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.

本研究提出了一种利用 TM-vector 来训练 IndRNN 的预测股票市场方向的方法，并将其与从 Twitter 中提取的特征和市场信息同时训练，以更准确地预测股票市场方向。通过对苹果公司每日股票价格的预测模型的测试，我们得到了接近 95% 的准确性，表明了 TM-vector 在股票市场预测中的有效性。