Stock Recommendation and Trade Assistance

Archana Purwar, Indu Chawla, Sarthak Jain, Rahul Malhotra and Dhanesh Chaudhary
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Archana Purwar: Jaypee Institute of Information Technology, Noida, India
Indu Chawla: Jaypee Institute of Information Technology, Noida, India
Sarthak Jain: Jaypee Institute of Information Technology, Noida, India
Rahul Malhotra: Jaypee Institute of Information Technology, Noida, India
Dhanesh Chaudhary: Jaypee Institute of Information Technology, Noida, India

International Journal of Information Technology Project Management (IJITPM), 2022, vol. 13, issue 3, 1-17

Abstract: Investing in the stock market has never been an easy task. This paper develops a stock recommendation and trade assistance that uses the past performance of the stock to predict its future performance using linear regression model. Linear regression model has given an accuracy of 99.8% as compared to support vector machine (SVM) which resulted into an accuracy of 94.6%. Data set used under the study was extracted from the historic stock data of reliance industries limited (RIL). To analyze whether to buy or sell the stock, four financial algorithms, namely Bollinger bands, moving average convergence/divergence indicator (MACD), money flow index (MFI), and relative strength index (RSI) are employed to find the composite result. Moreover, sentiment analysis of the news depending upon the earning calls and the annual general meetings is done to provide an overall stock and market sentiment analysis. In-depth balance sheet analysis of the company is also done using various instruments to make the trade assistance more accurate. The values for WACC, D/E ratio, and NPV obtained are 14.99, 0.76, and 8.9 lakh crores for RIL.

Date: 2022
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