Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data

TL;DR

This paper introduces a novel Chaos-Markov-Gaussian (CMG) framework combined with transformer models to improve short-term sentiment forecasting of financial OHLC data, addressing volatility and non-linearity challenges.

Contribution

The paper presents a new CMG framework integrating chaos theory, Markov models, and Gaussian processes, enhanced with transformers for better accuracy and efficiency in financial time series prediction.

Findings

CMG outperforms traditional models in accuracy

Framework is resource-efficient and generalizes well

Effective for various financial instruments

Abstract

Short-term sentiment forecasting in financial markets (e.g., stocks, indices) is challenging due to volatility, non-linearity, and noise in OHLC (Open, High, Low, Close) data. This paper introduces a novel CMG (Chaos-Markov-Gaussian) framework that integrates chaos theory, Markov property, and Gaussian processes to improve prediction accuracy. Chaos theory captures nonlinear dynamics; the Markov chain models regime shifts; Gaussian processes add probabilistic reasoning. We enhance the framework with transformer-based deep learning models to capture temporal patterns efficiently. The CMG Framework is designed for fast, resource-efficient, and accurate forecasting of any financial instrument's OHLC time series. Unlike traditional models that require heavy infrastructure and instrument-specific tuning, CMG reduces overhead and generalizes well. We evaluate the framework on market indices, forecasting sentiment for the next trading day's first quarter. A comparative study against statistical, ML, and DL baselines trained on the same dataset with no feature engineering shows CMG consistently outperforms in accuracy and efficiency, making it valuable for analysts and financial institutions.