How AI Can Help Find the Needle in the Haystack
Artificial intelligence is rapidly transforming the investment landscape in ways that extend far beyond algorithmic trading and robo-advisors. One of AI's most promising applications lies [...]
Financial Regulation and AI: A Faustian Bargain?
Financial regulation has always faced a trade-off between simplicity and precision. Simple rules are transparent and robust, but often miss where risks actually build up. More sophisticated tools can be more precise, but they are harder to understand, harder to explain, and sometimes change behavior in unexpected ways.
Can AI Read the News Better Than You? How ChatGPT Could Transform Momentum Investing
ChatGPT and similar large language models can enhance traditional investment strategies through superior interpretation of financial news. The improvements are economically meaningful, statistically robust, and persist under realistic implementation constraints.
How Geopolitics is Being Priced in Real Time
By reading earnings calls and analyst reports at scale, algorithms can identify who is applying pressure, who is being targeted, which instruments are used, and how firms respond. The result is a new way to observe geopolitical risk as it actually enters corporate decision making.
A Simple Guide to Using AI in Economic Analysis
The guest article below was submitted via a friend (Sailesh Radha) who develops software that helps investors analyze data (Borealis). We have no affiliation/connection to [...]
Should Limited Partners use AI to assist with investment decisions?
This paper reviews early evidence that algorithms can read GP reports, forecast cash flows, and benchmark funds. But it also shows where the limits lie.
When the Machine Becomes the Portfolio Manager
Today, machines are not only processing data but interpreting narratives, forecasting returns, and constructing investment theses once reserved for humans. This paper examines how AI is reshaping the role of the discretionary PM, arguing that the edge isn’t disappearing — it’s migrating.
When LLMs Go Abroad: Why U.S. AI Models Are Too Optimistic on Chinese Stocks
Large language models are increasingly being used to forecast stock prices and guide investment decisions. But what happens when these models cross borders?
Do Smart Machines Make Smarter Trades?
Can machine learning models help us exploit stock market anomalies more effectively? This paper says yes—but with a few important caveats. By applying gradient boosting algorithms to a wide array of established anomalies (like value, momentum, and quality), the authors show that machine learning methods can significantly improve the performance of long-short strategies.
The Virtue of Complexity in Return Prediction
This article explores how researchers forecast market returns by aggregating expected returns from individual stocks.
Artificial Intelligence and the Risks of Harking (Hypothesizing After-the-Fact)
Because AI systems can produce hundreds of seemingly coherent theoretical explanations for mined empirical results, investors need to establish high hurdles before allocating to anomaly-based strategies.
Training Machine Learning Models For Return Prediction
This paper investigates how modeling choices impact MLM outcomes such as cross-sectional return predictability.
Forecasting Earnings, Artificial Intelligence (AI) Versus Equity Analysts
A critical task in stock selection is identifying a firm’s true profitability. Given the potential of AI to deal with large data, an important question is: Can AI outsmart seasoned analysts?
Can Artificial Intelligence outsmart seasoned equity analysts?
If the task is to identify a firm’s true profitability, can AI outsmart seasoned analysts?
Artificial Intelligence, Textual Analysis and Hedge Fund Performance
There’s no reason to think that the use of AI should lead to persistent fund outperformance, with any advantages gained likely being short lived.
Artificial intelligence, firm growth, and product innovation
AI-powered growth concentrates among larger firms and is associated with higher industry concentration. Our results highlight that new technologies like AI can contribute to growth and superstar firms through product innovation.
From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses
An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine,” which also substantially reduces extreme errors.
Overvalued or New Paradigm?
Without question the topic of greatest debate among investors, including investment professionals, and financial economists, is whether or not the market, and the technology sector in particular, is overvalued. There are two very strong conflicting views regarding not only the current valuation of technology stocks, but also the valuation of the entire asset class of large-cap growth stocks. One side, I’ll call the “new paradigm” or “it’s different this time” school. The other side, I’ll call “the been there, done that” school. Its theme is those that don’t learn from the past are doomed to repeat the same mistakes. No two sides could have more different viewpoints. To understand each side, let’s imagine a dialogue between the two schools.
Creating Better Factor Portfolio via AI
Trading costs, discontinuous trading, missed trades, and other frictions, along with asset management fees can cause a shortfall between live and paper portfolios. The focus of this paper is to test an effective rebalancing method that prioritizes trades with the strongest signals to capture more of the factor premia while reducing turnover and trading costs.
Complexity is a virtue in return prediction
Simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations.