Futures price prediction modeling and decision-making based on DBN deep learning

Chen, Junhua; Hao, Yanhui; Wang, Hao; Wang, Tao; Zheng, Dingwen

doi:10.3233/ida-192742

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…In this paper, we conducted language recognition experiments using the bottleneck (BN) [15] and DBN methods with data from the NIST07 phonetic database. The experimental results show that the BN-DBN method can improve the recognition accuracy more effectively than the traditional language recognition methods MFCC and SDC [16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model

Liu

Zhou

2022

Wireless Communications and Mobile Computing

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Most of the traditional methods of English text chunk recognition are solved by setting the corresponding phrase identifier numbers and eventually transforming the chunk recognition problem into a lexical annotation problem. In language recognition, the traditional MFCC features are easily contaminated by noise and have weak noise immunity due to the insufficient amount of information on each frame of the signal. At the same time, SDC feature extraction methods commonly used today require artificial settings in parameter selection, which increases the uncertainty of recognition results. The method of identifying English text chunks by association evaluation of central word extensions identifies English text chunks from a different perspective. It has the following features: (i) each phase is considered as a cluster with the central word as the core, and the internal composition pattern of each phrase is fully considered; (ii) the results are dynamically evaluated using association and confidence. The results show that the proposed method can achieve higher recognition rate than traditional feature extraction methods. The recognition rate is faster, and the F -measure value of English block recognition reaches 94.05%, which is comparable to the best results so far.

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Section: Introductionmentioning

confidence: 99%

Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model

Liu

Zhou

2022

Wireless Communications and Mobile Computing

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“…This research provides an innovative example of deep learning applications in finance, offering powerful tools and methods to improve forecasting accuracy and profitability performance. In 2012, Jun-Hua Chen and his team conducted research on predicting crude oil futures prices using the Deep Belief Network (DBN) model [11]. Their experimental results unveiled the remarkable performance of deep learning models, particularly the Deep Belief Network (DBN), in the domain of financial time series modeling.…”

Section: Deep Learning-based Financial Time Series Forecastingmentioning

confidence: 99%

The Application and Prospects of Deep Learning in the Field of Quantitative Investment

Liu

2024

AEMPS

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This literature review provides a comprehensive overview of key developments in quantitative trading theory, machine learning-based financial time series forecasting, deep learning-based financial time series forecasting, and modern quantitative investment strategies. It highlights seminal contributions from renowned scholars and researchers in the field. This review first explores the Efficient Market Hypothesis (EMH) proposed by Eugene Fama in 1970 and its empirical validation, which lays the foundation for understanding stock market dynamics. It then focuses on the application of machine learning to financial time series forecasting, including Hulls Delta strategy, Hujll Js multifactor model regression series, and Junhua Chens seminal work in 2009, which emphasizes the role of machine learning in solving the challenges of financial time series data. Finally, an overview of the development of deep learning in financial time series forecasting is presented, including the comprehensive model of Bowie et al., the success of Junhua Chens Deep Belief Network (DBN), and the sequence data processing capabilities of the Multi-stage Attention Network (MAN) model. In terms of modern quantitative investment strategies, it covers research areas such as EBIT/EV-based stock ranking studies, higher-order moment analysis, frequent trading strategies, and investor sentiment indicators. In summary, this literature review showcases the evolution of quantitative trading theory, the emergence of machine learning and deep learning in financial time series forecasting, and the development of modern quantitative investment strategies, offering valuable insights and tools for investors, researchers, and practitioners in financial markets.

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“…In the realm of financial time series prediction, ANNs have found application due to their ability to simulate the abstract and concrete capabilities of the human brain. Neural networks possess distributed storage, parallel processing, and self-adjustment capabilities, making them well-suited for addressing complex nonlinear problems characterized by numerous influencing factors, instability, and stochasticity (Zareef et al, 2020;Chen et al, 2019). Energy futures price forecasting presents precisely such a challenge.…”

Section: Related Workmentioning

confidence: 99%

The Design of a Compound Neural Network-Based Economic Management Model for Advancing the Digital Economy

Shang,

Asif

2023

Journal of Organizational and End User Computing

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The rapid progress of the digital economy has brought forth a myriad of complexities in economic governance, particularly in the domains of stocks and network finance. The authors propose the exploration of an innovative economic management model founded on the compound neural network framework. Central to this approach is the utilization of the deep bidirectional long and short-term memory neural network model (Bi-LSTM) as the primary instrument for predictive analysis, complemented by the refinement and enhancement provided by the Markov chain model. Through comparative analysis of experiments, it is found that although the forecast price of this model has a certain lag, it has a more accurate judgment than other prediction models, and the accuracy and recall rate reach 87.66% and 86.31%. At the same time, the error evaluation index R2 is very close to the upper limit 1 of the index, and the mean absolute error MAE Hill inequality coefficient; TIC root; mean square error; RMSE; and symmetric mean percentage error (SMAPE) are 0.2654, 0.0124, 0.3481, and 0.3531, respectively.

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Futures price prediction modeling and decision-making based on DBN deep learning

Cited by 11 publications

References 17 publications

Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model

Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model

The Application and Prospects of Deep Learning in the Field of Quantitative Investment

The Design of a Compound Neural Network-Based Economic Management Model for Advancing the Digital Economy

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