Stock Price Prediction Using Fuzzy Time-Series Population Based Gravity Search Algorithm

Abstract

The main motive of this research is to predict the future stock value of the particular day with minimum variation from the actual value of stock. In this research, a genetic algorithm-based gravity search algorithm is proposed for stock market prediction. It will be helpful for short-term investors in the National stock market. Some important factors that affect the value of stock are total stocks traded, total turnover of the company, gross domestic product (GDP) of the country, GDP per capita and political or external factors are some of the main factors that affect the stock value of that particular day. Opening and closing values of the stock market were predicted with the help of the above factors. Each factor will be considered as an object with mass, the mass of every object will be based on the importance. With the help of a Gravitational Search Algorithm (GSA) [1], the converging point of the entire object is determined and it is said to be the optimal output of the algorithm. The input considered are opening, closing, low and high values for a period of one year.