Stock Market Prediction and Analysis using Supervised Learning

Abstract

The Stock Market Remains a Captivating Subject for Stockbrokers and Investors Seeking Financial Success Through Strategic Equity Trading. Informed Decisions Are Pivotal in Navigating the Dynamic Landscape of Stock Investments, Prompting the Adoption of Various Predictive Techniques. This Study Introduces a Novel Prediction Algorithm That Elucidates the Intricate Relationship Between Independent Variables—Comprising Opening and Closing Prices, High and Low Stock Values, and Trading Volume—and the Dependent Variable, Which Is the Stock Price. Leveraging a Deep Learning System, We Demonstrate the Efficacy of Generating Precise Stock Price Forecasts. Our Research Ambit Encompasses a Comprehensive Exploration of Diverse Deep-Learning Architectures Tailored to Anticipate Stock Prices for Global Conglomerates and Indian Enterprises. A Primary Objective Is to Conduct a Comparative Analysis of These Architectures, Discerning Their Respective Performances in Stock Price Prediction Scenarios. Notably, Long Short-Term Memory (Lstm) Algorithms Are Instrumental in Achieving Heightened Accuracy and Robust Prediction Outcomes. The Methodology Entails Meticulous Data Collection From Historical Stock Market Datasets Spanning Various Companies of Global and Indian Origin. Subsequent Data Preprocessing Involves Addressing Missing Values, Standardizing Features, and Structuring the Data Into Sequences Conducive to Lstm Model Input. The Lstm Architecture, Characterized by Its Adeptness in Capturing Long-Term Dependencies and Temporal Patterns, Forms the Cornerstone of Our Prediction Model. Through This Research Endeavor, We Aim to Provide Valuable Insights Into the Potential of Deep Learning Algorithms, Particularly Lstm, in Facilitating Informed Decision-Making for Investors and Stock Market Participants