A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China

This paper combines artificial neural networks (ANN), fuzzy optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high.Computational intelligence; artificial neural networks; fuzzy optimization; early warning system; economic crises

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.

Currency movement forecasting using time series analysis and long short-term memory

Foreign exchange is one type of investment, which its goal is to minimize losses that could occurred. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using time series analysis called Auto Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day

Prediction of Rupiah Against US Dollar by Using ARIMA

The currency exchanges rate is one of the most important things in the economy. The currency exchange rate is needed in the business word for example, investment and profit assessment. Prediction of rupiah rate is done to get the price of the rupiah against US dollar in the future to be used as consideration in decision-making, thereby reducing the risk of loss. Therefore, we need a method that can help in making business decisions about when to make the right trades with a high degree of accuracy. This study aims to predict the value of rupiah against US dollar by using ARIMA (Autoregressive Integrated Moving Average). This study uses four stages, including (1) the preparation of the dataset, (2) preprocessing of data, (3) the use of ARIMA models, (4) test accuracy. The data used for the test is the data rate from January 4th 2010 until June 24th 2016. The result showed that ARIMA method has an accuracy rate of 98.74%. Based on the result, it can be concluded that the development of the predictive value of the rupiah against the US dollar using ARIMA method was accurate to use

Forecast foreign exchange rate: the case study of PKR/USD

The main aim of this paper is to forecast the future values of the exchange rate of the USD. Dollar (USD) and Pakistani Rupee (PR). For this purpose was used the ARIMA model to forecast the future exchange rates, because the time series was stationary at first difference. Data reported to five years ranging from the first day of April 2014 to 31st March 2019. The results proved that ARIMA (1,1,9) is the most suitable model to forecast the exchange rate. The difference between the forecasted values and actual values are less than 1%; therefore, it was found that the ARIMA is robust and this model will be helpful for the government functionaries, monetary policymakers, economists and other stakeholders to identify and forecast the future trend of the exchange rate and make their policies accordingly.info:eu-repo/semantics/publishedVersio

Understanding SLL / US$ exchange rate dynamics in Sierra Leone using Box-Jenkins ARIMA approach

This study was carried out with the purpose of producing twelve out-of-sample forecast for a univariate exchange rate variable as a way of addressing challenges faced around dollarization issues in the domestic economy. In pursuit of this, the ARIMA model was utilised, with the best model [1,4,7] indicating that the Sierra Leone - Leone [SLL] currency will continue to depreciate against the United States Dollar [US$] throughout most part of the year 2020. This was done on the assumption of Ceteris Paribus condition, and most importantly on the view that past events of the univariate exchange rate variable is a determinant of future outcomes or performances. In a bid to moving forward, policy recommendations have suggested high level collaboration between relevant policy institutions like the Bank of Sierra Leone and the Ministry of Finance to address issues of concern, for example, a boost to the real sector and many more