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SUMMARY:Textual sentiment\, option information and stock predictability - 
 Cathy YH Chen\, Yanchu Liu and Wolfgang Karl Härdle
DTSTART:20170201T120000Z
DTEND:20170201T130000Z
UID:TALK69669@talks.cam.ac.uk
CONTACT:Professor John Rust
DESCRIPTION:A huge amount of literature show a predictability of options m
 arkets for future equity returns\, but to which the extent the predictabil
 ity can be created is a major research question. Is the predictability ste
 mming from information advantage or from investor sentiment for firm persp
 ective? The prevalence of social media platform along with textual\nanalyt
 ics enable us to distil sentiment and examine the source of predictive pow
 er. We find options markets react to sentiment from NASDAQ news. A higher 
 implied volatility\, higher out-of-money put price and a higher smirk can 
 be observed as more negative articles being posted which constitutes more 
 negative sentiment. While excluding the sentiment component\, we find the 
 predictability of option variables is very limited for high-attention firm
 s but still remains for low-attention firms. In sum\, the predictability o
 f options markets is not only attributed to information asymmetry but also
  sentiment.\n\nWolfgang Karl Hardle is Ladislaus von Bortkieviecz Professo
 r of Statistics at Humboldt-Universität zu Berlin and Director of CASE (t
 he Center for Applied Statistics & Economics).  He is also Director of the
  Collaborative Research Center CRC649 “Economic Risk” and of the Sino 
 German International Research Training Group IRTG1792 „High dimensional 
 non stationary time series analysis“ (WISE\, Xiamen University). His res
 earch focuses on dimension reduction techniques\, computational statistics
  and quantitative finance. He has published 34 books and more than 250 pap
 ers in top statistical\, econometrics and finance journals.  He is one of 
 the “Highly cited Scientist” according to the Institute or Scientific 
 Information. He has professional experience in financial engineering\, str
 uctured product design and credit risk analysis.\n\nCathy Yi-Hsuan Chen is
  a full-time\, 2-year tenure-track associate professor at the School of Bu
 siness & Economics in Humboldt-Universität zu Berlin\, and a principal in
 vestigator of the International Research Training Group 1792 – High Dime
 nsional Non Stationary Time Series. Her research interests focus on “tex
 t mining on finance analysis” and “risk analysis and management”. Sh
 e has dedicated herself recently on applying text mining techniques to dis
 til news flow from social media. Using the statistical analytics such as M
 achine Learning (e.g. SVM)\, Lexicon Projection\, Latent Semantic Analysis
 \, Latent Dirichlet Allocation and Topic Modelling\, she analyzes the news
  impact on financial markets. She has published in key journals and has wr
 itten important software for financial econometrics. She applies modern ec
 onometric techniques\, such as copulae and ultrahigh dimensional factor mo
 dels to financial data on systemic risk indicators. She has professional e
 xperience in derivative pricing and trading\, risk modeling and management
  in banking industry. She is currently heading a “transfer project” be
 tween Humboldt-Universität and Deutsche Bank\, and focusing on credit ris
 k modelling and stress testing.
LOCATION:Room W2.02\, Judge Business School\, Trumpington St.\, Cambridge
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