2 research outputs found
TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data
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.Comment: 24 pag
Forecasting of Bitcoin Illiquidity Using High-Dimensional and Textual Features
Liquidity is the ease of converting an asset (physical/digital) into cash or another asset without loss and is shown by the relationship between the time scale and the price scale of an investment. This article examines the illiquidity of Bitcoin (BTC). Bitcoin hash rate information was collected at three different time intervals; parallel to these data, textual information related to these intervals was collected from Twitter for each day. Due to the regression nature of illiquidity prediction, approaches based on recurrent networks were suggested. Seven approaches: ANN, SVM, SANN, LSTM, Simple RNN, GRU, and IndRNN, were tested on these data. To evaluate these approaches, three evaluation methods were used: random split (paper), random split (run) and linear split (run). The research results indicate that the IndRNN approach provided better results.Peer Reviewe