A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data
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Date
2023-06-08
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IEEE
Abstract
Several economists and social scientists have held a longstanding fascination with the practice of stock market prediction. As the stock market is essentially uncontrollable chaos, many experts believe that trying to predict it is futile. Due to the complexity of the numerous factors, accurate stock price predictions are notoriously difficult to achieve. While the market behaves more like a scale than a voting machine over the long run, its behavior may be predicted with some certainty. Information from Twitter is used into the algorithm. In this proposed method, a convolutional extreme learning machine model with kernel support was introduced (CKELM). To improve feature extraction and data classification, the CKELM model builds on the KELM’s hidden layer by adding convolutional and subsampling layers. The convolutional layer and the subsampling layer do not employ the gradient technique to fine-tune their parameters because some designs worked well with random weights. When compared to popular models like CNN and KELM, The proposed model fares quite well, with an accuracy of around 98.3 percent.
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ISME
Keywords
Social networking, Extreme learning machines, Computational modeling, Neural networks, Prediction algorithms, Feature extraction, Data models, Convolutional Neural Network (CNN), Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM)
Citation
V. Agarwal, P. Ravi Kumar, S. Shankar, S. Praveena, V. Dubey and A. Chauhan, "A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data," 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2023, pp. 78-82, doi: 10.1109/ICICCS56967.2023.10142522.