Sector-Specific Financial Forecasting With Machine Learning Algorithm and Shap Interaction Values

Abstract

This study examines the application of machine learning models to predict financial performance in various sectors, using data from 21 companies listed in the BIST100 index (2013-2023). The primary objective is to assess the potential of these models in improving financial forecast accuracy and to emphasize the need for transparent, explainable approaches in finance. A range of machine learning models, including Linear Regression, Ridge, Lasso, Decision Tree, Bagging, Random Forest, AdaBoost, Gradient Boosting (GBM), LightGBM, and XGBoost, were evaluated. Gradient Boosting emerged as the best-performing model, with ensemble methods generally demonstrating superior accuracy and stability compared to linear models. To enhance interpretability, SHAP (SHapley Additive exPlanations) values were utilized, identifying the most influential variables affecting predictions and providing insights into model behavior. Sector-based analyses further revealed differences in model performance and feature impacts, offering a granular understanding of financial dynamics across industries. The findings highlight the effectiveness of machine learning, particularly ensemble methods, in forecasting financial performance. The study underscores the importance of using explainable models in finance to build trust and support decision-making. By integrating advanced techniques with interpretability tools, this research contributes to financial technology, advancing the adoption of machine learning in data-driven investment strategies.

A Transfer Learning Approach for Testing the Adaptive Market Hypothesis: Evidence from BWP/USD to Cryptocurrency Markets

“…This shows how important recurrent calendar patterns and opening price discovery are in determining how exchange rates move. These results are in line with those of previous studies that show how interpretable machine learning methods help in locating the most important time-based factors in financial forecasting models; see, for instance, (Ergenç and Aktaş 2025). The findings indicate that periodic trade settlements, institutional foreign currency flows, and regular fiscal or commercial cycles are pivotal in BWP/USD dynamics (Honde 2024), especially in less liquid foreign exchange markets, where information is integrated at the commencement of the trading period.…”

A Transfer Learning Approach for Testing the Adaptive Market Hypothesis: Evidence from BWP/USD to Cryptocurrency Markets

“…This shows how important recurrent calendar patterns and opening price discovery are in determining how exchange rates move. These results are in line with those of previous studies that show how interpretable machine learning methods help in locating the most important time-based factors in financial forecasting models; see, for instance, (Ergenç and Aktaş 2025). The findings indicate that periodic trade settlements, institutional foreign currency flows, and regular fiscal or commercial cycles are pivotal in BWP/USD dynamics (Honde 2024), especially in less liquid foreign exchange markets, where information is integrated at the commencement of the trading period.…”

A Transfer Learning Approach for Testing the Adaptive Market Hypothesis: Evidence from BWP/USD to Cryptocurrency Markets

“…This shows how important recurrent calendar patterns and opening price discovery are in determining how exchange rates move. These results are in line with those of previous studies that show how interpretable machine learning methods help in locating the most important time-based factors in financial forecasting models; see, for instance, (Ergenç and Aktaş 2025). The findings indicate that periodic trade settlements, institutional foreign currency flows, and regular fiscal or commercial cycles are pivotal in BWP/USD dynamics (Honde 2024), especially in less liquid foreign exchange markets, where information is integrated at the commencement of the trading period.…”

A Transfer Learning Approach for Testing the Adaptive Market Hypothesis: Evidence from BWP/USD to Cryptocurrency Markets

“…This shows how important recurrent calendar patterns and opening price discovery are in determining how exchange rates move. These results are in line with those of previous studies that show how interpretable machine learning methods help in locating the most important time-based factors in financial forecasting models; see, for instance, (Ergenç and Aktaş 2025). The findings indicate that periodic trade settlements, institutional foreign currency flows, and regular fiscal or commercial cycles are pivotal in BWP/USD dynamics (Honde 2024), especially in less liquid foreign exchange markets, where information is integrated at the commencement of the trading period.…”