Determinants of FinTech Stock Returns: Evidence from the Warsaw Stock Exchange Using Machine Learning
DOI:
https://doi.org/10.15611/fins.2026.1.04Keywords:
FinTech, stock returns, machine learning, Random Forest, Warsaw Stock ExchangeAbstract
Aim: The aim of this study was to identify the key determinants of stock returns of FinTech companies listed on the Warsaw Stock Exchange and to evaluate the usefulness of machine learning methods in predicting the direction of FinTech stock returns in this emerging European market.
Methodology: The study was based on a sample of 12 FinTech companies observed over the period 2011–2025. The analysis employed financial indicators describing economic and financial condition of companies, including profitability, growth, leverage, and liquidity measures. A Random Forest classification model was applied, supported by correlation analysis, data preprocessing techniques and logistic regression used for additional robustness.
Findings: The results indicated that traditional correlation methods do not reveal significant relationships between financial variables and stock returns, suggesting a complex and nonlinear structure of the analysed market. The machine learning model achieved moderate predictive performance. Feature importance analysis identified operating profit margin, revenue growth, and return on equity as the most influential variables.
Implications: The findings highlight the limitations of traditional econometric approaches and support the application of machine learning methods in financial market analysis. For practitioners, the results suggest that operational performance and growth indicators are key factors in evaluating FinTech companies.
Originality/value: The article contributes to the literature by combining machine learning techniques with the analysis of stock return determinants in the FinTech sector within an emerging market context, which remains underexplored.
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Copyright (c) 2026 Katarzyna Perez, Katarzyna Czaplińska
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Accepted 2026-05-18
Published 2026-07-28
