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Real-Time News Analytics With Semantic Big Data Technologies

Real-Time News Analytics With Semantic Big Data Technologies. Dr. Volker Stümpflen and Michael Schramm Clueda AG 1 .4.2014. Clueda. Founded 2012 Spin -Off Institute for Bioinformatics a nd Systemsbiology of the Helmholtz Zentrum München

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Real-Time News Analytics With Semantic Big Data Technologies

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  1. Real-Time News AnalyticsWithSemantic Big Data Technologies Dr. Volker Stümpflen and Michael Schramm CluedaAG 1.4.2014

  2. Clueda • Founded 2012 • Spin-Off Institute forBioinformaticsandSystemsbiologyofthe Helmholtz Zentrum München • Real-time softwaresolutionsforsemanticandassociativeknowledgeprocessingandanalysis • >40 man years R&D • 30 employees • Partner: Baader Bank AG • Winner Best in Big Data Award 2013

  3. Why Big Data • Storage ischeap • Data isgloballyaccesible

  4. Big Data Processing isPossible (forEveryone)

  5. Newsflood Millionsoffinancialinstruments X tradersandanalysts 500.000 newsp.d. ~4 bnsentences p.a. Fromstocksto derivatives Increasing Decreasing time forincreasinginformation Isconstantandsmall Fromnewsagencies tosocialmedia channels (Blogs, Tweets) Stronglyincreasing

  6. News Moves Markets Cluedaanalysisready traderisbuying Price News reading Automated analysis Commercial advantage Time News published News readingfinished traderisbuying

  7. Big Data Problem: Big Data – Big Noise • Junk-In -> Junk-Out

  8. Big Data Problem: Correlation vs. Causality

  9. Needle in a Haystack

  10. User-Centric Decision Making See Concepts, relations and events as they happen in multiple information sources Data Understand Trends, mood and relationships using semantics and systems biology approaches Information Real-timeengine Answer Questions that only specialists could answer before Knowledge

  11. Market Moving Influences Events Market Moving Mood Insider Knowl. Sentiment Information

  12. Elementary Processing Steps RecognizingConcepts (Companies, Persons, ...) Generating Knowledge Networks Recognizing Relations and Events AdvancedAnalytics (e.g. Sentiment)

  13. Simple DetectionAndUtilizingOfConcepts • Applicationsand Problems Source : Preis, T., Moat, H. S. & Stanley, H. E. Quantifying Trading Behavior in Financial Markets Using Google Trends. Sci. Rep. 3, 1684 (2013).

  14. Concept Detection • Recognizing the meaning of unknown words • Self-learning capabilities based on machine learning approaches • After initial training knowledge base ist extended automatically

  15. Real-Time Event Detection and Processing … biglaunchcelebrations at hardwarestoreswithGalaxy Tab III werecanceled. Apple sues Samsung in Australia.Followingearlier legal disputes … Nokia Sony Motorola legal action Samsung NEGATIVE RELATION ACTING COMPANY RECEIVING COMPANY • Understands textual information and relations • Generates a semantic knowledge network • Identifies market moving news in real-time Apple Apple sues Samsung Sharp in Australia Microsoft Foxconn China LOCATION OF RELATION Rare Earths

  16. Event Determination With Big Data Analytics Price close = high move caused by news open measurement error market move low Time t0 t1 News Release

  17. Analysis Of News From One Year Event Type 2 Number of news Clustering Event Type 1 Optimal threshold Meaningful news events Threshold market move

  18. Event Types

  19. Statement-Centric Information CompressionandDetection • Approximately 30-40% of all newscontain redundant information • Onlyone out of 500 newsismarketmoving

  20. IdentifyRelevant Information from Noise

  21. BehaviouralFinance “We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA”

  22. Sentiment Detection • Simple approach: Counting positive and negative words • Problems

  23. Systemic Interrelations / Systemic Mood Nokia Nokia Sony Motorola Sony Motorola Samsung Samsung legal action Apple Sharp Sharp Microsoft Foxconn Foxconn China • Sentiment influences with systems biological methods • Mood propagation in networks • Identification of indirect mood drivers Rare Earths

  24. Sentiment works in multifactormodels

  25. Understanding ComplexSituations • Extractionfromnetworkswithmillionsofnodesandbillionsofedges

  26. Semantic Big Data News Analytics • Big Data is a reality • Big Data pitfalls • Junk in – Junk out • Correlation vs. Causation • Combinationwith intelligent methodsismandatory • Semanticanalysis • Network analysis • Itworks “Wir sparen mit der Software jeden Tag Tausende von Euros” UtoBaader - Baader Bank

  27. ThankYou! Volker Stümpflen Michael Schramm

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