Application of Artificial Neural Network Unified with Fuzzy Logic for Systematic Stock Market Prediction

Abstract

Prediction of the stock market can play a vital role in everyone’s life to attain sustainable growth. This can also lead to an attractive profit by making the proper choices in the financial stock market. The stock market prediction is a big challenge which requires extensive advanced tools and techniques to analyze the future and present data. The modern stock market institutions provide better self-trading and investment options, enabling regular people and traders to enter the market through numerous online applications and websites that are available via smart devices. Financial institutions are investing more and more in talent development so that investors may make the maximum money. There are a multitude of reasons for this, including volatility of market and a collection of other interrelated and independent parameters that control the market value of a different stock. Because of these factors, it is excessively difficult for any specialist in financial markets to accurately predict the market’s rise and collapse. This research paper proposed an artificial neural network (ANN) technique which combines with fuzzy logic to predict the stock market for short duration with high accuracy. First, historical and real-time data are used to classify and gather information for prediction. Then the short-duration prediction is done with a high accuracy of 96% and the error margin for a small instance is 2.3%. Through training, testing and validation it is clear that for sustainable short duration prediction, ANN integrated with fuzzy is a good choice.