Eleven lessons of quantitative portfolio theory have given us a toolkit. Markowitz's mean-variance framework provides the structural foundation for thinking about risk and return jointly. Tobin's two-fund separation tells us that, with a risk-free asset, every efficient portfolio is a mix of cash and a single tangency portfolio of risky assets.

Sharpe's CAPM shows that only systematic risk earns a premium, and Fama and French extend the systematic-risk dimension into multiple factors. Value-at-Risk and Conditional VaR / Expected Shortfall summarize the worst-case loss properties; drawdowns measure the path-dependent investor experience that variance does not. Correlation regimes remind us that diversification is conditional on the regime continuing.

Risk parity reframes asset allocation from capital to risk. The Kelly criterion provides an analytical foundation for position sizing. Tail-risk hedging adds explicit bounded-loss protection at a real but quantifiable cost.

None of these tools is a complete answer on its own; together, they constitute a working framework for building portfolios that are both analytically grounded and practically robust.