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    188宝金博页面版: ChatGPT基于的投资组合选择 ChatGPT-based Investment Portfolio Selection

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    内容提示： O. Romanko, A. Narayan, R. H. Kwon ChatGPT-based Investment Portfolio Selection 1 Oleksandr Romanko oleksandr.romanko@sscinc.com Akhilesh Narayan akhilesh.narayan@iitb.ac.in ? Roy H. Kwon rkown@mie.utoronto.ca 1 SS&C Algorithmics, 200 Front Street West, suite 2500, Toronto, ON, M5V3K2, Canada 2 Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India 3 Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Tor...

    文档格式：PDF | 页数：25 | 浏览次数：63 | 上传日期：2023-08-23 15:04:16 | 文档星级：
    O. Romanko, A. Narayan, R. H. Kwon ChatGPT-based Investment Portfolio Selection 1 Oleksandr Romanko oleksandr.romanko@sscinc.com Akhilesh Narayan akhilesh.narayan@iitb.ac.in ? Roy H. Kwon rkown@mie.utoronto.ca 1 SS&C Algorithmics, 200 Front Street West, suite 2500, Toronto, ON, M5V3K2, Canada 2 Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India 3 Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S3G9, Canada ChatGPT-based Investment Portfolio Selection Oleksandr Romanko 1,3 · Akhilesh Narayan 2 · Roy H. Kwon 3 First version: 11 August 2023 Abstract In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation of the output. Therefore, we take an alternative approach. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Subsequently, we compared various portfolio optimization strategies that utilized this AI-generated trading universe, evaluating those against quantitative portfolio optimization models as well as comparing to some of the popular investment funds. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio. But when stocks selection by ChatGPT is combined with established portfolio optimization models, we achieve even better results. By blending strengths of AI-generated stock selection with advanced quantitative optimization techniques, we observed the potential for more robust and favorable investment outcomes, suggesting a hybrid approach for more effective and reliable investment decision-making in the future. Keywords Portfolio optimization · Investment management · Generative AI · ChatGPT 1 Introduction The advent of generative artificial intelligence (AI) models, particularly large language models (LLMs) like ChatGPT, marks a significant development and can potentially disrupt different industries, including finance [1,10,11,13,17]. These models, based on the transformative architecture of generative AI, can generate detailed, contextually coherent outputs by analyzing massive text datasets, and have demonstrated impressive capabilities in tasks ranging from creative writing to complex problem-solving. The use of these models, ChatGPT in particular, for selection of investment strategies or "stock picking" has drawn significant attention [2,3,4,8,9,14] since the launch of ChatGPT in November 2022 and especially GPT-4 [15], its more advanced successor, in mid-March 2023. Other LLMs available at the time of writing this paper, such as Bard from Google or Claude 2 from Anthropic, may be considered as alternatives to GPT models from OpenAI. However, the question of how effective ChatGPT might be in selecting financial assets, such as stocks, for investing, remains open. One challenge arises from the black-box nature of generative AI models, which involve complex non-linearities, making it difficult to discern how they analyze their training data to define investment strategies. The specifics of the training data used for the GPT-4 model are unknown, adding

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