Soft Computing Based Stock Price Prediction
Trading in stock markets is not an easy task and requires expertise and knowledge that improve and increase the chances of making more profits and ensuring that you make profitable decisions at all times. Financial Experts and data analysts make use of various algorithms and neural networks to predict the value of stock depending upon the available information and then use the outcomes to analyze and make their trading decision. The main motive of this research is to predict the future stock value of the particular stock with minimum variation from the actual value of stock. In this research, a soft computing based mygrave 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 Mygrave Algorithm the converging point of the entire object is determined and it is said to be the optimal output of the algorithm. The inputs are opening, closing, low and high values of a stock for a period of one year (256 days). Stock Exchange data were collected from National Stock Exchange (NSE), India, 2016 and these data were given viii as an input to the proposed model to evaluate the performance of stock market. This research work outlines the basic elements influencing the future value of the stock and also suggests the best algorithm which gives the most efficient and accurate results.
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