We deconstruct the marketing hype surrounding "AI in finance" to analyze the actual mathematical and statistical methods used by quantitative funds.
Natural Language Processing
Why simply counting positive vs. negative words is obsolete. How quantitative researchers use transformer models (like FinBERT) to detect executive evasion, measure management uncertainty, and identify thematic shifts before they manifest in fundamental data.
Machine Learning / Statistics
If you backtest enough parameters, you will inevitably find a strategy that looks like a money printer. How to apply Deflated Sharpe Ratios, walk-forward optimization, and out-of-sample testing to prevent curve-fitting in neural networks.
Data Engineering
Satellite imagery of parking lots and scraped credit card receipts used to provide massive edge. Now they are commoditized. We analyze the half-life of alternative data signals in modern quantitative finance.
Fund Analysis
A deep dive into funds that claim to use proprietary AI to pick stocks. Why do their holdings often look identical to a standard momentum or quality factor portfolio? We deconstruct the black box.
Statistical Modeling
Markets don't behave linearly; they shift abruptly between risk-on and risk-off states. How quantitative researchers use Hidden Markov Models (HMMs) to classify latent market states and adjust risk exposure dynamically.