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Open AccessJournal ArticleDOI

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

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TLDR
This paper analyzes features with regard to collective sentiment and perception on stock relatedness and predict stock price movements by employing nonlinear models on the basis of tweets from Xueqiu, a popular Chinese Twitter-like social platform specialized for investors.
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This article is published in Journal of Computational Science.The article was published on 2017-11-07 and is currently open access. It has received 47 citations till now. The article focuses on the topics: Stock market & Stock market prediction.

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Citations
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What is Twitter

Journal ArticleDOI

Text Mining in Big Data Analytics

TL;DR: 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, are investigated.
Journal ArticleDOI

Which sentiment index is more informative to forecast stock market volatility? Evidence from China

TL;DR: In this article, the predictive ability of three sentiment indices constructed by social media, newspaper, and Internet media news to forecast the realized volatility (RV) of SSEC from in-and out-of-sample perspectives was investigated.
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LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

TL;DR: A new multioptimal combination wavelet transform (MOCWT) method with a novel threshold-denoising function is presented to reduce the degree of distortion in signal reconstruction, and experimental results clearly showed that the proposed MOCWT outperforms the traditional methods in the term of prediction accuracy.
References
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Journal ArticleDOI

Long short-term memory

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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The Cross‐Section of Expected Stock Returns

TL;DR: In this paper, Bhandari et al. found that the relationship between market/3 and average return is flat, even when 3 is the only explanatory variable, and when the tests allow for variation in 3 that is unrelated to size.
Proceedings ArticleDOI

A training algorithm for optimal margin classifiers

TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Journal ArticleDOI

The behavior of stock market prices

Proceedings ArticleDOI

What is Twitter, a social network or a news media?

TL;DR: In this paper, the authors have crawled the entire Twittersphere and found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.
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