[论文解读] TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

Faraz Sasani, Ramin Mousa+3 more|arXiv (Cornell University)|Mar 13, 2023

Stock Market Forecasting Methods被引用 10

一句话总结

TM-vector 提出一种预测方法，将一个 IndRNN 与一个将 Twitter 派生特征和市场数据结合的丰富表示共同训练，以预测股票走势，在 Dow Jones 30 成分股上取得显著结果（尤其是 Apple）。

ABSTRACT

Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.

研究动机与目标

• 通过利用社交媒体信号与市场数据来改进股票走势预测的动机。
• 开发一个统一表示（TM-vector），捕捉用户特征、互动和市场影响。
• 用含有 Twitter 特征和市场信息的神经网络模型（IndRNN）来预测市场方向。

提出的方法

• 将 TM-vector 定义为从 Twitter 特征和市场数据共同学习的联合表示。
• 使用 IndRNN（输入驱动的循环网络）来建模市场用户和股票走势的序列动态。
• 在预测管道中纳入用户层面的特征、跨用户互动及其市场影响。
• 将模型应用于 Dow Jones 30 股票并评估方向准确性，包括 Apple。

实验结果

研究问题

• RQ1 当将 Twitter 派生特征和市场数据融合为 TM-vector 时，是否能提高股票走势方向预测的准确性，超越传统方法？
• RQ2 在 TM-vector 框架下，个体用户特征和跨用户效应如何影响市场走势？
• RQ3 TM-vector 对于 Dow Jones 30 主要成分股，特别是 Apple 的预测准确性如何？

主要发现

• TM-vector 在 Apple 及其他 Dow Jones 30 股票的日常股票变动预测中表现出较高准确性（Apple 的准确性报道接近 95%）。
• 纳入用户特征和跨用户效应能提高预测性能，相较基线方法有改进。
• 同時使用 Twitter 特征和市场信息进行训练，可以为市场方向预测提供更丰富的表示。