Stock Analysis using Arima Time Series

Abstract

Stock market plays a pivotal role in the financial aspect of the nation's growth, but the stock market is highly volatile and complex in nature. It is affected by significant political issues, analyst calls, news articles, the company's future plans of expansion, growth, and many more. Public and private opinions about a wide variety of subjects are expressed and spread continually. Developing a program for stock analysis is an approach to be used to computationally measure customers’ perceptions. This paper reports on the design of stock analysis by extracting a vast amount of data on stocks in real-time, produced by Yahoo Finance, and provides an API to fetch the historical data, which can be used to predict the value of stocks in the future. R programming language is used in this development along with various models like Regression Analysis, which has both dependent and independent variables. The values of independent variables can be predicted by analyzing and working on dependent variables. However, in time-series data, the outcome has to be figured out on the basis of historical data. Hence, the data related to stocks can be predicted with a time series model.