A Novel Approach for Forecasting Price of Stock Market using Machine Learning Techniques

In today’s competitive business world, industries strive for rapid growth and leadership. Expanding a business requires additional capital, which can be raised through an initial public offering (IPO), angel investors, or business loans. As a company grows, it becomes difficult for individual investors to sustain operations with their capital alone, necessitating a constant influx of funds. Conducting an IPO not only raises capital but also enhances the company’s reputation and credibility. It can also allow founders or early-stage investors to sell part of their ownership. After an IPO, the company’s shares are publicly traded as stocks, offering various benefits when included in a public investment portfolio. Investing in stocks from different companies enables individuals to accumulate savings and safeguard their wealth against inflation and taxes. However, accurately predicting stock prices is crucial for maximizing investment returns. In this research paper, the main goal is to predict the stock price. To achieve this, a special Hybrid model called LSTM + GRU is used. Two case studies have also been done to support the result. The first case study is done with Tata Motors and the other is with Honda Motors. Different measures, such as RMSE, MAE, and MSE, are used to assess how well the model performs. The results are presented in a visually appealing way, allowing for easy understanding and comparison with other existing models. By conducting this research, our objective is to provide valuable insights into predicting stock prices, helping investors and decision-makers make informed choices