Forecasting exchange rate using deep belief networks and conjugate gradient method

Shen, Furao; Chao, Jinhui; Zhao, Jinxi

doi:10.1016/j.neucom.2015.04.071

Cited by 207 publications

(66 citation statements)

References 19 publications

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“…In order to calculate Q , known as the covariance of process noise, the change in asset price returns is calculated for a time interval. For this purpose, in a specified period such as in a day and in a week the change in asset price returns is calculated by equations (23) and (24), and it remains as a fixed number during the forecasting period.…”

Section: Artificial Neural Network For Filter Parameters and Tuningmentioning

confidence: 99%

“…The summation layer has two neurons, and the hidden layer uses a Gaussian transfer function in the radial basis function (RBF) in order to approximate the given function. For each pair of currencies, we train the GRNN through the supervised method of learning using the results of equations (23) and (24).…”

Section: Artificial Neural Network For Filter Parameters and Tuningmentioning

confidence: 99%

“…But forecasting accuracy evaluations based on error measures such as RMSE is not useful for distinguishing between the best and the worst model (Shen et al, 2015). To this end, the correct direction change prediction (%CDCP) is calculated.…”

Section: Forecasting Performancementioning

confidence: 99%

“…Utilization of publicly available intra-day high frequency FX data and applying more sophisticated modeling techniques might be a more effective means to explain the behavior of exchange rates in the short to medium term (Shen et al, 2015). Cai & Zhang (2016) stress that "…most of previous literature on intra-day exchange rate forecasting has focused on regular time intervals such as 30 minutes or one hour."…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Synergistic Forecasting Model for High-Frequency Foreign Exchange Data

Ebrahimijam¹,

Adaoglu²,

Gökmenoğlu³

2018

ECECSR

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show abstract

Section: Artificial Neural Network For Filter Parameters and Tuningmentioning

confidence: 99%

Section: Artificial Neural Network For Filter Parameters and Tuningmentioning

confidence: 99%

Section: Forecasting Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Synergistic Forecasting Model for High-Frequency Foreign Exchange Data

Ebrahimijam¹,

Adaoglu²,

Gökmenoğlu³

2018

ECECSR

View full text Add to dashboard Cite

show abstract

“…Recently, artificial neural network-based prediction models have again been attracting attention owing to the development of parallel processing technology, as well as algorithms that overcome the limitations of deep neural networks [11][12][13]. A typical algorithm used to train a deep neural network is the deep belief network (DBN).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability

2017

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Many studies have predicted the future performance of companies for the purpose of making investment decisions. Most of these are based on the qualitative judgments of experts in related industries, who consider various financial and firm performance information. With recent developments in data processing technology, studies have started to use machine learning techniques to predict corporate performance. For example, deep neural network-based prediction models are again attracting attention, and are now widely used in constructing prediction and classification models. In this study, we propose a deep neural network-based corporate performance prediction model that uses a company's financial and patent indicators as predictors. The proposed model includes an unsupervised learning phase and a fine-tuning phase. The learning phase uses a restricted Boltzmann machine. The fine-tuning phase uses a backpropagation algorithm and a relatively up-to-date training data set that reflects the latest trends in the relationship between predictors and corporate performance.

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Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

Yun

Huang

et al. 2022

Energy Science & Engineering

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Global carbon dioxide emissions have become a great threat to economic sustainability and human health. The carbon market is recognized as the most promising mean to curb carbon emissions, furthermore, carbon price forecasting will promote the role of the carbon market in emissions reduction and achieve reduction targets at lower economic costs for emission entities. However, there are still some technical problems in carbon price prediction, such as mode mixing and larger reconstruction error for the traditional empirical mode decomposition-type models. Therefore, the innovation of this paper is constructing a novel carbon price prediction model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-long short-term memory (LSTM), that combines the advantages of CEEMDAN in decomposing the multiscale time-frequency carbon price signals and the LSTM model in fitting the financial signals. The results show the proposed CEEMDAN-LSTM model has significant accuracy in predicting the complex carbon price signals. The prediction error and expectation indicators of root mean square error, mean absolute error, mean absolute percentage error, and direction accuracy are 0.638342, 0.448695, 0.015666, and 0.687631, respectively, which is better than other benchmark models. Further evidence convince that the short-term forecasting performance is superior to the long-term and medium-term performance. That evidence concludes that the proposed model is a reliable method to reveal the carbon price-driving mechanism from the point of multiscale time-frequency characteristics. Particularly, short-term forecasting is more accurate and can provide a valuable technical reference for reduction entities and green financial companies to judge the market situation and formulate quantitative transactions.

show abstract

Forecasting exchange rate using deep belief networks and conjugate gradient method

Cited by 207 publications

References 19 publications

A Synergistic Forecasting Model for High-Frequency Foreign Exchange Data

A Synergistic Forecasting Model for High-Frequency Foreign Exchange Data

Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

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