• Ravikant Narayan
• Urvashi Rakholiya

The stock market has become increasingly popular in recent years, attracting investors from all walks of life. Despite its unpredictability and complexity, the market provides a dynamic and ever-changing platform for traders to invest in shares, with the potential for significant gains and losses. Accurately forecasting stock prices is crucial for investors as it provides valuable insights into a company's financial health and growth prospects. With this information, investors can make informed decisions, mitigate risks, and capitalize on lucrative opportunities in the market. Extensive research has been dedicated to developing effective prediction methods, leveraging various mathematical models and machine-learning techniques. This research paper delves into the realm of stock market prediction, focusing on evaluating different machine-learning styles. We aim to comprehensively analyze and compare the performance of these techniques in forecasting stock market behavior. By understanding the strengths and limitations of each method, investors, financial analysts, and market participants can gain critical knowledge to optimize their trading strategies and decision-making processes. To achieve this goal, we explore an array of machine-learning algorithms, ranging from traditional linear regression models to sophisticated deep-learning approaches. These algorithms leverage historical stock market data, macroeconomic indicators, company financials, and sentiment analysis, among other factors, to predict future price movements and market trends. In addition to performance comparison, we examine the impact of various factors that influence the effectiveness of these machine-learning techniques. Factors such as data quality, feature engineering, model selection, hyperparameter tuning, and market conditions play pivotal roles in the accuracy of predictions. Understanding these factors will aid in refining the model-building process and enhancing overall forecasting capabilities. Our study encompasses an extensive dataset spanning multiple stock markets and periods, ensuring robustness and reliability in the findings. The machine-learning techniques objectively. Furthermore, we investigate the potential of ensemble methods, combining the strengths of multiple models to achieve enhanced prediction accuracy. Ensemble techniques, such as bagging, boosting, and stacking, have proven effective in diverse domains and are expected to demonstrate their value in stock market prediction. By the end of this research, readers will have a comprehensive understanding of the landscape of machine-learning techniques applied to stock market prediction. The findings will offer insights into which methods are most suitable for different market conditions and will aid in establishing the best.

Stock Market, Stock price prediction, LSTM, BILSTM, Linear regression models, Deep learning approaches, Historical stock market data, Macroeconomic indicators

"Navigating Stock Market Price Forecasting: Comparative Analysis of Conventional Statistics and Advanced Machine-Learning Methods", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.g315-g320, March-2024, Available :http://www.jetir.org/papers/JETIR2403644.pdf

"Navigating Stock Market Price Forecasting: Comparative Analysis of Conventional Statistics and Advanced Machine-Learning Methods", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppg315-g320, March-2024, Available at : http://www.jetir.org/papers/JETIR2403644.pdf