Modeling Asset Price Process: An Approach for Imaging Price Chart with Generative Diffusion Models

Jinseong Park (), Hyungjin Ko () and Jaewook Lee ()
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Jinseong Park: Seoul National University
Hyungjin Ko: Sungkyunkwan University
Jaewook Lee: Seoul National University

Computational Economics, 2025, vol. 66, issue 1, No 11, 349-375

Abstract: Abstract Artificial Intelligence (AI) models have been recently studied to discover data patterns for prediction and forecasting tasks in finance. However, the use of deep generative models in finance remains relatively unexplored. In this paper, we investigate the potential of deep generative diffusion models to estimate unknown dynamics using multiple simulations based on stock chart images. We first demonstrate a novel pre-processing framework and synthetic image generation using opening, high, low, and closing stock chart images to train neural networks. Without assuming the specific process as the underlying asset price process, we can generate synthetic data without predetermined assumptions of the underlying movements of stock prices by trained generative diffusion models. The experimental results demonstrate that the proposed method successfully replicates well-known asset price processes. With various simulation paths, we can also accurately estimate option pricing on the S &P 500. We conclude that financial simulation with AI can be a novel approach to financial decision-making.

Keywords: Deep learning; Generative diffusion model; Asset price process; Financial simulation; Price chart; Option pricing (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10668-4

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