Usefulness of AI Chatbots to Individual Investors

Can a generative artificial intelligence (AI) model, such as ChatGPT 4o, materially aid investors in understanding the implications of earnings conference call transcripts? In their December 2024 paper entitled “AI, Investment Decisions, and Inequality”, Alex Kim, David Kim, Maximilian Muhn, Valeri Nikolaev and Eric So conduct two surveys to explore how generative AI shapes investment decision-making based on anonymous earnings conference call transcripts of publicly traded firms. For the first survey, they: (1) divide participants into sophisticated and unsophisticated groups based on responses to initial questions; and, (2) ask ChatGPT 4o to generate one summary for individuals with little financial knowledge and another summary for individuals with college-level financial knowledge and stock investing experience. They then randomly assign each participant to receive raw conference call transcripts (the control), summaries for sophisticated investors or summaries for unsophisticated investors. They next present each participant with summaries for two distinct but similar firms, one at a time and ask each participant to:

1. Rate on a scale of -5 to 5 the likelihood that firm earnings will decrease or increase next year, and confidence in the estimate on a scale from 0 to 1.
2. Evaluate on a scale of -5 to 5 the overall sentiment as negative or positive, and confidence in the evaluation on a scale from 0 to 1.
3. Allocate a hypothetical $1,000 to the two stocks presented or to cash for either one day or one year.
4. Write a brief rationale for the asset allocation decision.

They record how much time each participant spends on each task.

For the second survey, they provide some participants with an AI chatbot pre-loaded with earnings call transcripts and some with only the raw transcripts (the control). They study interactions of participants with the chatbot and measure subsequent performances on investment tasks.

Their pool of end-of-fiscal-year earnings conference call transcripts spans 2010 through 2022 for 200 NYSE/NASDAQ stocks assigned to 100 economically similar pairs. Using the selected transcripts and associated 1-day and 1-year stock returns, they find that: (more…)