A Knowledge Graph-Gcn-Community Detection Integrated Model for Large-Scale Stock Price Prediction

Wang, Ting; Guo, Jiale; Shan, Yuehui; Zhang, Yueyao; Peng, Bo; Wu, Zhuang

doi:10.2139/ssrn.4229420

Cited by 3 publications

(6 citation statements)

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“…19 Moreover, they provide flexibility in exploring time series data of varying lengths, which is particularly beneficial for stocks with inconsistent trading histories. 53,63,89 However, DL models require significant computational power and resources for training, which may not be accessible to all. 51,61 The efficacy of these models heavily depends on the quality and quantity of the data; insufficient data can reduce their performance.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…19 Moreover, they provide flexibility in exploring time series data of varying lengths, which is particularly beneficial for stocks with inconsistent trading histories. 53,63,89…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…GraphCNNs are particularly applicable to SPF, where they can model the relationships between different stocks or between different features of a single stock. The work of Wang et al 89 proposed a new model for stock price prediction, integrating a knowledge graph, GraphCNN, and community detection. This model aims to overcome the limitations of existing models, which often neglect deeper influencing factors and rely on small-scale stock datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Hybrid DL approaches frequently combine DL techniques with traditional methods [71][72][73][74][75] or DL architectures with each other, such as CNN-LSTM, LSTM or BiLSTM with attention mechanisms (AMs), transformer models, and graph convolutional neural network (GraphCNN). [76][77][78][79][80][81][82][83][84][85][86][87][88][89][90] These hybrid DL models prove to be efficient in identifying complex patterns and relationships in data due to the high capacity and adaptability of DL architectures, especially in applications like SPF. Chandar 71 proposed a new method for stock trading by combining technical indicators and CNNs, termed TI-CNN.…”

Section: Hybrid Approachesmentioning

confidence: 99%

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A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

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We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

“…19 Moreover, they provide flexibility in exploring time series data of varying lengths, which is particularly beneficial for stocks with inconsistent trading histories. 53,63,89…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Hybrid Approachesmentioning

confidence: 99%

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A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

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“…The method's robustness is enhanced by verifying its alignment accuracy on real datasets, which is better than the baseline model. Literature [10] utilizes the triad of knowledge graph to describe the semantic relationship of stocks, combines the data mining algorithm with graph convolutional neural network to obtain the accurate clustering results of similar populations, and predicts the stock prices by taking the historical prices of the similar stocks as the input features, and proves the accuracy and stability of the prediction effect of the method. Literature [11] analyzes the issues that arise during the development process of the current recommender system and proposes a context-aware recommendation algorithm that utilizes knowledge graphs to enhance recommendation performance.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph-based Diversity Analysis of Supplier Holographic Portraits

Li,

Bian,

Wen

et al. 2024

Applied Mathematics and Nonlinear Sciences

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Fully understand the development of suppliers in order to make better supplier selection. This paper is based on the knowledge graph, through the knowledge updating of the knowledge graph combined with the Transformer model for knowledge extraction of supplier entity relationship, forming the ternary semantic information of supplier entity relationship. Then, based on the big data platform for the construction of supplier holographic portrait and knowledge storage, through information integration, analysis and other links to identify the supplier attributes for label definition. Taking cell phone product suppliers as an example, we use Python technology to obtain relevant data and validate the specific role of supplier holographic portrait in terms of the supplier’s comprehensive strength, behavioral prediction, transaction closeness, and comprehensive evaluation. The results show that: the correlation between the comprehensive strength of suppliers and the amount of winning bids is strong, and its R2 test result is 0.5924, and it can realize the behavioral prediction of suppliers in the supply chain. Supplier H offers a range of cell phone products in 2022, which is 17.62%<unk>21.17% higher than the benchmark market price. The holographic portrait of suppliers based on a knowledge graph combined with a big data platform can meet the need to carry out an all-around analysis of suppliers and provide more accurate support for diversified decision-making on the demand side.

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An enhanced Wasserstein generative adversarial network with Gramian Angular Fields for efficient stock market prediction during market crash periods

Ghasemieh,

Kashef

2023

Appl Intell

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A Knowledge Graph-Gcn-Community Detection Integrated Model for Large-Scale Stock Price Prediction

Cited by 3 publications

References 0 publications

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Knowledge Graph-based Diversity Analysis of Supplier Holographic Portraits

An enhanced Wasserstein generative adversarial network with Gramian Angular Fields for efficient stock market prediction during market crash periods

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