Machine learning prediction of stock markets

Download Machine learning prediction of stock markets

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  1. 1. Machine learning aided prediction of financial markets Nikola Miloevi Email: nikola.milosevic@manchester.ac.uk Blog: http://inspiratron.org Twitter: @dreadknight011
  2. 2. Who am I? BSc and MSc in EE and CS, University of Belgrade, Serbia PhD student University of Manchester OWASP (Open Web Application Security Project) local and project leader Mentor at Google Summer of Code Open Source contributor About 4 years experience in industry (Software testing and development Banking, Telco, SaaS, Mobile) Head of Technology at MUTIS Finance Society
  3. 3. Agenda Machine learning introduction Finance and its resistance to machine learning Approaches to investing Fundamental Behavioral Research examples Predicting long term stock prices using machine learning Predicting FOREX using social media Conclusion
  4. 4. What is Machine learning
  5. 5. How does it work? Machine learning is the subfield of computer science that "gives computers the ability to learn without being explicitly programmed" (Arthur Samuel, 1959)
  6. 6. Types of Machine learning
  7. 7. Achievements of ML
  8. 8. What about handling money? Not that much Fund manager often cant understand the model Who is accountable for losses? Data scientist/Quant/Software engineer? Fund manager? Data? Algorithm? Computer?
  9. 9. Machine learning as a black box or black magic
  10. 10. Understanding models Machine learning black box Machine learning black magic Complexity matches the problem Rules are not simple! Complex problems complex solutions (models) Shortcut understanding does not work Right cross validation, right dataset Machines make as many mistakes as human do (in many cases even less)
  11. 11. Approaches in finance H: Machine can learn same things human can Behavioural finance Long-term investing Short-term investing
  12. 12. Technical analysis - traditionally Graham criteria (1949) Stock Selection for the Defensive Investor: 1. Not less than $100 million of annual sales. [Note: This works out to $500 million today based on the difference in CPI/Inflation from 1971] 2-A. Current assets should be at least twice current liabilities. 2-B. Long-term debt should not exceed the net current assets. 3. Some earnings for the common stock in each of the past 10 years. 4. Uninterrupted [dividend] payments for at least the past 20 years. 5. A minimum increase of at least one-third in per-share earnings in the past 10 years. 6. Current price should not be more than 15 times average earnings. 7. Current price should not be more than 1-12 times the book value. Graham number = 22.5
  13. 13. Technical analysis Analyse technical indicators and ratios Over time Dependencies Graham model too strict It is hard to develop new models Mathematically demanding People are not good with numbers Time consuming Can machines help?
  14. 14. Predicting long term movement of stock price Use machine learning on past 2-3 year data Data obtained using Bloomberg terminal Data include 28 indicators Book value, Market capitalization, Change of stock Net price over the one month period, Percentage change of Net price over the one month period, Dividend yield, Earnings per share, Earnings per share growth, Sales revenue turnover, Net revenue, Net revenue growth, Sales growth, Price to earnings ratio, Price to earnings ratio -five years average, Price to book ratio, Price to sales ratio, Dividend per share, Current ratio, Quick ratio, Total debt to equity, margins, asset turnover
  15. 15. Predicting long term movement of stock price (2) Selected 1739 stocks from different indexes (S&P 1000, FTSE 100 and S&P Europe 350) Calculated which ones price grew more than 10% Used different Machine learning algorithms and 10 fold cross validation for evaluation Used Python for scripting and Weka toolkit for machine learning
  16. 16. Results Trial with all financial indicators as a features
  17. 17. Results (2) We performed feature selection Experiment with only 11 indicators
  18. 18. 11 indicators The performance turned out not to be significantly different, but it showed that only 11 indicators are enough
  19. 19. FinAnalyzer Tool that uses a model and Yahoo hidden API https://github.com/nikolamilosevic86/FinAnalyzer Open source (GPLv3) Decision support Not liable of any loses Join development (contact me)
  20. 20. Behavioural finance Psychology and emotion influence decision People follow their peers Sentiment of the information is shaping the decision
  21. 21. Social media and news
  22. 22. Sentiment and FOREX
  23. 23. Major forex pairs
  24. 24. System overview
  25. 25. Sentiment classification
  26. 26. Twitter sentiment - challenges Short text dense information Ungrammatical language Abbreviations Typos Emoticons Links, hashtags, mentions
  27. 27. Evaluation - sentiment
  28. 28. Correlations sentiment vs pair
  29. 29. Correlations with sentiment (What we found?) There is correlation between sentiment and market movement In time it is variable (some times 5 minutes, some times 5 hours) Some times ambiguous More research necessary When to enter/exit?
  30. 30. Conclusion Finance is all about information Information flooded world Machine learning, data science, text mining Here to HELP! Try them Predicting without emotional influence Accountability Consider all information for better prediction More than human can digest
  31. 31. Reference Milosevic, Nikola. Equity forecast: Predicting long term stock price movement using machine learning. arXiv preprint arXiv:1603.00751 (2016). https://arxiv.org/ftp/arxiv/papers/1603/1603.00751.pdf Martic, Miljan. Twitter sentiment analysis for foreign exchange market movement orediction. (2014).
  32. 32. Email: Nikola.milosevic@manchester.ac.uk Blog: http://inspiratron.org Twitter: @dreadknight011

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