2.5

CiteScore

8.8

Global Impact Factor

Structural Break Analysis of Indian IT Sector Stocks Using ARIMA: Evidence from the COVID-19 Pandemic


Paper ID: EIJTEM_2026_13_2_295-305

Author's Name: Kulkarni Parag, Sanjay Azade

Volume: 13

Issue: 2

Year: 2026

Page No: 295-305

Abstract:

Five major NSE-listed Indian information technology companies — Infosys (INFY), HCL Technologies (HCLTECH), Tech Mahindra (TECHM), Wipro (WIPRO), and Tata Consultancy Services (TCS) — form the empirical focus of this investigation into structural breaks in stock price dynamics. The Autoregressive Integrated Moving Average (ARIMA) framework is applied across a decade-long horizon from January 2016 to January 2026. India's national lockdown on March 24, 2020, and the COVID-19 pandemic declared a global health emergency on March 11, 2020, together constitute the primary macro shock examined here. Drawing on the full Box-Jenkins methodology alongside the Chow test, Zivot-Andrews test, and Bai-Perron multiple structural break tests, the paper documents how the dynamics of Indian IT markets shifted permanently during the pandemic. All five closing price series are confirmed as integrated of order one (I(1)) by Augmented Dickey-Fuller, KPSS, and Phillips-Perron tests, with log returns satisfying stationarity under all three criteria. AIC-guided ARIMA model selection produces ARIMA(2,1,2) for INFY and WIPRO, ARIMA(3,1,2) for HCLTECH, ARIMA(2,1,3) for TECHM, and ARIMA(0,1,0) for TCS — the last of these reflecting random walk behaviour in the most liquid stock. At both COVID event dates, Chow test F-statistics are strongly significant across all five stocks (all p

Keywords: ARIMA; Structural Break; COVID-19; NSE India; IT Sector; Stock Price Forecasting; Bai-Perron; Pre-Post COVID Regime

References:

[1] NASSCOM, Technology Sector in India: Annual Strategic Review 2023. New Delhi: National Association of Software and Service Companies, 2023.
[2] WHO, "WHO Director-General's opening remarks at the media briefing on COVID-19: 11 March 2020," World Health Organization, 2020. https://www.who.int
[3] G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, 1976.
[4] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. Melbourne: OTexts, 2021. https://otexts.com/fpp3/
[5] R. S. Tsay, Analysis of Financial Time Series, 3rd ed. Hoboken, NJ: Wiley, 2010.
[6] K. He, Q. Yang, L. Ji, J. Pan, and Y. Zou, "Financial time series forecasting with the deep learning ensemble model," Mathematics, vol. 11, no. 4, p. 1054, 2023.
[7] Z. Senol and M. Ozturk, "Stock market prediction using deep learning techniques," IEEE Access, vol. 10, pp. 6498–6508, 2022.
[8] J. Bai and P. Perron, "Estimating and testing linear models with multiple structural changes," Econometrica, vol. 66, no. 1, pp. 47–78, 1998.
[9] M. Bhatia, "Stock market efficiency and COVID-19 with multiple structural breaks: Evidence from India," Glob. Bus. Rev., 2022. https://doi.org/10.1177/09721509221110372
[10] M. Maiti, "Short-term impact of COVID-19 on Indian stock market," J. Risk Financial Manag., vol. 14, no. 11, p. 558, MDPI, 2021. https://doi.org/10.3390/jrfm14110558
[11] G. C. Chow, "Tests of equality between sets of coefficients in two linear regressions," Econometrica, vol. 28, no. 3, pp. 591–605, 1960.
[12] E. Zivot and D. W. K. Andrews, "Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis," J. Bus. Econ. Stat., vol. 10, no. 3, pp. 251–270, 1992.
[13] J. Bai and P. Perron, "Computation and analysis of multiple structural change models," J. Appl. Econometrics, vol. 18, no. 1, pp. 1–22, 2003.
[14] M. Dadhich, V. Chouhan, and A. Adholiya, "Predictive models for stock market index using stochastic time series ARIMA modeling in emerging economy," in Lect. Notes Mech. Eng., Springer Nature Singapore, 2021. https://doi.org/10.1007/978-981-16-0942-8_26
[15] P. K. Kandpal, Shourya, Y. Yadav, and N. Sharma, "Time series forecasting of NSE stocks using machine learning models (ARIMA, Facebook Prophet, and Stacked LSTM)," in Lect. Notes Netw. Syst., vol. 788, Springer, Singapore, 2023. https://doi.org/10.1007/978-981-99-6553-3_24
[16] Z. Fang, X. Ma, H. Pan, G. Yang, and G. R. Arce, "Movement forecasting of financial time series based on adaptive LSTM-BN network," Expert Syst. Appl., vol. 213, p. 119207, 2023.
[17] V. K. Rahi, A. Kumar, and R. P. Singh, "Stock price prediction using ARIMA with option chain data and technical indicators," in Innovations in Data Analytics, LNNS vol. 1410, pp. 63–75, Springer, 2025. https://doi.org/10.1007/978-981-96-6303-3_5
[18] S. Varshney and P. Srivastava, "A comparative study of future stock price prediction through artificial neural network and ARIMA modelling," Glob. Bus. Rev., 2023. https://doi.org/10.1177/09711023241230367
[19] J. N. C. Yong and Z. Cao, "COVID-19 and instability of stock market performance: Evidence from the U.S.," Financial Innov., vol. 7, no. 1, pp. 1–18, Springer, 2021. https://doi.org/10.1186/s40854-021-00229-1
[20] J. A. Ndako, T. Kumeka, F. Adedoyin, and S. Asongu, "Structural breaks in global stock markets: Are they caused by pandemics, protests, or other factors?" Transnatl. Corp. Rev., vol. 17, p. 200147, 2025. https://doi.org/10.1016/j.tncr.2025.200147
[21] K. S. Manu and A. S. Shetty, "Impact of COVID-19 on the performance of Indian stock market: An empirical analysis," Invest. Manag. Financial Innov., vol. 19, no. 2, 2022. https://doi.org/10.1177/22786821221127734
[22] B. K. Guru and A. Das, "COVID-19 and uncertainty spillovers in Indian stock market," MethodsX, vol. 8, p. 101199, 2021.
[23] A. F. Bariviera, I. Merediz-Solà, and A. Urquhart, "COVID-19 and persistence in the stock market: A study on NSE sectors," Int. J. Disclosure Gov., Springer, 2024. https://doi.org/10.1057/s41310-024-00250-7
[24] T. N. Bui and X. T. Nguyen, "COVID-19 structural breaks in ASEAN stock markets: A Bai-Perron multiple break analysis," J. Asian Finance Econ. Bus., vol. 9, no. 2, pp. 1–12, 2022.
[25] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed. Hoboken, NJ: Wiley, 2015.
[26] D. A. Dickey and W. A. Fuller, "Distribution of the estimators for autoregressive time series with a unit root," J. Am. Stat. Assoc., vol. 74, no. 366a, pp. 427–431, 1979.
[27] D. Kwiatkowski, P. C. B. Phillips, P. Schmidt, and Y. Shin, "Testing the null hypothesis of stationarity against the alternative of a unit root," J. Econometrics, vol. 54, no. 1–3, pp. 159–178, 1992.
[28] P. C. B. Phillips and P. Perron, "Testing for a unit root in time series regression," Biometrika, vol. 75, no. 2, pp. 335–346, 1988.
[29] R. F. Engle, "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation," Econometrica, vol. 50, no. 4, pp. 987–1007, 1982.
[30] R. Cont, "Empirical properties of asset returns: Stylized facts and statistical issues," Quant. Finance, vol. 1, no. 2, pp. 223–236, 2001.
[31] E. F. Fama, "Efficient capital markets: A review of theory and empirical work," J. Finance, vol. 25, no. 2, pp. 383–417, 1970.

View PDF