Capital Asset Pricing Model and Artificial Neural Networks: A Case of Pakistan’s Equity Market

Abstract

Artificial Neural Networks (ANN) approach is a relatively new and promising field of the prediction of stock price behavior. Neural networks approach is a mathematical model, flexible enough to accommodate both linear and non-linear aspect of stock returns. This paper applies the ANN to asset pricing models. It is found that the optimum number of neurons does not follow some mathematical rule rather it is based on the presentiment of the researcher to apply an exhaustive search for the number of optimum neurons. Another important finding is that the difference of errors between the testing and training dataset is minimum and the networks are not suffering from the over-fitting phenomenon. The predicted value of high beta portfolios is better than the low beta and mid beta portfolios. This finding reinforces the investment principle that the market compensates the high-risk portfolios more than other classes. The paper concludes that the proposed model achieves a significant improvement in the return on investment and the investors can magnify their profitability.