A Neural Network Measurement of Relative Military Security: The Case of Greece and Cyprus

This paper aims at introducing a relative security measure, applicable to evaluating the impact of arms races on the military security of allies. This measure is based on demographic criteria, which play a dominant role in a number of arms races involving military alliances. The case of Greece and Cyprus, on one hand, and Turkey on the other, is the one to which our relative security measure is applied and tested. Artificial neural networks were trained to forecast the future behaviour of relative security. The high forecasting performance permitted the application of alternative scenarios for predicting the impact of the Greek - Turkish arms race on the relative security of the Greek - Cypriot alliance.Arms Race, Neural Networks, Relative Military Security

Computational Intelligence in Exchange-Rate Forecasting

This paper applies computational intelligence methods to exchange rate forecasting. In particular, it employs neural network methodology in order to predict developments of the Euro exchange rate versus the U.S. Dollar and the Japanese Yen. Following a study of our series using traditional as well as specialized, non-parametric methods together with Monte Carlo simulations we employ selected Neural Networks (NNs) trained to forecast rate fluctuations. Despite the fact that the data series have been shown by the Rescaled Range Statistic (R/S) analysis to exhibit random behaviour, their internal dynamics have been successfully captured by certain NN topologies, thus yielding accurate predictions of the two exchange-rate series.Exchange - rate forecasting, Neural networks

Financial Versus Human Resources in the Greek-Turkish Arms Race: A Forecasting Investigation Using Artificial Neural Networks

This paper aims at forecasting the burden on the Greek economy resulting from the arms race against Turkey and at concentrating on the leading determinants of this burden. The military debt and the defence share of GDP are employed alternatively in order to approximate the measurement of the arms race pressure on Greece, and the method used is that of artificial neural networks. The use of a wide variety of explanatory variables in combination with the promising results derived, suggest that the impact on the Greek economy resulting from this arms race is determined, to a large extent, by demographic factors which strongly favour the Turkish side. Prediction on both miltary debt and defence expenditure exhibited highly satisfactory accuracy, while the estimation of input significance, indicates that variables describing the Turkish side are often dominant over the corresponding Greek ones.Greek Military Debt, Defence Expenditure, Neural Networks

The Greek Current Account Deficit:Is it Sustainable after all?

The large Greek current account deficit figures reported during the past few years have become the source of increasing concern regarding its sustainability. Bearing in mind the variety of techniques employed and the views expressed as regards the analysis and the assessment of the size of the current account deficit, this paper resorts to using neural network architectures to demonstrate that, despite its size, the current account deficit of Greece can be considered sustainable. This conclusion, however, is not meant to neglect the structural weaknesses that lead to such a deficit. In fact, even in the absence of any financing requirements these high deficit figures point to serious competitiveness losses with everything that these may entail for the future performance of the Greek economy.Neural Networks; Current Account Deficit Sustainability

Forecasting Exchange-Rates via Local Approximation Methods and Neural Networks

There has been an increased number of papers in the literature in recent years, applying several methods and techniques for exchange - rate prediction. This paper focuses on the Greek drachma using daily observations of the drachma rates against four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DM), the French Franc (FF) and the British Pound (GBP) for a period of 11 years, aiming at forecasting their short-term course by applying local approximation methods based on both chaotic analysis and neural networks.Key Words: Exchange Rates, Forecasting, Neural Networks

A Variational Recurrent Neural Network for Session-Based Recommendations using Bayesian Personalized Ranking

This work introduces VRNN-BPR, a novel deep learning model, which is utilized in session-based Recommender systems tackling the data sparsity problem. The proposed model combines a Recurrent Neural Network with an amortized variational inference setup (AVI) and a Bayesian Personalized Ranking in order to produce predictions on sequence-based data and generate recommendations. The model is assessed using a large real-world dataset and the results demonstrate its superiority over current state-of-the-art techniques

The Greek-Turkish Arms Race Using Artificial Neural Networks

The aim of this paper is to forecast the pressure due to this arms race between Greece and Turkey exercised on the economy of the former. This forecast, established on the basis of the most appropriate explanatory variables, will provide the opportunity to comment on the nature and relative importance of the explanatory variables that determine the burden of this arms race on the Greek economy, as this is approximated by either the military debt of the country or the defence share of GDP. The method of analysis used is that of Artificial Neural Networks, which has been considered preferable to the conventional estimation methods for the purposes of the present analysis for reasons analyzed later on in this paper