"Grant me the serenity to accept the things I cannot change,
Courage to change the things I can,
And wisdom to know the difference."
— Reinhold Niebuhr
Working Papers
Certainly! Generative AI and its Impact on Academic Writing (in Finance) (with Marie Dutordoir), submitted. [Working Paper]
Abstract
This paper investigates how the introduction of Large Language Models (LLMs) has affected academic writing in finance. Analyzing 41,489 articles from 34 finance journals, we find that readability declined and the use of LLM-associated terms increased following the release of ChatGPT at the end of 2022. These trends are more pronounced among authors from non-English-speaking countries and in lower-ranked journals. The effects are also stronger for authors affiliated with institutions in countries that place less emphasis on creativity, display lower skepticism toward technology, and have higher levels of moral ambiguity. Author-level analyses suggest that while LLM adoption increases publication quantity, it does not enhance publication quality or scholarly impact.
Feature in Generative AI in Higher Education, CXO Advisory Group, ESSEC Knowledge
Presentations (scheduled): Utrecht University (2025); Dutch Sustainable Finance Network Workshop (Rotterdam, 2025); Dutch Math Finance Afternoons (Utrecht, 2025); University of Gronningen (2025); Asia Conference on Business and Economic Studies (Ho Chi Minh City, 2025); AVSE FBNet (online, 2025); Radboud University Nijmegen (2025); Corporate Finance Days (Gronningen, 2025); a.s.r. (Zeist, 2025); German Finance Association Annual Meeting (Hagen, 2025); Vrije University Amsterdam (2025); Technische Universität Dresden (2025); Ruhr University Bochum (2025); University of Maastricht (2025); Audencia Business School (Nantes, 2026); emlyon (Lyon, 2026); KU Leuven (2026)
Financial Robo Advisors: A Comprehensive Review and Future Directions (with Mustafa Mourallah, Peter Öhman, and Duc Khuong Nguyen), submitted. [Working Paper]
Abstract
Financial Robo-Advisors (FRAs) enable households to participate in financial markets with a limited amount of money and without time or place constraints. While FRAs can help investors overcome behavioural biases, they also have disadvantages, such as relying on a limited number of inputs and lacking individualization. We conducted a systematic literature review on the nascent research on FRAs to synthesize previous research results. We identify two streams of literature: (1) asset management, which focuses on designing FRAs and improving the functioning of these machine advisors, and (2) behavioural finance, which investigates technology adoption and issues related to biased advice. Among other topics, future research should address why FRAs do not appeal to less financially literate people, who likely would benefit more than others from using FRAs.
Common Drivers of Commodity Futures? (with Tom Dudda, Tony Klein, and Duc K. Nguyen), R&R. [Working Paper]
Abstract
We investigate drivers of commodity futures returns using mixed-frequency vector autoregression. Slowing real economic activity and increasing macroeconomic uncertainty precede negative monthly returns. Stock markets predict daily but not long-term commodity returns. Information from these drivers yields significant trading profits, though predictability changes with financialization periods. In recent years, futures prices show less sensitivity to financial variables. Our findings indicate that financial hedgers (commodity index investors) enhance the predictive power of financial variables but reduce the informativeness of fundamental information for future commodity returns.
Best PhD Paper Award, Annual Conference of the British Accounting and Finance Association (Doctoral Masterclass)
Feature in Macrosynergy
Keynote: V International Workshop, Higher School of Economics (Moscow, 2022, online); International Conference on Climate and Energy Finance (Weihai, 2024); 5th International Conference on Research in Management & Technovation (Hanoi, 2024)
Presentations: ABN AMRO (2024); University of Basel (2024); German Finance Association Annual Meeting (Hohemheim, 2023); Annual Meeting of the Commodity & Energy Markets Association (Chicago, 2022); International Association for Applied Econometrics Annual Conference (London, 2022); 8th International Symposium of Environment and Energy Finance Issues (Paris, 2022); 5th Commodity Markets Winter Workshop (St. Johan im Pongau, 2022); 6th Vietnam Symposium in Banking and Finance (Hanoi, 2021, online); HvB PhD Seminar (Chemnitz, 2021); Utrecht University (2021); Annual Conference of the British Accounting and Finance Association (Doctoral Masterclass, online, 2021); Econometric Research in Finance (Warsaw, 2021); 10th INREC (Essen, 2021); TU Dresden (2018); TU Chemnitz (2018); Queen's University Belfast (2018)
Reinforcement Learning and Portfolio Allocation: Challenging Traditional Allocation Methods (with Matus Lavko and Tony Klein), R&R. [Working Paper]
Abstract
We test the out-of-sample trading performance of model-free reinforcement learning (RL) agents and compare them with the performance of equally-weighted portfolios and traditional mean-variance (MV) optimization benchmarks. By dividing European and U.S. indices constituents into factor datasets, the RL-generated portfolios face different scenarios defined by these factor environments. The RL approach is empirically evaluated based on a selection of measures and probabilistic assessments. Training these models only on price data and features constructed from these prices, the performance of the RL approach yields better risk-adjusted returns as well as probabilistic Sharpe ratios compared to MV specifications. However, this performance varies across factor environments. RL models partially uncover the nonlinear structure of the stochastic discount factor. It is further demonstrated that RL models are successful at reducing left-tail risks in out-of-sample settings. These results indicate that these models are indeed useful in portfolio management applications.
Presentations: European Financial Management Association (Cardiff, 2023); European Alternative Finance Research Conference (Utrecht, 2022); Cardiff FinTech Conference (2022); Finance and Innovation Symposium (Hanoi, 2022)
In Preparation
Macroeconomic Determinants of Realized Volatility: A Machine Learning Approach (with Andrii Babii)
Presentations: Utrecht University (2021); UNC-Chapel Hill (2022); Zeppelin University, Friedrichshafen (2022), University of Hannover (2022)
The Financialization of the European Futures Market for Carbon Emission Allowances (with Tom L. Dudda, Tony Klein, and Florentina Paraschiv)
Presentations: Zafin Finance and Sustainability Conference (Warsaw, 2022); Conference on INternational Finance; Sustainable and Climate Finance and Growth (Naples, 2022); Workshop on Carbon Finance (Hagen, 2022); ENERDAY (Dresden, 2022)
Permanent Working Paper (forever young)
Oil Price Changes and U.S. Real GDP Growth: Is this Time Different? (with Lanouar Charfeddine and Tony Klein). [Working Paper]
Presentations: SoFiE Summer School (Chicago, 2020); International Association for Applied Econometrics Annual Conference (Montreal, 2018), Commodity and Energy Markets Association (CEMA) Annual Meeting (Rome, 2018), Verein für Socialpolitik Annual Meeting (2018), International Ruhr Energy Conference (Essen, 2017), European Stability Mechanism (Luxembourg, 2017), John von Neumann Institute (Ho Chi Minh City, 2017)