Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

This Engage grant application aims to build a research partnership between our research group andx000D
Dimensional Research Canada. The research project proposes to investigate the use of advanced data miningx000D
techniques for detecting regime changes from financial time series data. We will design, implement and test ax000D
new method in order to obtain a set of rich features characterizing financial regimes. The method will be basedx000D
on our recent Model-based Categorical Sequence Clustering (MCSC) algorithm. It contains the followingx000D
steps: 1) representing financial time series via candlesticks and performing cluster analysis to convertx000D
candlestick representation to categorical representation; 2) performing hierarchical sequence clustering usingx000D
the MCSC algorithm; 3) exploring the variable-order Markov model of the MCSC to extract signature patterns;x000D
and 4) investigating the usefulness of these patterns to characterize regimes through a comparative study forx000D
predicting market returns. The innovative nature of the proposed methodology lies in its unsupervised learningx000D
approach to analyzing regimes and regime changes. It has the potential to overcome the major limitations of thex000D
conventional approaches. It will allow the discovery of long and statistically significant patterns, yielding morex000D
predictive and easier-to-interpret features. The project is expected to provide direct economic benefits to ourx000D
industry partner, by expanding the scope of its existing platform and its internal knowledge of how to developx000D
future products for complex time series analysis. It also has the potential to benefit the industry, public servicex000D
and scientific research sectors in general.