History of Quantitative Investing

Q: What is quantitative investing, and what is its theoretical foundation?

A: Quantitative investing uses mathematics, statistics, and computer technology to build systematic models for making and executing investment decisions.

Its theoretical foundation can be traced to Harry Markowitz's Modern Portfolio Theory in 1952. Markowitz introduced mathematics into finance through the mean-variance model and showed that investors could reduce portfolio risk through diversification without necessarily reducing expected return. This became a foundation for measuring risk and return quantitatively.

Q: What pricing models and core concepts shaped quantitative investing?

A: After Modern Portfolio Theory, two important pricing frameworks pushed quantitative finance forward.

• Capital Asset Pricing Model (CAPM): In the 1960s, William Sharpe and others developed CAPM from portfolio theory. It introduced concepts such as beta, the return associated with systematic risk, and alpha, the excess return from active skill. The model argues that expected return is mainly tied to systematic risk.
• Black-Scholes-Merton model: In 1973, Fischer Black, Myron Scholes, and Robert Merton proposed the option-pricing formula. Through no-arbitrage pricing, the model gave derivatives markets a quantitative pricing tool and helped shape modern Wall Street derivatives trading and risk management.

Q: When did Wall Street begin adopting quantitative investing at scale?

A: Practical quantitative investing expanded mainly from the late 1970s into the 1980s, when people with physics, mathematics, and computer backgrounds entered Wall Street. These professionals became known as quants.

Edward Thorp is often regarded as a pioneer of quantitative hedge funds. In 1969 he founded Princeton Newport Partners and used mathematical models for convertible bond arbitrage.

James Simons, a top mathematician, founded Renaissance Technologies in 1982. The Medallion Fund used data-driven statistical models to search for small market patterns and became a symbol of systematic quantitative trading.

D. E. Shaw and Citadel, founded from the late 1980s into the early 1990s, further combined computer algorithms with multi-strategy quantitative investing and helped institutionalize the field.

Q: How did multifactor models become mainstream in quantitative stock selection?

A: Multifactor stock selection is one of the most mature branches of quantitative investing.

Arbitrage Pricing Theory, proposed by Stephen Ross in 1976, argued that asset returns are driven by multiple macroeconomic or economic factors rather than a single market-risk factor.

In 1992, Eugene Fama and Kenneth French proposed the three-factor model. Their empirical work showed that market risk, size, and value could explain a meaningful part of long-term stock returns.

Later research and practice expanded the factor library to include momentum, quality, low volatility, and other factors. This formed the classic multifactor framework still used today.

Q: What changed after the start of the twenty-first century?

A: Quantitative investing faced several major shifts in market structure and technology.

The 2007 Quant Meltdown showed the danger of crowded factors. Several large quantitative funds held similar statistical arbitrage positions. When one fund was forced to unwind, the selling pressure spread across the market. This event made dynamic risk control and factor crowding much more important.

High-frequency trading grew after exchanges became electronic. By the mid-to-late 2000s, microsecond execution, market-making algorithms, and statistical arbitrage had become mature enough to capture very small price differences.

In the last decade, quantitative investing has moved beyond financial statements and price-volume data. Satellite images, text sentiment, and other alternative data entered the process, while machine learning and deep learning began to capture nonlinear signals that traditional linear models could miss.