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 - The Australasian Journal of Applied Finance

JF - The Australasian Journal of Applied Finance

SN - 0313-5934

IS - 3

ER -