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Project’s deliverables

  1. Anagnoste, S., Andrei, A.-V., Bolovăneanu, V., Cepoi, C.-O., Clodnitchi, R., Cramer, A.-A., Grecu, R.-A., Lessmann, S., Pele, D. T., Petukhina, A., & Strat, V. A. (2025). The role of AI in (re)shaping energy finance: A systematic literature review. Energy Strategy Reviews, 61, 101833. https://doi.org/10.1016/j.esr.2025.101833
  2. Pele, D. T., Bolovăneanu, V., Lin, M.-B., Ren, R., Ginavar, A. T., Spilak, B., Andrei, A.-V., Toma, F.-M., Lessmann, S., & Härdle, W. K. (2025). In the beginning was the word: LLM-VaR and LLM-ES. Expert Systems with Applications, 128676. https://doi.org/10.1016/j.eswa.2025.128676
  3. Agakishiev, I., Härdle, W. K., Kopa, M., Kozmik, K., & Petukhina, A. (2025). Multivariate probabilistic forecasting of electricity prices with trading applications. Energy Economics, 141, 108008. https://doi.org/10.1016/j.eneco.2024.108008
  4. Bokelmann, B., & Lessmann, S. (2025). Heteroscedasticity-aware stratified sampling to improve uplift modeling. European Journal of Operational Research, 325(1), 118–131. https://doi.org/10.1016/j.ejor.2025.02.030
  5. Gerling, C., & Lessmann, S. (2025). Multimodal Document Analytics for Banking Process Automation. Information Fusion, 102973. https://doi.org/10.1016/j.inffus.2025.102973
  6. Grecu, R. A., Cramer, A. A., Pele, D. T., & Lessmann, S. (2025). The link between energy prices and stock markets in European Union countries. The North American Journal of Economics and Finance, 78, 102420. https://doi.org/10.1016/j.najef.2025.102420
  7. Gurgul, V., Lessmann, S. & Härdle, W.K. (2025). Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2025.02.007
  8. Kozodoi, N., Lessmann, S., Alamgir, M., Moreira-Matias, L., & Papakonstantinou, K. (2025). Fighting sampling bias: A framework for training and evaluating credit scoring models. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2025.01.040
  9. Anderloni, L., Petukhina, A., & Tanda, A. (2024). Peer-to-peer lending: Exploring borrowers’ motivations and expectations. Journal of Small Business Management, 1–33. https://doi.org/10.1080/00472778.2024.2431232
  10. Fischer, T., Sterling, M., & Lessmann, S. (2024). FX-spot predictions with state-of-the-art transformer and time embeddings. Expert Systems with Applications, 249, 123538. https://doi.org/10.1016/j.eswa.2024.123538
  11. Medina-Olivares, V., Lessmann, S., & Klein, N. (2024). The Deep Promotion Time Cure Model. IEEE Transactions on Neural Networks and Learning Systems, online first. https://doi.org/10.1109/TNNLS.2024.3398559
  12. Teng, H.-W., Härdle, W. K., Osterrieder, J., Baals, L. J., Papavassiliou, V. G., Bolesta, K., Kabasinskas, A., Filipovska, O., Thomaidis, N. S., Moukas, A. I., Goundar, S., Nasir, J. A., Weinberg, A. I., Arakelian, V., Truică, C.-O., Akar, M., Kabaklarlı, E., Apostol, E.-S., Iannario, M., Bedowska-Sojka, B., Skaftadottir, H. K., Schwendner, P., Yıldırım, Ö., Shala, A., Pisoni, G., Coita, I. F., Korba, S., Hafner, C. M., Molnár, B., Xhumari, E., Pele, D. T., & Orhun, E. (in press). Digital assets: Risks, regulations, mitigation. Financial Innovation.
  1. Săvescu, A. R., Mazurencu-Marinescu-Pele, M., Alexandra-Ioana Conda, Bolovăneanu, V., & Pele, D. T. (2025). Infodemic insights: Mapping COVID-19’s digital discourse in Romania. Management & Marketing, 20(1), 15-34. https://doi.org/10.2478/mmcks-2025-0003
  2. Oțoiu, A., Manea, D.I., Paraschiv, D M. and Ștefan, A. (2025). The Role and Potential of Blockchain Technology for the Food Processing Industry: A Scoping Review. Amfiteatru Economic, 27(69), pp. 504-517. https://doi.org/10.24818/EA/2025/69/504 or https://www.webofscience.com/wos/woscc/full-record/WOS:001500039000014
  3. Otoiu, A., Titan, E., Paraschiv, D., Manea, DI. (2025). Job polarisation OR AND upgrading! Recent evidence from Europe. The Economic and Labour Relations Review.https://doi.org/10.1017/elr.2025.12 or https://www.webofscience.com/wos/woscc/full-record/WOS:001490470000001 
  4. Paraschiv, D.M., Muhammad, A., Petrariu, I.R., Gheorghe, M., Dieaconescu, R.I. and Istudor, M., (2024). Shaping Europe’s Digital and Sustainable Future: Analysis of the Digital Economy and Society Index in the Pre- and Post-Pandemic Period. Amfiteatru Economic, 26(Special Issue No. 18), pp. 1012-1030. https://doi.org/10.24818/EA/2024/S18/1012 or https://www.webofscience.com/wos/woscc/full-record/WOS:001379092800002
  5. Popescu, I.M., Zavatin, I., Manea, D.I., Pamfilie, R., Jurconi, A., (2024), Adapting the competences of the employed personnel in the context of the integration of artificial intelligence in organisations, Amfiteatru Economic, 26, Issue 67, Page 817-831, DOI10.24818/EA/2024/67/817 or https://www.webofscience.com/wos/woscc/full-record/WOS:001289160200008
  1. Grădinaru, G. I., Manea, D. I., Andreescu, F., Toma, D. A., & Paraschiv, L. I. (2024). Identifying the Main Factors of Elaborating “Smart City” Strategy Using Machine Learning. A Comparative Study Among Romanian Cities. Economic Computation and Economic Cybernetics Studies and Research, 58(3), 53–71. https://www.webofscience.com/wos/woscc/full-record/WOS:001317110900004
  1. Strat, V., Radev, D., Pele, D. T., Chinie, C., Grosu, F., Darie, F. C., Mare, C., Damian, V., & Coita, I. (2024). Fintech Report: Romania & Bulgaria, 2023 Edition. Bucharest: Editura ASE. ISBN 978-606-34-0517-4. https://bbs.ase.ro/wp-content/uploads/2024/04/RoFin.Tech_Romania-Bulgaria_Report2023_ebook.pdf

 

  1. Shahar, O., Lessmann, S., & Pele, D. T. (2025). Causality analysis of electricity market liberalization on electricity price using novel machine learning methods (arXiv:2507.12331). arXiv. https://arxiv.org/abs/2507.12331
  2. Velev, G., Lessmann, S. (2024). Interpretable, multi-dimensional Evaluation Framework for Causal Discovery from observational i.i.d. Data. arXiv preprint, https://arxiv.org/pdf/2409.19377
  3. Vöge, L., Gurgul, V., & Lessmann, S. (2024). Leveraging Zero-Shot Prompting for Efficient Language Model Distillation. arXiv preprint, arXiv:2403.15886. https://arxiv.org/abs/2403.15886
  4. Andrei, V.A., Velev, G., Toma, F.M., Pele, D.T., Lessmann, S. (2024). Energy Price Modelling: A Comparative Evaluation of Four Generations of Forecasting Methods.            arXiv preprint arXiv:2411.03372. https://arxiv.org/abs/2411.03372
  5. Bedowska-Sojka, B., Wojcik, P., & Pele, D. T. (2024). Predicting the Undead: Using Machine Learning to Forecast Cryptocurrency Zombies. SSRN. https://ssrn.com/abstract=4793792 or http://dx.doi.org/10.2139/ssrn.4793792
  6. Bettencourt, L. O., Tetereva, A., & Petukhina, A. (2024). Advancing Markowitz: Asset Allocation Forest. SSRN 4781685.
  7. Grecu, R., Cramer, A., Pele, D. T., & Lessmann, S. (2024). The Link between Energy Prices and Stock Markets in European Union Countries. SSRN. https://ssrn.com/abstract=4989890 or http://dx.doi.org/10.2139/ssrn.4989890
  8. Lin, M. B., Pele, D. T., & Ren, R. (2024). Understanding Blockchain Technology. In Handbook of Blockchain Analytics (Springer, Forthcoming). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4804484
  1. STAT of ML (Statistics of Machine Learning), October 5 – October 6, 2023, Czech Academy of Sciences, Prague, Czech Republic: Conference Website
    • Pele, D. T., Petukhina, A., Bolovaneanu, V., & Conda, A. Robustified Markowitz Approach for Diversified Portfolios with Crypto-Assets.
    • Gurgul, V., Ider, D., & Lessmann, S. Finetuning NLP Models for Financial Forecasting: Do or Don’t? A Cryptocurrency Return Prediction Case Study.
  2. The 17th International Conference on Computational and Financial Econometrics (CFE 2023), HTW Berlin, University of Applied Sciences, Berlin, Germany, 16-18 December 2023: Conference Website
    • Petukhina, A., Agakishev, I., Kozmik, K., Härdle, W. K., & Kopa, M. Multivariate Probabilistic Forecasting of Electricity Prices with Trading Applications.
    • Andrei, A.-V., & Pele, D. T. Deep Learning for Energy Forecasting: A Benchmark.
    • Grecu, R.-A., Pele, D. T., & Cramer, A. A. The Impact of Energy Prices on Stock Returns in Selected Central and Eastern European Countries.
    • Bolovaneanu, V., Moukas, A.-I., Petukhina, A., Pele, D. T., & Thomaidis, N. Optimizing Wind Energy Aggregation: A Comparative Analysis of Asset Allocation Techniques.
    • Conda, A., Petukhina, A., Melzer, A., Phan, M., Basangova, M., Alkhoury, S., & Bolovaneanu, V. Day-Ahead Probability Forecasting for Redispatch.
    • Lessmann, S., & Velev, G. Neural Architecture Search for Bitcoin Market Prediction.
    • Cepoi, C. O., Cramer, A. A., Clodnitchi, R., Pele, D. T., Strat, V., & Anagnoste, S. Forecasting Realized Volatility Using Machine Learning: The Case of EU Energy Listed Firms.
  3. Conference AI Innovations in Finance and Society, 30-31 October 2023, Universitat Pompeu Fabra, Barcelona, Spain: Conference Website
    • Pele, D. T. BRC Blockchain Research Center.
  4. The 18th International Conference on Business Excellence: Smart Solutions for a Sustainable Future, 21-23 March 2024, Bucharest, Romania: Conference Website
    • Grecu, R.-A., Cramer, A.-A., & Pele, D. T. The Link Between Energy Prices and Stock Markets in European Union Countries.
    • Cretulescu, A., & Popescu Cretulescu, A. Model Prediction XGBoost for Perspective of Closing Cernavoda Reactor 1 and the Impact on the Romanian Energy System.
    • Petukhina, A., Erlwein-Sayer, C., Phan, M. P., Basagnova, M., Bolovaneanu, V., & Conda, A. Day-Ahead Probability Forecasting for Redispatch 2.0 Measures.
    • Bolovaneanu, V., Pele, D. T., & Petukhina, A. Optimizing Wind Energy Aggregation: A Comparative Analysis of Asset Allocation Techniques.
    • Andrei, A.-V., Pele, D. T., & Lessmann, S. Benchmarking Generations of Time Series Forecasting Models: From Econometrics to LLM.
    • Cepoi, C.-O. Energy Uncertainty and Alternative-Nuclear Transition.
  5. The 3rd Quantum Computing Workshop on AI Optimization and Forecasting across Industries: Digital and Quantum Computing, 9-12 April 2024, National University of Singapore: Conference Website
    • Lessmann, S. Transfer learning for credit risk modeling.
  6. AI Finance Insights: Pioneering the Future of Fintech, COST FINAI Conference, 20-21 May 2024, Istanbul, Turkey: Conference Website
    • Pele, D. T. Benchmarking Generations of Time Series Forecasting Models.
  7. 33rd European Conference on Operational Research, Technical University of Denmark (DTU), 30 June – 3 July 2024: Conference Website
    • Agakishiev, I., Härdle, W. K., Kozmik, K., Kopa, M., & Petukhina, A. Multivariate Probabilistic Forecasting of Electricity Prices With Trading Applications.
    • Lessmann, S., Andrei, A.-V., Velev, G., & Pele, D. T. Energy Price Modelling: A Comparative Evaluation of Four Generations of Forecasting Methods.
    • Grecu, R.-A., Pele, D. T., & Alexandru-Adrian. The Impact of Energy Prices on European Stock Markets.
    • Cepoi, C. O., & Pele, D. T. Predicting Realized Volatility: Insights from EU Energy Listed Firms During Crises.
    • Bolovăneanu, V., Petukhina, A., Conda, A., Basangova, M., Erlwein-Sayer, C., Melzer, A., & Phan, M. P. Day-Ahead Probability Forecasting for Redispatch 2.0 Measures.
    • Lin, M.-B., Găman, Ş., Wang, R., & Pele, D. T. The Chronicles of Ethereum: An Event Study on EIPs.
    • Pele, D. T., Mazurencu Marinescu Pele, M., Conda, A., & Bag, R. C. Financial Risk Meter for the Romanian Stock Market.
    • Pele, D. T., Mazurencu Marinescu Pele, M., Găman, Ş., Conda, A., & Bag, R. C. BitMood: Analyzing Bitcoin Trends through Facebook Emotions with AI.
  8. The 7th International Conference on Econometrics and Statistics (EcoSta 2024), Beijing Normal University, Beijing, China, 17-19 July 2024
    • Bolovăneanu, V., Petukhina, A., Conda, A., Basangova, M., Erlwein-Sayer, C., Melzer, A., & Phan, M. P. Day-Ahead Probability Forecasting for Redispatch 2.0 Measures.
  9. The 17th International Conference on Applied Statistics (ICAS 2024), November 14-15, 2024, Bucharest, Romania: Conference Website
    • Tak, R., Pele, D. T., & Härdle, W. K. GAN application for automated trading systems.
    • Jheng, S.-L., Teng, H.-W., Härdle, W. K., Pele, D. T., & Costea, A. Financial risk meters in Taiwan’s high-cap sectors.
    • Ginavar, A., Pele, D. T., Manea, D. I., Conda, A. I., Mazurencu-Marinescu-Pele, M., & Stefan, A. Cryptocurrency market analysis: Insights from Metcalfe’s law and log-periodic power laws.
  10. The 4th Yushan Conference, 18th NYCU International Finance Conference, National Yang Ming Chiao Tung University, Taiwan, 5-6 December 2024: Conference Website
    • Lessmann, S. A Tale of Bias and Missingness in AI-based Scoring Systems: Evidence from the Credit Industry.
    • Petukhina, A. Day-ahead Probability Forecasting for Redispatch 2.0 Measures.
    • Spilak, B. Machine Learning for Waste Water Treatment Plant (WWTP) Control.
    • Teng, H.-W. AI in Finance.
    • Pele, D. T. In the Beginning was the Word: LLM Risk Measures.
    • Găman, Ş. Quantlet: The Code Snippet Knowledge Platform.
    • Jheng, S.-L. Financial Risk Meters in Taiwan’s High-Cap Sectors.
    • Zuo, X. A Safe Recomm System for Knowledge Platforms.
  11. 18th International Joint Conference on Computational and Financial Econometrics (CFE) and CMStatistics, King’s College London, 14-16 December 2024: Conference Website
    • Bolovăeanu, V. Bayesian Bandit Portfolio: Customized Thompson Sampling for Investor Preference.
  12. The 19th International Conference on Business Excellence: Leading Change in Disruptive Times, 20-22 March 2025, Bucharest, Romania: Conference Website
    • Pele, D.T. “In the Beginning Was the Word: LLM-Based Risk Forecasting – VaR and ES.”
    • Tak, R. & Pele, D.T. “LLM-Driven Stock Prediction: Capturing Market Trends with LLaMA.”
    • Ginavar, A.T., Conda, A.I., Mazurencu-Marinescu-Pele, M., & Pele, D.T. “Cryptocurrency Market Analysis: Insights from Metcalfe’s Law and Log-Periodic Power Laws.”
    • Jheng, S-L., Conda, A.I., Pele, D.T., & Härdle, W.K. “Cryptocurrencies in a Changing Financial Landscape: A Systematic Review.”
    • Petukhina, A. “Probabilistic forecasting of electricity prices with multivariate distributional copula regression” (with G. Mazzon and Daniele Marazzina).
  13. English Lecture Event – Unveiling Return Dynamics: Energy, Crypto, and Equity Factor Insights, Biomedical Artificial Intelligence Academy, Kaohsiung Medical University, June 17, 2025, Kaohsiung, Taiwan: Event Website
    • Pele, D. T., Conda, A.-I., Bolovăneanu, V., & Mazurencu-Marinescu Pele, M. Infodemic Insights: Mapping COVID-19’s Digital Discourse in Romania.
  14. 34th Southern District Statistical Seminar & 2025 Annual Meeting of the Chinese Society of Probability and Statistics, National Taipei University, 19-20 June 2025: Conference Website
    • Pele, D. T. LLM-Based Risk Measures.
  15. 5th Yushan-Turing/Euler Conference, June 21, 2025, Taipei, Taiwan: Conference Website
    • Pele, D. T. AI and Digital Finance.
  16. 34th European Conference on Operational Research, June 22-25, 2025, Leeds, UK: Conference Website
    • Lessmann, S. (2025). Fairness in Credit Scoring. Invited Talk presented at the 34th European Conference on Operational Research.
  17. 17th Annual Society for Financial Econometrics Conference (SOFIE), June 9-12, 2025, Cergy, France: Conference Website
    • Petukhina, A. (2025). Blockchain Characteristics and Systematic Risk: A Neural Network Based Factor Model for Cryptocurrencies?
  1. ASE Summer School 2023
  2. Workshop AI, Digital Assets and the Future of Energy Finance, 16-17 May 2024, Bucharest University of Economic Studies: https://www.meetup.com/fintech_ai_in_finance/events/299747969/
    • Pele, D. T. LLM VaR: In the Beginning Was the Word.
    • Grecu, R.-A., Cramer, A.-A., & Pele, D. T. The Link Between Energy Prices and Stock Markets in European Union Countries.
    • Bolovăneanu, V., Petukhina, A., Conda, A., Basangova, M., Erlwein-Sayer, C., Melzer, A., & Phan, M. P. Day-Ahead Probability Forecasting for Redispatch 2.0 Measures.
  3. MSCA DIGITAL Summer School, University of Twente, NL, 10-13 June 2024: https://www.digital-finance-msca.com/cost-finai-phd-school-2024
    • Pele, D. T. LLM Risk Measures.
  4. AI4EFin Workshop, Humboldt-Universität zu Berlin, Berlin, 05-06 September 2024:
    • Bolovaneanu, V. Day-ahead Probability Forecasting for Redispatch 2.0 Measures – Update.
    • Pele, D. T. LLM VaR: In the Beginning Was the Word.
    • Grecu, R.-A., Cramer, A.-A., & Pele, D. T. The Link Between Energy Prices and Stock Markets in European Union Countries.
    • Andrei, A. Energy Price Modelling: A Comparative Evaluation of Four Generations of Forecasting Methods.
    • Grecu, R.-A. Review Paper on Energy Finance.
    • Mazzon, G., & Petukhina, A. Multivariate Distributional Copula Regression: Probabilistic Forecasting of Electricity Prices.
    • Zharova, A. Recommendation Systems for Energy Efficiency in Residential Buildings.
  5. 2024 Summer School “Data Science for Sustainable Finance and Economics” at Hochschule für Technik und Wirtschaft Berlin, 09-13 September 2024: Summer_School_2024-29.pdf (htw-berlin.de)
    • Pele, D. T. Overview of XAI Methods
    • Petukhina A. Classification models for financial applications
  6. Research Seminar at the University of Pavia, June 2025:
    • Petukhina A. Advancing Markowitz: Asset Allocation Forest – Link

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