The dissertation implements a model driven statistical arbitrage strategy that uses the principal components from Principal Component Analysis as factors in a multi-factor stock model, to isolate the idiosyncratic component of returns, which is then modelled as an Ornstein Uhlenbeck process. The idiosyncratic process (referred to as the residual process) is estimated in discrete-time by an auto-regressive process with one lag (or AR(1) process). Trading signals are generated based on the level of the residual process. This strategy is then evaluated over historical data for the South African equity market from 2001 to 2013 through backtesting. In addition the strategy is evaluated over data generated from Monte Carlo simulations as well as bootstrapped historical data. The results show that the strategy was able to significantly out-perform cash for most of the periods under consideration. The performance of the strategy over data that was generated from Monte Carlo simulations demonstrated that the strategy is not suitable for markets that are asymptotically efficient
