An effective technique to forecast company stock prices using machine learning models

Researchers have worked extensively to develop accurate and reliable predictive algorithms for forecasting stock prices, with literature indicating that precise predictions are achievable through well-constructed and refined models. This paper aims to demonstrate a comprehensive array of stock price prediction models, encompassing time series, econometrics, statistics, and machine learning, using a dataset covering January 2017 to December 2022, including NIFTY50 and some other industry leaders from various sectors. The performance of these models was evaluated to determine the optimal strategy for different sectors. ARIMA served as the econometric model were served as time series models. Additionally, OLS was employed as the statistical model, alongside various machine learning and deep learning models such as Random Forest, KNN, CNN, and LSTM. These models were designed to predict stock prices and identify price movement directions. By integrating multiple models and techniques, this work is aiming to give insights of most effective methods for forecasting prices of stock direction among different industries.