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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03.12.2021

Faktor oder Sektor?

Institutional Money

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

Abstract

Faktor- und Sektor-Optimierungen im Rahmen von Asset Allocation-Strategien zeigen unterschiedliche Erfolge in unterschiedlichen Zeiten und Zeitfenstern: Wer wann die Nase vorne hat.

 

 

https://www.institutional-money.com/fileadmin/emagazin/2021_4_IM/124/index.html

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17.11.2021

Asset Pricing, Empirische Kapitalmarktforschung

News Embeddings for Sector Timing – A textual similarity based approach to determining trending sectors in news coverage

17.11.2021

Schmitz, Hans Christian Wolff, Dr. Dominik Prof. Dr. Dirk Neumann Dr. Bernhard Lutz

Abstract

In this study, we present an approach to quantify the relevance of daily newspaper articles  for individual companies and their corresponding sectors. Based on the textual content of daily newspaper, we utilize sentence and paragraph embeddings to compare news articles from daily newspapers over a time span of 15 years with company descriptions from Form 10-K filings. We measure the relevance of a news article as the similarity between the article and the company’s description. In contrast to prevalent approaches in the literature, our relevance estimation does neither rely on stock price data nor on a predefined mapping between news articles and companies. We evaluate the proposed methodology on several strategies on sector exchange traded funds and on the level of individual stocks. We find that the trading strategies based on our relevance estimation approach yield up to 15.38% out-of-sample annualized return after transaction costs.

17.11.2021

Asset Pricing, Empirische Kapitalmarktforschung

Decision Support for Classifying Financial News

17.11.2021

Schmitz, Hans Christian Wolff, Dr. Dominik Prof. Dr. Dirk Neumann Dr. Bernhard Lutz

Abstract

In this study, we evaluate several trading strategies based on the textual content of corporate disclosures using machine learning. To obtain a conservative estimate of profitability, we require orders to be placed for the close price of the current trading day and stocks must exhibit high liquidity to ensure proper order execution. Our evaluation based on 354,992 form 8-K filings and 10,204 ad hoc announcements shows that the proposed trading strategies yield up to 7.81% and 9.34% out-of-sample annualized return. More importantly, we find that the prevalent approach in the literature of estimating the stock market reaction of a disclosure based on the closing price of the past trading and omitting liquidity filters substantially overestimates profitability. We also provide useful insights for practitioners by describing feature importance to shed light onto how the machine learning models arrive at decisions.

 

 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3910451

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06.09.2021

Dividend Predictability and Higher Moment Risk Premia

Journal of Asset Management

Al-Jaaf, Aşty

Abstract

I use model-free methods to estimate the term structures of the variance risk premium (VRP) and the skewness risk premium (SRP) derived from dividend futures and options. I find that VRP is on average negative, whereas SRP is on average positive. They have unique characteristics and can hardly be explained by equity risk factors and equity moment risk premia. I present evidence that both dividend moment risk premia contain significant forecasting power for dividend futures returns in- and out-of-sample. Dividend futures returns are predicted by the VRP (SRP) in almost all setups with a negative (positive) sign.

24.08.2021

Empirische Kapitalmarktforschung, Risk & Optimization

Factor Investing und Asset-Allokationsstrategien

Absolut Alternative

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

Abstract

Factor Investing erlebte in den letzten Jahren ein enormes Wachstum.
In dem folgenden Fachbeitrag untersuchen Prof. Dr. Wolfgang Bessler,
Dr. Georgi Taushanov und Dr. Dominik Wolff, ob auf Faktoren
basierende Allokationsstrategien dem Anleger eine bessere Portfolioperformance
bieten als traditionelle Sektorallokationen. Dabei werden
die Performance und die Performanceunterschiede von Sektor- und
Faktor-Portfolios für verschiedene Allokationsstrategien und Portfolio-
Optimierungsansätze analysiert.

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)

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

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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