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

Ali Raza, Mubeen Javed, Adham Fayad and Asfand Yar Khan

Journal of Accounting and Finance in Emerging Economies, 2023, vol. 9, issue 3, 277-294

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.

Keywords: Advanced Deep Learning; Predictive Modelling; Stock Market Trends; Performance Metrics of Stock Market (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:src:jafeec:v:9:y:2023:i:3:p:277-294

DOI: 10.26710/jafee.v9i3.2739

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