Reference number : GC-2017-Q3-00610, amendment number : , agreement type : G, recipient legal name : Wang, Shengrui (Université de Sherbrooke), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : This Engage grant application aims to build a research partnership between our research group and_x000D_
Dimensional Research Canada. The research project proposes to investigate the use of advanced data mining_x000D_
techniques for detecting regime changes from financial time series data. We will design, implement and test a_x000D_
new method in order to obtain a set of rich features characterizing financial regimes. The method will be based_x000D_
on our recent Model-based Categorical Sequence Clustering (MCSC) algorithm. It contains the following_x000D_
steps: 1) representing financial time series via candlesticks and performing cluster analysis to convert_x000D_
candlestick representation to categorical representation; 2) performing hierarchical sequence clustering using_x000D_
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 for_x000D_
predicting market returns. The innovative nature of the proposed methodology lies in its unsupervised learning_x000D_
approach to analyzing regimes and regime changes. It has the potential to overcome the major limitations of the_x000D_
conventional approaches. It will allow the discovery of long and statistically significant patterns, yielding more_x000D_
predictive and easier-to-interpret features. The project is expected to provide direct economic benefits to our_x000D_
industry partner, by expanding the scope of its existing platform and its internal knowledge of how to develop_x000D_
future products for complex time series analysis. It also has the potential to benefit the industry, public service_x000D_
and scientific research sectors in general., program purpose french : This Engage grant application aims to build a research partnership between our research group and_x000D_
Dimensional Research Canada. The research project proposes to investigate the use of advanced data mining_x000D_
techniques for detecting regime changes from financial time series data. We will design, implement and test a_x000D_
new method in order to obtain a set of rich features characterizing financial regimes. The method will be based_x000D_
on our recent Model-based Categorical Sequence Clustering (MCSC) algorithm. It contains the following_x000D_
steps: 1) representing financial time series via candlesticks and performing cluster analysis to convert_x000D_
candlestick representation to categorical representation; 2) performing hierarchical sequence clustering using_x000D_
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 for_x000D_
predicting market returns. The innovative nature of the proposed methodology lies in its unsupervised learning_x000D_
approach to analyzing regimes and regime changes. It has the potential to overcome the major limitations of the_x000D_
conventional approaches. It will allow the discovery of long and statistically significant patterns, yielding more_x000D_
predictive and easier-to-interpret features. The project is expected to provide direct economic benefits to our_x000D_
industry partner, by expanding the scope of its existing platform and its internal knowledge of how to develop_x000D_
future products for complex time series analysis. It also has the potential to benefit the industry, public service_x000D_
and scientific research sectors in general., agreement title english : Sequence Clustering for Regime Change Detection, agreement title french : Sequence Clustering for Regime Change Detection, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-10-18, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada