A Deep Coupled LSTM Approach for USD/CNY Exchange Rate Forecasting

摘要

Forecasting CNY exchange rate accurately is a challenging task due to its complex coupling nature, which includes market-level coupling from interactions with multiple financial markets, macrolevel coupling from interactions with economic fundamentals, and deep coupling from interactions of the two aforementioned kinds of couplings. This study develops a new deep coupled long short-term memory (LSTM) approach, namely, DC-LSTM, to capture the complex couplings for USD/CNY exchange rate forecasting. In this approach, a deep structure consisting of stacked LSTMs is built to model the complex couplings. The experimental results with 10 years data indicate that the proposed approach significantly outperforms seven other benchmarks. The DC-LSTM is verified to be a useful tool to make wise investment decisions through a profitability discussion. The purpose in this article is to clarify the importance of coupling learning for exchange rate forecasting, and the usefulness of deep coupled model to capture the couplings.