Reasoning on Time-Series for Financial Technical Analysis

ArXiv ID: 2511.08616 “View on arXiv”

Authors: Kelvin J. L. Koa, Jan Chen, Yunshan Ma, Huanhuan Zheng, Tat-Seng Chua

Abstract

While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), a novel framework that combine verbal and latent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over time-series, we convert stock price data into textual annotations and optimize the reasoning trace using an inverse Mean Squared Error (MSE) reward objective. To produce time-series outputs from textual reasoning, we condition the outputs of a time-series backbone model on the reasoning-based attributes. Experiments on stock datasets across U.S., Chinese, and European markets show that VTA achieves state-of-the-art forecasting accuracy, while the reasoning traces also perform well on evaluation by industry experts.

Keywords: Large Language Models (LLMs), Technical Analysis, Time-Series Forecasting, Inverse Mean Squared Error (MSE), Verbal Reasoning, Equities (Stocks)

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper introduces a novel reinforcement learning objective (Time-GRPO) with clipped ratios and KL-divergence regularization, and conditions forecasts on reasoning attributes, showing moderate-to-high mathematical density. It also validates the model across U.S., Chinese, and European stock datasets, performing investment simulations and expert evaluations, demonstrating significant empirical validation and implementation focus.

  flowchart TD
    A["Research Goal: Combine verbal & latent reasoning<br>for accurate & interpretable stock forecasts"] --> B

    subgraph B ["Methodology: Verbal Technical Analysis VTA"]
        B1["Input: Stock Price Time-Series Data"] --> B2["Convert to Textual Annotations"]
        B2 --> B3["Verbal Reasoning via LLMs"]
        B3 --> B4["Inverse MSE Reward Optimization"]
        B4 --> B5["Conditional Time-Series Generation"]
    end

    B --> C["Output: Stock Price Forecasts & Reasoning Traces"]

    C --> D["Outcome: State-of-the-Art Accuracy<br>Interpretability validated by Experts<br>Across US, China, Europe Markets"]