Forecasting Stock Prices of Select Indian Private Sector Banks – A Time Series Approach


  • Rajveer S. Rawlin Assistant Professor, M. S. Ramaiah Institute of Management, Bangalore – 560054, Karnataka
  • Satya Surya Narayana Raju Pakalapati Graduate Student, M. S. Ramaiah Institute of Management, Bangalore – 560054, Karnataka



ARIMA, Banking, Forecasting, Stationarity, Time Series Analysis


Forecasting stock markets and individual stocks has been a well-researched area in the world of finance. Fundamental and technical analysis is widely used by investors in analysing stock prices. Researchers have used various methods to predict stock prices such as Hidden Markov models, genetic algorithms and neural networks (Enke, Grauer, and Mehdiyev, 2011; Hassan, Nath, and Kirley 2007). Time series analysis is used in forecasting asset prices (Long et al, 2021; Eita, 2012). Indian private sector banks are among the best-performing stocks on the Indian stock exchanges over the last decade, as they have consistently captured market share from their public sector counterparts. ARIMA is a useful technique to forecast stock and stock index prices (Box and Jenkins, 1970). This study aimed to evaluate the effectiveness of the ARIMA model in forecasting private bank stock prices in India. Forecasted values differed from actual prices, suggesting markets may be efficient and other variables may also prove to be influential in forecasting Indian private bank stock prices.


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How to Cite

Rawlin, R. S., & Raju Pakalapati, S. S. N. (2022). Forecasting Stock Prices of Select Indian Private Sector Banks – A Time Series Approach. SDMIMD Journal of Management, 13(1), 35–44.



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