Exploring the Potential of ChatGPT in Financial Decision Making

Exploring the Potential of ChatGPT in Financial Decision Making

Reza Gharoie Ahangar, Agata Fietko
DOI: 10.4018/978-1-6684-8386-2.ch005
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Abstract

This chapter investigates the potential role of OpenAI's ChatGPT in investments to assess its proposed solutions' accuracy, potential benefits and drawbacks, and implications for financial decisions. The study utilizes empirical examples to evaluate ChatGPT's performance in providing financial advice. The results indicate that while ChatGPT can define financial terms and explain them in detail, it cannot provide investment suggestions. Moreover, the analysis suggests that the ChatGPT can provide more accurate information when users break down their questions into more specific topics. The study also highlights the need for more comprehensive training data to allow ChatGPT to provide more accurate advice. In addition, the research advises investors to be aware of ChatGPT's limitations and recommends breaking down financial questions into more specific topics to maximize the benefits of this technology. It is concluded that ChatGPT can be a helpful resource for financial experts but not for those with limited financial expertise and knowledge.
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1. Introduction

1.1. History of ChatGPT

The growing public interest in Artificial Intelligence (AI) reflects the efforts of major technology companies, such as Google, Microsoft, and Baidu, to develop AI-based services and products. For example, Microsoft is a major investor in OpenAI, while Google has released an experimental AI service called Bard. Generative AI, such as Chinchilla AI and Large Language Models, are also becoming increasingly popular (Zarifhonarvar, 2023).

In 2020, OpenAI, a company founded in 2015, launched third generation of its autoregressive language model, Generative Pre-trained Transformer 3 (GPT-3), which uses deep learning to produce human-like text (Brown et al., 2020). This model was developed on a corpus of almost 500 billion unlabeled tokens, representing the largest natural language processing (NLP) model in the world with 175 billion parameters (Radford et al., 2018). OpenAI was founded with the goal of developing AI systems that can outperform humans in different tasks (OpenAI, 2015).

On November 30, 2022, OpenAI introduced ChatGPT, a chatbot that leverages reinforcement learning to interact with its users (Grant & Metz, 2022). Quickly gaining widespread attention, ChatGPT was referred to as the industry's next big disruptor due to the quality of its response output (Shankland, 2022; Vanian, 2022). Within one week of its release, the artificial intelligence system had attracted over a million users on various social media platforms (Zarifhonarvar, 2023). This was notably faster than Instagram and TikTok, which respectively took nine and thirty months to reach a hundred million active users.

Recent media reports suggest that ChatGPT may be a formidable contender to Google (Brown, 2022), potentially leading to digital transformation (Tyrrell, 2022). However, San Altman, CEO of the company, has noted that the platform is still in its early testing stage (Altman, 2022), limiting its current capabilities. Nevertheless, ChatGPT still presents a promising indication of the potential of AI.

Generative AI has the potential to increase worker productivity by 10% (Megahed et al., 2023). Moreover, this technology is estimated to create trillions of dollars in economic value (Huang et al., 2022). In 2022, OpenAI invested over $1 billion in generative AI (Griffith & Metz, 2023). Despite these potential advantages, there is a growing concern regarding the potential impact of artificial intelligence on the knowledge economy, education, and skilled workers (Krugman, 2022; Locke, 2022; Marche, 2022).

Key Terms in this Chapter

Business Analytics: The process of analyzing data to gain valuable insights, which helps to develop strategies to increase productivity, reduce costs, and improve strategic decision-making.

Artificial Intelligence: It is the ability of a computer program to think, learn, and perform similar to humans. This is achieved by programming algorithms that allow the machine to make decisions and solve problems.

Chat Generative Pre-Trained Transformer: This type of language model is based on a transformer architecture that is pre-trained on large amounts of conversational data and designed to generate natural language responses to input sentences.

Stock Market Recommendation: This is an opinion or advice on buying, holding, or selling a particular stock. It is typically provided by an experienced financial analyst or investment professional who has knowledge of the stock and the overall market.

Financial Decision Making: It is the process of analyzing data to extract information and using it to make informed decisions for financial goals.

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