A STUDY ON STOCK PREDICTIVE ANALYTICS ON THE BANKING STOCKS WITH REFERENCE TO ANGEL ONE Ltd

Authors:

Dr. P. JAYARAMI REDDY M.com, MBA, Ph.D. Faculty of JNTUASMS JNTUA SCHOOL OF MANAGEMENT STUDIES ANANTAPUR (India). E-mail: p.jayaramireddi@gmail.com

KOLISETTY BHARGAVI Student of JNTUA SCHOOL OF MANAGEMENT STUDIES ANANTAPUR (India) Email: kolisettybhargavi60@gmail.com

ABSTRACT:

This project focuses on applying time series analysis to the Indian banking sector, specifically examining State Bank of India (SBI) and HDFC Bank. Conducted with reference to Angel One Ltd. in Anantapur, the research shifts away from traditional stock advisory methods toward data-driven, algorithmic forecasting. By extracting a dynamic five-year trailing dataset of daily OHLCV market values via Python, the study builds a quantitative framework for timing market entries. Since raw financial data fluctuates constantly, the Augmented Dickey-Fuller test was used to evaluate stationarity, followed by mathematical differencing to stabilize the prices.

The core of the analysis relies on an advanced ARIMA(5,1,0) forecasting algorithm designed to predict a 30-day trading window by capturing weekly cyclical momentum. The mathematical results proved highly accurate, producing a Root Mean Square Error (RMSE) of roughly ₹10 for both banking assets. Additionally, a long-term CAGR projection highlighted the aggressive wealth-building potential of the public sector compared to the private sector over the evaluated timeframe. Ultimately, this algorithmic approach provides retail investors with a reliable, risk-adjusted tool for portfolio management.