Evaluating the Investment Climate for China’s Cross-Border E-Commerce: The Application of Back Propagation Neural Network

Lei, Yi; Qiu, Xiaodong

doi:10.3390/info11110526

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…e initial parameters of the model were given through the simulated annealing algorithm, and the parameters of the model were gradually optimized through the gradual changes in product sales. Finally, the effectiveness of the model is verified through evaluation indicators such as RMSE [23,24].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Research on the Construction of Crossborder e‐Commerce Logistics Service System Based on Machine Learning Algorithms

Jin-bo

2022

Discrete Dynamics in Nature and Society

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Based on machine learning algorithms, this paper designs a crossborder e-commerce logistics service system recommendation algorithm. First, we introduce the meaning of query recommendation, analyze the mechanism of e-commerce platform shopping search, redesign the query recommendation process on this basis, establish a Markov decision process model for the problem, and solve the optimal recommendation strategy through deep machine learning algorithms. Second, we design a simple calculation example, use Python programming through a simulated shopping environment, give the solution process of the optimal recommendation strategy in the whole process, and prove the feasibility of the algorithm. The sentiment synthesis word vector is used as the input data structure of the text, the convolutional neural network model and the recurrent neural network model in machine learning are independently designed and constructed, and a shunt is proposed. The rule (shunt) realizes the operation of judging the data and inputting the two machine learning networks. The shunt fully realizes the combination of the advantages of the local feature characterization of the convolutional neural network and the timing characteristics of the recurrent neural network and achieves a more efficient and accurate electrical system. Finally, through simulation experiments, a series of data processing work such as data outlier cleaning, sliding window construction features of data variables, and training set and test set division are designed to convert regression prediction problems into classification problems to predict commodity demand. At the same time, it also compared the effect of the time series model, random forest model, GBDT, single Xgboost model, and the model used in this topic and analyzed the reasons for this difference and the application of each model.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Research on the Construction of Crossborder e‐Commerce Logistics Service System Based on Machine Learning Algorithms

Jin-bo

2022

Discrete Dynamics in Nature and Society

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“…The first topic covers a range of investment analysis studies that have some form of ML approach built into the investment analysis. The range is quite wide, for example, of the sample studies, Liu (2008) proposes using NN to improve discounted cash flow methods, Lei and Qiu (2020) uses an ML approach to understand the investment climate in countries on the ‘Belt and Road’ economic expansion initiative and Liao and Ho (2010) examines how to value investments based on a combination of real options and fuzzy logic. The interesting takeaway here, is that all subfields of investment analysis are potentially up for grabs.…”

Section: Topics Of ML and Financementioning

confidence: 99%

Machine learning in finance: A topic modeling approach

Aziz

Dowling

Hammami

et al. 2021

Euro Fin Management

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We identify the core topics of research applying machine learning to finance. We use a probabilistic topic modeling approach to make sense of this diverse body of research spanning across multiple disciplines. Through a latent Dirichlet allocation topic modeling technique, we extract 15 coherent research topics that are the focus of 5942 academic studies from 1990 to 2020. We find that these topics can be grouped into four categories: Priceforecasting techniques, financial markets analysis, risk forecasting and financial perspectives. We first describe and structure these topics and then further show how the topic focus has evolved over the last three decades. A notable trend we find is the emergence of text-based machine learning, for example, for sentiment analysis, in recent years. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic EUROPEAN FINANCIAL MANAGEMENTWe thank John A. Doukas, the editor, and an anonymous referee of European Financial Management as the study has enormously benefited from their comments. We also thank Muhammad Farooq Ahmad and participants of IFABS 2019, Angers France for their valuable comments. Saqib Aziz and Michael Dowling acknowledge financial assistance from the B<>COM project: Prospect 2030. The views expressed in this article are those of the authors and all errors are our own. modeling of topics for deep comprehension of a body of literature.

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“…Especially this year, under the impact of the global epidemic, e-commerce has played an important role in counteracting the impact of the epidemic, promoting consumer recovery and driving global economic recovery with its ability to break through time and space constraints, reduce intermediate links and resolve information asymmetry between supply and demand. As the country with the largest scale of e-commerce, China has a mature logistics system, an aggregated manufacturing industry chain, an efficient operation model, a specialised platform and a relatively well-established business ecosystem, and has a first-mover advantage in areas such as logistics, manpower, and payment ( Lei and Qiu, 2020 ). As Chinese e-commerce has become relatively mature in the domestic market, it has turned to apply its rich experience and mature development system to the international market, and cross-border e-commerce has thus grown rapidly.…”

Section: Introductionmentioning

confidence: 99%

Research on the Evaluation of Cross-Border E-Commerce Overseas Strategic Climate Based on Decision Tree and Adaptive Boosting Classification Models

Lei

Qiu

2021

Front. Psychol.

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At present, China’s cross-border e-commerce has ushered in a golden period of development. When developing cross-border e-commerce, enterprises should first assess the market climate of the target country and reasonably select the target country. Based on the PESTEL theory, an evaluation index system is established for China’s cross-border e-commerce overseas strategic climate. Taking “One Belt, One Road” as the opportunity and background, the overseas strategic climate of cross-border e-commerce in 62 countries along the “One Belt, One Road” is selected as the research object, and the Decision Tree and Adaptive Boosting classification methods in machine learning are applied to train and predict the established index system. Finally an overall picture of the overseas strategic climate of the 62 countries is obtained. The results are compared and analysed in depth to identify the most suitable countries for cross-border e-merchants and to provide reference for cross-border e-merchants investors.

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Evaluating the Investment Climate for China’s Cross-Border E-Commerce: The Application of Back Propagation Neural Network

Cited by 3 publications

References 41 publications

[Retracted] Research on the Construction of Crossborder e‐Commerce Logistics Service System Based on Machine Learning Algorithms

[Retracted] Research on the Construction of Crossborder e‐Commerce Logistics Service System Based on Machine Learning Algorithms

Machine learning in finance: A topic modeling approach

Research on the Evaluation of Cross-Border E-Commerce Overseas Strategic Climate Based on Decision Tree and Adaptive Boosting Classification Models

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