Decoding Financial Data: Machine Learning Approach to Predict Trading Actions

• Autorzy: Fung Yat Chun , Amonov Bekzod

Identyfikatory

DOI

10.15439/2024F4556

• Konferencja: Federated Conference on Computer Science and Information Systems (19 ; 08-11.09.2024 ; Belgrade, Serbia)
• Języki publikacji: EN

Abstrakty

EN

This paper presents a study on predicting stock trends using a dataset consisting of key financial indicators from 300 S&P 500 companies over a decade. Each company is characterized by 58 financial indicators along with their 1-year changes, offering valuable insights into potential trends. The objective is to develop predictive models to accurately forecast trading actions (buy, sell, hold) based on fundamental financial data. Three machine learning models---Random Forest, CatBoost, and XGBoost classifiers---were trained, employing two distinct voting mechanisms. The first voting mechanism was utilized in the competition, while the second was developed post-competition after the test labels were released. Notably, the second model was trained solely on the training data. The results demonstrate that both voting mechanisms effectively capture trends, as reflected by the average error cost measure, evaluated using the provided error cost matrix.

Słowa kluczowe

EN

cost; uncertainty; computational modeling; stacking; training data; company; forestry; predictive models; market research; vector

PL

koszt; niepewność; modelowanie obliczeniowe; nakładanie; firma; leśnictwo; modele predykcyjne; badania rynku; wektor

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2024
• Tom: Vol. 39
• Strony: 739--744
• Opis fizyczny: Bibliogr. 7 poz., rys., tab., wykr.

Twórcy

autor

Fung Yat Chun

• TU Dortmund University Department of Statistics, Data Science Dortmund, Germany

autor

Amonov Bekzod

• TU Dortmund University Department of Statistics, Data Science Dortmund, Germany

Bibliografia

• 1. L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “Catboost: Unbiased boosting with categorical features,” Advances in Neural Information Processing Systems, vol. 31, pp. 6638–6648, 2018.
• 2. S. Van Buuren and K. Groothuis-Oudshoorn, “Mice: Multivariate imputation by chained equations in r,” Journal of statistical software, vol. 45, no. 3, 2011.
• 3. L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001.
• 4. A. Liaw and M. Wiener, “Classification and regression by randomforest,” R news, vol. 2, no. 3, pp. 18–22, 2002.
• 5. T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016, pp. 785–794.
• 6. K. Gurney, An introduction to neural networks. CRC press, 1997.
• 7. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons, 2004.

Uwagi

Track: Data Mining Competition