Nepal Stock Exchange Market Prediction using Support Vector Regression and Back- Propagation Neural Network

Abstract

Acquisition of knowledge by analyzing the large volume of data from the various perspective and summarizing into useful information has become essential in recent years. Stock market prediction is interesting and challenging research in data science incorporated with artificial intelligence. In this research work, Nepal Stock Exchange data has been used to predict the stock price for a next day. Data sets are collected from Nepal Stock Exchange. Data preprocessing is performed in order to compute an accurate result. The data collection of the year 2016 is used for this work. The data belong to promoter share and unwanted feature eliminated from considered data. Overall stock data is further divided into the different sector of investment in stock market. Data sets are normalized for better performance, before applying the machine learning methods. Min-Max and Z-score normalization two methods are used for this work. Support Vector Machine and Artificial Neural Network are applied in order to predict stock price in the market. In order to measure the performance of two learning models mean square error, mean absolute error, root mean square error and coefficient of determination are used. The prediction of stock market using two models found better on Min-Max normalized data. SVR found better than BPNN on predicting stock market of different investment sector.