QUANTIFYING THE EFFECTS OF ONLINE BULLISHNESS ON INTERNATIONAL FINANCIAL MARKETS
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1 QUANTIFYING THE EFFECTS OF ONLINE BULLISHNESS ON INTERNATIONAL FINANCIAL MARKETS Huina Mao School of Informatics and Computing Indiana University, Bloomington, USA ECB Workshop on Using Big Data for Forecasting and Statistics, April , Frankfurt, Germany,
2 Our Research Team Huina Mao Indiana University, Bloomington Dr. Scott Counts Microsoft Research, Redmond Dr. Johan Bollen Indiana University, Bloomington
3 Investor Sentiment Theory DeLong et al. (1990) Introduced 24 years ago. Now, the question is no longer, as it was a few decades ago, whether investor sentiment affects stock prices, but rather how to measure investor sentiment and quantify its effects. (Baker and Wurgler 2007) Baker and Wurgler, Investor Sentiment in the Stock Market, Journal of Economic Perspectives, vol 21 (7), 2007
4 Surveys Gallup Consumer Confidence Index Investor Intelligence Daily Investor Sentiment
5 University of Michigan Consumer Sentiment Index monthly poll: 500 telephone interviews & 5 questions Would you say that you (and your family living there) are better off or worse off financially than you were a year ago? "Now looking ahead do you think that a year from now you (and your family living there) will be better off financially, or worse off, or just about the same as now? "Now turning to business conditions in the country as a whole do you think that during the next twelve months we ll have good times financially, or bad times, or what?"...
6 Investor Intelligence (since 1963) Weekly advisors sentiment report surveys: percentage of advisors bullish, bearish views of over 100 independent investment newsletters Daily Sentiment Index (since 1987) Interview small traders for their bullish or bearish feeling on US future markets
7 Pros and Cons of Surveys Pros Explicit Representative samples Straightforward-to-conduct Controlled design Cons Small-scale Reliability and validity may be an issue Expensive-to-conduct Low frequency (weekly, monthly, or annually) Released with delay
8 Proxies of Investor Sentiment Edmans et.al. "Sports sentiment and stock returns." The Journal of Finance 62.4 (2007): Hirshleifer et.al. "Good day sunshine: Stock returns and the weather." The Journal of Finance 58.3 (2003): Sports/weather Sentiment
9 Proxies of Investor Sentiment Edmans et.al. "Sports sentiment and stock returns." The Journal of Finance 62.4 (2007): Hirshleifer et.al. "Good day sunshine: Stock returns and the weather." The Journal of Finance 58.3 (2003): Sports/weather Sentiment INDIRECT Indicator
10 Other Investor Sentiment Measurements News Pessimism Index (Tetlock 2007): Harvard Dictionary Negative Words Stock Message Board Text Classification (Antweiler and Frank 2004): Machine Learning Classifiers: Support Vector Machine and Naive Bayes. Output: Bullish, bearish and neutral References: Tetlock, P. Giving content to investor sentiment: The role of media in the stock market, 62 (3), pp: , The Journal of Finance, 2007 Antweiler, W. and Frank, M., Is all that talk just noise? The information content of internet stock message boards Th Journal of Finance, 59 (3), pp: , 2004
11 The Advent of Big Data
12 Literature review: Predicting socio-economic indicators from large-scale data Google search predict flu (Ginsberg et.al 2008) Blog sentiment predict stock market (Gilbert 2010) Twitter predict box office (Asur 2010) Google search predict unemployment claims (Ettredge 2005) Google search predict car sales, travel, health (Choi 2009) Google search reveals investor attention (Da 2011) Yahoo search predicts consumer behavior (Goel 2010) Mobile communication reveal economic prosperity (Eagle 2010)
13 Why Twitter? (launched July 2006 by Jack Dorse) Twitter Statistics (2013) Total number of active registered Twitter users: 554,750,000 Number of new Twitter users signing up everyday: 135,000 Number of Tweets that happen every second: 9,100 Average number of tweets per day: 58 Million Number of Twitter search engine queries every day: 2.1 billion Percent of Twitters who do not tweet but watch other people tweet: 40%
14
15 Twitter Mood and Stock Market
16 Social Mood From Twitter 1.75 OpinionFinder day after election Thanksgiving CALM -1 pre- election anxiety ALERT 1-1 SURE election results 1 z-scores -1 1 VITAL pre! election energy 1-1 KIND -1 HAPPY Thanksgiving happiness 1-1 Oct 22 Oct 29 Nov 05 Nov 12 Nov 19 Nov 26 Bollen, Mao, Zeng, Twitter mood predict the stock market, Journal of Computational Science, vol 2(1), pp: 1-8,2011
17 Twitter Mood Predicts the Stock Market DJIA z-score bank bail-out Calm z-score 2 DJIA z-score Calm z-score Aug 09 Aug 29 Sep 18 Oct 08 Oct 28 Bollen, Mao, Zeng, Twitter mood predict the stock market, Journal of Computational Science, vol 2(1), pp: 1-8,2011
18 Research Question: How to measure investor sentiment from Twitter?
19 Investor Sentiment Surveys Investor Intelligence (since 1963) weekly advisors sentiment report surveys percentage of advisors bullish or bearish views of over 100 independent investment newsletters Daily Sentiment Index (since 1987) daily polls Interview traders for their bullish or bearish feeling on US future markets
20 Measuring Investor Sentiment Strategy 1 Strategy 2 All Twitter Users Sentiment Features Investor Users Investor Sentiment Features Tweets Tweets Investor Sentiment Investor Sentiment
21 Measuring Investor Sentiment We adopt: Strategy 1 Strategy 2 All Twitter Users Sentiment Features Investor Users Investor Sentiment Features Tweets Tweets Investor Sentiment Investor Sentiment
22 Why we used 2 terms only: bullish and bearish? Word Frequency signaling market remain sentiment territory 9870 pattern 8486 turning 8433 ahead 8241 chief 8079 outlook 7852 Google n-grams (LDC) The 2 words are very reliable and simple indicators of investment sentiment in online language Proof: - We looked up Google bigrams of form (bullish, x) and (bearish, x) (N=314M) - Linguistic context is majority investor/sentiment-related
23 Twitter and Google Bullishness Twitter Data (daily) From January 2010 to December 2012, N=45M/day Bullish tweet = tweet that contains bullish Bearish tweet = tweet that contains bearish Bullish & bearish tweets: 0.31 million. Google Data (weekly) From January 2007 to December 2012 (313 weeks) Bullish search = bull market Bearish search = bear market Search volumes from Google Trends. Twitter and Google Bullishness:
24 Bullish & Bearish Tweet Volume Weekly Pattern Autoregression Fast Fourier Transform Correlation Coefficient Magnitude Lag Periodicty (Days) Day of Week Tweet Volume Mon Tu Wed Th Fr Sat Sun
25 Twitter Bullishness vs. Survey (Daily Sentiment Index) Pearson correlation: r=0.3 p < day MA: day MA: 0.7
26 Investor Sentiment Theory DeLong et al. (1990) Noise trader s irrationality can drive the asset price to deviate from its fundamental value temporarily after which it will reverse to the mean
27 Two Hypothesizes If Twitter bullish and bearish tweets are only reactions to market changes, we may observe contemporaneous correlation, but no prediction. If Twitter Bullishness is an indicator of investor sentiment, we may observe short-term lead and reversal in a long-run.
28 Vector Autoregression R: return; T: Twitter Bullishness; Vol: trading volume. Exogenous variables: VIX, Daily Sentiment Index, and calendar controls.
29 Result 1 Predicting Daily Stock Returns of Dow Jones, S&P 500, Russell 1000 and Russell 2000 Using Twitter Bullishness. Twitter Bullishness vs. Daily Sentiment Index Linear Pearson Correlation Coefficient: 0.30 (p < 0.01) Daily Sentiment Index: beta_t-1 = 2.26 (p =0.1) Twitter Bullishness Index: beta_t-1 = (p < 0.01) These two are related, but different.
30 Result 2 Predicting Stock Returns of US, UK, CA, and CN Using Twitter Bullishness
31 Result 3 Stock Market Price Against Google Bullishness United States United Kingdom Normalized Score DJIA G.Bullishness Normalized Score FTSE100 G.Bullishness Canada China Normalized Score GPSTSE G.Bullishness Normalized Score SSE G.Bullishness
32 Result 4 Predicting Weekly Stock Returns Using Google Bullishness
33 Result 4 Google Bullishness vs. Investor Intelligence (survey) Pearson correlation = 0.54 (p < 0.01)
34 Conclusion Investor sentiment measurement from Twitter/Google complement (even substitute) survey measures. Twitter Bullishness Index is predictive of daily stock returns. Our results support investor sentiment theory.
35 Thank you! Contacts Huina Mao Indiana University, Bloomington, USA Dr. Scott Counts Microsoft Research, Redmond, USA Prof. Johan Bollen Indiana University, Bloomington, USA
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