Harnessing Investor Sentiment Using Big Data Analytics

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This study examines the statistical and economic significance of investor sentiment, based on general business news, on stock market returns and volatility. Using big data analytics, our findings reveal that sentiment does not affect market returns. However, sentiment does influence volatility, with negative (positive) sentiment increasing (decreasing) volatility. Investor sentiment is also economically significant; we demonstrate that an ETF-based trading strategy can be used to capitalize on the predictive capability of investor sentiment. This paper summarizes the research findings made by Johnman, Vanstone and Gepp (2018) from a more practical perspective.

Published in The Australasian Journal of Applied Finance, formerly known as JASSA.
Original languageEnglish
JournalThe Australasian Journal of Applied Finance
Volume2019
Issue number3
Publication statusPublished - 2019

Fingerprint

Investor sentiment
Sentiment
Statistical significance
Market returns
Stock market returns
Stock market volatility
Applied finance
Economic significance
Trading strategies
News

Cite this

@article{498f923db6f248a9830dcf2d74191121,
title = "Harnessing Investor Sentiment Using Big Data Analytics",
abstract = "This study examines the statistical and economic significance of investor sentiment, based on general business news, on stock market returns and volatility. Using big data analytics, our findings reveal that sentiment does not affect market returns. However, sentiment does influence volatility, with negative (positive) sentiment increasing (decreasing) volatility. Investor sentiment is also economically significant; we demonstrate that an ETF-based trading strategy can be used to capitalize on the predictive capability of investor sentiment. This paper summarizes the research findings made by Johnman, Vanstone and Gepp (2018) from a more practical perspective.Published in The Australasian Journal of Applied Finance, formerly known as JASSA.",
author = "Mark Johnman and Adrian Gepp and Vanstone, {Bruce J}",
note = "The Australasian Journal of Applied Finance - formerly known as JASSA.",
year = "2019",
language = "English",
volume = "2019",
journal = "JASSA",
issn = "0313-5934",
publisher = "Financial Services Institute of Australasia",
number = "3",

}

Harnessing Investor Sentiment Using Big Data Analytics. / Johnman, Mark; Gepp, Adrian; Vanstone, Bruce J.

In: The Australasian Journal of Applied Finance, Vol. 2019, No. 3, 2019.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Harnessing Investor Sentiment Using Big Data Analytics

AU - Johnman, Mark

AU - Gepp, Adrian

AU - Vanstone, Bruce J

N1 - The Australasian Journal of Applied Finance - formerly known as JASSA.

PY - 2019

Y1 - 2019

N2 - This study examines the statistical and economic significance of investor sentiment, based on general business news, on stock market returns and volatility. Using big data analytics, our findings reveal that sentiment does not affect market returns. However, sentiment does influence volatility, with negative (positive) sentiment increasing (decreasing) volatility. Investor sentiment is also economically significant; we demonstrate that an ETF-based trading strategy can be used to capitalize on the predictive capability of investor sentiment. This paper summarizes the research findings made by Johnman, Vanstone and Gepp (2018) from a more practical perspective.Published in The Australasian Journal of Applied Finance, formerly known as JASSA.

AB - This study examines the statistical and economic significance of investor sentiment, based on general business news, on stock market returns and volatility. Using big data analytics, our findings reveal that sentiment does not affect market returns. However, sentiment does influence volatility, with negative (positive) sentiment increasing (decreasing) volatility. Investor sentiment is also economically significant; we demonstrate that an ETF-based trading strategy can be used to capitalize on the predictive capability of investor sentiment. This paper summarizes the research findings made by Johnman, Vanstone and Gepp (2018) from a more practical perspective.Published in The Australasian Journal of Applied Finance, formerly known as JASSA.

M3 - Article

VL - 2019

JO - JASSA

JF - JASSA

SN - 0313-5934

IS - 3

ER -