Forschung

Zentrale Aufgabe des IQAM Research ist die quantitative Kapitalmarktforschung und die Erstellung wissenschaftlicher Arbeiten in Kooperation mit Hochschulen und Partnern. Mit modernen Methoden werden Kapitalmarktphänomene empirisch untersucht und erklärt sowie grundsätzliche Fragestellungen der Kapitalanlage bearbeitet.

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28.05.2021

Factor-Investing and Asset Allocation Strategies: A Comparison of Factor Versus Sector Optimization

Journal of Asset Management

Wolff, Dr. Dominik Prof. Dr. Wolfgang Bessler Dr. Georgi Taushanov

Abstract

With greater economic and financial market integration, it is critical for asset managers to choose the investment universe that provides superior diversification and performance op-portunities. Therefore, it is important to investigate whether international diversification benefits arise from industry rather than country allocations. We employ various asset alloca-tion strategies such as 1/N, ‘Risk-Parity’, Minimum-Variance as well as Mean-Variance, Bayes-Stein and Black-Litterman to analyze whether an industry-based or a country-based approach provides a superior performance. We also investigate time-varying effects for ex-pansionary and recessionary sub-periods, equity-only and equity-bond portfolios as well as portfolios with and without short positions. For the 1986-2020 period, we find that industry-based asset allocation strategies attain higher Sharpe and Omega ratios and higher alphas compared to country-based allocations. The Sharpe ratio differences are economically rele-vant yet statistically insignificant in many analyzed settings. The outperformance of sector allocations are independent of the optimization approach and implemented constraints and whether bonds are included in the investment universe. This is consistent with the observa-tion that countries have become more integrated and higher correlated than industries, re-sulting in lower country and relatively higher industry diversification benefits. Especially for periods with unpredictable shocks, industry allocations have superior performance. Our results have important implications for international asset allocation decisions.

27.05.2021

Asset Pricing, Empirische Kapitalmarktforschung

Stock Picking with Machine Learning

Wolff, Dr. Dominik Echterling, Dr. Fabian

Abstract

We combine insights from machine learning and finance research to build machine learning algorithms for stock selection. Our study builds on weekly data for the historical constituents of the S&P 500 over the period from 1999 to 2019 and includes typical equity factors as well as additional fundamental data, technical indicators, and historical returns. Deep Neural Networks (DNN), Long Short-Term Neural Networks (LSTM), Random Forest, Boosting, and Regularized Logistic Regression models are trained on stock characteristics to predict whether a specific stock outperforms the market over the subsequent week. We analyze a trading strategy that picks stocks with the highest probability predictions to outperform the market. Our empirical results show a substantial and significant outperformance of machine learning based stock selection models compared to a simple equally weighted benchmark. Moreover, we find non-linear machine learning models such as neural networks and tree-based models to outperform more simple regularized logistic regression approaches. The results are robust when applied to the STOXX Europe 600 as alternative asset universe. However, all analyzed machine learning strategies demonstrate a substantial portfolio turnover and transaction costs have to be marginal to capitalize on the strategies.

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30.04.2021

Empirische Kapitalmarktforschung, Risk & Optimization

Optimal Asset Allocation Strategies for International Equity Portfolios

Journal of International Financial Markets, Institutions & Money

Wolff, Dr. Dominik Taushanov, Georgi Bessler, Wolfgang

Abstract

Although most academic studies conclude that mutual funds cannot outperform a passive investment strategy in the long run, there is some recent empirical evidence that a persistent outperformance can be achieved in an out-of-sample framework when using sophisticated optimization techniques. These empirical findings are for equity-bond-commodity-portfolios, international equity-bond portfolios and US-industry portfolios. The latter can even be further improved when return predictions are included. Given these empirical findings, we analyze in this study whether an industry-based or a country-based optimization model performs best. We employ a variety of optimization- and weighting-techniques to compare the country- and the sector-based allocation strategies. These include naive ‘equally weighted’ (1/N) portfolio, the two risk-based asset allocation rules ‘risk-parity’ (RP) and minimum-variance (MinVar) as well as three portfolio optimization approaches mean-variance (MV), Bayes-Stein (BS) and the Black-Litterman (BL) model. We also include simple return prediction models. To determine whether one approach is persistently superior to the other approach, we analyze time varying effects based on the state of the economy, i.e. expansionary or recessionary periods. Moreover, we investigate investment style or investor clientele ef-fects, the full period and different sub-periods, equity-only and equity-bond portfolios as well as aggressive and conservative investments styles. Summarizing of all analyzed cases, we find strong evidence for the fact that in the long run the sector-oriented asset allocation provided a higher performance than the country-allocation. In almost all the observed cases, the sector-based allocation achieved higher Sharpe ratios than the country-based allocation.

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28.02.2020

Asset Pricing, Empirische Kapitalmarktforschung

Factor-based Investing in Government Bond Markets: The Current State of Research

Journal of Asset Management volume 21, pages 94–105 (2020)

Bektic, Dr. Demir Hachenberg, Britta Schiereck, Dirk

Abstract

Factor investing has become very popular during the last decades, especially with respect to equity markets. After extending Fama–French factors to corporate bond markets, recent research more often concentrates on the government bond space and reveals that there is indeed clear empirical evidence for the existence of significant government bond factors. Voices that state the opposite refer to outdated data samples. By the documentation of rather homogeneous recent empirical evidence, this review underlines the attractiveness of more sophisticated investment approaches, which are well established in equity and even in corporate bond markets, to the segment of government bonds.

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07.12.2019

Asset Pricing, Empirische Kapitalmarktforschung

Extending Fama-French Factors to Corporate Bond Markets

Journal of Portfolio Management, forthcoming

Bektic, Dr. Demir Wenzler, Dr. Josef-Stefan Wegener, Dr. Michael Spielmann, Timo Prof. Dr. Dirk Schiereck

Abstract

The explanatory power of size, value, profitability and investment has been extensively studied for equity markets. Yet, the relevance of these factors in global credit markets is less explored although equities and bonds should be related according to structural credit risk models. We investigate the impact of the four Fama-French factors in the U.S. and European credit space. While all factors exhibit economically and statistically significant excess returns in the U.S. high yield market, we find mixed evidence for U.S. and European investment grade markets. Nevertheless, we show that investable multi-factor portfolios outperform the corresponding corporate bond benchmarks on a risk-adjusted basis. Finally, our results highlight the impact of company level characteristics on the joint return dynamics of equities and corporate bonds.

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25.07.2019

Asset Pricing, Empirische Kapitalmarktforschung

Machine Learning Approaches for Equity Market Predictions

Journal of Asset Management, forthcoming

Wolff, Dr. Dominik Neugebauer, Dr. Ulrich

Abstract

We empirically analyze equity premium predictions with ‘traditional’ linear models and machine learning approaches. Based on a commonly used dataset of equity market predictors extended by additional fundamental, macroeconomic, sentiment and risk indicators, we find mixed results for machine learning algorithms for equity market predictions. In contrast to sophisticated linear models such as penalized least squares or principal component regressions (PCR), the analyzed machine learning algorithms fail to significantly outperform the historical average benchmark forecast. However, an investment strategy that uses machine learning predictions in a market timing strategy, outperforms a passive buy-and-hold investment. Compared to sophisticated linear prediction models, in our problem set, machine learning algorithms do not improve forecast accuracy.

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10.05.2019

Asset Pricing, Empirische Kapitalmarktforschung, Risk & Optimization

Systematic or Idiosyncratic? Spillover Effects in Corporate Bond Markets and Portfolio Implications

Bektic, Dr. Demir Evangelos Salachas

Abstract

Periods with high financial distress and uncertainty are characterized by increased co-movement in corporate bond markets. In this paper, we study the dynamic interactions among corporate bond returns in a period from 2004 to 2016 that covers important macroeconomic, financial and political events. In particular, we provide a framework for the evaluation of contagion among corporate bonds in different regions during a period with increased financial turmoil. We measure contagion in terms of dynamic spillovers, which capture the degree of homogeneity in bond returns. Our specification distinguishes two sources of bond risk: the systematic risk and the idiosyncratic risk. To account for a market-level analysis of co-movement we employ a panel VAR model in which the variables (bond markets) are treated as endogenous. Based on our results, the systematic risk component accounts for a larger portion of variation in bond returns relative to the idiosyncratic component, indicating the existence of homogeneity in global corporate bond markets. The emerging markets are also net receivers of international shocks, whereas innovations in U.S. bond markets contribute importantly to the instability in global bond markets.

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16.01.2019

Empirische Kapitalmarktforschung, Risk & Optimization

R Tutorial on Machine Learning

WILMOTT magazine

Huber, Dr. Claus

Abstract

Nonlinearity in financial market returns is commonplace, and in particular in hedge fund returns. Hedge funds are known to generate option-like returns based on the products they trade, as well as their trading strategies. This tutorial describes how Kohonen’s self-organizing map (SOM), a method of machine learning, can help to analyze nonlinearity in returns. We focus on simple examples that help the reader to understand where nonlinear hedge fund returns come from, why linear correlation analysis is inappropriate, and how SOMs can help to visualize nonlinear returns to enhance risk analysis. R code and step-by-step instructions enable the reader to reproduce the creation of the SOM. Readers are encouraged to change parameters and study the impacts on results.

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01.11.2018

Asset Pricing, Empirische Kapitalmarktforschung

Financial crises, price discovery and information transmission: a high-frequency perspective

Financial Markets and Portfolio Management (FMPM), 32(4), pp. 333-365

Stein, Dr. Michael

Abstract

This paper examines the price discovery processes before and during the 2007–2009 subprime and financial crisis, as well as the subsequent European sovereign crisis, for American and German stock and bond markets, as well as for U.S. Dollar/Euro FX. Based on 5-s intervals, we analyze how asset prices interact conditional on macroeconomic announcements from the USA and Germany. Our results show significant co-movement and spillover effects in returns and volatility, reflecting systematic information transmission mechanisms among asset markets. We document strong state dependence with a substantial increase in inter-asset spillovers and feedback effects during times of crisis.

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15.10.2018

Empirische Kapitalmarktforschung, Risk & Optimization

Return Prediction Models and Portfolio Optimization: Evidence for Industry Portfolios

Universität Gießen

Wolff, Dr. Dominik Bessler, Dr. Wolfgang

Abstract

An essential motive for investing in commodities is to enhance the performance of portfolios traditionally including only stocks and bonds. We analyze the in-sample and out-of-sample portfolio effects resulting from adding commodities to a stock-bond portfolio for commonly implemented asset-allocation strategies such as equally and strategically weighted portfolios, risk-parity, minimum-variance as well as reward-to-risk timing, mean-variance and Black-Litterman. We analyze different commodity groups such as agricultural and livestock com-modities that currently are critically discussed. The out-of-sample portfolio analysis indicates that the attainable benefits of commodities are much smaller than suggested by previous in-sample studies. Hence, in-sample analyses, such as spanning tests, might exaggerate the ad-vantages of commodities. Moreover, the portfolio gains greatly vary between different types of commodities and sub-periods. While aggregate commodity indices, industrial and precious metals as well as energy improve the performance of a stock-bond portfolio for most asset-allocation strategies, we hardly find positive portfolio effects for agriculture and livestock. Consequently, investments in food commodities are not essential for efficient asset allocation.

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