Hello!
Hello!
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I am a Quantitative Researcher at Alipes Capital and an external lecturer at Copenhagen Business School.
My primary research area is empirical asset pricing with a particular focus on how to use machine learning in financial settings. For more information about me or my research, check out my CV, or reach out:
E-mail: kristofferhalskov@gmail.com
Published Papers:
Published Papers:
With Peter Feldhütter and Arthur Krebbers
Forthcoming at the Journal of Financial Economics
Abstract:
We examine the pricing of sustainability-linked bonds (SLBs), where the cash flows depend on the bond issuer achieving one or more Environmental, Social and Governance (ESG) goals. Investors are willing to accept a 1-2bps lower yield due to the bond's ESG label, providing evidence of investors caring about environmental impact. Furthermore, we find the average probability of missing the target is 14%-39% so firms set ESG targets that are easy to reach. We find that the SLB market is efficient: the prices of SLBs depend strongly on the size of the potential penalty and there is no evidence of mispricing. Finally, our results suggest that SLBs serve as financial hedges against ESG risk.
Working Papers:
Working Papers:
Abstract:
This paper proposes a new type of modelling framework that use machine learning techniques to estimate the parameters of structural models: Deep Structural Models (DSMs). I implement a DSM with a simple Merton (1974) model as a foundation, and show that the DSM jointly estimates expected equity returns and (co)variances with higher predictive power than leading benchmark models. The model is used to form long-short and mean variance efficient portfolios with significantly higher average excess returns, alphas, and Sharpe ratios, compared to those formed on the basis of a state-of-the-art machine learning model. Economically, the DSM suggests that systematic risk compensation is the largest contributor to the average expected asset return of firms, while mispricing is the primary driver of the dispersion of expected returns. Finally, the DSM provides evidence that firm leverage is the main reason for an increased equity premium during economic recessions.
Abstract:
This paper present a novel decomposition of expected returns for merger arbitrage trades, utilizing modern machine learning techniques to model individual components. This decomposition lead to better proxies for expected returns than relying on realized merger arbitrage returns. Additionally, they yield large economic gains for merger arbitrage investors in terms of both absolute and risk-adjusted returns. Finally, the decomposed expected return estimates provide new insight into the aggregate market, by showing a persistent decrease for the cross-sectional average expected merger arbitrage return, over the last decade.
Machine Learning and Financial Stability in Credit Markets
Machine Learning and Financial Stability in Credit Markets
With Peter Feldhütter, Tarun Ramadorai, and Ansgar Walther
Draft Coming Soon
Abstract:
In this paper, we assess the performance of state-of-the-art machine learning models for predicting firm-level default probabilities across the entire term structure. Our analysis reveals that models based on decision tree algorithms consistently outperform traditional statistical models across all time horizons. Further, we investigate the implications of adopting these machine learning models within the banking sector for predicting defaults, with a specific focus on their impact on financial stability. Our findings suggest that constructing loan portfolios based on these advanced predictive models, does not decrease financial stability, although the resulting loan portfolios are much less diversified.
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