Predicting FTSE 100 returns and volatility using sentiment analysis
نویسندگان
چکیده
منابع مشابه
Predicting Stock Market Returns and Volatility with Investor Sentiment: Evidence from Eight Developed Countries
We test the predictive ability of investor sentiment on the return and volatility at the aggregate market level in the U.S., four largest European countries and three Asia-Pacific countries. We find that in the U.S., France and Italy periods of high consumer confidence levels are followed by low market returns. In Japan both the level and the change in consumer confidence boost the market retur...
متن کاملIntraday asymmetric liquidity and asymmetric volatility in FTSE-100 futures market
Available online 2 November 2013 In this study, we use both quote and trade data for the FTSE-100 futures for 2001–2004 in order to examine asymmetric volatility in the context of extreme sells. We define extreme sells as ask quotes that involve large percentages of total depth, selling orders executed at prices much closer to bids than to asking prices, and consecutive sell-initiated trades. S...
متن کاملComovement and FTSE 100 Index Changes
We employ the Barberis, Shleifer and Wurgler (2004) methodology to investigate the impact of changes to the FTSE 100 index on return comovement over the 1992-2002 period. For FTSE stock inclusions the average increase in the beta coe¢ cient is 0.38 in univariate regressions for weekly returns and 0.60 in bivariate regressions that control for the return on non-FTSE stocks. Stocks deleted from t...
متن کاملPricing Options Using Implied Trees: Evidence from Ftse-100 Options
of High Performance Computing are gratefully acknowledged. The authors also wish to thank the two referees for their insightful comments that helped to improve the this article in significant ways. MATLAB (a mathematical, financial, and statistical software language) was used for the programming throughout the study. *Correspondence author, School of Business, Singapore Management University, 4...
متن کاملNews versus Sentiment: Predicting Stock Returns from News Stories
This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, bu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Accounting & Finance
سال: 2018
ISSN: 0810-5391
DOI: 10.1111/acfi.12373