A Study on Selected Stock Price of Fast-Moving Consumer Goods Sector

Abstract

This project will try to predict stock prices in the FMCG sector, especially of key companies such as ITC, Procter & Gamble, and Hindustan Unilever Limited. This paper aims to build a model using past data to predict future stock prices with the help of advanced statistical models. Techniques to be applied will range from exponential smoothing to the ARIMA models. By implementing these methodologies, the present study would seek to establish patterns and trends in stock price oscillations to make more accurate predictions. Besides forecasting, the paper compares the performances of different models to develop the best approach for the FMCG sector. The analysis involves model comparisons based on their predictive accuracy and robustness. This data will be retrieved from renowned financial platforms like Money Control and Yahoo Finance due to their reliability in terms of historical stock data. Extensive data pre-processing was performed, followed by an in-depth analysis and Python visualization. The result of this research will contribute a lot to the study of stock price forecasting as it improves any of the predictions. Furthermore, the results will be obtained with more information about various forecasting techniques that would lead an investor to informed decisions about the FMCG sector.