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A Bayesian Network Approach to Stock Price Movement Prediction

Dambo Itari, Ebuete, Ibim Yarwamara, ABSTRACT, Keywords, ., INTRODUCTION

Abstract

This study presents a Bayesian Network (BN) model for predicting Apple Inc. stock price movements from 2018 to 2025. The model incorporates temporal dependencies and multiple financial indicators, including technical indicators, market trends, earnings reports, and news sentiment. A time-based split was used to train and test the model, ensuring realistic evaluation and avoiding look-ahead bias. Performance was assessed using confusion matrices, macro- averaged multi-class metrics, ROC-style analysis, cross-validation, and probability calibration. The BN model achieved an accuracy of 0.78, macro-averaged precision 0.75, recall 0.74, and F1- score 0.74, demonstrating balanced performance across Up, Down, and Stable movements. ROC analysis showed strong discriminative ability for directional movements (AUC Up: 0.84, Down: 0.81) and reasonable discrimination for Stable movements (AUC: 0.73). Comparative evaluation with Logistic Regression confirmed the BN model’s superiority in capturing conditional dependencies and non-linear relationships. The results highlight the robustness and practical applicability of BN for large-cap stock prediction.

Keywords

Bayesian Network ModelStock PredictionApple Inc.Temporal Dependencies

References

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