Marcel Müller

Marcel Müller

Postdoctoral Researcher

Karlsruhe Institute of Technology

Welcome!

I am a postdoctoral researcher in finance at the Karlsruhe Institute of Technology. My work leverages large datasets and machine-learning methods to uncover the mechanisms of financial markets, with applications in asset pricing, asset management, and decentralized finance. In addition to my research, I teach courses on investments, asset pricing, decentralized finance, and machine learning. I have also served as a data-science consultant for banks, insurance companies, small and medium-sized enterprises, and cryptocurrency exchanges, helping them develop data-driven strategies and analytical tools.

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Interests
  • Asset Pricing
  • Asset Management
  • Decentralized Finance
  • Machine Learning
Education

  • PhD in Finance, 2020

    Karlsruhe Institute of Technology

  • MSc in Industrial Engineering and Management, 2013

    Karlsruhe Institute of Technology

  • BSc in Industrial Engineering and Management, 2010

    Karlsruhe Institute of Technology

News

[31/03/25] New Paper Same Same but Different: The Risk Profile of Corporate Bond ETFs is online.

[12/13/24] Updated version of the paper Expected Bond Liquidity is online.

[09/09/24] Paper Expected Bond Liquidity scheduled for presentation at the Paris December Finance Meeting.

[01/30/24] Paper Expected Bond Liquidity scheduled for presentation at the SGF Conference in Zurich.

[09/04/23] Paper Expected Bond Liquidity scheduled for presentation at the Fixed Income and Financial Institutions Conference at the University of South Carolina.

Working Papers

Same Same But Different: The Risk Profile of Corporate Bond ETFs
with Johannes Dinger, Aleksandra Rzeźnik, and Marliese Uhrig-Homburg
We show that, while corporate bond ETFs systematically exhibit lower liquidity risk than the bonds they hold, they also face heightened intermediary risk. This effect is more pronounced for high-yield ETFs, for those with less liquid portfolios, and for funds reliant on weaker Authorized Participants. A stylized model reveals how partial segmentation between ETF and bond markets drives these diverging exposures. Overall, investors of corporate bond ETFs effectively trade reduced liquidity risk for increased intermediary risk, highlighting a fundamental trade-off embedded in the ETF structure.
SSRN
Same Same But Different: The Risk Profile of Corporate Bond ETFs
Climate Change, Energy Prices, and the Returns of Proof-of-Work vs. Proof-of-Stake Crypto Assets
with Fabian Eska
From December 2021, Proof-of-Work (PoW) crypto assets earn a systematic risk premium of 20% p.a. over Proof-of-Stake (PoS) crypto assets. This finding aligns with asset pricing theory, suggesting that energy-intensive assets, such as PoW assets, should be systematically riskier than their less energy-intensive PoS counterparts due to the cyclicality of energy prices. We show that contemporaneously, the systematic part of the returns from a portfolio that is long PoW and short PoS covaries negatively with innovations in climate change concerns and with innovations in the oil price. A one standard deviation increase in climate change concerns is associated with 25% of a standard deviation decrease in systematic PoW minus PoS returns. For an oil price shock, the corresponding number is 11%. Prior to 2021, PoS assets were systematically riskier than PoW assets. We show that this can be attributed to the cyclicality of the opportunity cost associated with PoS, which dominates the energy-related risk premium of PoW in this period of the sample.
PDF
Climate Change, Energy Prices, and the Returns of Proof-of-Work vs. Proof-of-Stake Crypto Assets
Expected Bond Liquidity
with Michael Reichenbacher, Philipp Schuster, and Marliese Uhrig-Homburg
We propose a machine learning methodology for predicting the future liquidity distribution of individual bonds in the U.S. corporate bond market and use it to compute two forward-looking illiquidity measures: expected illiquidity and expected tail illiquidity as measure for downside liquidity risk. We find that bonds characterized by higher expected illiquidity have elevated systematic risk premiums, whereas expected tail illiquidity is predominantly reflected in the alpha. Investors in corporate bond funds preemptively sell their shares in response to anticipated liquidity declines in underperforming funds. All effects are much stronger compared to the standard approach of using today’s realized liquidity.
Code Dataset SSRN
Expected Bond Liquidity
Asset Pricing with Slanted News
with Marliese Uhrig-Homburg
We argue that media slant constitutes a source of ambiguity and show that the uncertainty stemming from slanted news is priced in the cross section of US stocks. Our identification of slanted news stocks is based on a combination of a news proxy using Wikipedia page view data and mutual fund managers' aggregated portfolio positions. We find that slanted news stocks earn a premium of roughly 1% in announcement months over their unslanted peers, which peaks on the announcement day itself. Our results further show that the premium is compensating for the exposure to a slanted news mimicking factor.
PDF
Asset Pricing with Slanted News
Fake Alpha
with Tobias Rosenberger and Marliese Uhrig-Homburg
We develop a model in which mutual fund investors chase CAPM alpha. Managers can generate CAPM alpha either by discovering mispricing, True Alpha, or by loading on risk factors that are beyond the scope of the CAPM, Fake Alpha,. Investors cannot distinguish between the two different types of alpha and thus confuse Fake Alpha with True Alpha. We show that this confusion ex-post causes negative CAPM alpha in equilibrium states. Empirical results support our theoretical predictions. The average CAPM alpha is significantly negative, and retail funds with large loads of Fake Alpha provide investors significantly lower CAPM alpha than their peers.
SSRN
Fake Alpha
Drivers of Sovereign Recovery Risk
with Marliese Uhrig-Homburg
What determines the recovery of sovereign bond holders in the face of a credit event? This paper studies empirical determinants for sovereign recovery risk. Guided by theoretically backed hypotheses we use a sample of 102 past restructurings and empirically test the relation between haircut sizes and their economic drivers. We find a significant linkage of the haircut size to a debtor’s ability to repay as well as his willingness. Distinguishing between excusable and strategic defaulters in a new way enables us to empirically show that punishment is of markedly increased effectiveness amongst the strategic cohort. Based on these results we develop a forecasting-model for predicting haircuts conditional on the restructurings taking place within the year ahead and assess the performance of the model by applying it to a sample of the 45 restructurings observed from 1991 to present.
SSRN
Drivers of Sovereign Recovery Risk

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