Quantitative Analysis（量化分析）
A data-and-model process whose reliability depends on research design, implementation, costs, and changing market regimes.
Quantitative analysis uses mathematical, statistical, and computational methods to study data and make or support decisions. A backtest is historical simulation, not proof of a durable edge, and results can be distorted by overfitting, data leakage, survivorship bias, transaction costs, and regime change.
Frequently Asked Questions
Can a successful backtest predict future returns?
No. Backtests are conditional on historical data and design choices. Out-of-sample testing, realistic execution, multiple-testing controls, robustness checks, and live monitoring can reduce but not eliminate model risk.
What is overfitting?
Overfitting occurs when a model captures noise or idiosyncrasies in the research sample rather than a repeatable relationship. More parameters, repeated searches, and selective reporting increase the risk.
What costs should a quant strategy include?
Commissions, bid-ask spreads, market impact, borrowing, financing, taxes, latency, data, implementation shortfall, turnover, and capacity constraints can materially reduce returns.
Are factor premiums guaranteed?
No. Definitions vary, relationships can weaken or reverse, crowding can change returns, and published results may be affected by data mining. Factor exposure also creates drawdowns and tracking error.