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Forecasting exchange rate using deep belief networks and conjugate gradient method · Study

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ArticleForecasting exchange rate using deep belief networks and conjugate gradient method

Furao Shen2015

Article2015

Forecasting exchange rate using deep belief networks and conjugate gradient method

Furao Shen · Chao Jing · Jinxi Zhao

234 citations·0/0 files stored·Neurocomputing

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

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References

1988Economic prediction using neural networks: the case of IBM daily stock returns 1995Forecasting exchange rates using feedforward and recurrent neural networks 1998Neural network forecasting of the British Pound/US Dollar exchange rate

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2021A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions 2023Financial applications of machine learning: A literature review 2019Portfolio formation with preselection using deep learning from long-term financial data 2021Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators 2020Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading 2021Deep learning-based exchange rate prediction during the COVID-19 pandemic 2022StockNet—GRU based stock index prediction 2017Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms

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Year2015

Typearticle

Published2015-05-05

VenueNeurocomputing

ISSN-L0925-2312

Languageen

Citations234

Files0 public / 0

ShelfStock Market Forecasting Methods

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doi10.1016/j.neucom.2015.04.071

openalex_workW222543348

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