Fusion Strategies for Multi-Class Stock Movement Prediction: Balancing Temporal, Spatial, and Tabular Models

Accurate short-horizon stock-movement forecasting remains a central problem in computational finance, where even small directional errors can accumulate into significant trading risk. The most challenging regime is the neutral state—intervals with minor price changes that are easily masked by noise. To address this challenge, we compare three complementary learning paradigms and their combinations across multiple lookback horizons for three representative equities (AAPL, GOOG, TSLA). We evaluate Long Short-Term Memory (LSTM) networks for temporal dynamics, Convolutional Neural Networks (CNNs) on polar-transformed price images for spatial pattern extraction, and XGBoost on tabular technical indicators for structured feature learning. Empirical results (Appendices A–C) reveal distinct horizon-dependent behaviors: CNNs excel at ultra-short windows (W = 1–3) with perfect accuracy and neutral-F1 ≈ 1.00 but deteriorate rapidly as horizons lengthen; LSTMs gain overall accuracy with longer windows (W = 30–60o) but lose sensitivity to neutral segments; and XGBoost remains the most stable single model, maintaining accuracy ≈ 0.89–0.93, low loss ≈ 0.4–0.6, and neutral-F1 ≈ 0.89–0.96 across assets. Building on these complementary patterns, we propose fusion frameworks that integrate CNN and XGBoost outputs through weighted voting, cascaded thresholds, and probability-smoothed blending. The best configuration—probability-smoothed fusion—achieves roughly a 3–4 percentage-point improvement in neutral-F1 over the strongest standalone model while preserving comparable accuracy and calibration loss. The LSTM is retained solely as a benchmark to illustrate sequence-model trade-offs and is not included in the fusion. Together, the results demonstrate that combining spatial and tabular perspectives yields more balanced recognition of neutral states without sacrificing directional accuracy. Accuracy measures overall correctness, loss captures probabilistic calibration, and F1 quantifies class-wise precision–recall balance. Viewed jointly, these metrics show that CNN–XGBoost fusion produces smoother and more interpretable predictions across assets and horizons. Such stability can reduce overtrading during ambiguous market phases, improving risk-adjusted decision-making in algorithmic trading strategies.