Revolutionizing Stock Market Intelligence: A Deep Dive into Machine Learning for IT Sector Price Predictions

This study challenges the efficient market hypothesis by presenting a diverse approach to stock price prediction, incorporating statistical, machine learning, and deep learning models with daily stock data. Demonstrating effectiveness in capturing volatile patterns, the framework integrates artificial intelligence with traditional time-series analysis methods, including autoregressive integrated moving average (ARIMA), long short- term memory (LSTM), and Facebook Prophet. A noteworthy advancement is the exceptional forecasting accuracy of the predictive toolkit, enabling users to create and share personalized stock portfolios based on advanced, unique forecasts. In the realm of business intelligence, the framework employs a novel machine- learning approach for predicting stock price movements in the information technology sector. Emphasizing the significance of accurate stock price prediction, the paper advocates for continuous exploration of diverse machine learning techniques. In conclusion, the review highlights the synergy between traditional time-series analysis and modern machine-learning techniques with substantial potential for navigating the complexities of financial markets and empowering users in making well-informed investment decisions.