Modelling and forecasting interest rates during stages of the economic cycle: A knowledge-discovery approach. (February 2016)

• Record Type: Journal Article
• Authors: Diaz, David; Theodoulidis, Babis; Dupouy, Carlos
• Is Part Of: Expert systems with applications. Volume 44(2016)
• Page Start: 245
• Page End: 264
• Publication Date: 2016-02
• Subjects: Interest rates -- Yield curve -- Economic cycles -- Forecasting -- Data mining -- Decision trees; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.09.010
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Proposes a knowledge discovery methodology to model and forecast economic variables. Demonstrates the deployment of the methodology to forecast interest rates. The interpretability of the results is improved through the use of decision trees. Comprehensive critical review of existing theoretical and empirical studies. Demonstrates that economic stage modelling improves the forecasting results. Abstract: Modelling the structure of risk-free rates and their relation to other economic and financial variables during different stages of the economic cycles has attracted much interest from both the theoretical and practical perspectives. The previous literature has emphasized the deployment of expert systems and knowledge-discovery approaches motivated by the need to address the limitations of the econometric models. However, it has failed to address the interpretability aspects and, more importantly, the need to provide methodological support that allows the deployment of such techniques in a more systematic way. This approach entails the definition of a process that includes the usual steps taken by experts to address similar problems and allows the relative merits of different techniques in relation to common goals and objectives to be gauged. This paper addresses the interpretability and the lack of methodological support by proposing a knowledge-discovery methodology that includes a minimal common number of steps to model, analyse, evaluate and deploy different non-linear techniques and models. Furthermore, the interpretability is addressed through the use of open-box techniques, such as decision trees. The proposed methodology helps to discover and describe hidden patterns, allowing for the study and characterization of economic cycles, and economic cycle stages, as well as the description of the historic relationships between interest rates and other relevant economic variables. These patterns can also be used in the forecasting of economic cycle stages, interest rates and other related variables of concern. The output of the methodology can provide actionable information for market agents, such as monetary authorities, financial institutions, and individual investors, as well as for the academic community, to increase further the knowledge and understanding of financial markets, thus enriching and complementing existing financial theories.