Non-parametric forecasting for conditional asset allocation

Conditional asset allocation (CAA) involves using key past economic and financial data to produce forecasts of expected returns for the various asset classes involved in the asset allocation decision. Traditional forecasting models for asset returns, in particular linear regression models and ARIMA time series based models, often provide economically meaningful asset allocation decisions. These results, however, only seem to possess power in-sample, and the out-of-sample performance of such methodologies suggests, consistent with efficient market theory, that there is no economic benefit from undertaking conditional asset allocation.