Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100

ArXiv ID: 2506.06345 “View on arXiv”

Authors: Sukru Selim Calik, Andac Akyuz, Zeynep Hilal Kilimci, Kerem Colak

Abstract

Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 indices, covering the period from January 2015 to March 2025. Models including DLinear, LTSNet, Vanilla Transformer, and Time Series Transformer are employed, with input features enriched by technical indicators. SHAP and LIME techniques are used to provide transparency into the influence of individual features on model outputs. The results demonstrate the strong predictive capabilities of transformer models and highlight the potential of interpretable machine learning to empower individuals in making informed investment decisions and actively engaging in financial markets.

Keywords: Transformer Models, Explainable AI (XAI), Time Series Forecasting, SHAP (SHapley Additive exPlanations), Technical Indicators, Equities

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced transformer architectures (DLinear, LTSNet, TST) and XAI techniques (SHAP/LIME) which involve dense mathematical foundations, while demonstrating strong empirical rigor with a decade-long backtest on BIST100 data using multiple models and technical indicators.
  flowchart TD
    Start["Research Goal:<br>Predict stock prices with<br>high accuracy & interpretability"] --> Data["Data Source:<br>BIST100 (5 high-volume banks<br>XBANK & XU100 indices)<br>Jan 2015 - Mar 2025"]
    
    Data --> Features["Feature Engineering:<br>Technical Indicators<br>+ Raw Time Series Data"]
    
    Features --> Models["Computational Models:<br>DLinear, LTSNet,<br>Vanilla & Time Series Transformers"]
    
    Models --> XAI["Explainability Process:<br>SHAP & LIME analysis<br>on model predictions"]
    
    XAI --> Outcomes["Key Outcomes:<br>1. High prediction accuracy<br>2. Transparent feature influence<br>3. Actionable investment insights"]