20 research outputs found

    Does Non-linearity Matter in Retail Credit Risk Modeling?

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    In this research we propose a new method for retail credit risk modeling. In order to capture possible non-linear relationships between credit risk and explanatory variables, we use a learning vector quantization (LVQ) neural network. The model was estimated on a dataset from Slovenian banking sector. The proposed model outperformed the benchmarking (LOGIT) models, which represent the standard approach in banks. The results also demonstrate that the LVQ model is better able to handle the properties of categorical variables.retail banking, credit risk, logistic regression, learning vector quantization

    Emerging Economies Crises: Lessons from the 1990’

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    The paper examines the financial crises of the 1990s. They represent a new kind of crises, as they do not seem to conform to the so-called first generation and second generation literature on currency crises. The outburst of the Asian crises brought a new challenge for economic policy. The attention has been placed on the self-fulfilling character of the speculative attacks and microeconomic weaknesses. The first part of the paper reviews the recent theoretical literature on financial crises, the second part addresses some lessons for emerging economies. The authors consider the policies to manage financial crises and reduce the risks associated with them.financial crises, emerging markets, contagion

    Croatian and Slovenian Mutual Funds and Bosnian Investments Funds (in English)

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    The paper provides a stock-market-performance analysis for three emerging European stock markets: Croatia, Slovenia, and Bosnia and Herzegovina. Using monthly observations we perform a detailed study of the performance of Croatian and Slovenian mutual funds and Bosnian investment funds. The risk-return measures of the funds are assessed using the Sharpe ratio, Treynor ratio, information ratio, Jensen’s alpha, and an appraisal ratio. Furthermore, we analyze the timing ability of the funds. Descriptive statistics for the returns are given and different statistic tests are calculated in order to test ordinary-least-squares assumptions in the data. The results are also estimated by applying the bootstrap method.stock market, mutual fund, investment fund, risk/return measures

    A Nonlinear Approach to Forecasting with Leading Economic Indicators

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    The classical NBER leading indicators model was built solely within a linear framework. With recent developments in nonlinear time-series analysis, several authors have begun to examine the forecasting properties of nonlinear models in the field of forecasting business cycles. The research presented in this paper focuses on the development of a new approach to forecasting with leading indicators based on neural networks. Empirical results are presented for forecasting the Index of Industrial Production. The results demonstrate that a superior performance can be obtained relative to the classical model.

    Forecasting with leading economic indicators - a non-linear approach

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    Leading economic indicators have a long tradition in forecasting future economic activity. Recent developments, however, suggest that there is scope for adding extensions to the methodology of forecasting major economic fluctuations. In this paper, the author tries to develop a new model, which would outperform the forecast accuracy of classical leading indicators model. The use of artificial neural networks is proposed here. For demonstration a case study for Slovene economy is included. The main finding is that, at the twelve months forecasting horizon, a stable and improved forecast accuracy could be achieved for in- and out-of-sample data.leading economic indicators, neural network, forecasting, aggregate economic activity

    Behavioural patterns as determinants of market movements: evidence from an emerging market

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    This article aims to empirically support the hypothesis that behavioural patterns are key determinants of market movements. We developed a model for predicting market psychology which is based on the application of a self-organizing network algorithm. The estimated model is applied to a mechanical trading system, which independently adopts investment decisions based on the current daily data. The model was tested on the data for daily trading on the Slovenian stock market as an example of an emerging capital market. The performance of the model supports the suggested hypothesis.