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Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 39

Predicting Stock Trends Using Common Financial Indicators: A Summary of FedCSIS 2024 Data Science Challenge Held on KnowledgePit.ai Platform

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DOI: http://dx.doi.org/10.15439/2024F7912

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 731–737 ()

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Abstract. Predictive analytics aims to empower finance professionals to make data-driven decisions, anticipate customer behavior, and navigate the complexities of the financial landscape. One of the tasks in this domain is the prediction of stock trend movements. The goal of the FedCSIS 2024 Data Science Challenge was to investigate the feasibility of a predictive model that would allow for accurate forecasts of stock trend movements based on the financial fundamental data. Such a model could have a vital role in algorithmic or manual trading, providing trading signals for making decisions about the time and direction of stock trades. We describe the prepared dataset and competition task. We also summarize the outcomes of the competition and provide insights about the most successful ML techniques used by participants. Finally, we discuss our ideas for a continuation of this study.

References

  1. Oskar Triebe, Hansika Hewamalage, Polina Pilyugina, Nikolay Laptev, Christoph Bergmeir, and Ram Rajagopal, “NeuralProphet: Explainable Forecasting at Scale,” arXiv preprint, p. https://arxiv.org/abs/2111.15397, 2021.
  2. Mahinda Mailagaha Kumbure, Christoph Lohrmann, Pasi Luukka, and Jari Porras, “Machine Learning Techniques and Data for Stock Market Forecasting: A Literature Review,” Expert Systems with Applications, vol. 197, pp. 116659, 2022.
  3. Temitope Kehinde, Felix T. S. Chan, and Sai Ho Chung, “Scientometric Review and Analysis of Recent Approaches to Stock Market Forecasting: Two Decades Survey,” Expert Systems with Applications, vol. 213, pp. 119299, 2023.
  4. Mathurin Aché, Andrzej Janusz, Kamil Zbikowski, Dominik Ślęzak, Marzena Kryszkiewicz, Henryk Rybinski, and Piotr Gawrysiak, “ISMIS 2017 Data Mining Competition: Trading Based on Recommendations,” in Foundations of Intelligent Systems – 23rd International Symposium, ISMIS 2017, Warsaw, Poland, June 26-29, 2017, Proceedings, 2017, vol. 10352 of Lecture Notes in Computer Science, pp. 697–707.
  5. Weiwei Jiang, “Applications of Deep Learning in Stock Market Prediction: Recent Progress,” Expert Systems with Applications, vol. 184, pp. 115537, 2021.
  6. Gaurang Sonkavde, Deepak Sudhakar Dharrao, Anupkumar M. Bongale, Sarika T. Deokate, Deepak Doreswamy, and Subraya Krishna Bhat, “Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications,” International Journal of Financial Studies, vol. 11, no. 3, pp. 94, 2023.
  7. Deepak Kumar, Pradeepta Kumar Sarangi, and Rajit Verma, “A Systematic Review of Stock Market Prediction Using Machine Learning and Statistical Techniques,” Materials Today: Proceedings, vol. 49, pp. 3187–3191, 2022.
  8. Nusrat Rouf, Majid Bashir Malik, Tasleem Arif, Sparsh Sharma, Saurabh Singh, Satyabrata Aich, and Hee-Cheol Kim, “Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions,” Electronics, vol. 10, no. 21, pp. 2717, 2022.
  9. Isaac Kofi Nti, Adebayo Felix Adekoya, and Benjamin Asubam Weyori, “A Comprehensive Evaluation of Ensemble Learning for Stock-Market Prediction,” Journal of Big Data, vol. 7, no. 1, pp. 20, 2020.
  10. Andrzej Janusz and Dominik Ślęzak, “KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions,” in Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, Sofia, Bulgaria, September 4-7, 2022, 2022, vol. 30 of Annals of Computer Science and Information Systems, pp. 393–398.
  11. Kenniy Olorunnimbe and Herna Viktor, “Deep Learning in the Stock Market – A Systematic Survey of Practice, Backtesting, and Applications,” Artificial Intelligence Review, vol. 56, no. 3, pp. 2057–2109, 2023.
  12. Prakash Balasubramanian, Chinthan P, Saleena Badarudeen, and Harini Sriraman, “A Systematic Literature Survey on Recent Trends in Stock Market Prediction,” PeerJ Computer Science, vol. 10, pp. e1700, 2024.
  13. Htet Htet Htun, Michael Biehl, and Nicolai Petkov, “Survey of Feature Selection and Extraction Techniques for Stock Market Prediction,” Financial Innovation, vol. 9, no. 1, pp. 26, 2023.
  14. Andrzej Janusz, Adam Krasuski, Sebastian Stawicki, Mariusz Rosiak, Dominik Ślęzak, and Hung Son Nguyen, “Key Risk Factors for Polish State Fire Service: A Data Mining Competition at Knowledge Pit,” in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Warsaw, Poland, September 7-10, 2014, 2014, vol. 2 of Annals of Computer Science and Information Systems, pp. 345–354.
  15. Eftim Zdravevski, Petre Lameski, Andrea Kulakov, and Dejan Gjorgjevikj, “Feature Selection and Allocation to Diverse Subsets for Multilabel Learning Problems with Large Datasets,” in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Warsaw, Poland, September 7-10, 2014, 2014, vol. 2 of Annals of Computer Science and Information Systems, pp. 387–394.
  16. Jan Lasek and Marek Gagolewski, “The Winning Solution to the AAIA’15 Data Mining Competition: Tagging Firefighter Activities at a Fire Scene,” in 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódź, Poland, September 13-16, 2015, 2015, vol. 5 of Annals of Computer Science and Information Systems, pp. 375–380.
  17. Marek Grzegorowski and Sebastian Stawicki, “Window-based Feature Extraction Framework for Multi-sensor Data: A Posture Recognition Case Study,” in 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódź, Poland, September 13-16, 2015, 2015, vol. 5 of Annals of Computer Science and Information Systems, pp. 397–405.
  18. Andrzej Janusz, Dominik Ślęzak, Marek Sikora, and Łukasz Wróbel, “Predicting Dangerous Seismic Events: AAIA’16 Data Mining Challenge,” in Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, Gdańsk, Poland, September 11-14, 2016, 2016, vol. 8 of Annals of Computer Science and Information Systems, pp. 205–211.
  19. Marek Grzegorowski, “Massively Parallel Feature Extraction Framework Application in Predicting Dangerous Seismic Events,” in Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, Gdańsk, Poland, September 11-14, 2016, 2016, vol. 8 of Annals of Computer Science and Information Systems, pp. 225–229.
  20. Łukasz Grad, “Helping AI to Play Hearthstone Using Neural Networks,” in Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, September 3-6, 2017, 2017, vol. 11 of Annals of Computer Science and Information Systems, pp. 131–134.
  21. Quang Hieu Vu, Dymitr Ruta, Andrzej Ruta, and Ling Cen, “Predicting Win-rates of Hearthstone Decks: Models and Features that Won AAIA’2018 Data Mining Challenge,” in Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, Poznań, Poland, September 9-12, 2018, 2018, vol. 15 of Annals of Computer Science and Information Systems, pp. 197–200.
  22. Andrzej Janusz, Łukasz Grad, and Marek Grzegorowski, “Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction,” in Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019, Leipzig, Germany, September 1-4, 2019, 2019, vol. 18 of Annals of Computer Science and Information Systems, pp. 3–6.
  23. Andrzej Janusz, Mateusz Przyborowski, Piotr Biczyk, and Dominik Ślęzak, “Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit,” in Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, FedCSIS 2020, Sofia, Bulgaria, September 6-9, 2020, 2020, vol. 21 of Annals of Computer Science and Information Systems, pp. 77–80.
  24. Dymitr Ruta, Ling Cen, and Quang Hieu Vu, “Deep bi-directional lstm networks for device workload forecasting,” in Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, FedCSIS 2020, Sofia, Bulgaria, September 6-9, 2020, 2020, vol. 21 of Annals of Computer Science and Information Systems, pp. 115–118.
  25. Andrzej Janusz, Antoni Jamiołkowski, and Michał Okulewicz, “Predicting the Costs of Forwarding Contracts: Analysis of Data Mining Competition Results,” in Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, Sofia, Bulgaria, September 4-7, 2022, 2022, vol. 30 of Annals of Computer Science and Information Systems, pp. 399–402.
  26. Eyad Kannout, Michał Grodzki, and Marek Grzegorowski, “Considering Various Aspects of Models’ Quality in the ML Pipeline – Application in the Logistics Sector,” in Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, Sofia, Bulgaria, September 4-7, 2022, 2022, vol. 30 of Annals of Computer Science and Information Systems, pp. 403–412.
  27. Michał Czerwiński, Marcin Michalak, Piotr Biczyk, Błażej Adamczyk, Daniel Iwanicki, Iwona Kostorz, Maksym Brzeczek, Andrzej Janusz, Marek Hermansa, Łukasz Wawrowski, and Artur Kozłowski, “Cybersecurity Threat Detection in the Behavior of IoT Devices: Analysis of Data Mining Competition Results,” in Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023, Warsaw, Poland, September 17-20, 2023, 2023, vol. 35 of Annals of Computer Science and Information Systems, pp. 1289–1293.
  28. Chang Lin, “Tackling Variable-length Sequences with High-cardinality Features in Cyber-attack Detection,” in Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023, Warsaw, Poland, September 17-20, 2023, 2023, vol. 35 of Annals of Computer Science and Information Systems, pp. 1295–1299.
  29. Marc Boullé, “Predicting Dangerous Seismic Events in Coal Mines under Distribution Drift,” in Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, Gdańsk, Poland, September 11-14, 2016, 2016, vol. 8 of Annals of Computer Science and Information Systems, pp. 221–224.
  30. Jan Kanty Milczek, Robert Bogucki, Jan Lasek, and Michal Tadeusiak, “Early Warning System for Seismic Events in Coal Mines Using Machine Learning,” in Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, Gdańsk, Poland, September 11-14, 2016, 2016, vol. 8 of Annals of Computer Science and Information Systems, pp. 213–220.
  31. Milena Trajanoska, Pavel Gjorgovski, and Eftim Zdravevski, “Application of Diversified Ensemble Learning in Real-life Business Problems: The Case of Predicting Costs of Forwarding Contracts,” in Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, Sofia, Bulgaria, September 4-7, 2022, 2022, vol. 30 of Annals of Computer Science and Information Systems, pp. 437–446.
  32. Andrzej Janusz, Daniel Kałuża, Agnieszka Chadzyńska-Krasowska, ̨ Bartek Konarski, Joel Holland, and Dominik Ślęzak, “IEEE BigData 2019 Cup: Suspicious Network Event Recognition,” in 2019 IEEE International Conference on Big Data (IEEE BigData), Los Angeles, CA, USA, December 9-12, 2019, 2019, pp. 5881–5887.
  33. Andrzej Janusz, Guohua Hao, Daniel Kałuża, Tony Li, Robert Wojciechowski, and Dominik Śl ̨ezak, “Predicting Escalations in Customer Support: Analysis of Data Mining Challenge Results,” in 2020 IEEE International Conference on Big Data (IEEE BigData 2020), Atlanta, GA, USA, December 10-13, 2020, 2020, pp. 5519–5526.
  34. Tianqi Chen and Carlos Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, 2016, pp. 785–794.
  35. Andrzej Janusz, Dominik Ślęzak, Sebastian Stawicki, and Krzysztof Stencel, “A Practical Study of Methods for Deriving Insightful Attribute Importance Rankings Using Decision Bireducts,” Information Sciences, vol. 645, pp. 119354, 2023.
  36. Christoph Schröer, Felix Kruse, and Jorge Marx Gómez, “A Systematic Literature Review on Applying CRISP-DM Process Model,” in CENTERIS 2020 – International Conference on ENTERprise Information Systems / ProjMAN 2020 – International Conference on Project MANagement / HCist 2020 – International Conference on Health and Social Care Information Systems and Technologies 2020, 2021, vol. 181 of Procedia Computer Science, pp. 526–534.