Multi-source information fusion and deep-learning-based characteristics measurement for exploring the effects of peer engagement on stock price synchronicity

Li, Lin; Zhu, Feng; Sun, Hui; Hu, Yiyi; Yang, Yunyun; Jin, Dawei

doi:10.1016/j.inffus.2020.11.006

Cited by 33 publications

(18 citation statements)

References 68 publications

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“…They effectively analyzed the changes in financial market risks (Xu and Gao 2019). Li et al (2021a) introduced new comprehensive analysis indexes into the traditional financial index system based on Hidden Markov Model (HMM), integrated economic statistical structure data, and Internet information. They significantly improved the risk prediction ability of the model (Li et al 2021a).…”

Section: Research On Financial Risk Management (Frm)mentioning

confidence: 99%

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Elston

2022

Oper Manag Res

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A sound credit assessment mechanism has been explored for many years and is the key to internet finance development, and scholars divide credit assessment mechanisms into linear assessment and nonlinear assessment. The purpose is to explore the role of two important data analytics models including machine learning and deep learning in internet credit risk assessment and improve the accuracy of financial prediction. First, the problems in the current internet financial risk assessment are understood, and data of MSE (Micro small Enterprises) are chosen for analysis. Then, a feature extraction method based on machine learning is proposed to solve data redundancy and interference in enterprise credit risk assessment. Finally, to solve the data imbalance problem in the credit risk assessment system, a credit risk assessment system based on the deep learning DL algorithm is introduced, and the proposed credit risk assessment system is verified through a fusion algorithm in different models with specific enterprise data. The results show that the credit risk assessment model based on the machine learning algorithm optimizes the standard algorithm through the global optimal solution. The credit risk assessment model based on deep learning can effectively solve imbalanced data. The algorithm generalization is improved through layer-by-layer learning. Comparison analysis shows that the accuracy of the proposed fusion algorithm is 25% higher than that of the latest CNN (Convolutional Neural Network) algorithm. The results can provide a new research idea for the assessment of internet financial risk, which has important reference value for preventing financial systemic risk.

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Section: Research On Financial Risk Management (Frm)mentioning

confidence: 99%

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Elston

2022

Oper Manag Res

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“…Human behavior recognition research can be divided into two categories: the first is image vision recognition, which uses image equipment to generate pictures or videos and monitor user behavior. Reference [ 17 ] employs a convolutional neural network to learn behavior data automatically using a big data analysis method to select the best features. A feature fusion method is proposed in light of the overfitting phenomenon of data labels in the learning process, which strengthens the ability of feature learning and improves the discrimination of behavior features.…”

Section: Related Workmentioning

confidence: 99%

Human Health Activity Recognition Algorithm in Wireless Sensor Networks Based on Metric Learning

Sun

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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Wireless sensor network is an ad hoc network with sensing capability. Usually, a large number of sensor nodes are randomly deployed in an unreachable environment or complex area for data collection and transmission, which can realize the perception and monitoring of the target area or specific objects and transmit the obtained data to the remote end of the system. Human health activity recognition algorithm is a hot topic in the field of computer. Based on the small sample problem and the linear indivisibility of real samples encountered in metric learning, this paper proposes a human activity recognition algorithm for wireless sensor networks. Human activity recognition algorithm for wireless sensor networks uses human activity recognition algorithm to solve the singularity of intraclass divergence matrix, so as to reduce the impact of small sample problem. The algorithm maps two different feature spaces to the high-dimensional linearly separable kernel space through the corresponding kernel function, calculates the distance between samples in the two projected feature subspaces to obtain two distance measurement functions, and finally linearly combines them with weights to obtain the final distance measurement function.

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“…For the detection of data conflict in multisource data fusion, the abnormal points in conflict are regarded as outliers, and the point outlier detection technology is used to detect and process the conflict [19][20][21][22]. In the traditional data mining work, outlier detection is carried out by using statistics, clustering, classification, proximity, and other methods [23][24][25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

Zhang

Sun

2022

Wireless Communications and Mobile Computing

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Considering the credit index calculation differences, semantic differences, false data, and other problems between platforms such as Internet finance, e-commerce, and health and elderly care, which lead to the credit deviation from the trusted range of credit subjects and the lack of related information of credit subjects, in this paper, we proposed a crossplatform service credit conflict detection model based on the decision distance to support the migration and application of crossplatform credit information transmission and integration. Firstly, we give a scoring table of influencing factors. Score is the probability of the impact of this factor on credit. Through this probability, the distance matrix between influencing factors is generated. Secondly, the similarity matrix is calculated from the distance matrix. Thirdly, the support vector is calculated through the similarity matrix. Fourth, the credit vector is calculated by the support vector. Finally, the credibility is calculated by the credit vector and probability.

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Multi-source information fusion and deep-learning-based characteristics measurement for exploring the effects of peer engagement on stock price synchronicity

Cited by 33 publications

References 68 publications

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Human Health Activity Recognition Algorithm in Wireless Sensor Networks Based on Metric Learning

A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

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