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Two useful papers on Sharpe ratios and their estimation errors, time aggregation etc. - The seminal paper by Andrew Lo, The Statistics of Sharpe Ratios. researchgate.net/publication/22… - Matteo Riondato, Sharpe Ratio: Estimation, Confidence Intervals, and Hypothesis Testing, Two Sigma

A useful repo. github.com/EliteQuant/Eli…

🔓 Open-sourcing our crypto market dataset pipeline This is the code we used to build the database of our new paper on crypto: Catching Crypto Trends: A Tactical Approach for Bitcoin and Altcoins Most datasets only include active tokens, introducing survivorship bias. We fixed

From Man Group on the role of patience in trend following. man.com/insights/why-p…

New paper on trend following: “It is quite surprising that a single EMA is optimal for capturing trends… using a complex mixture of sophisticated indicators is unnecessary... simplicity can indeed be beautiful.” Paper: arxiv.org/abs/2504.10914

🔥 New paper! We show GPT‑4o can perfectly recall tons of econ & market data inside its training window—so those glossy “LLM beats Wall St.” back‑tests may just be parroted history. Yuehua Tang The Memorization Problem: Can We Trust LLMs' Economic Forecasts?

Hou et al. find strong evidence that earnings momentum works best in neglected stocks, while price momentum is strongest in widely watched stocks. “Overall, our findings strongly support the hypothesis that investor attention amplifies price momentum while diminishing earnings

NEW PAPER OUT! Should ML models specialize in sectors like human analysts? 🤖 🧠 We tested Generalist, Specialist, and Hybrid models. Key findings: 🔴 Specialist < Generalist 🟢 Hybrid > both Full paper below 👇

A fresh roundup of the latest investing research is out. Some of the topics covered: ➢ Currency Returns Around Macro News ➢ Drivers of Corporate Bond Returns ➢ Machine Learning and Return Predictability ➢ Volatility Forecasts and VIX Futures Trading ➢ Great Blogs,

New article from Man Group on market timing. man.com/insights/marke…

New paper by Cartea et al. suggests that the benefits of complex models depend critically on data quality. Essentially, complex models are likely to start overfitting to noise. "...excessive complexity in the presence of limited data and high noise levels may lead to factor

My brilliant partner Antti Ilmanen’s latest on his lifelong obsession with expected returns. This one on how people actually form them (rational or not). Link below.

A fresh roundup of the latest investing research is out. Some of the topics covered include: ➢ Commodities as Diversifiers ➢ Identifying Mispriced Stocks ➢ Lecture Notes on Quantitative Methods ➢ Option Anomalies ➢ Great Blogs, Repositories, and Podcasts ➢ ...and much

Ernie Chan and his coauthor are soon releasing a new trading book, this one focused on Generative AI. amazon.com/Generative-AI-…

New Blog! When the Equity Premium Fades, Alpha Shines wp.me/p1SgTN-unW via CFA Institute Research and Policy Center

Looking forward to reading this one over the weekend.

Psst! Wanna see some double digit Sharpe Ratios? qoppac.blogspot.com/2025/05/can-i-…

📢 Local or Global Factor Models? New ML Evidence! Prior studies: Regional > global However, they rely on ex-post analysis and only use linear methods. We compare models OOS & employ both linear and ML algorithms. 📊 Key results 📄 Full paper 👇papers.ssrn.com/sol3/papers.cf…