Determinants Of The Index Of Prices And Quotations On The Mexican Stock Exchange: Sensitivity Analysis Based On Artificial Neural Networks

This study applies a neural network (NN) methodology to determine the relative variable impact between variables, established by financial theory, as determinants of Index of Prices and Quotations (IPC) on the Mexican Stock Exchange. A NN model is proposed because these variables have nonlinear and nonparametric behavior. NN models are most appropriate and efficient than conventional linear models in these situations. The architecture used to implement the network was Multilayer Perceptron (MLP) with a hidden layer. The following variables were used in the input layer: Index Dow Jones Industrial Average (DJIA), Consumer Price Index (CPI), International Reserves (RI), Yields on Treasury Certificates (CETES), Monetary Aggregate (M1), and Exchange Rate (TC). The algorithms applied to assess variable contribution were Connection Weights Approach and Garson’s Algorithm. Then, a comparative analysis of the results of each algorithm was performed. We conclude the variable International Reserves has the greatest impact. Moreover, results allow quantifying the impact of each proposed variable on the IPC of the Mexican Stock Exchange. This study adds to research that demonstrates the efficiency of artificial neural networks for the simulation and provides a methodology to determine relative importance of financial economic variables