Advanced Deep Learning-Based Predictive Modelling for Analyzing Trends and Performance Metrics in Stock Market

Abstract

Objective: This study's main goal is to investigate how deep learning approaches may be used to analyze stock market performance. The complex patterns and nonlinear interactions present in stock market data may be difficult to completely capture using traditional approaches, which are mostly based on statistical models.
 Methodology: Our work uses a large dataset of historical stock prices, macroeconomic indices, and other crucial financial factors to address this. Simple Moving Averages (SMA) are one of the feature engineering approaches that are used to combine fundamental and technical indicators. To capture the temporal dynamics of the stock market, the study goes further into a variety of deep learning architectures, including as long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
 Findings: The results show that thorough feature engineering combined with deep learning approaches may effectively capture the complexity of the stock market and provide forecasts that are more accurate.
 Implications: This highlights how deep learning may revolutionize financial market research and points to a paradigm change toward more trustworthy instruments for investors and decision-makers.