Study of Stock Prediction Based on Social Network

Zheng, Chen; Du, Xiaoyong

doi:10.1109/socialcom.2013.141

Cited by 12 publications

(6 citation statements)

References 3 publications

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“…Chen and Du proposed a stock forecasting method that combines sentiment analysis and online social behavior analysis. By constructing social behavior graphs and calculating key features, it finds the correlation between transaction volume or price and these features [22]. Wang et al used delayed neural network models to predict public housing prices in Singapore.…”

Section: Related Workmentioning

confidence: 99%

Data prediction model in wireless sensor networks based on bidirectional LSTM

Cheng

Xie

et al. 2019

J Wireless Com Network

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The data collected by the wireless sensor nodes often has some spatial or temporal redundancy, and the redundant data impose unnecessary burdens on both the nodes and networks. Data prediction is helpful to improve data quality and reduce the unnecessary data transmission. However, the current data prediction methods of wireless sensor networks seldom consider how to utilize the spatial-temporal correlation among the sensory data. This paper has proposed a new data prediction method multi-node multi-feature (MNMF) based on bidirectional long short-term memory (LSTM) network. Firstly, the data quality is improved by quartile method and wavelet threshold denoising. Then, the bidirectional LSTM network is used to extract and learn the abstract features of sensory data. Finally, the abstract features are used in the data prediction by adopting the merge layer of the neural network. The experimental results show that the proposed MNMF model has better performance compared with the other methods in many evaluation indicators.

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Section: Related Workmentioning

confidence: 99%

Data prediction model in wireless sensor networks based on bidirectional LSTM

Cheng

Xie

et al. 2019

J Wireless Com Network

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“…The authors Zheng Chen and Xiaoqing Du (2013) [16] ,in their work developed a unique Social Network Systems Weibo.com and Renren.com to play the roles of Twitter and Face book, as Chinese people don't have access to Face book and Twitter. They have used a unique Chinese Stock forum Guba that has several topics that focus more on stock exchange.…”

Section: A Stock Trends Prediction On Social Network Information Psychological States Of Users and Collective Sentiment Analysismentioning

confidence: 99%

Role of Social Sentiment Analysis in Stock Trends Forecasting

2019

ijrte

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Social media like Face book, Twitter have attracted attention from various sectors of study in recent years. Most of the people share ideas, opinions on various topics such as Stock Market Prediction, Digital marketing, Movie review, Election Results Prediction and Product reviews etc,. Forecasting Financial Market is considered to be one of the significant applications of Sentiment Analysis on Social Data like Face book, Twitter. It is essential to accurately predict the movements in stock trends, as the stock market trends are volatile. In the past few years several researches have been carried out for predicting the future trends of stock market through sentiment analysis on social media comments. This paper gives the survey on the various techniques, tools and methodologies adopted by several researchers on Stock Market Prediction based on sentiment analysis of Social networks

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“…They use them in support vector machine (SVM) to forecast stock price movements. Chen et al (2013) developed a graph about people's online behaviors using the data from an online financial community. They studied the correlations between these graph properties and the stock trading prices and trading volumes.…”

Section: The Technology Perspective: Predicting Stock Prices In the Bmentioning

confidence: 99%

Systemic risk management and investment analysis with financial network analytics: research opportunities and challenges

Schwabe

2015

Financial Innovation

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Recent economic crises like the 2008 financial tsunami has demonstrated a critical need for better understanding of the topologies and various economic, social, and technical mechanisms of the increasingly interconnected global financial system. Such a system largely relies on the interconnectedness of various financial entities such as banks, firms, and investors through complex financial relationships such as interbank payment networks, investment relations, or supply chains. A network-based perspective or approach is needed to study various financial networks in order to improve or extend financial theories, as well as develop business applications. Moreover, with the advance of big data related technologies, and the availability of huge amounts of financial and economic network data, advanced computing technologies and data analytics that can comprehend such big data are also needed. We referred this approach as financial network analytics. We suggest that it will enable stakeholders better understand the network dynamics within the interconnected global financial system and help designing financial policies such as managing and monitoring banking systemic risk, as well as developing intelligent business applications like banking advisory systems. In this paper, we review the existing research about financial network analytics and then discuss its main research challenges from the economic, social, and technological perspectives. AbstractRecent economic crises like the 2008 financial tsunami has demonstrated a critical need for better understanding of the topologies and various economic, social, and technical mechanisms of the increasingly interconnected global financial system. Such a system largely relies on the interconnectedness of various financial entities such as banks, firms, and investors through complex financial relationships such as interbank payment networks, investment relations, or supply chains. A network-based perspective or approach is needed to study various financial networks in order to improve or extend financial theories, as well as develop business applications. Moreover, with the advance of big data related technologies, and the availability of huge amounts of financial and economic network data, advanced computing technologies and data analytics that can comprehend such big data are also needed. We referred this approach as financial network analytics. We suggest that it will enable stakeholders better understand the network dynamics within the interconnected global financial system and help designing financial policies such as managing and monitoring banking systemic risk, as well as developing intelligent business applications like banking advisory systems. In this paper, we review the existing research about financial network analytics and then discuss its main research challenges from the economic, social, and technological perspectives.

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Study of Stock Prediction Based on Social Network

Cited by 12 publications

References 3 publications

Data prediction model in wireless sensor networks based on bidirectional LSTM

Data prediction model in wireless sensor networks based on bidirectional LSTM

Role of Social Sentiment Analysis in Stock Trends Forecasting

Systemic risk management and investment analysis with financial network analytics: research opportunities and challenges

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