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
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?
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)
