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- 1. Machine learning in stock market analysis
- 2. Agenda • Economic concepts • Can we predict the future price of a stock? • Hidden Markov Models • Building a virtual investor • Experimental results • Demo: Ben Investment Assistant • Conclusions and future work
- 3. Economic concepts • Stock Markets • Stock price and volume • Other indicators
- 4. Prediction of stock prices • Random walk and the Efficient Market Hypothesis • Dow Theory • Conclusions
- 5. Hidden Markov Models • Intuitive description • Example:
- 6. Building a virtual investor • He learns from historical financial data • Based on what he learned he makes decisions (Buy/Sell/Hold) • What data do we provide?
- 7. Preparing data • We apply the EWMA financial technique to eliminate noise by smoothing the series. • We consider for each the day the rate of growth by applying the natural logarithm for the daily return • How do we make use of this data?
- 8. Computations • Modeling observations: Multivariate Gaussian mixtures • Re-estimations: – What is the probability of being at state 2 at time 4? – What is the probability of being at state 2 at time 4 at mixture 3? – How do we re-estimate the model?
- 9. Computations Forward procedure: Backward procedure:
- 10. Computations
- 11. Computations
- 12. The algorithm
- 13. Experimental results • Tests conducted for 14 randomly selected companies from different sectors: financial, utilities, technology, services and healthcare. • We obtained to over 100% in revenues, and we suffered losses only when a company suffered a huge depreciation in its stock price. • A few examples...
- 14. Goldman Sachs (NYSE:GS) Above is the Goldman Sachs stock price evolution (June 07 – June 08) Above is the account evolution for investing in Goldman Sachs during June 07 – June 08 (After a year it generated a 53.3% revenue)
- 15. Royal Gold (NYSE:RGLD) Above is the Royal Gold stock price evolution for the testing period Above is the account evolution for investing in Royal Gold (It generated a 50.3% revenue in 97 days)
- 16. An extreme case I (NYSE:MBI) Above is the MBIA stock price evolution for June 07 – June 08 Above is the account evolution for investing in MBIA. The system does a good job at minimizing losses (only 26.2% loss)
- 17. An extreme case II (NYSE:MBI) Above is the MBIA stock price evolution for June 07 – June 08 Using Auto-regression trees. A 74.2% loss
- 18. Demo: Investing in Google • Ben Investment Assistant was done using: • Windows Presentation Foundation, Sql Server, Analysis Services, ADOMD.NET, AMO, .NET 3.5, C# 3.0, Linq to SQL on Windows Vista Business. • 3-tier architecture, highly scalable
- 19. Conclusions • Due to our results we can invalidate the assumption that past data has no use. • Because the algorithm behaves like an investor we can have losses if the company suffers a severe depreciation of value.
- 20. Future work • If we let Ben make decisions on a diversified portfolio we might almost be certain of a profitable outcome. • We can expand the vector of observations to include more data (for example a news index calculated with text mining and Google search API)
- 21. Thank you!

Full NameComment goes here.janerussell85Interesting stock market program/model. I've had some success on the ASX of late, trading strategically based on fundamentals, but also applying similar technical analysis. As a part time ESL tutor I have ample time to make good trading decisions. I utilise Stock Market Charting Software to assist in making good decisions.gemoor21 year agoRichard Dingwall, Software Developer at Provoke Solutions 4 years ago