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Tatiana Silinskaya , Ruslan Novikov , Evgeny Polyakov , Alexander Porshnev, Vladimir Rossohin

iCare’14, September, 21-24, Perm. Relationship between of emotional states of Twitter users and stock market indicators: search for causality. Tatiana Silinskaya , Ruslan Novikov , Evgeny Polyakov , Alexander Porshnev, Vladimir Rossohin

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Tatiana Silinskaya , Ruslan Novikov , Evgeny Polyakov , Alexander Porshnev, Vladimir Rossohin

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  1. iCare’14, September, 21-24, Perm Relationship between of emotional states of Twitter users and stock market indicators: search for causality Tatiana Silinskaya, RuslanNovikov, EvgenyPolyakov, Alexander Porshnev, Vladimir Rossohin National Research University Higher School of Economics, Nizhny Novgorod

  2. Twitter as data source Reaction on Associated Press tweet on White House explosions (Tue Apr 23, 2013 7:01pm EDT) http://www.reuters.com/article/2013/04/23/net-us-usa-whitehouse-ap-idUSBRE93M12Y20130423 Twitter Political Index: A Comparison to Gallup (with 30-day moving averages – August 1, 2010 – July 31, 2012 ---- Twitter ---- Gallup https://blog.twitter.com/2012/a-new-barometer-for-the-election

  3. Twitter - as moodmeter Psycho-linguistics Math-linguistics (machine learning) Behavioral economics Econometrics Prediction System

  4. Sentiment analysis of twits • Zhang et al. (2009) Correlation Hope+Fear+Worry%-3-mean DJIA = − 0.726** NASDAQ =− 0.728** S&P=− 0.713** VIX= 0.633** (98 days) • Bollenet al. (2011) – 87.6%.for DJIA (0,1) (non filtered) weakness: only 21 days of testing • Vu et al. (2012) – 82.93% for Apple (AAPL), Amazon (AMZN) 75.00%, Google (GOOG)-80.49%, Microsoft (MSFT)-75.61% (filtered by NER) weakness: only 41 days of testing • Chen et al. (2013) “happy” and “sad” sentiment 70% accuracy Weakness: 33 days simulation • Porshnev A. et al. (2013) – 68.60% (S&P500), 73.3% (DJIA) (from 75 up 256 days for testing) weakness no econometric model

  5. Data (English language) • Period: February 13, 2013 till September 29, 2013 • Finance historical data: finance.yahoo.com InversotPoint.com Historical data • Twitter API 755’000 101 messages

  6. Method Simple sentiment analysis • Frequency of tweets with words from created emotional dictionaries (8 emotions, 217 words). For example calm dictionary: impassive, motionless, stationary, still, calm, etc. • Frequency of emotional words : worry, fear, hope etc. • Frequency of emoticons, interjections (lol, wth, etc.) DJIA, S&P500, NASDAQ • Historical volatility • Trading volumes • Open Prices • Close Prices Granger causality test (with lag from 1 to 7 days)

  7. DJIA volatility

  8. NASDAQ volatility

  9. S&P500 volatility

  10. Correlation between volatility and emotions worry hope calm angry tired sad DJIA 0,41 0,29 0,13 0,36 0,37 0,31 NASDAQ 0,43 0,37 0,23 0,41 0,44 0,34 S&P500 0,45 0,35 0,20 0,40 0,41 0,33

  11. Liquidity (trade volume) • DJIA (no significant relationships) • S&P500 • NASDAQ

  12. Results for Granger causality DJIA

  13. Main results • Emotions angry, sad, tired are Granger cause for volatility of DJIA, S&P500, NASDAQ • Emotion fearful – Granger cause Open, Close, Min and Max Prices

  14. Model ARMA(p,q)-GARCH(k,m) Indexes’ volatility, with p, q, k and m ranges from 0 to 3, choosing with best MSE criteria The influence of sentiment words (x-axis) with 95% confidence intervals

  15. Further plans • Testing econometric models of mood influence • Different market situations (application of Markov-chain models) • Using non-normalized words, emoticons, interjections • Compare with realistic (5 minutes) volatility

  16. Thank you for attention, aporshnev@gmail.com

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