This paper describes a new approach for asset allocation and risk management called funds exchange. The funds exchange generically describes short-term trading of (broadly-based) mutual funds or indices based on statistical strategies aimed at achieving improved returns and, at the same time, reducing market risk (i.e., market exposure). Unlike many statistically-based trading and advisory systems trying to predict and benefit from the major (big) changes in the stock market, the funds exchange approach tries to capitalize on the short-term (daily) market volatility, i.e. small daily changes. This paper describes concepts and assumptions underlying this approach, and mathematical formulation of the funds exchange approach as a problem of predictive learning. Finally we show empirical evidence that the proposed approach can indeed provide improved returns and reduce market risk for SP 500 mutual funds.