Measuring the accuracy of currency crisis prediction with combined classifiers in designing early warning system

Ramli, Nor Azuana; Ismail, Mohd Tahir; Wooi, Hooy Chee

doi:10.1007/s10994-014-5447-y

Cited by 9 publications

(17 citation statements)

References 22 publications

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“…As already stated above, the DNDT algorithm was applied to solve the research question raised, but we have also used different methods in the construction of the currency crisis prediction model. The use of different methods aimed to achieve a robust model which is contrasted not only through a classification technique but also by applying all those that have shown success in the previous literature [1,2,[12][13][14]. Specifically, logistic regression, artificial neural networks, support vector machines, and AdaBoost were used.…”

Section: Methodsmentioning

confidence: 99%

“…To estimate the model, we started from the quotient between the probability that an event will occur and the probability that it will not occur. The probability of an event occurring is determined by Expression (1).…”

Section: Logistic Regressionmentioning

confidence: 99%

“…The sample used in this study comprised 162 developed, emerging, and developing countries with information for the period 1970-2017 (Appendix A). The granularity of the data was annual, following the format data of previous works [1,12,38]. The dataset of the present study had 7708 observations, being 236 crisis observations.…”

Section: Data and Variablesmentioning

confidence: 99%

“…Reference [1] used computational combinations with support vector machine (SVM), logistic regression, and logical analysis of data tree (LADTree), based on the k-nearest neighbor. The results showed that the computational classifiers were more accurate than the traditional statistical methods, obtaining a level above 90%.…”

Section: Introductionmentioning

confidence: 99%

“…However, the event was about more than just speculation on the Thai currency and saw the collapse of Asian stock markets. The financial crisis that began with the devaluation of the Thai baht exchange rate resulted in a sharp increase in interest rates and the collapse of many companies, as well as an increase in the cost of credit and a general fall in GDP in the region [1]. This resulted in foreign and national investors pulling out investment.…”

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Currency Crises Prediction Using Deep Neural Decision Trees

et al. 2019

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Featured Application: The superiority of a novel computational technique (deep neural decision trees) for prediction of currency crises over other methodologies and the construction of new crisis prediction models more precise than existing ones.Abstract: Currency crises are major events in the international monetary system. They affect the monetary policy of countries and are associated with risks of vulnerability for open economies. Much research has been carried out on the behavior of these events, and models have been developed to predict falls in the value of currencies. However, the limitations of existing models mean further research is required in this area, since the models are still of limited accuracy and have only been developed for emerging countries. This article presents an innovative global model for predicting currency crises. The analysis is geographically differentiated for regions, considering both emerging and developed countries and can accurately estimate future scenarios for currency crises at the global level. It uses a sample of 162 countries making it possible to account for the regional heterogeneity of the warning indicators. The method used was deep neural decision trees (DNDTs), a technique based on decision trees implemented by deep learning neural networks, which was compared with other methodologies widely applied in prediction. Our model has significant potential for the adaptation of macroeconomic policy to the risks derived from falls in the value of currencies, providing tools that help ensure financial stability at the global level. Appl. Sci. 2019, 9, 5227 2 of 18 effect of contagion on other countries. However, the event was about more than just speculation on the Thai currency and saw the collapse of Asian stock markets. The financial crisis that began with the devaluation of the Thai baht exchange rate resulted in a sharp increase in interest rates and the collapse of many companies, as well as an increase in the cost of credit and a general fall in GDP in the region [1]. This resulted in foreign and national investors pulling out investment. Not only did this crisis affect Asian countries, but it also had a negative impact on other emerging economies, especially in Latin America, showing that currency crises are not limited to a specific economy. Globalization can increase the economic difficulties of societies and affect the structure of national economies after the real economy has suffered a damaging impact [2]. Figure 1 shows the number of currency crises per year at the international level. Appl. Sci. 2019, 8, x FOR PEER REVIEW 2 of 19of contagion on other countries. However, the event was about more than just speculation on the Thai currency and saw the collapse of Asian stock markets. The financial crisis that began with the devaluation of the Thai baht exchange rate resulted in a sharp increase in interest rates and the collapse of many companies, as well as an increase in the cost of credit and a general fall in GDP in the region [1]. This resulted in foreig...

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Section: Methodsmentioning

confidence: 99%

Section: Logistic Regressionmentioning

confidence: 99%

Section: Data and Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Currency Crises Prediction Using Deep Neural Decision Trees

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Anovel HEOMGA Approach for Class Imbalance Problem in the Application of Customer Churn Prediction

et al. 2021

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Making class balance is essential when learning from highly skewed datasets; otherwise, a learner may classify all instances to a negative class, resulting in a high false-negative rate. As a result, a precise balancing strategy is required. Many researchers have investigated class imbalance using Machine Learning (ML) methods due to their powerful generalization performance and interpreting capabilities, comparing with random sampling techniques, to handle the problem of class imbalance in the preprocessing phase to facilitate learning process and improve performance results of learners. In this research, an effective method called HEOMGA is presented by combining Heterogeneous Euclidean-Overlap Metric (HEOM) and Genetic Algorithm (GA) for oversampling minority class. The HEOM is employed to define a fitness function for the GA. To assess the performance of the proposed HEOMGA method, three benchmark datasets from UCI repository in the domain of customer churn prediction are examined using three different ML learners and evaluated with three performance metrics. The experiment results show the effectiveness of the proposed method compared to some popular oversample methods, such as SMOTE, ADASYN, G SMOTE, and Gaussian oversampling methods. The HEOMGA method significantly outperformed the other oversampling methods in terms of recall, G mean, and AUC when the Wilcoxon signed-rank test is used.

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Measuring the accuracy of currency crisis prediction with combined classifiers in designing early warning system

Cited by 9 publications

References 22 publications

Currency Crises Prediction Using Deep Neural Decision Trees

Currency Crises Prediction Using Deep Neural Decision Trees

Bibliography

Anovel HEOMGA Approach for Class Imbalance Problem in the Application of Customer Churn Prediction

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