Web Media and Stock Markets: A Survey and Future Directions from a Big Data Perspective

Qing Li, Yan Chen, Jun Wang, Yuanzhu Chen, Hsinchun Chen

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation.

Original languageEnglish (US)
Article number8068217
Pages (from-to)381-399
Number of pages19
JournalIEEE Transactions on Knowledge and Data Engineering
Volume30
Issue number2
DOIs
StatePublished - Feb 1 2018

Fingerprint

Systems science
Management information systems
Finance
Computer science
Big data
Financial markets

Keywords

  • big data
  • Computing methodologies
  • financial market
  • news
  • social media
  • stocks
  • text mining

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective. / Li, Qing; Chen, Yan; Wang, Jun; Chen, Yuanzhu; Chen, Hsinchun.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 30, No. 2, 8068217, 01.02.2018, p. 381-399.

Research output: Contribution to journalArticle

@article{8047cb2ab0db42c4939e65a1bde34a01,
title = "Web Media and Stock Markets: A Survey and Future Directions from a Big Data Perspective",
abstract = "Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation.",
keywords = "big data, Computing methodologies, financial market, news, social media, stocks, text mining",
author = "Qing Li and Yan Chen and Jun Wang and Yuanzhu Chen and Hsinchun Chen",
year = "2018",
month = "2",
day = "1",
doi = "10.1109/TKDE.2017.2763144",
language = "English (US)",
volume = "30",
pages = "381--399",
journal = "IEEE Transactions on Knowledge and Data Engineering",
issn = "1041-4347",
publisher = "IEEE Computer Society",
number = "2",

}

TY - JOUR

T1 - Web Media and Stock Markets

T2 - A Survey and Future Directions from a Big Data Perspective

AU - Li, Qing

AU - Chen, Yan

AU - Wang, Jun

AU - Chen, Yuanzhu

AU - Chen, Hsinchun

PY - 2018/2/1

Y1 - 2018/2/1

N2 - Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation.

AB - Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation.

KW - big data

KW - Computing methodologies

KW - financial market

KW - news

KW - social media

KW - stocks

KW - text mining

UR - http://www.scopus.com/inward/record.url?scp=85040659538&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85040659538&partnerID=8YFLogxK

U2 - 10.1109/TKDE.2017.2763144

DO - 10.1109/TKDE.2017.2763144

M3 - Article

AN - SCOPUS:85040659538

VL - 30

SP - 381

EP - 399

JO - IEEE Transactions on Knowledge and Data Engineering

JF - IEEE Transactions on Knowledge and Data Engineering

SN - 1041-4347

IS - 2

M1 - 8068217

ER -