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

An econo-physics view on the historical dynamics of the Albanian currency vs. Euro exchange rates

The descriptive analysis for the very long-term behavior of the Euro/ALL exchange rates has identified a near to average .revert behavior which contradict some econometric arguments and economical level of the country. Apparent anxious regimes have continuously ended up without crashing and generally the national currency of the not competitive economy has shown a nearly stabilized dynamics toward EU currency. Some of those properties have been explained herein by employing the analysis of the system from complexity and econo-physics point of view. So, by approaching the trend we obtained that the time precursor is characterized by local self-organization regimes that never organized in long scale to produce dangerous move. Thermodynamic–like processes have acted constantly as stabilizer of the national currency value. More details and features have been considered by analyzing the distributions and multifractal structure of the series in the framework of the non-equilibrium statistical mechanics approach. Gathering the information about the stationarity of the states, presences of regimes and their properties, we realized to identify the optimal condition for measurement, modeling and steadfast descriptive statistics. Finally, by using neural network we have realized a forecasting example for one month time interval. The work aims to reveal the importance of interdisciplinary consideration for better results in the study of complex socioeconomic systems

Forecasting foreing exchange reserves using Bayesian Model Averaging-Naïve Bayes

Foreign exchange reserves are used by governments to balance international payments and make stable the exchange rate. Numerous works have developed models to predict foreign exchange reserves; however, the existing models have limitations and the literature demands more research on the subject given that the accuracy of the models is still poor, and they have only been used for emerging countries. This paper presents a new prediction model of foreign exchange reserves for both emerging countries and developed countries, applying a method of Bayesian model averaging-Naïve Bayes, which shows better precision results than the individual classifier. Our model has a great potential impact on the adequacy of macroeconomic policy against the risks derived from balance of payment crises providing tools that help to achieve financial stability on a global level

Neural Networks In Business Time Series Forecasting: Benefits And Problems

Many studies examine the use of Neural Networks (NNs) as a tool for business time series forecasting, but the findings have been mixed and inconsistent. This paper explores the conditions under which NNs can improve business time series forecasting based on studies that compare NNs with traditional statistical models. The findings are that NNs generally outperform alternatives when data are nonlinear or discontinuous, building effective NNs for time series forecasting, including designing and selecting the structure, simulation functions, stopping rules, training algorithms and evaluation criteria remains challenging. A case study is discussed to illustrate these findings, and implications for future research and practice are also provided

Review on Financial Forecasting using Neural Network and Data Mining Technique

The rise of economic globalization and evolution of information technology, financial data are being generated and accumulated at an extraordinary speed. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial data to support companies and individuals in strategic planning and investment decision-making. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. There has been a large body of research and practice focusing on exploring data mining techniques to solve financial problems. This paper describes data mining in the context of financial application from both technical and application perspective by comparing different data mining techniques

Integrated computational intelligence and Japanese candlestick method for short-term financial forecasting

This research presents a study of intelligent stock price forecasting systems using interval type-2 fuzzy logic for analyzing Japanese candlestick techniques. Many intelligent financial forecasting models have been developed to predict stock prices, but many of them do not perform well under unstable market conditions. One reason for poor performance is that stock price forecasting is very complex, and many factors are involved in stock price movement. In this environment, two kinds of information exist, including quantitative data, such as actual stock prices, and qualitative data, such as stock traders\u27 opinions and expertise. Japanese candlestick techniques have been proven to be effective methods for describing the market psychology. This study is motivated by the challenges of implementing Japanese candlestick techniques to computational intelligent systems to forecast stock prices. The quantitative information, Japanese candlestick definitions, is managed by type-2 fuzzy logic systems. The qualitative data sets for the stock market are handled by a hybrid type of dynamic committee machine architecture. Inside this committee machine, generalized regression neural network-based experts handle actual stock prices for monitoring price movements. Neural network architecture is an effective tool for function approximation problems such as forecasting. Few studies have explored integrating intelligent systems and Japanese candlestick methods for stock price forecasting. The proposed model shows promising results. This research, derived from the interval type-2 fuzzy logic system, contributes to the understanding of Japanese candlestick techniques and becomes a potential resource for future financial market forecasting studies --Abstract, page iii

Dynamic aperiodic neural network for time series prediction

There are many things that humans find easy to do that computers are currently unable to do. Tasks such as visual pattern manipulating objects by touch, and navigating in a complex world are easy for humans. Yet, despite decades of research, we have no viable algorithms for performing these and other cognitive functions on a computer. In this study, we used a bio-inspired neural network called a KA­ set neural network to perform a time series predictive task. The results from our experiments showed that the predictive accuracy with this method was better in most markets than results obtained using a random walk method

Forecasting Long-Term Government Bond Yields: An Application of Statistical and AI Models

This paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets.interest rates; forecasting; neural networks; fuzzy logic.