ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
Authors: 
Shengkun Wang, Yangxiao Bai, Kaiqun Fu, Linhan Wang, 
Chang-Tien Lu, Taoran JiAuthors Info & Claims
Shengkun Wang, Yangxiao Bai, Kaiqun Fu, Linhan Wang, Chang-Tien Lu, Taoran JiAuthors Info & ClaimsPages 538 - 542
Abstract
For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.
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Published: 15 March 2024
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ASONAM '23
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ASONAM '23: International Conference on Advances in Social Networks Analysis and Mining
November 6 - 9, 2023
Kusadasi, Turkiye
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ASONAM '23 Paper Acceptance Rate 53 of 145 submissions, 37%;
Overall Acceptance Rate 116 of 549 submissions, 21%
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