Quantitative investing uses mathematical models, statistical analysis, and computer algorithms as core tools to systematically conduct securities analysis and trading decisions. Unlike fundamental investing, which relies on personal judgment, industry research, and management meetings, quantitative investing searches large historical datasets for statistically testable regularities (“factors”) and encodes these regularities into automatically executed trading strategies.
## The Core Logic of Quantitative Investing
**Market inefficiency hypothesis**: quantitative investing rests on the theoretical foundation that markets are not fully efficient (i.e., weak or semi-strong form), meaning systematic, data-discoverable price anomalies exist. These anomalies may originate from: investor behavioral biases (anchoring, disposition effect, overreaction/underreaction), liquidity premiums, and structural factors (institutional holding constraints, tax effects). The core of quantitative strategy is identifying these anomalies and profiting consistently before they are fully priced in.
**Factors and Alpha**: the core concepts of modern quantitative investing. A “factor” is a variable that systematically explains stock excess returns — such as the value factor (low P/E stocks outperform long-term), momentum factor (recently rising stocks tend to continue short-term), and quality factor (high-ROE companies outperform). “Alpha” is pure excess return after stripping out factor exposures — the core competitive advantage of quant funds.
**Backtestability**: the most important feature of quantitative strategies is the ability to test them with historical data (backtesting), quantitatively evaluating historical performance (annualized return, Sharpe ratio, maximum drawdown). But backtesting has fundamental pitfalls: overfitting (strategy performs extremely well on historical data but has no predictive power for the future); look-ahead bias (unconsciously using data that would not have been available at strategy execution time).
## Major Quantitative Strategy Types
**Statistical Arbitrage**: exploiting historical correlations between different assets, building long-short paired trades when correlations deviate, profiting when they revert. Suited to deep data analysis and short-cycle trading.
**Trend Following/CTA**: systematically identifying and following asset price trends, trading with momentum. Bridgewater Pure Alpha and Winton are representative funds.
**Multi-factor Stock Selection**: simultaneously using multiple factors to score and rank stocks, holding high-score stocks and shorting low-score stocks. Domestic firms Squant, Jinkui, and Mingshi are representative institutions.
See [Factor Investing Guide](https://sunqi.org/factor-investing-guide-en/) and [AQR Capital quantitative research](https://www.aqr.com/Insights/).
