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Keywords

ChatGPT, Natural Language Processing, Artificial Intelligence, AI, corporate governance, institutional investors, proxy voting, index funds, securities regulation, outsourcing voting decisions, small investment funds, small passive funds, low-cost business model, shareholder interests, proxy advisors, bias, proxy voting guidelines

Abstract

Released in November 2022, Chat Generative Pre-training Transformer (“ChatGPT”), has risen rapidly to prominence, and its versatile capabilities have already been shown in a variety of fields. Due to ChatGPT’s advanced features, such as extensive pre-training on diverse data, strong generalization ability, fine-tuning capabilities, and improved reasoning, the use of AI in the legal industry could experience a significant transformation. Since small passive funds with low-cost business models generally lack the financial resources to make informed proxy voting decisions that align with their shareholders’ interests, this Article considers the use of ChatGPT to assist small investment funds, particularly small passive funds, in making more accurate and informed proxy voting decisions.

This Article proposes that small passive funds can improve their proxy voting accuracy and personalization by leveraging advanced AI language models such as ChatGPT. This would enable them to better serve their shareholders and navigate the competitive market. To test ChatGPT’s potential, the author conducted an experiment using a zero-shot GPT-4 model to generate detailed proxy voting guidelines and applied them to a real-world proxy statement. The model successfully identified conflicts of interest in the election of directors and generated comprehensive guidelines with weight for each variable. However, the model also identifies some of ChatGPT’s limitations, such as token limitations, long-range dependencies, and likely ESG inclination. Additionally, this Article recognizes that investment funds may face other challenges when outsourcing voting decisions to AI, such as data and algorithm biases, cybersecurity and privacy concerns, and regulatory uncertainties.

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