Poddig, Thorsten
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Poddig, Thorsten
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Poddig, Thorsten
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Item-typ:Veröffentlichung, Covariances vs. characteristics: what does explain the cross section of the German stock market returns?(2016-02-12); ; The characteristics book-to-market equity ratio, size and momentum are highly correlated with the average returns of common stocks. Fama and French (J Financ Econ 33(1):3–56, 1993), (J Finance 50(1):131–155, 1995) and (J Finance 51(1):55–84, 1996) argue (for size and the book-to-market equity ratio) that the relation between returns and characteristics arises because the characteristics are proxies for exposures to common risk factors. We examine the question whether the characteristics or the covariance structure of returns explain the cross-sectional dispersion in German stock market returns. Our results suggest that widely accepted factors SMB, HML or WML are not priced.Wissenschaftlicher ArtikelBand:9Heft:April143 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, An investor’s perspective on risk-models and characteristic-models(2016-03-27); ; Purpose – In capital markets, research risk factor loadings and characteristics are considered as opposing explanations for the cross-sectional dispersion in average stock returns. However, there is little known about the performance an investor would obtain who believes either in the characteristics explanation (CB-investor) or in the risk factor loadings explanation (RB-investor). The purpose of this paper is to compare the performance of CB- and RB-investors. Design/methodology/approach – To compare the competing strategies, the authors propose a simple new approach to equity portfolio optimization in the style of Brandt et al. (2009) by modeling the portfolio weight in each asset as a function of the asset’s risk factor loadings or characteristics. The authors perform an empirical analysis on the German stock market, exploiting the risk factor loadings from the Carhart (1997) four-factor model and the respective characteristics size, book-to-market equity ratio and momentum. Findings – The results show that investment strategies relying on characteristics (particularly on momentum) outperform risk-based investment strategies in horse races. These findings hold in- and out-of-sample. Furthermore, the characteristics-based investment strategies outperform a value-weighted market portfolio strategy in- and out-of-sample. Originality/value – The authors introduce a portfolio optimization approach that enables investors to directly link portfolio decisions to the firm’s characteristics or risk factor loadings.Wissenschaftlicher ArtikelBand:17Heft:3145 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Zertifikatebewertung auf Grundlage der Monte Carlo Verfahren(2015-09-24); ; Eine objektive Bewertung von Zertifikaten, deren Preise i. d. R. von Emittenten gestellt werden, ist wesentlich für ihre Akzeptanz beim Anleger. Dieser Beitrag stellt die simulationsbasierte Bewertung von Zertifikaten dar. Die simulationsbasierte Bewertung mittels indirekter Ermittlung der Auszahlungsstruktur (indirektes Bewertungsverfahren) ist als der vielversprechendste Ansatz identifiziert, der mit wenigen Annahmen auskommend viele entscheidungsnützliche Informationen anbietet und sich vergleichsweise einfach implementieren lässt.Wissenschaftlicher ArtikelBand:22Heft:5123 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Non-standard errors in the cryptocurrency worldMotivated by recent findings from the equity market, we investigate non-standard errors in cryptocurrency research. We examine ten prevalent decisions related to data sources, sample preparation, and portfolio construction, generating 20,736 research designs for 43 sorting variables. Our findings reveal remarkable variation in portfolio performance tied to seemingly trivial choices. The non-standard errors in cryptocurrency studies not only surpass those in the stock market but also clearly exceed standard errors—though varying considerably across coin characteristics. Notwithstanding the above, the most prominent cryptocurrency factors, such as size and momentum, remain consistently robust across numerous specifications. Lastly, we find that reducing the influence of the smallest coins effectively decreases the non-standard errors.Wissenschaftlicher ArtikelHeft:9234 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Asset Pricing in Digital Assets(2022-05-16); ; Digital assets are continually evolving into a “mainstream” asset class. Institutional interest is growing every day, with the market capitalization of digital assets rising in value and importance not only for retail investors, but also for global banks, hedge funds and regulators. This chapter provides an overview for interested readers of how digital assets compare to traditional asset classes, how a blockchain works, and an assessment of whether academic research has uncovered first trading strategies and other relevant findings in this emergent asset class of virtual tokens.BerichtBand:0015412 1602 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Pockets of Predictability: A Replication(Wiley, 2025-08); ; Farmer, Schmidt, and Timmermann (FST) document time-variation in market return predictability, identifying “pockets” of significant predictability through kernel regressions. However, our analysis reveals a critical discrepancy between the method outlined by FST and the code actually implemented. Instead of using a one-sided kernel, which guarantees out-of-sample forecasts, they perform in-sample estimation with a two-sided kernel. As a result, future information leaks into the forecasting model, undermining its reliability. Rectifying this error qualitatively alters the findings, invalidating most conclusions of the FST study. Thus, attempts to exploit such “pockets”—should they exist—offer little help in forecasting market returns.Wissenschaftlicher ArtikelBand:80Heft:617 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Liquidity-driven approach to dynamic asset allocation: evidence from the German stock market(2015-09-24); ; ; Fluctuations in market-wide liquidity may offer opportunities of earning illiquidity premiums. For the US stock market, an investment strategy that profitably exploits these market-wide liquidity fluctuations is proposed by Xiong (J Portf Manag 39(3):102–111, 2013), who focus on an in-sample analysis. In this article, we firstly replicate the liquidity-driven investment strategy of Xiong (J Portf Manag 39(3):102– 111, 2013) for the German stock market showing that a successful harvesting of illiquidity premiums is possible as well. Secondly, we extend the study design of Xiong (JPortfManag39(3):102–111,2013)inthatweconductastrictout-of-sampleanalysis. Our results show that the initial superior in-sample results drastically deteriorate in an out-of-sample framework rendering the practical application of the liquidity-driven investment strategy for the German stock market impossible. Lastly, we modify the rather static investment methodology by a novel approach in which the asset allocation responds flexibly to market-wide liquidity fluctuations. This modification leads to significant performance improvements.Wissenschaftlicher ArtikelBand:29Heft:November153 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Machine learning techniques for cross-sectional equity returns’ predictionWe compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees and neural networks. Our analysis is based on monthly data on nearly 12,000 individual stocks from 16 European economies over almost 30 years from 1990 to 2019. We find that the prediction of stock returns can be decisively improved through machine learning methods. The outperformance of individual (combined) machine learning models over the benchmark model is approximately 0.6% (0.7%) per month for the full cross-section of stocks. Furthermore, we find no model breakdowns, which suggests that investors do not incur additional risk from using machine learning methods compared to the traditional benchmark approach. Additionally, the superior performance of machine learning models is not due to substantially higher portfolio turnover. Further analyses suggest that machine learning models generate their added value particularly in bear markets when the average investor tends to lose money. Our results indicate that future research and practice should make more intensive use of machine learning techniques with respect to stock return prediction.Wissenschaftlicher ArtikelBand:4592 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, A Trend Factor for the Cross Section of Cryptocurrency Returns(Cambridge University Press, 2025-07); ; ; ; We propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across different time horizons. Using data on more than 3,000 coins, we employ machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts cryptocurrency returns. The effect cannot be subsumed by known factors and remains robust across different subperiods, market states, and alternative research designs. Moreover, it survives the impact of transaction costs and persists in big and liquid coins. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns.Wissenschaftlicher ArtikelBand:60Heft:751 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns(With Intelligence, 2025-04); ; ; The article examines the cross-sectional predictability of factor returns. Using machine learning techniques, the authors extract information from a comprehensive set of factor characteristics measuring their past returns, risks, and spreads. Applying this method to a repertoire of US stock market anomalies, they find robust predictability in their performance. The decile of factors with the highest expected return outperforms those with the worst outlook by up to 1.39% per month. The alphas are robust to many considerations but gradually decline over time. Predictability is mainly driven by factor momentum, which captures most of the cross-sectional variation in anomaly returns.Wissenschaftlicher ArtikelBand:51Heft:678