Identifying Feature Stock Price by Considering Most Influential Parameters using Prediction Methods in Indian Stock Exchange
1K.Sudhakar, Assistant Professor G.Pulla Reddy Engineering College (Autonomous): Kurnool, A.P, India.
2S.Naganjaneyulu, Professor of Information Technology Laki Reddy Bali Reddy Engineering College (Autonomous): Mylavaram, Krishna District, Andhra Pradesh India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1-4 | Volume-8 Issue-4, November 2019. | Retrieval Number: A9253058119/2019©BEIESP | DOI: 10.35940/ijrte.A9253.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Before the evaluation of big data analytics predicting the optimal share price in the stock market is very difficult, by applying the big data analytics it is easy to predict frequent patterns and feature outcomes in any domain. So in this paper we consider the financial domain to predict feature outcomes of share prices in the Indian stock exchange. We first gathered the dataset with duration 2011-2016 financial years of TCS Company, the reason to choose TCS dataset it is a trust based company and datasets are available at open access with all parameters. Market price per share is strongly effect by company’s variable like price earnings, dividend yield, dividend per share, earnings per share, book value per share, and return on equity, after observing the results we conclude that the variables price earnings, book value per share and firm size are important determinants of share prices in the Indian stock market. The regression model achieved a high R2 (0.94) for the closed price and book value per share variable and also the model achieved a high R2 (0.98) for the closed price and price earnings.
Keywords: Market Price Of Share, Regression, Firm Specific Variables.
Scope of the Article: Regression and Prediction.