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Predicting the rankings of financial analysts, supporting big data projects and other applications of label ranking methods (Part I)

MLKDD@São Carlos
by

Carlos Soares

on 20 July 2013

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Transcript of Predicting the rankings of financial analysts, supporting big data projects and other applications of label ranking methods (Part I)

Predicting the rankings of financial analysts, supporting big data projects and other applications of label ranking methods
(part I)

linkedin: http://pt.linkedin.com/in/cpsoares
plaxo: search “carlos soares universidade do porto”

machine learning/data mining
[management] information systems
decision support systems
activities
Carlos Soares

data mining/machine learning
(+web+text)
business intelligence
machine learning + optimization

... AI
Cheng, W., Hühn, J. & Hüllermeier, E., 2009. Decision tree and instance-based learning for label ranking. In L. Bottou & M. Littman, eds. Proceedings of the 26th International Conference on Machine Learning (ICML-09). Montreal, Canada: Omnipress, pp. 161–168.offspring
2+8 Ph.D.
14+8 M.Sc.
publications
3 books
a few dozen papers
conferences
ECMLPKDD 2012
KDD 2009___CHROME_BUG___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___CHROME_BUG___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___CHROME_BUG___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cooperation with portuguese and foreign universities
U. Leiden, U. Genebra, U. Masaryk, U.P. Valência, USP, UF Pernambuco, U. Waikato
… and companies
creativesystems, bivolino, SONAE, palco principal, ALERT, flex2000, declarativa, sensoria/paletadeideias, portalexecutivo.com/portal ver, STCP, INE, daimlerchrysler
R&ID and consulting
Carlos Soares
http://tinyurl.com/nto2hzv
1997
2012
2004
1990
1995
label ranking:
overview

Artur Aiguzhinov, Carla Rebelo, Cláudio Sá, Geraldine Ribeiro, Jorge Kanda,
Wouter Duivesteijn
colleagues
affected by many different factors
difficult to collect
news and rumours
subjective
investor confidence indices
Understanding the rankings of financial analysts
Aiguzhinov, Soares and Serra, FMA (2010)
yet, some analysts seem to be able to do it
making money on the stock market
[machine learner's view]

time series forecasting
predict rankings of financial analysts
http://careers.stateuniversity.com/pages/210/Financial-Analyst.html
starmine
... including oscar-like cerimony
predict the best analyst
classification
label ranking
distances
distribution of permutations
decomposition
e.g., pairwise comparisons and regression
3 main approaches
TDIDT
with new splitting criterion:

minimize target variable variance in nodes
Predictive Clustering Trees
A Similarity-based Adaptation of Naive Bayes for Label Ranking: Application to the Metalearning Problem of Algorithm Recommendation
Aiguzhinov, Soares and Serra, DS (2010)
Estimate a posteriori "ranking probability" based on Bayes theorem
Naive Bayes
Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results
Brazdil, Soares and Pinto da Costa, MLj (2003)

Instance-based label ranking using the Mallows model
Cheng, W. & Hüllermeier, E., Preference Learning (2008)
k-NN
A Weighted Rank Measure of Correlation
Pinto da Costa and Soares
Australian and New Zealand Journal of Statistics (2005)
the higher the rank, the higher its importance
Weighted Rank Correlation (linear)
evaluation
Spearman rank correlation
Ranking with Predictive Clustering Trees
Todorovski, Blockeel and Dzeroski, ECML (2002)

Empirical Evaluation of Ranking Trees on Some Metalearning Problems
Rebelo and Soares and Pinto da Costa, M-PREF (2008)

Decision tree and instance-based learning for label ranking
Cheng, W., Hühn, J. & Hüllermeier, E., ICML (2009)
Mining Association Rules for Label Ranking
Sá, Soares, Jorge, Azevedo and Pinto da Costa, PAKDD (2010)
Association Rules
LRAR: AR with a ranking in the consequent
model
identify relationships between
attributes
and
target rankings
training data
new quarter
predictions
characteristics of the market
observed ranking of analysts according to some utility function
single observation: one quarter
Log Ranking Accuracy
Weighted Rank Correlation quadratic)
compare prediction to target ranking
same distance function
new prediction method
algorithms
a priori
conditional probabilities
Multilayer Perceptron
Individual Weight-based Signed Global Approach
Global Approach
Local Approach
APPL
ZNGA
DELL
APPL
0.7
ZNGA
0.1
DELL
0.6
VIEWS
APPL
5%
ZNGA
3%
DELL
10%
MARKET
APPL
8%
ZNGA
1%
DELL
11%
PORTFOLIO
Black-Litterman model
CONFIDENCE
making money on the stock market
with label ranking

(almost) making money on the stock market
with label ranking

PROJECTS
TEACHING
RESEARCH
SELECTED RESULTS
Multilayer Perceptron for Label Ranking
Ribeiro, Duivesteijn, Soares and Knobbe ICANN (2012)
Decision tree and instance-based learning for label ranking
Cheng, Hühn, & Hüllermeier, ICML (2009)
Estimating the Predictive Accuracy of a Classifier
Bensusan& Kalousis, ECML (2001)
Label ranking by learning pairwise preferences
Hüllermeier, E. et al., AI (2008)
issues
representation
evaluation
other applications
offspring
2+8 Ph.D.
14+8 M.Sc.
publications
3 books
few dozen papers
conferences
ECMLPKDD 2012
KDD 2009
students
Alípio Jorge, Ana Paula Serra, André Carvalho, Arno Knobbe, Joaquim Costa, Joost Kok, Pavel Brazdil, Paulo Azevedo
accuracy
top-N evaluation
utility
but analysts do not make recommendations regularly
prediction
observed
Kendall rank correlation
cost
1
2
3
4
total orders
1
2
2
3
partial orders
1
2
3
?
incomplete orders
e.g. 2 analysts make exactly the same recommendation
e.g. one analyst does not make a recommendation
or
a new analyst starts making recommendations
election results/polls
source: wikipedia
F1 races
source: formula1.com
reranking
1
2
3
4
predicted
1
2
3
4
tried 1st label
2
3
4
...tried 2nd label
1
...should I still try the 3rd?...
preprocessing
discretization
applying F&I's method directly doesn't work
Multi-Interval Discretization of Continuous Attributes for Label Ranking
Sá, Soares, Knobbe, Azevedo, Jorge, DS (2013)
ranking probability
... taking dispersion into account
Full transcript