International Journal of Computer Science Engineering and Applications · 2014 · 280 citations · 18 references
BusinessEconomicsFinanceEconometricsForecastingTime Series AnalysisFinancial ForecastingForecast EvaluationTime Series EconometricsTime Series ForecastingTime Series ModelingTime Series PredictionArima ModelsAutoregressive Integrated Moving AverageAutoregressive Integrated Moving Average ForecastingAutoregressive Moving Average ModelsFinancial BenefitFinancial Time Series ForecastingForecast Model ComparisonPredictive Price Modeling
Stock price prediction has always attracted interest because of the direct financial benefit and the associated complexity. From our literature review, we felt the need of a study having sector specific analysis with a broad range of stocks. In this paper, we have conducted a study on the effectiveness of Autoregressive Integrated Moving Average (ARIMA)model, on fifty six Indian stocks from different sectors. We have chosen ARIMA model, because of its simplicity and wide acceptability of the model. We also have studied the effect on prediction accuracy based on various possible previous period data taken. The comparison and parameterization of the ARIMA model have been done using Akaike information criterion (AIC). The contribution of the paper , are a) coverage of a good number of Indian stocks b) Analysis of the models based on sectors c) Analysis of prediction accuracy based on the varying span of previous period data.
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