Visual predictions of currency crises using self‐organizing maps

Throughout the 1990s, four global waves of financial turmoil occurred. The beginning of the 21st century has also suffered from several crisis episodes, including the global financial crisis of 2008–2009. However, to date, the forecasting results are still disappointing. This paper examines whether new insights can be gained from the application of the self‐organizing map (SOM) – a non‐parametric neural‐network‐based visualization tool. We develop a SOM‐based model for prediction of currency crises. We evaluate the predictive power of the model and compare it with that of a classical probit model. The results indicate that the SOM‐based model is a feasible tool for predicting currency crises. Moreover, its visual capabilities facilitate the understanding of the factors and conditions that contribute to the emergence of currency crises. Copyright © 2011 John Wiley & Sons, Ltd.     

A pattern‐based approach to extract REA value models from business process models

Business models are economic models that describe the rationale of why organizations create and deliver value. These models focus on what organizations offer and why. Business process models capture business activities and the ways in which they are accomplished (i.e. their coordination). They explain who is involved in the activities, and how and when these activities should be performed. This paper discusses the alignment between business models and business process models. It proposes a novel systematic method for extracting a value chain (i.e. business model) expressed in the Resources, Events, Agents (REA) ontology from a business process model expressed in Business Process Model and Notation?. Our contribution is twofold: (1) from a theoretical standpoint we identified a set of structural and behavioural patterns that enable us to infer the corresponding REA value chain; (2) from a pragmatic perspective, our approach can be used to derive useful knowledge about the business process and serve as a starting point for business analysis.     

Estimating Probabilities of Recession in Real Time Using GDP and GDI

This work estimates Markov switching models on real‐time data and shows that the growth rate of gross domestic income (GDI), deflated by the gross domestic product (GDP) deflator, has done a better job recognizing the start of recessions than has the growth rate of real GDP. This result suggests that placing an increased focus on GDI may be useful in assessing the current state of the economy. In addition, the paper shows that the definition of a low‐growth phase in the Markov switching models changed considerably from 1978 to 2005. The models increasingly came to define this phase as an extended period of around zero rather than negative growth, diverging somewhat from the traditional definition of a recession.