5 papers in the archive (2023–2026), average empirical rigor 6.4/10, average math complexity 3/10. Quadrant mix: Philosophers ×1, Street Traders ×4.
Works mostly in Crypto Markets & DeFi, NLP & LLMs in Finance, Factor Investing & Asset Pricing, Market Microstructure.
Frequent co-authors: Aman Saggu (5), Friedrich-Philipp Wazinski (2), Benjamin Schellinger (1), Friedrich Wazinksi (1), Ingo Fiedler (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
The introduction of OpenAI’s large language model, ChatGPT, catalyzed investor attention towards artificial intelligence (AI) technologies, including AI-related crypto assets not directly related to ChatGPT. Utilizing the synthetic difference-in-difference methodology, we identify significant ‘ChatG
Stablecoins represent a critical bridge between cryptocurrency and traditional finance, with Tether (USDT) dominating the sector as the largest stablecoin by market capitalization. By Q1 2025, Tether directly held approximately $98.5 billion in U.S. Treasury bills, representing 1.6% of all outstandi
This paper investigates the potential of blockchain-based fan tokens, a class of crypto asset that grants holders access to voting on club decisions and other perks, as a mechanism for stimulating democratized decision-making and fan engagement in the sports and esports sectors. By utilizing an exte
Biodiversity loss is accelerating at an unprecedented pace, threatening ecosystem stability, economic resilience, and human well-being, with billions required to reverse current trends. Against this backdrop, biodiversity finance has emerged as a rapidly expanding but highly fragmented field spannin
This paper investigates retail investor motivations for digital real estate ownership in the crypto-metaverse. Utilizing a detailed financial behavior survey of metaverse landowners’ intrinsic and extrinsic motivations, we apply principal components analysis to uncover four distinct motivational gro