This AI Research Shows How ChatGPT Can Be Used For Finance Research

Artificial intelligence language models are computer programs that use machine learning algorithms to analyze and understand human language. These models are designed to automatically process large amounts of text data and use that information to generate text that mimics human language. ChatGPT is the most famous artificial intelligence language model released recently by OpenAI. ChatGPT is based on the GPT-3 language model, which uses deep learning techniques to generate human-like responses to text input. Language models like ChatGPT have numerous applications in natural language processing, chatbots and virtual assistants, content creation, healthcare, finance, and law. They can assist in analyzing large volumes of text data, understanding human language, and generating contextually appropriate responses. Language models have the potential to revolutionize the way we interact with technology and enhance a wide range of applications, making them more efficient and effective. In light of ChatGPT’s significance and great success, an Irish research team recently conducted a study to determine whether ChatGPT can significantly assist with finance research. And the findings of this study are expected to be applicable across various research domains.

Concretely, the article’s authors conducted an empirical study to test the effectiveness of ChatGPT in finance research. They focused on the first four stages of the research process:

  • Idea generation
  • Prior literature synthesis
  • Data identification and preparation
  • Testing framework determination and implementation

To conduct the study, the authors chose cryptocurrencies as the finance topic and concentrated on letter-style articles of about 2000-2500 words. They requested ChatGPT to generate output for the four stages of the research process. In addition, they used three versions of ChatGPT output for each step of the research process. The first version was based purely on the AI-generated output, the second version was based on AI-generated output with minimal guidance from an experienced researcher, and the third version was based on AI-generated output with significant input and guidance from an experienced researcher. The generated output for each stage was graded by a panel of experienced academic authors and reviewers on a scale of 1 to 5, with 5 being the highest grade. The panel evaluated the output based on several criteria: originality, coherence, relevance, and overall quality. The authors also analyzed the impact of private data and researcher domain expertise input on the quality of the generated output. They found that the output quality improved significantly when there was more private data and an experienced researcher provided more guidance and input.

The evaluation of the study was conducted using Qualtrics. It involved a team of experienced authors and reviewers who assessed the likelihood of the output being acceptable for a minimum ABS2-level finance journal. The study’s three versions were evaluated based on specific criteria, and all received positive ratings. Notably, the third version, which integrated private data and researcher domain expertise, received the highest rating. However, the platform encountered challenges in generating suitable testing frameworks and literature reviews.

In conclusion, artificial intelligence language models, such as ChatGPT, have various applications in various fields, including finance research. The study conducted in this article found that ChatGPT can significantly assist in the early stages of the research process, particularly when combined with private data and guidance from an experienced researcher. However, challenges still exist in generating suitable testing frameworks and literature reviews using language models. Overall, the potential of language models to revolutionize how we interact with technology and enhance various applications is promising.


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Mahmoud is a PhD researcher in machine learning. He also holds a
bachelor's degree in physical science and a master's degree in
telecommunications and networking systems. His current areas of
research concern computer vision, stock market prediction and deep
learning. He produced several scientific articles about person re-
identification and the study of the robustness and stability of deep
networks.

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