By combining public and private strategies, the firm observes that public markets react more quickly to crises. This provides predictive insights into the slower-moving private markets, creating an informational edge to anticipate cycles and opportunities before they fully materialize.

Hedge funds have a constant, daily need to make informed buy, sell, or hold decisions, creating a clear business problem that data solves. Corporations often lack this frequent, high-stakes decision-making cycle, making the value proposition of external data less immediate and harder to justify.

With information now ubiquitous, the primary source of market inefficiency is no longer informational but behavioral. The most durable edge is "time arbitrage"—exploiting the market's obsession with short-term results by focusing on a business's normalized potential over a two-to-four-year horizon.

An estimated 80-90% of institutional trading is driven by quant funds and multi-manager platforms with one-to-three-month incentive cycles. This structure forces a short-term view, creating massive earnings volatility. This presents a structural advantage for long-term investors who can underwrite through the noise and exploit the resulting mispricings caused by career-risk-averse managers.

Long-term economic predictions are largely useless for trading because market dynamics are short-term. The real value lies in daily or weekly portfolio adjustments and risk management, which are uncorrelated with year-long forecasts.

Hudson River Trading shifted from handcrafted features based on human intuition to training models on raw, internet-scale market data. This emergent approach, similar to how ChatGPT is trained, has entirely overtaken traditional quant methods that relied on simpler techniques like linear regression.

Cliff Asnes explains that integrating machine learning into investment processes involves a crucial trade-off. While AI models can identify complex, non-linear patterns that outperform traditional methods, their inner workings are often uninterpretable, forcing a departure from intuitively understood strategies.

AI models can predict short-term stock prices, defying the efficient market hypothesis. However, the predictions are only marginally better than random, with an accuracy akin to "50.1%". The profitability comes not from magic, but from executing this tiny statistical edge millions of times across the market.

Demanding interpretability from AI trading models is a fallacy because they operate at a superhuman level. An AI predicting a stock's price in one minute is processing data in a way no human can. Expecting a simple, human-like explanation for its decision is unreasonable, much like asking a chess engine to explain its moves in prose.