ChatGPT’s crystal ring: simulating auditors’ use of machine learning in stock price prediction

Abstract

This study investigates the influence of technological factors on the intent to use Machine Learning (ML) tools such as Python for the purpose of predicting stock prices. Further, it investigates the moderate impact of Artificial Intelligence (AI) models usage, in particular ChatGPT, on these associations. The outcomes of a simulation involving 400 auditors, accounting for the heterogeneity of their competencies, were obtained through code utilisation based on the Python programming language. The technological factors drawn from diffusion of innovation theory (DOI), including relative advantages, Complexity, compatibility, observability, and triability, all showed positive associations with behavioural intent. The use of ChatGPT significantly fortified these connections. These results suggest a fruitful symbiotic outcome may be achieved by combining AI capabilities with these variables. The findings underscore the significance of planning for the adoption of AI in financial decision-making and auditing and also illustrate the potential of AI in these areas.

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