Trend-encoded Probabilistic Multi-order Model: A Non-Machine Learning Approach for Enhanced Stock Market Forecasts

ArXiv ID: 2502.08144 “View on arXiv”

Authors: Unknown

Abstract

In recent years, the dominance of machine learning in stock market forecasting has been evident. While these models have shown decreasing prediction errors, their robustness across different datasets has been a concern. A successful stock market prediction model minimizes prediction errors and showcases robustness across various data sets, indicating superior forecasting performance. This study introduces a novel multiple lag order probabilistic model based on trend encoding (TeMoP) that enhances stock market predictions through a probabilistic approach. Results across different stock indexes from nine countries demonstrate that the TeMoP outperforms the state-of-the-art machine learning models in predicting accuracy and stabilization.

Keywords: probabilistic model, trend encoding, prediction accuracy, stock market forecasting, equities

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper presents a probabilistic model with multiple lag orders and trend encoding, involving statistical and fuzzy logic concepts, indicating moderate math complexity. Empirically, it tests the model across nine international stock indices and compares it to state-of-the-art ML models, showing a data-heavy and backtest-ready approach.

  flowchart TD
    A["Research Goal: Non-ML Approach for Robust Stock Forecasting"] --> B["Methodology: Trend-encoded Multi-order Probabilistic Model"]
    B --> C["Input: Global Stock Index Data<br>9 Countries"]
    C --> D["Computation: Trend Encoding &<br>Multi-order Probabilistic Analysis"]
    D --> E["Outcomes: Superior Accuracy &<br>Stabilized Forecasts vs. SOTA ML"]