Traditional approaches for time series modeling assume a linear underlying relationship among the past and the future values of a time series, but they may be totally inappropriate if the underlying process is nonlinear. Artificial Neural Networks (ANNs) are a particularly promising branch on soft computing techniques, as they possess the ability to determine non-linear relationships, and are p...

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for timely systemic risk monitoring of large European banks and insurance companies. We predict firms’ systemic relevance as the marginal impact of individual downside ris...

This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2...

The financial market dynamics can be characterized by macro-economic, micro-financial and market risk indicators, used as leading indicators by market professionals. In this article, we propose a method to identify market states integrating two classification algorithms: a Robust Kohonen Self-Organising Maps one and a CART one. After studying the market’s states separation using the former, we ...

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. This paper proposes a Hierarchical Radial Basis Function Network (HiRBF) model for forecasting three major international currency exchange rates. Based on the pre-defined instruction sets, HRBF model can be created and evolved. The ...

Forecasting the short run behavior of foreign exchange rates is a challenging problem that has attracted considerable attention. High frequency financial data are typically characterized by noise and non–stationarity. In this work we investigate the profitability of a forecasting methodology based on unsupervised clustering and feedforward neural networks and compare its performance with that o...

Principle Component Analysis (PCA) is used to reduce dimensionality and noise, while still preserving the majority of the variance in the data. It however gives little guarantee on the predictive value of the remaining data. This paper proposes an inverted Principle Component Analysis (iPCA), to achieve dimensionality and noise reduction, by removal of the largest principle components. In addit...