Prediction of Stock Market Indices-Using SAS
Abstract— The SAS© System has a powerful suite of tools for analyzing and forecasting data taken over a selected time period. The paper concentrates more on Stock Market (NSE-Nifty, India) & its prediction, by and large a risky venture. Knowledgeable investors base their predictions either on the basis of Fundamental Analysis, or Technical Analysis, or both. But most of the investors rely on the tips given by the experts for Stock Market Predictions. However there are many such models available such as Interrupted Time Series, Auto Regression (AR), Exponential Smoothening, Moving Average (MA), and Distributed Lags Analysis. The procedures FORECAST, ARIMA process will be illustrated.
analysis, traditional time series analysis and machine learning methods. The analyzing and predicting of the indices is in one line-To reap returns while investing on the index derivatives, Index derivatives provide investors the exposure to price movements of entire indices through a single futures or options contract. Using index options, a very interesting kind of “portfolio insurance” can be obtained, whereby an investor gets paid only if the market index drops. If one does not want to bear index fluctuations in the coming weeks, then the index futures or index options can be used to reduce (or even eliminate) the consequent index exposure. This is far more convenient than distress selling of the underlying equity in the portfolio. Thus, such prediction of indices would help the investor to play safe in the dynamic volatile market arena. Analyzing What? Analyzing the indices as in NIFTY MIDCAP 50, one can determine various trends the market has seen on day-to-day basis-the process which includes determining the trends underneath the data series (at a stretch of 13 years from 19972010) brought down from various secondary sources. Moreover, these trends can be used to identify the patterns in the series and thus laying the foundation for forecasting adopting a couple of iterative processes which will be explained in the following sections. Details of the data: Observations-3282, Type-Daily Tools used: SAS© and Eviews. II. DATA FEATURES AND EXAMPLE The first step in time series analysis, or any good statistical analysis for that matter, is to plot the data. However, unless the user knows what to look for in the plots this exercise is futile. Features of interest usually include: Trend – an overall long term upward or downward movement in the data. Do we handle this by differencing the data or fitting a low order polynomial in time? Seasonality – a component of the series repeats periodically, for example, retail sales have a tendency to be high around November and December and lower near the first of the year. Do we look at seasonal differences or put in seasonal “indicator variables”?
Keywords— forecasting, prediction, stock market prediction Time series analysis. I. INTRODUCTION Stock market is the market for securities where organized issuance and trading of Stocks takes place either through exchanges or over-the-counter in electronic or physical form. It plays an important role in channelizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. Determination of stock price considers that market is supreme and it discounts everything (economical, political and all related factors). It presumes that all the investors behave rationally and the value of the asset is estimated based on future expectations. Hence, with every new information, the future expectation of the market is liable to change and consequently the stock prices. As the new information is erratic in nature so it influences the price in a random way. The purpose of this trend analysis is to enable the organizations/individuals improve their knack of trading their index options/futures in the available markets (NSE, BSE).The...
References:  www.nse-india.com (NSE National Stock Exchange, India) as a secondary data source for Nifty daily.  http://www.statsoft.com/textbook/time-series-analysis/  http:/webspace.qmul.ac.uk/dsgpollock/public_html/courses /tseries/1trends.pdf  www.nse-india.com/content/fo/fo_NIFTYMIDCAP50.htm  Brocklebank, J. C. and D. A. Dickey (2003) SAS for Forecasting Time Series, SAS Institute, Cary, N.C.  http://nseguide.com/press-releases/nse-index-weightsbased-on-nse-daily-bhav-copy/  http://www.hkbu.edu.hk/~billhung/econ3600/application/a pp03/app03.html  http://webspace.qmul.ac.uk/dsgpollock/public_html/course s/tseries/8idntify.pdf  http://www.duke.edu/~rnau/411arim.htm
“Model Selection is seldom Precise in Time Series modeling, because it’s An Art more Than a Science”
Siddhartha Reddy.B is with IBS Bangalore, Karnataka, INDIA. Ph: 91+9886768492,e-mail:Siddhartha.email@example.com
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