Dr. Johannes Stübinger

Lehrbeauftragter

Kurzlebenslauf

Forschungsschwerpunkte

• Machine learning (boosting, random forests, generalized linear models, etc.)
• High-performance computing (GPUs, Amazon Web Services)
• Big Data Analytics
• Capital market anamolies
• Daily and high-frequency data

Lehre

• WS 19/20: Extremwertstatistik mit Anwendungen in Finanz- und Versicherungsmärkten
• SS 19: Anwendung statistischer Methoden im Risikomanagement von Finanzinstituten
• SS 19: Mathematik für die Studiengänge Künstliche Intelligenz, Elektrotechnik, Medientechnik, Bauingenieurwesen, Umweltingenieurwesen
• WS 18/19: Extremwertstatistik mit Anwendungen in Finanz- und Versicherungsmärkten
• SS 18: Nichtparametrische statistische Verfahren
• SS 18: Statistik
• WS 17/18: Rechnergestützte Fallstudienübung Statistik
• WS 17/18: Angewandte Analyse von Zeitreihen- und Finanzmarktdaten
• SS 17: Nichtparametrische statistische Verfahren
• SS 17: Statistik
• WS 16/17: Angewandte Analyse von Zeitreihen- und Finanzmarktdaten
• WS 16/17: Rechnergestützte Fallstudienübung Statistik
• SS 16: Statistik
• WS 15/16: Statistik

Publikationen mit Begutachtungsprozess

• Stübinger, J., 2019. Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500. Quantitative Finance, 19(6), 921-935.
• Stübinger, J., Schneider, L., 2019. Statistical arbitrage with mean-reverting overnight price gaps on high-frequency data of the S&P 500. Journal of Risk and Financial Management, 12(2), 51.
• Endres, S., Stübinger, J., 2019. Optimal trading strategies for Levy-driven Ornstein-Uhlenbeck processes, Applied Economics, 51(29), 3153-3169.
• Endres, S., Stübinger, J., 2019. A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns. Quantitative Finance, 19(10), 1727-1740.
• Stübinger, J., 2019. The power of machine learning in the biological context. Biostatistics and Biometrics, 9(4), 1.
• Knoll, J., Stübinger, J., Grottke, M., 2019. Exploiting social media with higher-order factorization machines: Statistical arbitrage on high-frequency data of the S&P 500. Quantitative Finance, 19(4), 571-585.
• Knoll, J., Stübinger, J., 2019. Machine-learning-based statistical arbitrage football betting, KI, forthcoming.
• Stübinger, J., Endres, S., 2018. Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. Quantitative Finance, 18(10), 1735-1751.
• Stübinger, J., Knoll, J., 2018. Beat the bookmaker – Winning football bets with machine learning (Best Refereed Application Paper). In: Bramer M., Petridis M. (eds).  Artificial Intelligence XXXV. Springer, Cham, 219-233.
• Stübinger, J., Walter, D., Knoll, J., 2018. Financial market predictions with factorization machines: Trading the opening hour based on overnight social media data. The Economics and Finance Letters, 5(2), 28-45.
• Stübinger, J., Mangold, B., Krauss, C., 2018. Statistical arbitrage with vine copulas. Quantitative Finance, 18(11), 1831-1849.
• Krauss, C., Stübinger, J., 2017. Nonlinear dependence modeling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100. Applied Economics 49(52), 5352-5369.
• Stübinger, J., Bredthauer, J., 2017. Statistical arbitrage pairs trading with high-frequency data. International Journal of Economics and Financial Issues, 7(4), 650-662.

Konferenzen

• Beat the bookmaker – Winning football bets with machine learning (Best Refereed Application Paper). Thirty-eighth SGAI International Conference on Artificial Intelligence, Cambridge, December 2018.
• Using higher-order factorization machines for enterprise value prediction. Research Series of GfK, Nürnberg, October 2017.
  
• Financial market predictions with Factorization Machines: Trading the opening hour based on overnight social media data. CEQURA Conference 2017 on Advances in Financial and Insurance Risk Management, München, September 2017.