Predicting FTSE 100 returns and volatility using sentiment analysis

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Predicting FTSE 100 returns and volatility using sentiment analysis. / Johnman, Mark; Vanstone, Bruce J; Gepp, Adrian.
In: Accounting and Finance , Vol. 58, No. S1, 01.11.2018, p. 253-274.

Research output: Contribution to journalArticlepeer-review

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Johnman M, Vanstone BJ, Gepp A. Predicting FTSE 100 returns and volatility using sentiment analysis. Accounting and Finance . 2018 Nov 1;58(S1):253-274. doi: 10.1111/acfi.12373

Author

Johnman, Mark ; Vanstone, Bruce J ; Gepp, Adrian. / Predicting FTSE 100 returns and volatility using sentiment analysis. In: Accounting and Finance . 2018 ; Vol. 58, No. S1. pp. 253-274.

RIS

TY - JOUR

T1 - Predicting FTSE 100 returns and volatility using sentiment analysis

AU - Johnman, Mark

AU - Vanstone, Bruce J

AU - Gepp, Adrian

PY - 2018/11/1

Y1 - 2018/11/1

N2 - We investigate the statistical and economic effect of positive and negative sentiment on daily excess returns and volatility in the FTSE 100 index, using business news articles published by the Guardian Media Group between 01/01/2000 and 01/06/2016. The analysis indicates that while business news sentiment derived from articles aimed at retail traders does not influence excess returns in the FTSE 100 index, it does affect volatility, with negative sentiment increasing volatility and positive sentiment reducing it. Further, an ETF‐based trading strategy based on these findings is found to outperform the naïve buy‐and‐hold approach.

AB - We investigate the statistical and economic effect of positive and negative sentiment on daily excess returns and volatility in the FTSE 100 index, using business news articles published by the Guardian Media Group between 01/01/2000 and 01/06/2016. The analysis indicates that while business news sentiment derived from articles aimed at retail traders does not influence excess returns in the FTSE 100 index, it does affect volatility, with negative sentiment increasing volatility and positive sentiment reducing it. Further, an ETF‐based trading strategy based on these findings is found to outperform the naïve buy‐and‐hold approach.

U2 - 10.1111/acfi.12373

DO - 10.1111/acfi.12373

M3 - Article

VL - 58

SP - 253

EP - 274

JO - Accounting and Finance

JF - Accounting and Finance

SN - 0810-5391

IS - S1

ER -