On the understanding of profiles by means of post-processing techniques: an application to financial assets

Abstract

In last years, mining financial data has taken remarkable importance to complement classical techniques. Knowledge Discovery in Databases provides a framework to support analysis and decision-making regarding complex phenomena. Here, clustering is used to mine financial patterns from Venezuelan Stock Exchange assets (Bolsa de Valores de Caracas), and two major indexes related to that market: Dow Jones (USA) and BOVESPA (Brazil). Also, from a practical point of view, understanding clusters is crucial to support further decision-making. Only few works addressed bridging the existing gap between the raw data mining (DM) results and effective decision-making. Traffic lights panel (TLP) is proposed as a post-processing tool for this purpose. Comparison with other popular DM techniques in financial data, like association rules mining, is discussed. The information learned with the TLP improves quality of predictive modelling when the knowledge discovered in the TLP is used over a multiplicative model including interactions.

Keywords:

• clustering
• Knowledge Discovery in Databases
• data mining
• patterns interpretation
• post-processing
• traffic lights panel
• financial assets
• Venezuela Stock Exchange
• association rules
• frequent itemsets
• general linear model

2010 AMS Subject Classifications:

• 62H30
• 68T10
• 68U35
• 97R40
• 91G80

Acknowledgements

We want to thank Albany Sanchez and Maria Elena Naranjo (Universidad de Los Andes, Venezuela) for their valuable collaboration in exploratory analysis of financial data set used in this work, as well as their ETL process (Extract, Transform and Load) in data, prior association rules mining and their meetings and reports with Venezuelan financial analysts that were extremely useful in the discussion and conclusions of this research.