Official COST FinAI Publications

Here you find a list of all academic publications that were created in the context of our COST FinAI Action.

Please kindly add the appropriate acknowledgement. For academic articles, that would be:

• This publication is based upon work from COST Action 19130, supported by COST (European Cooperation in Science and Technology), www.cost.eu

COST FinAI Publications

1. COST FinAI presentations and executive summaries

Academic peer-reviewed papers

1. Wahlstrøm, R. R., Paraschiv, F., Schürle, M. A (2021) Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Models. Computational Economics https://doi.org/10.1007/s10614-021-10113-w

Articles published in the book of the conference’s proceedings

1. Barjaktarović, L., Barjaktarović, M., Konjikušić, S., Echo state networks usage of stock price predictions, The Book of Proceedings Singidunum University International Scientific Conference FINIZ 2020 - People in the Center of Process Automation, p. 97-102, [https:doi.org/10.15308/finiz-2020-97-102; available at: http://portal.finiz.singidunum.ac.rs/paper/42580]

Working papers

1. Devine, M.T, Russo, M., Cuffe, P., Blockchain electricity trading using tokenised power delivery contract.

2. K. Khowaja, D. Saef, S. Sizov, and W. K. Härdle. Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition. IRTG 1792 Discussion Paper 2020-026, 2020.

3. Paraschiv, F., Schmid, M., Wahlstrøm, R. R. (2021) Bankruptcy prediction of privately held SMEs using feature selection methods.

4. Wei, L., Paraschiv, F. (2021) Modelling the Evolution of Wind and Solar Power Infeed Forecasts. Available at SSRN: [1]

5. Wei, L., Denis, M.B. (2021) Day-ahead electricity prices prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling.

6. Escobar, D., Paraschiv, F., Schürle, M. (2021) Pricing electricity futures with distortion functions under model ambiguity.

7. Ren, R., Althof, M., & Härdle, W. K. (2020). Tail Risk Network Effects in the Cryptocurrency Market during the COVID-19 Crisis. Available at SSRN: http://dx.doi.org/10.2139/ssrn.3753421.

8.Ben Amor, S., Althof, M., & Härdle, W. K. (2021). FRM Financial Risk Meter for Emerging Markets. Available at: arXiv: https://arxiv.org/abs/2102.05398.

9. Hadji Misheva, B, Osterrieder, J., Hirsa, A., Kulkarni, O. and Fung Lin, S. (2021). Explainable AI in Credit Risk Management. Available at: arXiv: https://arxiv.org/abs/2103.00949.

10. Hirsa, A., Osterrieder, J., Hadji Misheva, H., Cao, W., Fu, Y., Sun, H. and Wai Wong, K. (2021). The VIX index under scrutiny of machine learning techniques and neural networks. Available at: arXiv: https://arxiv.org/abs/2102.02119.

11. Ren, R., Lu, M., Li Y., & Härdle, W. K. (2020). Financial Risk Meter Based on Expectiles. Available at SSRN: http://dx.doi.org/10.2139/ssrn.3809329.

12. Galena Pisoni, Bálint Molnár, Adam Tarcsi. Comparison of two technologies for digital payments: challenges and future directions, February 2021,Conference: REV2021 18th International Conference on Remote Engineering and Virtual Instrumentation Online Engineering and Society 4.0 At: Hong Kong , China, Available at:https://www.researchgate.net/publication/349646943_Comparison_of_two_technologies_for_digital_payments_challenges_and_future_directions