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AI, Opinion Ecosystems and Finance
The episode discusses one of the papers to be presented at the 9th Annual Data Science in Finance Conference by the Society of Quantitative Analysis (SQA) and the Chartered Financial Analysts (CFA) Society of New York on Thursday, January 8, 2026.
This research explores how Generative AI impacts financial markets by comparing its use on two distinct social media platforms: Seeking Alpha and WallStreetBets. Using GPTZero to detect AI-generated content, the authors find that a platform's governance and user demographics determine whether AI improves or harms information quality. On the curated Seeking Alpha, AI acts as a tool for information enhancement, helping sophisticated investors synthesize fundamental data and improve market efficiency. Conversely, on the unmoderated WallStreetBets, AI is often used for information distortion, amplifying emotional narratives and speculative "lottery-like" trading behaviors. Ultimately, the study concludes that the technology's market impact is not inherent but is instead shaped by the institutional environment and community norms.
Reference
Hirshleifer, David and Hirshleifer, David and Peng, Lin and Wang, Qiguang and Zhang, Weicheng and Zhang, Xiaoyan, AI, Opinion Ecosystems, and Finance (July 01, 2025). Available at SSRN: https://ssrn.com/abstract=5452175
Podcast Disclaimer
This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.
This episode is based on the reference(s) listed above and was generated using Notebook LM and potentially other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.
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By kathrynj2The episode discusses one of the papers to be presented at the 9th Annual Data Science in Finance Conference by the Society of Quantitative Analysis (SQA) and the Chartered Financial Analysts (CFA) Society of New York on Thursday, January 8, 2026.
This research explores how Generative AI impacts financial markets by comparing its use on two distinct social media platforms: Seeking Alpha and WallStreetBets. Using GPTZero to detect AI-generated content, the authors find that a platform's governance and user demographics determine whether AI improves or harms information quality. On the curated Seeking Alpha, AI acts as a tool for information enhancement, helping sophisticated investors synthesize fundamental data and improve market efficiency. Conversely, on the unmoderated WallStreetBets, AI is often used for information distortion, amplifying emotional narratives and speculative "lottery-like" trading behaviors. Ultimately, the study concludes that the technology's market impact is not inherent but is instead shaped by the institutional environment and community norms.
Reference
Hirshleifer, David and Hirshleifer, David and Peng, Lin and Wang, Qiguang and Zhang, Weicheng and Zhang, Xiaoyan, AI, Opinion Ecosystems, and Finance (July 01, 2025). Available at SSRN: https://ssrn.com/abstract=5452175
Podcast Disclaimer
This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.
This episode is based on the reference(s) listed above and was generated using Notebook LM and potentially other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.