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Zentrale Aufgabe des IQ-Kap 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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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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01.07.2018

Asset Pricing, Empirische Kapitalmarktforschung

Residual Equity Momentum Spillover in Global Corporate Bond Markets

Journal of Fixed Income, 2019, 28 (3), 46-54.

Bektic, Dr. Demir

Abstract

I present an improved equity momentum measure for corporate bonds and study the Euro denominated global investment grade corporate bond market between 2000 and 2016. I document economically meaningful and statistically significant corporate bond return predictability. In contrast to the widely used total equity return, momentum as measured by the residual (idiosyncratic) equity return appears to further enhance risk-adjusted performance of corporate bond investors. Additional support for this conjecture is obtained from tests for various asset pricing factors and transaction costs, as exposure to these risk factors cannot explain this abnormal pattern in returns.

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01.07.2018

Empirische Kapitalmarktforschung, Risk & Optimization

Machine Learning for visual risk analysis and hedge fund selection

The Hedge Fund Journal

Huber, Dr. Claus

Abstract

The Self-Organising Map (SOM) is a Machine Learning tool to identify similarities in high-dimensional data. We show how it can be applied for manager selection to build robust portfolios. We describe a method that utilises some of the natural features of the SOM, e.g., the ability to process non-linearity in hedge fund returns and its visualisation capabilities that can be deployed for risk analysis, i.e., avoid managers with similar risk profiles and identify managers with unique risk profiles. Resulting portfolios exhibit significantly enhanced return/risk metrics and drawdown measures.

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12.03.2018

Empirische Kapitalmarktforschung, Risk & Optimization

Risk-Based Commodity Investing

Simone Bernardi, Markus Leippold, Harald Lohre

Lohre, Dr. Harald

Abstract

Pursuing risk-based allocation across a universe of commodity assets, we find two alternative notions of risk parity to provide convincing results, diversified risk parity (DRP) and principal risk parity (PRP). DRP strives for maximum diversification along the uncorrelated risk sources embedded in the underlying commodities, while PRP budgets risk proportional to the risk source’s relevance in terms of their variance. These strategies are characterized by concentrated allocations that are actively adjusted to changes in the underlying risk structure. We also document competing risk-based allocation techniques to be rather similar to the 1/N-strategy or market indices in picking on concentrated market risk. Finally, we demonstrate how to enhance given risk-based allocation strategies by means of common commodity anomalies while preserving a meaningful degree of diversification.

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06.11.2017

Asset Pricing, Empirische Kapitalmarktforschung

ESG Factors in Corporate Bond Returns: Perspectives for Academic Research and Investors

Journal of Environmental Law and Policy, 2017, 40 (4), 293-298.

Bektic, Dr. Demir

Abstract

In this article, I analyze the latest research on environmental, social, and governance (ESG) factors and corresponding corporate bond returns. Since the development of sustainable and ethical investing, there has been a vigorous and ongoing debate on whether ESG factors in corporate bond markets enhance returns. Unfortunately, empirical evidence on ESG factors in corporate bond markets is mixed and inconclusive. Some evidence supports positive returns, other evidence suggests a negative relation, and a third strand of the literature finds that the relation is unstable. However, research on this topic is seemingly contradictory and in this article, I address this disconnect in recent empirical research.

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