Factor Allocation as Reverse Attribution
提出一种从因子波动率推导预期因子阿尔法的方法，将因子分配视为反向归因，帮助管理者在难以准确估计阿尔法时构建主动管理因子组合。
Multifactor models and the construction of factor portfolios are by now pervasive in investment management. Active factor allocation can be challenging, however, given the difficulty in generating accurate point estimates for <i>ex ante</i> factor alphas. To overcome this challenge, the present article describes a technique that gives managers the ability to generate expected factor alphas from factor volatilities. An important component of the framework is the way it characterizes factor allocation as reverse attribution. Starting with a portfolio alpha target and risk budget, the methodology proceeds to derive factor weights by finding the benchmark-relative factor loadings that satisfy the alpha and risk objectives. Furthermore, because effective factor allocation is forward looking, the framework described in the article also allows managers to adjust factor volatility values in a manner that is consistent with their subjective beliefs regarding a factor’s likely marginal alpha contribution. The framework presented thus serves as a quantitatively grounded yet practical framework for building actively managed factor portfolios. <b>TOPICS:</b>Factor-based models, portfolio construction, risk management, performance measurement <b>Key Findings</b> ▪ Performance attribution can be applied in reverse to inform forward-looking factor allocation. ▪ It is possible to derive expected factor alphas from factor volatilities and a target portfolio alpha value and risk budget. ▪ A simple, yet statistically driven, approach to calibrating <i>ex ante</i> factor volatilities can be implemented by coupling a manager’s subjective views with an assumed distribution for factor variances.