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

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  • Peiwan Wang
  • Chenhao Cui
  • Yong Li

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.

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  • Peiwan Wang & Chenhao Cui & Yong Li, 2025. "Trend-encoded Probabilistic Multi-order Model: A Non-Machine Learning Approach for Enhanced Stock Market Forecasts," Papers 2502.08144, arXiv.org, revised Feb 2025.
    • Handle: RePEc:arx:papers:2502.08144
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    References listed on IDEAS

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    1. Bitencourt, Hugo Vinicius & de Souza, Luiz Augusto Facury & dos Santos, Matheus Cascalho & Silva, Rodrigo & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha, 2023. "Combining embeddings and fuzzy time series for high-dimensional time series forecasting in internet of energy applications," Energy, Elsevier, vol. 271(C).
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