Forecasting NIFTY 50 benchmark Index using Seasonal ARIMA time series models

@article{Tewari2020ForecastingN5,
  title={Forecasting NIFTY 50 benchmark Index using Seasonal ARIMA time series models},
  author={Amit Tewari},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.08979}
}
  • Amit Tewari
  • Published 2020
  • Computer Science, Economics, Mathematics
  • ArXiv
  • This paper analyses how Time Series Analysis techniques can be applied to capture movement of an exchange traded index in a stock market. Specifically, Seasonal Auto Regressive Integrated Moving Average (SARIMA) class of models is applied to capture the movement of Nifty 50 index which is one of the most actively exchange traded contracts globally [1]. A total of 729 model parameter combinations were evaluated and the most appropriate selected for making the final forecast based on AIC criteria… CONTINUE READING

    Figures, Tables, and Topics from this paper

    References

    SHOWING 1-3 OF 3 REFERENCES
    Stock Price Prediction Using the ARIMA Model
    • 186
    • PDF
    Time Series Analysis, Forecasting and Control.
    • 8,532
    Information Theory and an Extension of the Maximum Likelihood Principle
    • 17,361