Consequence of Different Parameters on the High Stock Index of Allahabad Bank Using Deep Learning Techniques

Abstract

Stock market is one of exciting and demanding monetary activities for individual investors, and financial analysts. The stock market is an inter-connected important economic international business. Prediction of stock price has become a crucial issue for stock investors and brokers. The stock market is able to influence the day to day life of the common people. The stock markets contribute a large scope in economic development of India. The banking industry grip majority share between other industries in Indian stock trading consequence. The investors in the stock market use to bear certain risk for their predictable returns in the future. Investment decisions are usually taken by considering different fundamental factors both internal and external. Apart from fundamental factors which replicated in the security prices, there are numerous additional factors that can influence investment are stock prices, volume of trading, spread, turnover etc. The paper explores the effect of different variables on the high stock price of Allahabad Bank considering daily data over the period 4 Jan 2010 to 23 Apr 2020. For the study the weighted average price (WAP), number of shares, number of trades, total turnover (in INR), deliverable quantity, percent deliverable quantity to traded quantity, spread high and low, spread open and close and the high stock price of the organization were noted. High stock price was considered as output while other parameters were used as input. Pipeline Pilot module of Biovia software (Dassault Systems of France) was used for analysis. The software provides different built-in components to develop a machine learning model and use the model for prediction.