Effect of External Factor on Share Price Forecasting

Abstract

Generally, stock markets represent the economic status of a country. Different stock markets have different characteristics based on the economics of the countries they represent [1]. Stock markets are complex, nonlinear, dynamic and chaotic [2]. Prediction of stock prices is one of the major challenges in the hands of business analysts for the interest of the financial and economic personnel and in general the policy makers and planners of a country. Techniques of prediction vary greatly according to the availability of information, quality of modeling and the underlying assumptions used. Neural networks are regarded as more suitable for stock prediction than other techniques mainly due to its ability to adaptation to the nature of observations (data); for instance in situation like financial markets in general it is impossible to incorporate the effects of some socio-economic factors directly in the models (e.g. Exchange rate of other non-western currencies, GDP, Consumer indices, etc.) [1]. We used the S&P SL20 index of Colombo Stock Exchange (CSE) in Sri Lanka and exchange rate between US $ and LKR data from 02nd January 2013 to 31st December 2013. The best feed forward ANN model and suitable parameters of each model are selected by using training data set with trial and error technique. The accuracy of each model was compared via Absolute fraction of variance (R2), Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE) and visually. The results examined that the exchange rates which is an external factor affected the share price forecasting in ANNs.