Stock Price Prediction Using Data Mining with Soft Computing Technique

Stock costs forecast are interesting and valuable research theme. It provides huge revenue if the prediction favors’ and it makes a huge fall if it goes wrong. Developed nations economies are estimated by their stock trading sector. As of now, financial exchanges are viewed as a celebrated revenue generating field on the grounds that much of the time it gives large benefits with minimal loss and hence generally considered to be safe place of return. Financial exchange with its tremendous and dynamic data handling capacity is considered as a predominant place for profit lovers as well as for research scientists. In this paper, the concept of k-closest neighbor method was appled to select the data from the data set and later the selected datas are used to predict the future stock price using soft computing technique. In soft computing technique genetic algorithmic approach was implemented with a novelty for predicting and improving the performance of the result. Stocks of various companies are analyzed and the results are fine-tuned so the the trust worthable technique was developed. The experimental results show that this combined technique can well be suited for predicting the stock price. KeywordsSoft computingLong-short-filled term memoryStock priceData miningPredictionClustering technique