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Forecasting risk using auto regressive integrated moving average approach: an evidence from S&P BSE Sensex

Author

Listed:
  • Madhavi Latha Challa

    (School of Management Studies, Vignan’s Foundation for Science, Technology & Research)

  • Venkataramanaiah Malepati

    (Institute of Management Studies, Golden Valley Integrated Campus (GVIC))

  • Siva Nageswara Rao Kolusu

    (School of Management Studies, Vignan’s Foundation for Science, Technology & Research)

Abstract

The primary objective of the paper is to forecast the beta values of companies listed on Sensex, Bombay Stock Exchange (BSE). The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies. To reach out the predefined objectives of the research, Auto Regressive Integrated Moving Average method is used to forecast the future risk and returns for 10 years of historical data from April 2007 to March 2017. Validation accomplished by comparison of forecasted and actual beta values for the hold back period of 2 years. Root-Mean-Square-Error and Mean-Absolute-Error both are used for accuracy measurement. The results revealed that out of 30 listed companies in the BSE Sensex, 10 companies’ exhibits high beta values, 12 companies are with moderate and 8 companies are with low beta values. Further, it is to note that Housing Development Finance Corporation (HDFC) exhibits more inconsistency in terms of beta values though the average beta value is lowest among the companies under the study. A mixed trend is found in forecasted beta values of the BSE Sensex. In this analysis, all the p-values are less than the F-stat values except the case of Tata Steel and Wipro. Therefore, the null hypotheses were rejected leaving Tata Steel and Wipro. The values of actual and forecasted values are showing the almost same results with low error percentage. Therefore, it is concluded from the study that the estimation ARIMA could be acceptable, and forecasted beta values are accurate. So far, there are many studies on ARIMA model to forecast the returns of the stocks based on their historical data. But, hardly there are very few studies which attempt to forecast the returns on the basis of their beta values. Certainly, the attempt so made is a novel approach which has linked risk directly with return. On the basis of the present study, authors try to through light on investment decisions by linking it with beta values of respective stocks. Further, the outcomes of the present study undoubtedly useful to academicians, researchers, and policy makers in their respective area of studies.

Suggested Citation

  • Madhavi Latha Challa & Venkataramanaiah Malepati & Siva Nageswara Rao Kolusu, 2018. "Forecasting risk using auto regressive integrated moving average approach: an evidence from S&P BSE Sensex," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-17, December.
    • Handle: RePEc:spr:fininn:v:4:y:2018:i:1:d:10.1186_s40854-018-0107-z
    DOI: 10.1186/s40854-018-0107-z
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    References listed on IDEAS

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    Cited by:

    1. Madhavi Latha Challa & Venkataramanaiah Malepati & Siva Nageswara Rao Kolusu, 2020. "S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-19, December.
    2. Liu, Tao & Guan, Xinyue & Wei, Yigang & Xue, Shan & Xu, Liang, 2023. "Impact of economic policy uncertainty on the volatility of China's emission trading scheme pilots," Energy Economics, Elsevier, vol. 121(C).
    3. Changshi Liu & Gang Kou & Yi Peng & Fawaz E. Alsaadi, 2019. "Location-Routing Problem for Relief Distribution in the Early Post-Earthquake Stage from the Perspective of Fairness," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    4. Jian Liu & Ziting Zhang & Lizhao Yan & Fenghua Wen, 2021. "Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    5. Indrajit Banerjee & Atul Kumar & Rupam Bhattacharyya, 2020. "Examining the Effect of COVID-19 on Foreign Exchange Rate and Stock Market -- An Applied Insight into the Variable Effects of Lockdown on Indian Economy," Papers 2006.14499, arXiv.org, revised Sep 2020.
    6. Rupel Nargunam & William W. S. Wei & N. Anuradha, 2021. "Investigating seasonality, policy intervention and forecasting in the Indian gold futures market: a comparison based on modeling non-constant variance using two different methods," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-15, December.

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    More about this item

    Keywords

    Akaike Information Criteria (AIC); Bombay Stock Exchange (BSE); Auto Regressive Integrated Moving Average (ARIMA); Beta; Time series;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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