Abstract
Stock market volatility is important for investment, option pricing and financial market regulation. In recent years, stock market analysis and prediction have the greatest significance for many professionals in the fields of finance and stock exchange. There are many methods available in the literature to solve the problem of future prediction. The present study provides a detailed comparison of Single Exponential Smoothing (SES) model and Autoregressive Integrated Moving Average (ARIMA) model. Future values are forecasting using SES and ARIMA model. Forecasted values for different îś values are calculated from SES method and ARIMA (0, 2, 3). Also, Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square (RMSE) and Mean Absolute Percentage Error (MAPE) are calculated individually for both methods.
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Acknowledgements
The authors acknowledge University Grants Commission (UGC) for providing the infrastructure facility under the scheme of SAP (DRS-I). The first author acknowledges UGC for financial support to carry out this research work under Basic Science Research Fellowship (BSRF) for meritorious students.
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Abdul Azees, S.A., Sasikumar, R. (2019). Comparison Study on Exponential Smoothing and ARIMA Model for the Fuel Price. In: Deep, K., Jain, M., Salhi, S. (eds) Logistics, Supply Chain and Financial Predictive Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-13-0872-7_3
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DOI: https://doi.org/10.1007/978-981-13-0872-7_3
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