Portfolio analysis is essentially a statistical technique. However, because the ‘‘true’’ population parameters for the input data (expected returns, variances, and covariances) are unobservable, sample statistics must be estimated. Thus, the efficient portfolios generated by portfolio analysis are no better than the statistical input data on which they are based.
Factor models are applied by portfolio managers to analyze the potential returns on a portfolio of risky assets, to choose the optimal allocation of their funds to different assets and to measure portfolio risk. The theory of linear regression-based factor models applies to most portfolios of risky assets, excluding options portfolios but including alternative investments such as real estate, hedge funds, and volatility, as well as traditional assets such as commodities, stocks, and bonds.
The expected return of a financial asset can be modeled as a function of various theoretical factors according to three main categories: macroeconomic, statistical, and fundamental. Employing multiple factors addresses their cyclicality and increases diversification. However, there is no free lunch attached to factor investing.
