Forecasting Stock Trends of Bharat Heavy Electricals Limited: An Application of Geometric Brownian Motion
Ronit Paul, Tanusree Deb Roy
Abstract
This study evaluates the effectiveness of Geometric Brownian Motion (GBM) in predicting the stock market behavior of Bharat Heavy Electricals Limited (BHEL), specifically focusing on forecasting the open and close stock prices over a forthcoming ten-day period. Using historical share price data from July 14, 2023 till March 31, 2024, the GBM model is applied to predict stock price movements from April 1, 2024, to April 15, 2024. The results demonstrated high accuracy in the model's predictions, with discrepancies between forecasted and actual values generally narrow, highlighting specific instances of notable precision. Despite its effectiveness, the study identified limitations in predicting higher stock price values, suggesting a need for further model optimization to enhance accuracy at these data points. The findings emphasize the utility of advanced statistical models like GBM (Geometric Brownian Motion) in financial forecasting, aiding traders and investors in making informed decisions, with recommendations for future refinements to increase the model's applicability and precision across different market conditions.
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