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

Cao, Wei; Zhu, Weidong; Wang, Wenjun; Demazeau, Yves; Zhang, Chen

doi:10.1109/mis.2020.2977283

Cited by 28 publications

(17 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After years of improvement and optimization, although significant improvements have been achieved in the accuracy of the model, it still cannot surpass the maximum hidden layer setting of 2 layers due to the problem of nonconvex optimization. Therefore, the difficulty of using deep networks to improve accuracy has been unable to move forward [ 25 – 30 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Exchange Rate Forecasting Based on Deep Learning and NSGA‐II Models

Zhao

Liu

Liang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Today, the global exchange market has been the world’s largest trading market, whose volume could reach nearly 5.345 trillion US dollars, attracting a large number of investors. Based on the perspective of investors and investment institutions, this paper combines theory with practice and creatively puts forward an innovative model of double objective optimization measurement of exchange forecast analysis portfolio. To be more specific, this paper proposes two algorithms to predict the volatility of exchange, which are deep learning and NSGA-II-based dual-objective measurement optimization algorithms for the exchange investment portfolio. Compared with typical traditional exchange rate prediction algorithms, the deep learning model has more accurate results and the NSGA-II-based model further optimizes the selection of investment portfolios and finally gives investors a more reasonable investment portfolio plan. In summary, the proposal of this article can effectively help investors make better investments and decision-making in the exchange market.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Exchange Rate Forecasting Based on Deep Learning and NSGA‐II Models

Zhao

Liu

Liang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Through a profitability debate, the DC-LSTM has been proven to be a beneficial tool for making prudent investment decisions. The goal of this study is to explain why coupling learning is important for exchange rate forecasting and why a deep coupled model is effective for capturing the couplings [7]. Wei and Li [61] present a unique Multi-Channel LSTM network for foreign exchange rate prediction using time series data from various time scales.…”

Section: Related Researchmentioning

confidence: 99%

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

Ayitey

Appiahene

Appiah

2022

Journal of Electrical Systems and Inf Technol

View full text Add to dashboard Cite

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

show abstract

“…In addition, the internationalization of CNY has profoundly influenced the global market. In 2020, US$2.591 trillion worth of goods and services were exported by China, accounting for 13.8% of overall global exports 2 . In addition, China brought in US$ 163 billion of inflows in 2020, surpassing the United States as the largest recipient of foreign direct investment (FDI) 3 .…”

Section: Introductionmentioning

confidence: 99%

Improving exchange rate forecasting via a new deep multimodal fusion model

Windsor

Cao

2022

Appl Intell

Self Cite

View full text Add to dashboard Cite

Exchange rates are affected by the impact of disparate types of new information as well as the couplings between these modalities. Previous work mainly predicted exchange rates solely based on market indicators and therefore achieved unsatisfactory results. In response to such an issue, this study develops an inventive multimodal fusion-based long short-term memory (MF-LSTM) model to forecast the USD/CNY exchange rate. Our model consists of two parallel LSTM modules that extract abstract features from each modality of information and a shared representation layer that fuses these features. In terms of the text modality, bidirectional encoder representations from transformers (BERT) is applied to conduct a sentiment analysis on social media microblogs. Compared to previous studies, we incorporate not only market indicators but also investor sentiments into consideration, treating the two types of data differently to match their exclusive characteristics. In addition, we apply the multimodal fusion technique and contrive a deep coupled model rather than a shallow and simple model to reflect the couplings between the two modalities. As a consequence, the experimental results obtained over a 15month period exhibit the superiority of the proposed approach over nine baseline algorithms. The purpose of our study is to demonstrate that it is practicable and effective to incorporate multimodal fusion into financial time series forecasting.

show abstract

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

Cited by 28 publications

References 11 publications

[Retracted] Exchange Rate Forecasting Based on Deep Learning and NSGA‐II Models

[Retracted] Exchange Rate Forecasting Based on Deep Learning and NSGA‐II Models

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

Improving exchange rate forecasting via a new deep multimodal fusion model

Contact Info

Product

Resources

About