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19 OctMon
Mon
CFA Institute Research and Policy Center: PublicationsAI Score5454
A former equity strategy partner and independent investment firm operator used AI in retirement to backtest his long-standing personal strategy, which combined Peter Lynch's growth stock selection, Hilary Kramer's undervalued obscure stock selection, and earnings revision discipline. He corrected for survivorship bias, used point-in-time data, calculated pre-fee returns, and compared it against random stock selection from the same investment pool. The results showed that the strategy did not outperform random selection, with ordinary index funds performing better than the strategy.
Moontower has released this issue, mentioning the Metagame event held at Lighthaven in Berkeley from November 6 to 8. Moontower readers can enjoy a $50 discount on regular tickets. This issue also shares AI-related thoughts, TIPS investment analysis, crude oil market learning resources, and pre-sale information for "How I Invest Volume 2", which will be shipped on October 20.
Itaú is implementing its AI First strategy in customer and employee services through the conversational interface ia.i and the IARA governance platform. This application scenario operates in one of Brazil's most heavily regulated industries.
The AI boom is becoming an ESG test. The water and electricity consumption of AI data centers will affect corporate profit margins, capital expenditures, and return on investment. Cross-industry investment portfolios may collectively face ESG exposure from data center expansion and power bottlenecks.
Benefit Street Partners speeds up question answering with OpenAI's Codex tool. The firm's Anant Kumar uses Codex to conduct code reviews, improve follow-up efficiency, and build tools to compare loan valuations and analyze credit agreements.
Moontower host Kris Abdelmessih breaks down an interview with LTCM co-founder Victor Haghani, arguing that investment decisions consist of two parts: instrument selection and position sizing, and that LTCM's failure stemmed from flawed position sizing rather than poor instrument selection.
Most portfolio managers and analysts in the investment industry have not yet adopted large language models in their daily work, with only a few well-resourced large institutions having implemented them. The article notes that LLMs can improve investment decision hit rates and long-term productivity, and that current barriers to adoption stem primarily from non-technical factors such as organizational processes, incentive structures, and psychological safety.
New York University Professor Aswath Damodaran reviewed the performance of US stocks in September 2026: the 10-year US Treasury yield rose from 4.75% to 5.29% during the month, an increase of 54 basis points, placing it in the top 10% of 770 monthly changes since 1962. Despite this, the total market capitalization of US stocks still increased by $2.5 trillion during the month, with nearly $1.5 trillion of that increase coming from the technology sector.
AQR co-founder and dean of Northwestern University’s Kellogg School of Management co-authored an article pointing out that AI can automate most analytical and technical capabilities in quantitative investment, making creativity and emotional intelligence enduring competitive advantages that are difficult to replace. AQR is leveraging the Quanta Academy, launched in 2015, to expand relevant capability development, while Kellogg School of Management also promotes students’ simultaneous development of analytical skills and soft skills through programs such as MBAi. Research shows that the impact of AI on empathy is not fixed, and depends on tool design choices and organizational culture building.
NYU valuation professor Aswath Damodaran published a lengthy article proposing a four-stage framework for revolutionary change, arguing that AI is currently entering its coming-of-age phase, shifting from investment and infrastructure building to commercial implementation. The article notes that since the release of ChatGPT in November 2022, the Mag Seven contributed 45% of the total increase in U.S. stock market capitalization from 2022 to 2025. Six leading AI companies have cumulatively spent $1.7 trillion in capital expenditures, while the current upper bound of total revenue from AI products and services is only about $250 billion.
Bridgewater has published an AI policy commentary arguing that AI is the most impactful technological change of our time, and that policy must be enacted immediately to balance its productivity benefits against risks of social disruption and safety incidents. Bridgewater proposes that US AI policy must prioritize societal resilience to technological shocks and establish a robust AI safety regulatory framework; otherwise, AI development will be forced to stall due to political unsustainability or major incidents.
Drawing on two academic papers, the author identifies three structural limitations of using LLMs in trading research: First, training data is saturated with trading common sense and lacks mechanism-driven edge thinking. Second, across 35+ models, accuracy in time-series value tracking declines logarithmically as the number of historical updates increases, and neither prompt engineering nor expanding the context window can fix this. Third, outputs from 25+ models on open-ended questions show 71%–82% similarity, and models with different architectures converge to the same common answers.
