Big Data and High Quality Sentiment Analysis for Stock Trading and Business Intelligence. Dr. Sulkhan Metreveli Leo Keller

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2 Big Data and High Quality Sentiment Analysis for Stock Trading and Business Intelligence Dr. Sulkhan Metreveli Leo Keller

3 The greed

4 The money

5 And wrong assumptions

6 Primary Motivator: The Need to Know

7 And to Make it Work

8 And specially: the hardest thing is to make it work on financial markets

9 Mission At Calfor Finance, we try to use big data and predict the price change

10 The Data and available information for Financial Businesses have grown substantially Bloomberg Thomson Reuters Six Internet Interactive Data Argus

11 We are not the first one s who want to find this vein of gold

12 Different studies have shown a clear relation between (social) media content and market developments Product sales: Berger and Fitzsimmons (2008) the brand chatter in social media Promotional and ad campaigns: Jansen et. al. (2009) - electronic word of mouth Book sales: Gruhl et. Al. (2005) - the volume of online chat for book sales Movie & Theater sales; Mishne and Glance (2006) - the blog sentiments Political voting: Keller and Bisang (2010) - sentiment of forum contribution Car sales: Choi and Varian (2009) correlation with the number of search queries 12

13 Different studies have shown a clear relation between (social) media content and market developments Stock market development: - Tetlock (2007) He could in a early study proof how media coverage (column in Wall Street Journal) affects investor sentiment - Cowgill and Zitzewitz (2008) demonstrated a significant correlation between the mood of the Google's employees and the stock price - Engelberg, Sasseville, Williams (2009) proved the effect of advice doled out by TV personality Jim Cramer on his CNBC show Mad Money, to the stock prize - Bollen, Mao, Zeng (2010) analyzed the tone of micro-blogs, to accurately predict the direction of the stock market in the days that followed - Gloor et al. (2010) showed negative correlation between the amount of mood (hope, fear and worry) and the performance of the stock market index (Dow Jones) 13

14 In the last 3 years we coached 2 studies dealing with these challenges Jonathan Frei: Moods in Social Networks as an Indicator for Stock Market Performance Bachelor thesis, presented to Prof. Dr. Elgar Fleisch, University of St.Gallen (HSG); Julian Keller: Internet forums and their relevance for the development of ETF products; Bachelor thesis, presented to Prof. Andreas Grünbichler, University of St. Gallen (HSG); 14

15 Jonathan Frei: Investigates the relationship between Twitter comments of employees, the stock price development and the trading volumes of 6 companies 1. Keyword search in Twitter for the companies Cisco, Dell, Google, Intel, Microsoft, Oracle 2. Selecting all 1004 contributors that might be qualified employees 3. Creating a classifier using Multinomial Naive Bayes algorithms was programmed and trained with manually classified tweets and then used to determine the mood of the tweets 4. The classifier was trained using 30 positive and 30 negative attributes 5. The validation of this classifier (by hand) showed an accuracy of 74% 15

16 Cisco timeline of the stock price development and the mood of the employees tweets in the period April 2010 to April

17 The relationship between Cisco weekly Stock-price and mood no correlation was found 17

18 The relationship between Cisco daily volume of stock trades and the volume of tweets good correlation was found 18

19 For a large Investment Bank we were running a semantic analysis solution for Structured Products (ETF) 19 Sources: all relevant German - online news - Forums - Blogs - Twitter Products: - ETF - ETT - ETC - ETN - ETP - CMCI Questions: - Perception of products - Channels of interests - Strengths and weaknesses - New trends and products Issuer: - BlackRock - ishares - Lyxor - Axa Investment - Bank J. Bär - Comestage -..

20 We started a first analysis in September 2010 To compare the Gold-ETF discussion in all channels from with the trading volume of Gold ETF at the Zurich Stock Exchange Number of POST s to the product group GOLD ETF in all channels

21 21/06/ /06/ /06/ /06/ /06/ /06/ /06/ /06/ /06/ /06/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/2010 We started a first analysis in September 2010 To compare the Gold-ETF discussion in all channels from with the trading volume of Gold ETF at the Zurich Stock Exchange Number of POST s to the product group GOLD ETF in all channels Trading volume of a Gold ETF at the Zzrich stock exchange 4 2 0

22 21/06/ /06/ /06/ /06/ /06/ /06/ /06/ /06/ /06/ /06/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/ /08/2010 And we found interesting patterns Number of POST s to the product group GOLD ETF in all channels Trading volume of a Gold ETF at the Zurich stock exchange 4 2 0

23 Development of the gold price and the volume of two different Gold-ETFs

24 The development of the ZKB-Gold ETF Fonds volume and the discussion of the ETF Gold in the forums shows up a high statistic relevance (0,7 0,4)

25 The correlation between the development of the changes in the crude oil funds volumes and the forums discussion are strong in the beginning and weaker after the first market introduction (12 18 months) 25

26 Three dimensions of media analysis Environment Data Tools

27 The problem we address and problems we should address Environment Data In most cases we are very much concerned with the quality of tools In some cases we address the issue of data quality And we ignore the environment Tools

28 Media environment and news diffusion asymmetry

29 The speed of diffusion: the information diffusion loop

30 Solving the problems By addressing the problems of media environment and data quality we can solve many problems in regard of analysis tools performance

31 Data quality The machine learning tool that sorts high quality data versus low quality data is of the utmost importance

32 Testing it: The model

33 Testing it: first generation news delivery systems

34 Testing it: second and third generation news delivery systems

35 Calfor Finance: the key elements of our sentiment analysis method An innovative subject related keyword detection-entity extraction-machine learning engine a unique proprietary dynamic sentiment weighting technology developed by our scientists at University Zürich ( ) Entity Extraction Machine Learning Co-Occurence Calfor Unique Sentiment Analysis Context related Developed at University Zürich ( ) This include a dynamic sentiment reassessment method to reassess the sentiment value of words in the context of the Finance Industry Dynamic Reassessment of Sentiment Values of sentiment words

36 Sentiment score detection process Semi-supervised machine learning process The random walk process Permanent weighting and reassessment of the sentiment polarity scores of ALL words in dictionary. Consider this What was the polarity score of the word Ukraine in 2012? What is it today? What was the polarity score of the word Putin in 2012? What is it today? Before you answer, make sure you take into account the origin of the data you analyse What was the polarity score of the words Bernard Madoff in 2007? What is it today?

37 For our sentiment trading machines we create real time sentiment signals using open source data streams Using all available online sources: Online News Twitter Forum Blog Facebook Youtube Flickr Instagram Vimeo Dailymotion LinkedIn Google+ Foursquare Mixcloud Soundcloud Creating the sentiment signals in real time (millisec./1 /1 /5 /10 /../60 /..) Feeding/supporting different trading decision processes Our test s showed that we can outperform the Index systematically by more than 35%

38 Our sentiment trading machines are successful with small data streams for small caps All available online data are used Sentiment analysis shows strong correlation with stock price development Sentiment analysis shows bad correlations with quantitative document occurrence

39 Calfor Finance has shown in many scientific tests that our sentiment trading can outperform the index significantly Directional Relational Random Long only Index Sentiment

40 Scientific significance tests: Our tests are based on statistical bootstrapping methods. Different trading models are tested. Our relational trading model shows statistically significant better performance compared to Index trading (+ 37%) Sentiment Directional: buy when sentiment is positive and sell when sentiment is negative (constant trading volume) Long: buy only if sentiment is positive Sentiment: buy/sell more if the sentiment is stronger (dynamic trading volume) Index: Index based trading Random: buy and sell randomly

41 The Calfor Finance sentiment trading machine : the process Client Our sentiment sentiment strategy trading model Market Execution On Oanda Online Trading Platform Stock & Index Portfolio

42 Actual performance of one of our sentiment trading machines: Traded product: S&P 500 Client Trading Platform: Oanda sentiment Traded since: strategy Gain in 20 days 4.23% Data sources: Social Media/Twitter NoD/h: > doc Trading strategy: sentiment trading* Trade cycle: Permanent Data Stream * Our Sentiment Trading machine automatically executes buy and sell processes based on sentiment signals only. The weights of the signals are constantly re-assessed (our dynamic sentiment weighting and sentiment reassessment technology )

43 S&P500 sentiment trading machine Trade strategy 1 S&P500 Price Chart 8. July 15. August and our day trading history Our trades (daily 100 units/4% of Investment) are executed at market opening NYSE the sentiment signal represents all S&P500 comments on Twitter in the last 24h (ca Tweets) and leads to a trade signal (s/h/l)

44 S&P500 sentiment trading versus index trading Sentiment Trading Each trade is executed with the same volume (100 unit/day) depending on the sentiment value the system buys, holds or sells Index Trading Every day there is a buy (100 units) value growth with index growth

45 S&P500 sentiment trading versus index trading Sentiment Trading We can see the aggregated daily win/loss position independent from the daily trading volume and trading transaction s/h/l and the total value development Index Trading We can see the aggregated daily win/loss position independent from the daily trading volume and trading transaction s/h/l and the total value development

46 S&P500 sentiment trading versus index trading Sentiment Trading Performance sheet Index Trading Performance Sheet

47 S&P500 sentiment trading versus index trading

48 S&P500 sentiment trading machine Trade strategy 2 intra-day trading: The intra-day trading machine creates constantly a sentiment signal with the twitter messages of the last 30min. The trade stays open as long as the signal is constant. If it turns we close the trade and start immediately a new one Within 6 hours we executed 6 trades on NYSE - 5 trades created a win - 1 trade ended up in a loss only due to broker commission charges - The absolute gain on total asset: 4,68%

49 Conclusions and Discussion In order to use our analysis tools optimally, a good tool is not enough. We need to consider the media type and data quality. Could your data analysis tool have various results on various media types? Is it possible to create an universal quality filter for the data? Is there such thing as objective quality? Can we actually predict the market if we take into the account that markets are rational?

50 More information and updates

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