Application of Time Series Analysis in the Daily Stock Exchange Data

Mohankumari C1, Vishukumar M2 and Nagaraja Rao Chillale3

1Assistant Professor, Department of Statistics, School of Applied Sciences, REVA University, INDIA. 2Professor, Department of Mathematics, School of Applied Sciences, REVA University, INDIA. 3Associate Professor, Department of Statistics, Bangalore University, Bangalore, INDIA.

DOI : http://dx.doi.org/10.29055/jcms/938


Forecasting is a necessity of human life and a common problem in all branches of learning, financial and economic problems are domains in which forecasting is of major importance. In the field of stock exchange, the basic goal of market participants is to predict the future trends of stock price and determine the best time to execute transactions in order to optimize investment decisions. A stock, also known as equity of share is a portion of the ownership in a corporate by an individual. Hence, a stock of a company entitles its holder a share in its profit. Only by issuing shares a corporate company can mobilize huge capitals. The stock market is a field of financial game and it can fetch bigger financial benefits compared to fixed deposits with banks. The stability as well as the inflation of the economy of a country is swiftly and better reflected by the trend in the stock market. So the study of the fluctuations in the stock market becomes important. There are many approaches to know the depth of an analysis of stock price variation. So we have arrived to propose forecasting methods such as Time Series Analysis to provide better accuracy in forecasting as compared to traditional methods. Published stock data obtained from National Stock Exchange (NSE) are used with stock price predictive model developed. The results obtained revealed that the ARIMA model has a strong potential for short-term prediction and can compete favourably with existing prediction techniques for stock price prediction.

Keywords :Time Series Analysis, ARIMA models.

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