Evolutionary support vector machine for RMB exchange rate forecasting

Fu, Sibao; Li, Yongwu; Sun, Shaolong; Li, Hongtao

doi:10.1016/j.physa.2019.01.026

Cited by 44 publications

(17 citation statements)

References 39 publications

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“…where ER t is the Exchange Rate value at time t), then the hidden abstract patterns are learned through the gating mechanism. It is easy to find that the complex interactions between the indicators are encoded with the weight parameters U and W in Equations ( 1) to (6). Therefore, the hidden features of market-level coupling at time t could be represented by h C1 t , where:…”

Section: The Proposed Methodologymentioning

confidence: 99%

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

et al. 2020

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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, macro-level 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 paper is to clarify the importance of coupling learning for exchange rate forecasting, and the usefulness of deep coupled model to capture the couplings.

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Section: The Proposed Methodologymentioning

confidence: 99%

“…Simple machine learning methods with shallow architectures (e.g. SVR [6]) cannot incorporate the three kinds of couplings with different working mechanisms listed above. Recently, with the development of deep learning and FinTech, deep neural networks (e.g.…”

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confidence: 99%

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

et al. 2020

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“…In 2019, Fu used SVR to predict the CNY exchange rate. The results showed that the proposed evolutionary SVR was a promising method for predicting the CNY exchange rate [19]. In 2019, Zhelev proposed to apply LSTM to financial market time series [20].…”

Section: Literature Reviewmentioning

confidence: 98%

A Prediction Model of CNN-TLSTM for USD/CNY Exchange Rate Prediction

Wang

et al. 2021

IEEE Access

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The Exchange rate affects the economic development of various countries. To grasp the information of the changeable exchange rate in time, it is necessary to predict the exchange rate price. This paper proposes the CNN-TLSTM model to predict the United States Dollar/Chinese yuan (USD/CNY) exchange rate closing price of the next trading day. The model consists of two parts, namely convolutional neural networks(CNN)and tanh long short-term memory (TLSTM). The function of CNN is to extract feature factors from the input data. TLSTM is used to receive the output data of CNN for prediction, and finally obtain the prediction result. TLSTM is a new model proposed in this paper to improve the internal structure of the long short-term memory (LSTM). Its advantage is to change the range of the output value of the input gate, retain more data features, and prevent the output value of the input gate in LSTM from being overfitting. This paper selects the exchange rate of USD/CNY data and some stock data for each trading day from January 2, 2006, to October 30, 2020, as the experimental data. To prove the effectiveness of the CNN-TLSTM prediction model, the model is compared with multilayer perceptron (MLP), CNN, recurrent neural network (RNN), LSTM, and CNN-LSTM models. mean absolute percentage error (MAPE), mean square error (MSE), and R-squared (R 2 ) are used for comparative analysis. The experimental results show that the CNN-TLSTM model has the best predictive effect on the USD/CNY exchange rate closing price of the next trading day.

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“…ey demonstrated that such a model is suitable for processing 2D structural exchange rate data. Fu et al [49] developed evolutionary support vector regression (SVR) models to forecast four Renminbi (RMB, Chinese yuan) exchange rates (CNY against USD, EUR, JPY, and GBP). ey also demonstrated that the proposed model outperforms the multilayer perceptron (MLP) neural network, Elman neural network, and SVR models in terms of level forecasting accuracy measures.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder‐LSTM Techniques

Jung

Choi

2021

Complexity

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Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange (FX) market has become an important focus of both academic and practical research. There are many reasons why FX is important, but one of most important aspects is the determination of foreign investment values. Therefore, FX serves as the backbone of international investments and global trading. Additionally, because fluctuations in FX affect the value of imported and exported goods and services, such fluctuations have an important impact on the economic competitiveness of multinational corporations and countries. Therefore, the volatility of FX rates is a major concern for scholars and practitioners. Forecasting FX volatility is a crucial financial problem that is attracting significant attention based on its diverse implications. Recently, various deep learning models based on artificial neural networks (ANNs) have been widely employed in finance and economics, particularly for forecasting volatility. The main goal of this study was to predict FX volatility effectively using ANN models. To this end, we propose a hybrid model that combines the long short-term memory (LSTM) and autoencoder models. These deep learning models are known to perform well in time-series prediction for forecasting FX volatility. Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index (FXVIX) as a measure of FX volatility. In particular, the three major FXVIX indices (EUVIX, BPVIX, and JYVIX) from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model. Our hybrid model utilizes an LSTM model as an encoder and decoder inside an autoencoder network. Additionally, we investigate FXVIX indices through subperiod analysis to examine how the proposed model’s forecasting performance is influenced by data distributions and outliers. Based on the empirical results, we can conclude that the proposed hybrid method, which we call the autoencoder-LSTM model, outperforms the traditional LSTM method. Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. Our findings have important implications for practitioners. Because forecasting volatility is an essential task for financial decision-making, this study will enable traders and policymakers to hedge or invest efficiently and make policy decisions based on volatility forecasting.

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Evolutionary support vector machine for RMB exchange rate forecasting

Cited by 44 publications

References 39 publications

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

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

A Prediction Model of CNN-TLSTM for USD/CNY Exchange Rate Prediction

Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder‐LSTM Techniques

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