Sensitivity analysis, neural networks, and the finance

The paper investigates whether the sensitivity analysis can be used not only as a tool to read the knowledge embedded in artificial neural networks (ANNs), but also as a tool to evaluate the effectiveness of ANN learning. The simulation of the Black-Scholes formula is employed for this object. The Black-Scholes formula, in which the mapping between the call price and five relevant variables is a mathematically closed form, is suitable for verifying the validity of the methodology of sensitivity analysis in reading ANN knowledge. As for the validity of evaluating the effectiveness of ANN learning, two different ANNs are set up, and their sensitivity analyses on learning patterns are compared. The experimental results show that both values of sensitivity analysis of ANNs and partial derivative of the Black-Scholes formula are consistent. Furthermore, they indicate that the sensitivity analysis can be used as a tool to evaluate the effectiveness of ANN learning