The optimal training interval for amultilayer perceptron on a day to dayestimation of the Swedish OMXS30index

Abstract: The stock market plays a big role in our current nancial system and the uctuations onit are believed to depend on many dierent factors. One of the factors that are believedto be correlated to the stock market are macroeconomic variables, that is, variables thatindicate the status of the economical situation. Examples of such macroeconomic variablesare unemployment rate, loan interests and ination. Earlier attempts to predictthe stock market have been made by using process demanding methods such as arti-cial neural network. A multilayer perceptron is a self learning system that goes underthe category of an articial neural network. Such a network learns by being trainedon old data sets and has the capacity to identify relationships between dierent data.This method has been used in earlier studies to predict the stock market with goodresults. The problem statement of this report is to nd the optimal training interval fora multilayer perceptron on a day to day estimation of the Swedish OMXS30 index. Theinput to the algorithm consisted of 38 parameters, which in this case was a collectionof individual companies stock prices, foreign stock indexes, macroeconomic variables,previous and current values of the OMXS30 index. The results from the simulationsthat were executed on old stock data shows that 180 to 200 days of training yielded thebest results, where eight of nine periods over seven years (2007-2014) yielded prot. Theresults from the simulations during the periods with increasing index were sometimesbelow the index gain, but always with a prot. During periods of index decrease theresults were sometimes with a prot and sometimes non-prot. In the case of indexdecrease the result was always above the total index decrease. The conclusion is as theresults shows, that the optimal training interval is 180 to 200 days for the simulationsrun in the study of this report.1