VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis

• URL: http://arxiv.org/abs/2505.18570v3
• Date: Wed, 11 Jun 2025 18:38:02 GMT
• Title: VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis
• Authors: Tina Khezresmaeilzadeh, Parsa Razmara, Seyedarmin Azizi, Mohammad Erfan Sadeghi, Erfan Baghaei Potraghloo,
• Abstract summary: We introduce VISTA (Vision-Language Inference for Stock Time-series Analysis), a training-free framework for multi-modal stock forecasting.<n>We benchmark VISTA against standard baselines, including ARIMA and text-only LLM-based prompting methods.<n>We show that VISTA outperforms these baselines by up to 89.83%, demonstrating the effectiveness of multi-modal inference for stock time-series analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stock price prediction remains a complex and high-stakes task in financial analysis, traditionally addressed using statistical models or, more recently, language models. In this work, we introduce VISTA (Vision-Language Inference for Stock Time-series Analysis), a novel, training-free framework that leverages Vision-Language Models (VLMs) for multi-modal stock forecasting. VISTA prompts a VLM with both textual representations of historical stock prices and their corresponding line charts to predict future price values. By combining numerical and visual modalities in a zero-shot setting and using carefully designed chain-of-thought prompts, VISTA captures complementary patterns that unimodal approaches often miss. We benchmark VISTA against standard baselines, including ARIMA and text-only LLM-based prompting methods. Experimental results show that VISTA outperforms these baselines by up to 89.83%, demonstrating the effectiveness of multi-modal inference for stock time-series analysis and highlighting the potential of VLMs in financial forecasting tasks without requiring task-specific training.

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