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638 VI-D1 Modeling Financial Uncertainty with Multivariate Temporal Entropy-based Curriculums ...
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Abstract:
Full paper: https://www.auai.org/uai2021/pdf/uai2021.638.pdf Abstract: In the financial realm, profit generation greatly relies on the complicated task of stock prediction. Lately, neural methods have shown success in exploiting stock affecting signals from textual data across news and tweets to forecast stock performance. However, the dynamic, stochastic, and variably influential nature of text and prices makes it difficult to train neural stock trading models, limiting predictive performance and profits. To transcend this limitation, we propose a novel multi-modal curriculum learning approach: FinCLASS, which evaluates stock affecting signals via entropy-based heuristics and measures their linguistic and price-based complexities in a time-aware, hierarchical fashion. We show that training financial models can benefit by exposing neural networks to easier examples of stock affecting signals early during the training phase, before introducing samples having more complex linguistic and price-based temporal ...
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Keyword:
Machine Learning; Neural Network
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URL: https://dx.doi.org/10.48448/5xbw-jq75
https://underline.io/lecture/28944-638-vi-d1-modeling-financial-uncertainty-with-multivariate-temporal-entropy-based-curriculums
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GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion ...
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