Abstract

Over the years, researchers have strived to develop reliable and accurate predictive models for stock price prediction. The literature suggests that well-designed and refined predictive models can provide painstakingly precise estimates of future stock values. This project aims to showcase a comprehensive set of models for predicting stock prices, including time series, econometric, statistical, and machine learning-based approaches. The dataset includes ten industry leaders from different sectors and NIFTY50, spanning from January 2017 to December 2022. The models' performance was evaluated to determine which approach performs best for different sectors. The time series models employed include Holt's Linear Trend and Holt-Winters Exponential Smoothing, while the econometric model utilized is ARIMA. Additionally, the statistical model adopted is OLS, while several machine learning and deep learning models incorporated a range of techniques such as Random Forest, KNN, CNN, LSTM, etc. Besides predicting stock prices, the models were also designed to determine the direction of stock price movements. This project aims to provide insights into the best methods for predicting stock prices and direction across different industries by combining different models and approaches.