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A TIME SERIES DATA-BASED PREDICTION APPROACH FOR STOCK MARKET VOLATILITY
ABSTRACT
Time series analysis and forecasting is of vital significance owing to its widespread use in various practical domains. Time series data refers to an ordered sequence or a set of data points that a variable takes at equal time intervals. Stock Market is considered to be one of the most highly complex financial systems which consists of various components or stocks, the price of which fluctuates greatly with respect to time. Stock market forecasting involves uncovering the market trends with respect to time. All the stock market investors aim to maximise the returns over their investments and minimise the risks associated. Stock markets being highly sensitive and susceptible to quick changes, the main aim of stock trend prediction are to develop new innovative approaches to foresee the stocks that result in high profits.
This research tries to analyse the time series data of Indian stock market and build a statistical model that
could efficiently predict the future stocks.
I. INTRODUCTION
Future being a mystery is always a challenging task to predict. From the ages, human nature has always been more curious about the future. Forecasting refers to an approach of predicting what is likely to occur in the future
by observing what has happened earlier in the past and what is occurring at present. In other words, it is just similar
to driving a car in forward direction by keeping an eye on the rear-view mirror of a car. Forecasting is an important
problem but with vital importance in all areas of real world like business and industry, medicine, social science,
politics, finance, government, economics, environmental sciences and others. In recent years, with the rise of social
media and other promising applications, stock market forecasting has attracted huge interest from people in general and business in particular. Advances in financial sectors are responsible for growth and stability of overall economy [1] . In business domain, forecasting is considered as one of the difficult tasks owing to the various complexities of the market [2] [3] [4] . But it is important since it helps to plan for future by providing a solid idea about how to allocate the resources and plan for foreseen costs in the forthcoming period of time. Investors always try to monitor the risks in real time so that the return on investments could be higher. Forecasting helps in safeguarding the trade of securities among the buyers and the sellers as well as elimination of the risks involved.
This paper discusses an ARIMA (Auto Regressive Integrated Moving Average) model for prediction of stock market movement. An ARIMA model is a vibrant uni-variate forecasting method to project the future values of a time series. The remaining of the paper is arranged as follows. Section II describes the forecasting process. Section III discusses the forecasting techniques, while section IV discusses the financial forecasting. Section V presents the Time Series Analysis. In section VI, we try to explain in detail, the various statistical models for forecasting. Data collection and methodology are discussed in section VII, while the final section of this paper provides a brief conclusion.
A TIME SERIES DATA-BASED PREDICTION APPROACH FOR STOCK MARKET VOLATILITY
