Time-series data analysis for Stock Market Prediction using R,

Abstract

Prediction of the stock market is gathering of previous historical data of shares or any traded securities in the market and trying to forecast the future value of the company's share/securities. This stock prediction using historical time series data, based on certain preconceived notions and some constant factors and hence becomes difficult to predict for long term range. (Figlewski, 1994) (Mondal, 2014).Time series analysis like moving average, autoregression integrated. Moving Average and Holtwinters Models are being used. This paper proposes the best fit model for stock prediction using BSE (Bombay Stock Exchange data ) for 10 years.

Purpose: A typical mistake investor does many a time a wrong timing of investment. Investing at an appropriate time can get gains. Investors often fail to understand when to buy the stock, when to sell, how long to hold as many times misconceptions. (Grinblatt, 2000). Thus we have attempted to find the best method for Prediction. Also attempted to predict nearing to accurate Prediction and can generate profits to the investors. (Zweig, 1973).However, the limitation is the Time series analysis model is helpful in short period forecasting (Alwadi, 2018, October). (Chng, 2019) (Ariyo, 2014, March). Stock market prediction for the more extended period is a challenge because of the very nature of Data without seasonality or any trend.