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文库首页安全技术网络安全20210116_鲍余薇_论文展示1

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2. Model Design 3. Empirical Result 2. Model Design: Data 2. Model Design: Varia

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The Twitter myth revisited: Intraday

investor sentiment, Twitter activity and

individual-level stock return volatility

Behrendt, S., & Schmidt, A.

Journal of Banking & Finance, 2018.

Yuwei Bao

2021/01/16

Contents

1. Introduction

• Background & Motivation

• Literature

• Research Objective

• Contribution

2. Model Design

• Data

• Variable

• Method

3. Empirical Result

4. Conclusion

2021/1/16 Yuwei Bao 2

1. Introduction: Background & Motivation

• Intraday volatility assessment and forecasting

have gained importance for highly active

investors.

• Not only has the speed of trading increased

rapidly, but also the way investors can

comment or share on social media platforms.

• Stock prices, reﬂecting the trading activities of

both institutional and retail investors, might

reﬂect retail investor trading activities

inﬂuenced by sentiment.

2021/1/16 Yuwei Bao 3

• Sentiment may influence the market:

Ø Kyle (1985) & Black (1986): Define professional or institutional

investors as rational informed investors, individual or retail

investors as noise traders who have psychological biases.

Ø De Long et al. (1990) & Shleifer and Vishny (1997): Propose

rational investors could bet against sentiment driven noise

traders to make a proﬁt, with caution to the costs and risks.

Ø Barber & Odean (2007): Retail investors trade excessively in

attention-grabbing stocks.

Ø Kumar and Lee (2006) & Barber et al. (2009): Retail investors

trade in concert.

1. Introduction: Literature

2021/1/16 Yuwei Bao 4

• Twitter as a proxy for investor sentiment:

Ø Bollen et al. (2011): Derive six social mood dimensions from

Tweets, indicating that predictions of the DJIA are improved

through some of them.

Ø Sprenger et al. (2014a): Derive good and bad news from more

than 400,000 Tweets related to the S&P 500, ﬁnd these news

have an impact on the market.

Ø Sprenger et al. (2014b): Discover a relationship between stock

related Twitter sentiment and returns, volume of Tweets and

trading volume of the respective stock, disagreement and

return volatility.

1. Introduction: Literature

2021/1/16 Yuwei Bao 5

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