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2025
Predicting retail customers' distress in the finance industry: An early warning system approach (2025)Journal of Retailing and Consumer Services, 82. Article 104101. Beltman, J., Machado, M. R. & Osterrieder, J. R.https://doi.org/10.1016/j.jretconser.2024.104101
2024
How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review (2024)International Journal of Information Management Data Insights, 4(2). Article 100234. Amato, A., Osterrieder, J. R. & Machado, M. R.https://doi.org/10.1016/j.jjimei.2024.100234Stylized facts of metaverse non-fungible tokens (2024)Physica A, 653. Article 130103. Chan, S., Chandrashekhar, D., Almazloum, W., Zhang, Y., Lord, N., Osterrieder, J. & Chu, J.https://doi.org/10.1016/j.physa.2024.130103Leveraging network topology for credit risk assessment in P2P lending: A comparative study under the lens of machine learning (2024)Expert systems with applications, 252(Part B). Article 124100. Liu, Y., Baals, L. J., Osterrieder, J. & Hadji-Misheva, B.https://doi.org/10.1016/j.eswa.2024.124100Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics (2024)Finance Research Letters, 63. Article 105308. Liu, Y., Baals, L. J., Osterrieder, J. & Hadji-Misheva, B.https://doi.org/10.1016/j.frl.2024.105308Towards a new PhD Curriculum for Digital Finance (2024)Open Research Europe, 4. Article 7 (E-pub ahead of print/First online). Baals, L. J., Osterrieder, J. R., Hadji-Misheva, B. & Liu, Y.https://doi.org/10.12688/openreseurope.16513.1SNSF Narrative Digital Finance: a tale of structural breaks, bubbles & market narratives (2024)[Other contribution › Other contribution]. Osterrieder, J. & Hopp, C.
2023
Modelling taxpayers’ behaviour based on prediction of trust using sentiment analysis (2023)Finance Research Letters, 58(Part C). Article 104549. Coita, I. F., Belbe, S. (., Mare, C. (., Osterrieder, J. & Hopp, C.https://doi.org/10.1016/j.frl.2023.104549Examining share repurchase executions: insights and synthesis from the existing literature (2023)Frontiers in Applied Mathematics and Statistics, 9. Article 1265254. Osterrieder, J. & Seigne, M.https://doi.org/10.3389/fams.2023.1265254Share buybacks: A theoretical exploration of genetic algorithms and mathematical optionality (2023)Frontiers in Artificial Intelligence, 6. Article 1276804. Osterrieder, J.https://doi.org/10.3389/frai.2023.1276804Navigating the Environmental, Social, and Governance (ESG) landscape: constructing a robust and reliable scoring engine - insights into Data Source Selection, Indicator Determination, Weighting and Aggregation Techniques, and Validation Processes for Comprehensive ESG Scoring Systems (2023)Open Research Europe, 3. Article 119 (E-pub ahead of print/First online). Liu, Y., Osterrieder, J., Hadji Misheva, B., Koenigstein, N. & Baals, L.https://doi.org/10.12688/openreseurope.16278.1The Great Deception: A Comprehensive Study of Execution Strategies in Corporate Share Buy-Backs (2023)[Working paper › Preprint]. Seigne, M. & Osterrieder, J.Preface (2023)In Enterprise Applications, Markets and Services in the Finance Industry: 11th International Workshop, FinanceCom 2022, Twente, The Netherlands, August 23–24, 2022, Revised Selected Papers (pp. vii-viii) (Lecture notes in business information processing; Vol. 467). van Hillegersberg, J., Osterrieder, J., Rabhi, F., Abhishta, A., Marisetty, V. & Huang, X.https://doi.org/10.1007/978-3-031-31671-5Digital Finance: Reaching New Frontiers (2023)Open Research Europe, 3. Article 38. Osterrieder, J., Hadji Misheva, B. & Machado, M.https://doi.org/10.12688/openreseurope.15386.1
2022
Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies (2022)[Working paper › Preprint]. ArXiv.org. Lu, J. & Osterrieder, J.Editorial: Artificial intelligence in finance and industry: Highlights from 6 European COST conferences (2022)Frontiers in Artificial Intelligence, 5. Article 1007074. Henrici, A. & Osterrieder, J.https://doi.org/10.3389/frai.2022.1007074Discussion on: “Programmable money: next generation blockchain based conditional payments” by Ingo Weber and Mark Staples (2022)Digital Finance, 4(2-3), 137-138. Osterrieder, J.https://doi.org/10.1007/s42521-022-00063-9Simulating financial time series using attention (2022)[Working paper › Preprint]. Fu, W., Hirsa, A. & Osterrieder, J.Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network (2022)[Working paper › Preprint]. Zejnullahu, F., Moser, M. & Osterrieder, J.https://doi.org/10.48550/arXiv.2206.14267High-Frequency Causality in the VIX Index and its derivatives: Empirical Evidence (2022)[Working paper › Preprint]. Farokhnia, K. & Osterrieder, J.AI for trading strategies (2022)[Working paper › Preprint]. Jevtic, D., Deleze, R. & Osterrieder, J.The Efficient Market Hypothesis for Bitcoin in the context of neural networks (2022)[Working paper › Preprint]. Kraehenbuehl, M. & Osterrieder, J.Enterprise Applications, Markets and Services in the Finance Industry: 11th International Workshop, FinanceCom 2022, Twente, The Netherlands, August 23–24, 2022, Revised Selected Papers (2022)[Book/Report › Book editing] 11th International Workshop on Enterprise Applications, Markets and Services in the Finance Industry, FinanceCom 2022. Springer. van Hillegersberg, J., Osterrieder, J., Rabhi, F., Abhishta, A., Marisetty, V. & Huang, X.https://doi.org/10.1007/978-3-031-31671-5
2021
Wasserstein GAN: Deep Generation applied on Bitcoins financial time series (2021)[Working paper › Preprint]. Samuel, R., Nico, B. D., Moritz, P. & Joerg, O.Deep reinforcement learning on a multi-asset environment for trading (2021)[Working paper › Preprint]. Hirsa, A., Osterrieder, J., Hadji-Misheva, B. & Posth, J.-A.Generative Adversarial Networks in finance: an overview (2021)[Working paper › Preprint]. Eckerli, F. & Osterrieder, J.The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets (2021)Frontiers in Artificial Intelligence, 4. Article 668465. Posth, J.-A., Kotlarz, P. K., Hadji-Misheva, B., Osterrieder, J. & Schwendner, P.https://doi.org/10.3389/frai.2021.668465Explainable AI in Credit Risk Management (2021)[Working paper › Working paper]. ArXiv.org. Osterrieder, J., Misheva, B. H., Hirsa, A., Kulkarni, O. & Lin, S. F.https://doi.org/10.48550/arXiv.2103.00949The VIX index under scrutiny of machine learning techniques and neural networks (2021)[Working paper › Working paper]. ArXiv.org. Hirsa, A., Osterrieder, J., Misheva, B. H., Cao, W., Fu, Y., Sun, H. & Wong, K. W.https://doi.org/10.48550/arXiv.2102.02119Audience-Dependent Explanations for AI-Based Risk Management Tools: A Survey (2021)Frontiers in Artificial Intelligence, 4. Article 794996. Hadji Misheva, B., Jaggi, D., Posth, J.-A., Gramespacher, T. & Osterrieder, J.https://doi.org/10.3389/frai.2021.794996
2020
Neural networks and arbitrage in the VIX: A deep learning approach for the VIX (2020)Digital Finance, 2, 97-115. Osterrieder, J., Kucharczyk, D., Rudolf, S. & Wittwer, D.https://doi.org/10.1007/s42521-020-00026-y
2019
Editorial: AI and financial technology (2019)Frontiers in Artificial Intelligence, 2. Article 25. Giudici, P., Hochreiter, R., Osterrieder, J., Papenbrock, J. & Schwendner, P.https://doi.org/10.3389/frai.2019.00025Neural Networks and Arbitrage in the VIX: A Deep Learning Approach for the VIX (2019)[Working paper › Working paper]. Kucharczyk, D., Osterrieder, J., Rudolf, S. & Wittwer, D.https://doi.org/10.2139/ssrn.3305686The VIX volatility index-A very thorough look at it (2019)SSRN ELibrary. Article 3311727. Osterrieder, J., Vetter, L. & Röschli, K.Editorial on the Special Issue on Cryptocurrencies (2019)Digital Finance, 1, 1-4. Osterrieder, J. & Barletta, A.https://doi.org/10.1007/s42521-019-00015-wMachine Learning Tools for Probability of Default and Rating Downgrades of Corporate and Government Bonds (2019)SSRN ELibrary. Article 3461558. Choudary, J. & Osterrieder, J.
2018
Pattern Learning Via Artificial Neural Networks for Financial Market Predictions (2018)SSRN ELibrary. Article 3243479. Gabler, A., Perez, D., Sutter, U., Kucharczyk, D., Osterrieder, J. & Reitenbach, M.Pricing, Loss and Sensitivity Analysis of Barrier Options via Regression (2018)SSRN ELibrary. Article 3194111. Gabler, A., Wiegand, M. & Osterrieder, J.
2017
GARCH Modelling of Cryptocurrencies (2017)Journal of Risk and Financial Management, 10(4). Article 17. Chu, J., Chan, S., Nadarajah, S. & Osterrieder, J.https://doi.org/10.3390/jrfm10040017A Dynamic Market Microstructure Model with Market Orders and Random Order Book Depth (2017)[Working paper › Working paper]. Osterrieder, J.https://doi.org/10.2139/ssrn.2984315A Statistical Analysis of Cryptocurrencies (2017)Journal of Risk and Financial Management, 10(2). Article 12. Chan, S., Chu, J., Nadarajah, S. & Osterrieder, J.https://doi.org/10.3390/jrfm10020012A statistical risk assessment of bitcoin and its extreme tail behavior (2017)Annals of Financial Economics, 12(01). Article 1750003. Osterrieder, J. & Lorenz, J.https://doi.org/10.1142/s2010495217500038A Statistical Analysis of Carry Trading (2017)SSRN ELibrary. Fritzmann, S., Jaggi, D. & Osterrieder, J.https://doi.org/10.2139/ssrn.2993902Bitcoin and Cryptocurrencies—Not for the Faint-Hearted (2017)International Finance and Banking, 4(1), 56-94. Osterrieder, J., Strika, M. & Lorenz, J.https://doi.org/10.5296/ifb.v4i1.10451GARCH Modeling of Cryptocurrencies (2017)[Working paper › Working paper]. Chu, J., Chan, S., Nadarajah, S. & Osterrieder, J.https://doi.org/10.2139/ssrn.3047027Momentum and trend following trading strategies for currencies revisited-combining academia and industry (2017)SSRN ELibrary. Article 2949379. Rohrbach, J., Suremann, S. & Osterrieder, J.https://doi.org/10.2139/ssrn.2949379The Statistics of Bitcoin and Cryptocurrencies (2017)In Proceedings of the 2017 International Conference on Economics, Finance and Statistics (ICEFS 2017) (Advances in Economics, Business and Management Research; Vol. 26). Osterrieder, J.https://doi.org/10.2991/icefs-17.2017.33
2009
Simulation of a limit order driven market (2009)The Journal Of Trading, 4(1), 23-30. Lorenz, J. & Osterrieder, J.https://jot.pm-research.com/content/4/1/23
2007
Arbitrage, the limit order book and market microstructure aspects in financial market models (2007)[Thesis › PhD Thesis - Research external, graduation external]. Osterrieder, J. R.
2006
A Theoretical Model of the Limit Order Book and Some Applications (2006)SSRN ELibrary. Osterrieder, J.https://doi.org/10.2139/ssrn.881274Arbitrage opportunities in diverse markets via a non-equivalent measure change (2006)Annals of Finance, 2, 287-301. Osterrieder, J. R. & Rheinländer, T.https://doi.org/10.1007/s10436-006-0037-z