Exploiting investors social network for stock prediction in China's market

Zhang, Xi; Shi, Jiawei; Wang, Di; Fang, Binxing

doi:10.1016/j.jocs.2017.10.013

Cited by 63 publications

(34 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The axis of the work of Zhang et al [200] is Xueqiu, a Chinese platform that is similar to Twitter but specifically for investors. In their study, they classified the tweets by polarity, implementing the naive Bayes network, and predicted the stock price movements by using SVM and the perceptron network [200].…”

Section: Other Social Media Platformsmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

View full text Add to dashboard Cite

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

show abstract

Section: Other Social Media Platformsmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

View full text Add to dashboard Cite

show abstract

“…Ziniu Hu et al [7] proposed a Hybrid Attention Networks (HAN), which takes into account the use of analytical news factors to predict a stock trend in learning framework. Xi Zhang et al [8] proposed that the investor social networks is also an emotion-oriented forecasting method, which utilizes emotional factors and stock correlation characteristics to model. In their further article [9], they considered the relationship between the single source and the multisource data and then employed the coupling matrix and tensor decomposition framework to study the impact of online news and user sentiment on stock price changes.…”

Section: Related Workmentioning

confidence: 99%

LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

Liang

Sun

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For profit maximization, the model-based stock price prediction can give valuable guidance to the investors. However, due to the existence of the high noise in financial data, it is inevitable that the deep neural networks trained by the original data fail to accurately predict the stock price. To address the problem, the wavelet threshold-denoising method, which has been widely applied in signal denoising, is adopted to preprocess the training data. The data preprocessing with the soft/hard threshold method can obviously restrain noise, and a new multioptimal combination wavelet transform (MOCWT) method is proposed. In this method, a novel threshold-denoising function is presented to reduce the degree of distortion in signal reconstruction. The experimental results clearly showed that the proposed MOCWT outperforms the traditional methods in the term of prediction accuracy.

show abstract

“…Xueqiu website provides real-time quotes, actual exchanges, and real trades for users and is a popular platform for investors to communicate and share their experiences regarding investment strategies. Because of the low handing fees, the Xueqiu website attracts millions of investors by the end of 2015 [35] us, Xueqiu is undoubtedly one of the best material delivery platforms in many Internet platforms.…”

Section: Investor Attention Indexmentioning

confidence: 99%

Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks

Guo

Feng

2019

Complexity

View full text Add to dashboard Cite

e relationship between investor attention and stock prices has been a topic of interest in economics. Previous studies have shown that the correlation relationship between the two changes with time. However, there are few studies to explore the timevarying evolution of the relationship, as well as the transmission characteristics under important cycles. us, this paper is dedicated to discover the dynamic transmission characteristics of the correlation between investor attention and stock price. We selected the typical stocks of China's energy industry, PetroChina and Sinopec, as the research objects, as they occupy a large market share and are representative. And the transaction data and attention data are used to build investor attention indicator. In order to reproduce the dynamic transmission process of correlation at di erent cycles, sliding time window and complex network are applied. e results show that PetroChina and Sinopec stocks have a weakly negative correlation between investor attention and stock price from 2017 to 2018. However, from the perspective of di erent cycles, the correlation has time-varying characteristics. As the cycle grows, the types of transmission patterns of the ve consecutive days of correlation between the two become less, but the transmission intensity between the modes increases and the transition becomes more regular and inclined. In addition, by mining the important transmission modes and main transmission paths under important periods, we nd that the series modes of uncorrelated or weakly positive correlation for ve consecutive days dominate the transition of modes in the networks. Also, the closed loop formed by these two important modes and related modes is the main transmission path. ese ndings can reveal the rules of the typical stock market in China's energy industry and help investors with di erent investment cycle preferences make sound decisions.

show abstract

Exploiting investors social network for stock prediction in China's market

Cited by 63 publications

References 51 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks

Contact Info

Product

Resources

About