover, we find factor loadings on different risk factors, suggesting that alternative UCITS and hedge funds pursue different strategies. Finally, we assess the degree of the value added for an investor in terms of enhanced diversification benefits by implementing a spanning test and find that both groups are different asset classes with time-varying diversification properties.

The hedge fund literature has predominantly focused on the return and risk characteristics as well as the portfolio properties of different hedge fund styles. However, relatively little is known about how hedge fund investors should allocate their capital across various hedge fund styles. The aim of this study is to develop a rules-based framework that can be used by investors to optimize their hedge fund style allocation. We construct four hedge fund style indices using a sample of 6088 hedge funds from the Lipper TASS Hedge Fund Database over the January 1995–September 2010 time period. We also develop technical and fundamental indicators on the basis of the style return drivers from earlier hedge fund studies. We use these indicators to generate trading recommendations through a style allocation model that systematically over- and underweights the four major hedge fund styles. The empirical results indicate that our hedge fund style allocation model delivers an outperformance of up to 1 per cent per year over an equally weighted portfolio.

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 knowledge of risk factors that determine an industry's expected stock returns is important to assess whether this industry serves as a separate asset class. This study analyses the macroeconomic risk factors that drive expected stock returns in the shipping industry and its three sectors: container, tanker, and bulker shipping. Our sample consists of the monthly returns of 48 publicly-listed shipping companies over the period from January 1999 to December 2007. We use shipping stocks together with a set of country or other industry indices to estimate the macroeconomic risk profiles and the corresponding factor risk premiums. Using a Seemingly Unrelated Regressions (SUR) model to estimate factor sensitivities, we document that shipping stocks exhibit remarkably low stock market betas. We also provide evidence that a multidimensional definition of risk is necessary to capture the risk-return spectrum of shipping stocks. A one-factor model produces large pricing errors, and hence it must be rejected based on tests of the model's orthogonality conditions using the Generalized Method of Moments (GMM). In contrast, when the change in the trade-weighted value of the US$, the change in G-7 industrial production, and the change in the oil price are added as additional risk factors, the resulting multifactor model is able to explain the cross-section of expected stock returns. The risk-return profile of shipping stocks differs from country and other industry indices. However, the sensitivities to global systematic risk factors are similar across all three sectors of the shipping industry. Overall, our results suggest that shipping stocks have the potential to serve as a separate asset class. Our findings also have important implications for computing the cost of equity capital in the shipping industry.

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.

This paper adapts Meucci's [2006a, 2006b] copula opinion pooling (COP) framework to examine whether fixed income hedge fund strategies enhance the risk-return spectrum of traditional bond portfolios. In contrast to the Black-Litterman setup, the COP approach does not rely on linear dependencies, and avoids the problems associated with the assumption of normally distributed asset returns. We analyze three scenarios that represent investor expectations about the performance of fixed income portfolios, and we add fixed income hedge fund strategies such as fixed income arbitrage, convertible bond arbitrage, and distressed securities, given expected shortfall constraints. Our results suggest that investor market expectations and attitudes toward potential losses are both important in determining the relative weight of hedge funds in the optimal portfolio. Depending on the model parameters, the allocation to hedge funds can vary greatly, from 0% to 85%.

We investigate the conditional performance of a sample of German equity mutual funds over the period from 1994 to 2003 using both the beta-pricing approach and the stochastic discount factor (SDF) framework. On average, mutual funds cannot generate excess returns relative to their benchmark that are large enough to cover their total expenses. Compared to unconditional alphas, fund performance sharply deteriorates when we measure conditional alphas. Given that stock returns are to some extent predictable based on publicly available information, conditional performance evaluation raises the benchmark for active fund managers because it gives them no credit for exploiting readily available information. Underperformance is more pronounced in the SDF framework than in beta-pricing models. The fund performance measures derived from alternative model specifications differ depending on the number of primitive assets taken to calibrate the SDF as well as the number of instrument variables used to scale assets and/or factors.

We analyze the heterogeneity in asset allocation decisions of different investor groups in response to changes in the macroeconomic environment. Using a new data set that includes the monthly portfolio holdings of private, commercial, and institutional investors deposited with Swiss banks, we estimate the relationship between equity and bond holdings and common business cycle indicators. Regression analysis indicates that private investors do not systematically move from stocks into bonds by selling stocks to institutional investors and purchasing bonds from them in adverse macroeconomic states. A VAR-error correction framework including cointegration and error correction restrictions suggests that the investment behavior of commercial investors leads and private investors follow in their investment decisions only slowly over time. The asset allocation decisions of institutional investors are not affected by the actions of private and commercial investors. Our results refute a principle of “institutional irrelevance”.

Recent empirical work shows evidence for higher valuation of firms in countries with a better legal environment. We investigate whether differences in the quality of firm‐level corporate governance also help to explain firm performance in a cross‐section of companies within a single jurisdiction. Constructing a broad corporate governance rating (CGR) for German public firms, we document a positive relationship between governance practices and firm valuation. There is also evidence that expected stock returns are negatively correlated with firm‐level corporate governance, if dividend yields are used as proxies for the cost of capital. An investment strategy that bought high‐CGR firms and shorted low‐CGR firms earned abnormal returns of around 12% on an annual basis during the sample period.

It is well known that asset allocation policy is the major determinant of fund performance. We apply the technique introduced by Ibbotson and Kaplan (2000) to German and Swiss mutual fund data. Our results show that more than 80 percent of the variability in returns of a typical fund over time is explained by asset allocation policy, roughly 60 percent of the variation among funds is explained by policy, and more than 130 percent of the return level is explained, on average, by the policy return level.

In this article we have demonstrated the intuition behind the portfolio optimization model presented by BLACK and LITTERMAN (1992). Their approach helps to alleviate many of the problems associated with the implementation of traditional MARKOWITZ (1952) approach. Their advice is intuitive and consistent with a normal investment behavior of an average investor. The asset manager starts from the market portfolio (or some strategic weighting scheme), which constitutes a neutral point of reference. Starting from all positive weights, he or she should then deviate toward the most favoured asset classes by taking appropriate long and short positions. The technique allows to distinguish between strong views and vague assumptions, which is reflected by the optimal amount of deviation from the equilibrium weighting scheme. This technique reduces the problem associated with estimation errors, and leads to more intuitive and less sensitive portfolio compositions. In addition, the BLACK-LITTERMAN approach is very flexible with regards to expressing a variety of possible views.