VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis
This paper presents VISTA, a training-free multimodal framework combining visual and textual data to improve stock forecasting accuracy.
What it examines
The paper introduces VISTA, a training-free framework for stock forecasting using Vision-Language Models. By combining textual and visual data with chain-of-thought prompts, it seeks to improve predictive accuracy over traditional methods and address the challenges of forecasting volatile stock prices.
What it concludes
The paper concludes that using visual and textual data with reasoning prompts enhances stock forecasting accuracy compared to traditional models. Results suggest such multimodal methods can be applied in financial analysis, risk assessment, and economic planning, with further research needed to explore broader market applications.
Evidence objects
Researchers unveil VISTA, a training-free multimodal framework combining textual narratives and visual line charts with historical stock data to achieve up to 89.83% superior forecasting accuracy over traditional models innovatively.
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The study innovatively integrates Vision-Language Models with chain-of-thought prompting, enabling step-by-step reasoning that uncovers nuanced trends and seasonal patterns often overlooked by conventional numerical forecasting approaches, yielding outstanding predictive insights.
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Employing structured multimodal prompts, the research democratizes access to advanced financial forecasting tools, reducing error metrics consistently compared to baselines, though minor model inconsistencies indicate further refinement is necessary urgently.
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Leveraging a multimodal, training-free approach, the paper uniquely merges visual line graph representations with textual numerical data using vision-language models for stock forecasting. Its innovative zero-shot capabilities and chain-of-thought prompting strategy add novelty to traditional methods. Relevant for quantitative finance, the method is fresh, compelling, combining LLM and VLM insights.
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Raw abstract and provenance
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… ▽ More 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. △ Less
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