Search

Purpose – When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue. Design/methodology/approach – This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model. Findings – The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating indicators, the MFOAGRNN forecast model has better forecast ability than the other three forecasting models. Originality/value – This paper explored the correlation between oil and gold prices, and built oil and gold prices forecasting models. In addition, this paper proposes a modified FOA (MFOA), where an escape parameter Δ is added to Si. The findings showed that the forecasting model that combines MFOA and GRNN has the best ability to forecast the closing price of oil and gold. [ABSTRACT FROM AUTHOR]

Purpose – In order to avoid enterprise crisis and cause the domino effect, which influences the investment return of investors, the national economy, and financial crisis, establishing a complete set of feasible financial early warning model can help to prevent the possibility of enterprise crisis in advance, and thus, reduce the influence on society and the economy. The purpose of this paper is to develop an efficient financial crisis warning model. Design/methodology/approach – First, the fruit fly optimization algorithm (FOA) is used to adjust the coefficients of the parameters in the ZSCORE model (we call it the FOA_ZSCORE model), and the difference between the forecasted value and the real target value is calculated. Afterward, the generalized regressive neural network (GRNN model), with optimized spread by FOA (we call it FOA_GRNN model), is used to forecast the difference to promote the forecasting accuracy. Various models, including ZSCORE, FOA_ZSCORE, FOA_ZSCORE+GRNN, and FOA_ZSCORE+FOA_GRNN, are trained and tested. Finally, different models are compared based on their prediction accuracies and ROC curves. Furthermore, more appropriate parameters, which are different from the parameters in the original ZSCORE model, are selected by using the multivariate adaptive regression splines (MARS) method. Findings – The hybrid model of the FOA_ZSCORE together with the FOA_GRNN offers the highest prediction accuracy, compared to other models; the MARS can be used to select more appropriate parameters to further improve the performance of the prediction models. Originality/value – This paper proposes a hybrid model, FOA_ZSCORE+FOA_GRNN which offers better performance than the original ZSCORE model. [ABSTRACT FROM AUTHOR]