Reducing Exchange Rate Risks in International Trade: A Hybrid Forecasting Approach of CEEMDAN and Multilayer LSTM

Abstract

In international trade, it is common practice for multinational companies to use financial market instruments, such as financial derivatives and foreign currency debt, to hedge exchange rate risks. Making accurate predictions and decisions on the direction and magnitude of exchange rate movements is a more direct way to reduce exchange rate risks. However, the traditional time series model has many limitations in forecasting exchange rate, which is nonlinear and nonstationary. In this paper, we propose a new hybrid model of complete ensemble empirical mode decomposition (CEEMDAN) based multilayer long short-term memory (MLSTM) networks. It overcomes the shortcomings of the classic methods. CEEMDAN not only solves the mode mixing problem of empirical mode decomposition (EMD), but also solves the residue noise problem which is included in the reconstructed data of ensemble empirical mode decomposition (EEMD) with less computation cost. MLSTM can learning more complex dependences from exchange rate data than the classic model of time series. A lot of experiments have been conducted to measure the performance of the proposed approach among the exchange rates of British pound, the Australian dollar, and the US dollar. In order to get an objective evaluation, we compared the proposed method with several standard approaches or other hybrid models. The experimental results show that the CEEMDAN-based MLSTM (CEEMDAN–MLSTM) goes on better than some state-of-the-art models in terms of several evaluations.

Design of Fuzzy Aided Cognition System for International Economic and Trade Information under Supply Chain Management

“…However, due to different models and research methods adopted by different economists, their conclusions are quite different. The domestic and foreign research results on international economy and trade mainly focus on the following five aspects: trade promoting economic growth, export promoting economic growth, import promoting economic growth, trade policy and economic growth, and the mechanism of trade promoting economic growth, and on this basis, the use of modern measurement methods for research [ 6 – 8 ]. With the advancement of science and technology, as well as the arrival of the big data era, it is becoming increasingly important to realize cognition and judgment similar to that of the human brain, to discover new relevance and fuzzy auxiliary cognitive modes, and to make correct decisions, all of which present new opportunities and challenges for cognitive computing technology development.…”

A New Hybrid Forecasting Model Based on SW‐LSTM and Wavelet Packet Decomposition: A Case Study of Oil Futures Prices

“…Recently, many researchers have combined decomposition methods with machine learning algorithm to establish hybrid forecasting models. Lin et al [34] proposed the CEEMDAN-LSTM model to the forecast of exchange rate. Niu et al [32] and He et al [45] applied the VMD-LSTM model to the forecasting fields of stock prices and exchange rate movements.…”

“…e EMD, FEEMD, and VMD methods also have some certain limitations, for example, inadequate mathematical explanations, the boundary effects, noise oversensitivity, and pattern overlap. ese may cause excessive decomposition of the original data and adversely affect the prediction results [33,34]. On the other hand, the well-known deep learning model causes overfitting problems and is always based on historical information without thinking over the statistical regularity of behavior in the financial market, which leads to deficient precision [10,32].…”

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