Machine learning based customer meta-combination brand equity analysis for marketing behavior evaluation

Xu, Zheng; Zhu, Guiyan; Metawa, Noura; Zhou, Qingyuan

doi:10.1016/j.ipm.2021.102800

Cited by 71 publications

(36 citation statements)

References 13 publications

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“…Accrued earnings' management refers to the purpose of whitewashing financial statements and distorting earnings by corporate executives through using the accounting treatment methods [ 9 ]. Many scholars found that the modified Jones model was more suitable for China's capital market [ 10 – 11 ]. The specific model is as follows:

…”

Section: Methodsmentioning

confidence: 99%

Research on Earnings Management of Growth Enterprise Market in China Stock Market: Comparative Analysis Based on the BPNN, GBDT, and MLR Models

Jia

Xue

2022

Computational Intelligence and Neuroscience

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This study, based on 2011–2020 China’s listed companies on GEM as research samples, introduces the BPNN (BP neural network) and GBDT (Gradient Boosting Decision Tree) model into the research of the relationship between internal governance and earnings management, which will be comparatively analyzed with the empirical results of the traditional multiple linear regression model, so as to study its validity and predictive power in the earnings’ management research field. The results show the following. (1) The matching effect of the multiple linear regression model is poor in the analysis of GEM, with a high rate of experimental data distortion. However, the prediction ability of BPNN and gradient lifting tree model is much better than that of the multiple linear regression model. (2) The gradient lifting tree model is comparatively more suitable for the study of accrual earnings’ management, while BP neural network is more suitable for the study of real earnings’ management. Through the above research, new ideas will be provided for the application research of machine learning in the future.

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…”

Section: Methodsmentioning

confidence: 99%

Research on Earnings Management of Growth Enterprise Market in China Stock Market: Comparative Analysis Based on the BPNN, GBDT, and MLR Models

Jia

Xue

2022

Computational Intelligence and Neuroscience

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“…The iterative formula calculates hðtÞ sequences and converts them into probability integrals. According to the result of the probability integral conversion, the maximum likelihood estimation method is used to obtain a series of correlation coefficients and degrees of freedom of t-Copula [21,22].…”

Section: Data Selectionmentioning

confidence: 99%

Financial Asset Risk Measurement Based on Smart Sensor Big Data Security Analysis and Bayesian Posterior Probability Model

2022

Wireless Communications and Mobile Computing

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Following the speeding up of a process of financial globalization, the risks faced by financial markets have become more complex and diversified. Correlated patterns among financial assets exhibit characteristics of nonlinearity, asymmetry, and tail correlation. The original linear correlation analysis method is no longer suitable, but relevant information describing financial risks. In order to confirm whether an asset is safe, the key is to study and master its volatility, and this is based on our mastery of volatility measurement skills. This article is based on smart sensor big data security analysis and Bayesian analysis. The risk measurement of financial assets based on the empirical probability model is studied. The GARCH- t (1,1) model is selected according to the Akaike information criterion (AIC) after the generalized autoregressive conditional heteroskedasticity (GARCH) model is established by the EViews software. According to the results of probability integral transformation, a series of correlation coefficients and degrees of freedom of t -copula are obtained by the maximum likelihood estimation method. This paper uses the risk-adjusted return on capital (RAROC) method to evaluate the risk performance of financial assets. Financial institutions can only retain and absorb the financial market risks that cannot be avoided and transferred. The edge user node sends the service request to the edge server node. The edge server uses the model proposed in this paper to evaluate the user’s trust and selects the corresponding service level according to the trust level corresponding to the calculated credibility results. The data show that the edge calculation takes 0.2581 seconds, while the linear search takes about 64 seconds. The results show that intelligent edge computing improves the accuracy and efficiency of financial asset risk measurement.

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“…Therefore the work of this stage is to perform two-degree clustering on the subclusters according to the input data, and finally achieve the clustering results of the expected number of clusters. As shown in from formulas ( 10 ) to ( 17 ) [ 18 , 19 ].

where Δ BIC ( J ) represents the difference between the BIC values of adjacent clusters.…”

Section: Internet Of Things Big Data Processing Technologymentioning

confidence: 99%

Online Shopping Brand Sales Based on IoT Big Data Processing

Zhang

2022

Computational Intelligence and Neuroscience

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As a pair of closely related technical fields, “Internet of Things” and “big data” are gradually applied to the analysis of online shopping, which is very beneficial to promote the healthy and orderly development of the online shopping industry. This article uses the big data processing technology of the Internet of Things to analyze the influencing factors of online shopping brand sales based on the data collected by online questionnaires. It draws certain rules from it, and provides relevant marketing strategies for online shopping brand sales to assist its market positioning. According to the survey, the five aspects of online evaluation, brand awareness, online shopping community, online public opinion influence of related brands, and other brand information acquisition channels all have an impact on online brand sales. Especially women aged 25–35 will pay attention to online information, and 195 people will not choose to buy from brands with negative news. And during the purchase process, 186 people will check the online reviews of related brand products. Among them, there are 183 men aged 25–35 who will pay attention to online reviews.

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Machine learning based customer meta-combination brand equity analysis for marketing behavior evaluation

Cited by 71 publications

References 13 publications

Research on Earnings Management of Growth Enterprise Market in China Stock Market: Comparative Analysis Based on the BPNN, GBDT, and MLR Models

Research on Earnings Management of Growth Enterprise Market in China Stock Market: Comparative Analysis Based on the BPNN, GBDT, and MLR Models

Financial Asset Risk Measurement Based on Smart Sensor Big Data Security Analysis and Bayesian Posterior Probability Model

Online Shopping Brand Sales Based on IoT Big Data Processing

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