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 paper introduces a scalable framework for financial portfolio management using high-dimensional Conditional Autoencoders (CAEs) to identify latent asset-pricing factors. While traditional methods often restrict the number of factors to prevent overfitting, this study utilizes up to 50 latent factors coupled with an uncertainty-aware selection process. By employing diverse forecasting models like ZS-Chronos and Q-Boost, the authors rank these factors based on their predictive stability and prune the less reliable ones. The findings demonstrate that selecting the most predictable subset significantly improves risk-adjusted returns, achieving high Sharpe and Sortino ratios. Ultimately, the study concludes that ensemble strategies combining these varied predictive signals offer superior, market-neutral performance even during volatile periods.

Reference

Ryan Engel, Yu Chen, Pawel Polak, and Ioana Boier. 2025. Scaling Conditional Autoencoders for Portfolio Optimization via Uncertainty-Aware Factor Selection. In 6th ACM International Conference on AI in Finance (ICAIF ’25), November15–18, 2025, Singapore, Singapore. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3768292.3770415

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.

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 Apple’s App Tracking Transparency (ATT) policy served as a privacy-driven shock that disrupted the alternative data landscape in financial markets. By restricting cross-app tracking, the policy degraded the quality of mobile traffic signals, which were previously used by investors to predict firm performance. The authors demonstrate that mutual funds and financial analysts who relied on this data experienced a significant decline in their trading edge and forecasting accuracy. Consequently, the market's ability to price stocks efficiently weakened, leading to increased information frictions and higher trading costs for affected companies. Ultimately, the study highlights the fragility of non-traditional data and warns that privacy regulations can have unintended "ripple effects" on global capital allocation.

Reference

Abis, Simona and Tang, Huan and Bian, Bo, Breaking the Data Chain: The Ripple Effect of Data Sharing Restrictions on Financial Markets (July 01, 2025). The Wharton School Research Paper, Available at SSRN: https://ssrn.com/abstract=5334566 or http://dx.doi.org/10.2139/ssrn.5334566

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.

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 Wall Street Bets. Using GPT Zero 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 Wall Street Bets, 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 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.