This paper presents a novel deep learning approach for predicting stock market trends, aiming to improve the accuracy and efficiency of stock market forecasting. Traditional financial models often suffer from high complexity and limited predictive performance, which has motivated the exploration of deep learning techniques in this domain. The proposed model utilizes a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), leveraging the CNN's ability to capture spatial patterns and RNN's capability to understand temporal dynamics. The method is trained on historical stock market data, incorporating various technical indicators and fundamental analysis factors. Extensive experiments conducted on real-world datasets demonstrate the superior predictive power of the proposed model compared to benchmark models, such as linear regression, support vector machines, and LSTM networks. Furthermore, the model's robustness and generalization capability are validated through cross-validation and out-of-sample testing. The findings suggest that the proposed deep learning approach can serve as a valuable tool for investors and policymakers in making informed decisions, while providing new insights into the complex dynamics of the stock market.
Johnson, E. A Novel Deep Learning Approach for Predicting Stock Market Trends. Transactions on Applied Soft Computing, 2019, 1, 1. https://doi.org/10.69610/j.tasc.20190830
AMA Style
Johnson E. A Novel Deep Learning Approach for Predicting Stock Market Trends. Transactions on Applied Soft Computing; 2019, 1(1):1. https://doi.org/10.69610/j.tasc.20190830
Chicago/Turabian Style
Johnson, Emily 2019. "A Novel Deep Learning Approach for Predicting Stock Market Trends" Transactions on Applied Soft Computing 1, no.1:1. https://doi.org/10.69610/j.tasc.20190830
APA style
Johnson, E. (2019). A Novel Deep Learning Approach for Predicting Stock Market Trends. Transactions on Applied Soft Computing, 1(1), 1. https://doi.org/10.69610/j.tasc.20190830
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