Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Charalampos M. Liapis, Sotiris Kotsiantis
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引用次数: 0

Abstract

The use of deep learning in conjunction with models that extract emotion-related information from texts to predict financial time series is based on the assumption that what is said about a stock is correlated with the way that stock fluctuates. Given the above, in this work, a multivariate forecasting methodology incorporating temporal convolutional networks in combination with a BERT-based multi-label emotion classification procedure and correlation feature selection is proposed. The results from an extensive set of experiments, which included predictions of three different time frames and various multivariate ensemble schemes that capture 28 different types of emotion-relative information, are presented. It is shown that the proposed methodology exhibits universal predominance regarding aggregate performance over six different metrics, outperforming all the compared schemes, including a multitude of individual and ensemble methods, both in terms of aggregate average scores and Friedman rankings. Moreover, the results strongly indicate that the use of emotion-related features has beneficial effects on the derived forecasts.
基于时间卷积网络和bert的金融预测多标签情绪分析
将深度学习与从文本中提取情感相关信息的模型结合起来预测金融时间序列,是基于这样一个假设:关于股票的言论与股票波动的方式相关。鉴于上述情况,本文提出了一种将时间卷积网络与基于bert的多标签情感分类过程和相关特征选择相结合的多元预测方法。本文介绍了一系列广泛的实验结果,其中包括对三种不同时间框架的预测和捕捉28种不同类型的情感相关信息的各种多元集成方案。研究表明,所提出的方法在六个不同的指标上表现出总体表现的普遍优势,优于所有比较的方案,包括大量的个人和集合方法,无论是在总体平均分还是弗里德曼排名方面。此外,研究结果强烈表明,情绪相关特征的使用对导出的预测有有益的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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