International Markets for China's Electronics Products An Interview with Ouyang Zhongmou, President of the China Electronics Import-Export Corporation

It’s estimated that by the year 2000, the international electronics market will be worth US$ 2 trillion, making the electronics industry the world’s largest. How to expand its share of this international market will be a challenge for the China Electronics Import-Export Corporation. Recently, our reporter interviewed Mr. Ouyang Zhongmou, president of CEIEC. Question: How is your corporation getting on as China’s largest foreign

Electronic market models for decision support systems on the Web

With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). We propose a formal definition and a conceptual framework for Web-based open DSS (WODSS). The formal definition gives an overall view of WODSS, and the conceptual framework based on browser/broker/server computing mode employs the electronic market to mediate decision-makers and providers, and facilitate sharing and reusing of decision resources. We also develop an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that drive WODSS operate efficiently.

Shopping System Model in Electronic Commerce

Despite its name, e-commerce is not fully automated. Typically, online transactions require a significant level of human intervention. Customers undertake the search for products, services, potential vendors and business partners. They evaluate alternatives, decide what goods to buy and when, from which vendor, and how much they are willing to pay for. They engage in potential negotiations, carry out the transactions and so on. As those markets become mature, new products and services, as well as new practices, are beginning to appear. There is already ample evidence that agent-based technology will be crucial for these developments, reflecting that both buyers and sellers being vigorously seek techniques to improve their performance. This work surveys several of these agents that help electronic commerce systems by describing their roles in the context of a Consumer Buying Behavior (CBB) model. The CBB model presented augments traditional marketing models with concepts from Software Agents research to accommodate electronic markets.

What Has Become of China's Rural Household Electronic Appliance Market?

In China,almost all name brand household electronic appliance enterprises are striving to explore the rural market. But why is that the peasants are for inferior brands?Is it because the name brand household electronic appliance enterprises have not made enough efforts,or the rural market is too particular?

Online risk‑based portfolio allocation on subsets of crypto assets applying a prototype‑based clustering algorithm

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

Analysis of the cryptocurrency market using different prototype-based clustering techniques

Since the emergence of Bitcoin,cryptocurrencies have grown significantly,not only in terms of capitalization but also in number.Consequently,the cryptocurrency market can be a conducive arena for investors,as it offers many opportunities.However,it is difficult to understand.This study aims to describe,summarize,and segment the main trends of the entire cryptocurrency market in 2018,using data analysis tools.Accord-ingly,we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies,and one that looks for associations between the clustering results,and other factors that are not involved in clustering.Particularly,the methodology involves applying three different partitional clustering algorithms,where each of them use a different representation for cryptocurrencies,namely,yearly mean,and standard deviation of the returns,distribution of returns that have not been applied to financial markets previously,and the time series of returns.Because each representation provides a different outlook of the market,we also examine the integration of the three clustering results,to obtain a fine-grained analysis of the main trends of the market.In conclusion,we analyze the association of the clustering results with other descriptive features of cryptocurrencies,including the age,technological attributes,and financial ratios derived from them.This will help to enhance the profiling of the clusters with additional descriptive insights,and to find associations with other variables.Consequently,this study describes the whole market based on graphical information,and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly,and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period,we found that the market can be typically segmented in few clusters(five or less),and even considering the intersections,the 6 more populations account for 75%of the market.Regarding the associations between the clusters and descriptive features,we find associations between some clusters with volume,market capitalization,and some financial ratios,which could be explored in future research.

A MULTI-OBJECTIVE ROBUST OPERATION MODEL FOR ELECTRONIC MARKET ENABLED SUPPLY CHAIN WITH UNCERTAIN DEMANDS

A multi-objective robust operation model is proposed in this paper for an electronic market enablexi supply chain consisting of multi-supplier and multi-customer with uncertain demands. Suppliers in this supply chain provide many kinds of products to different customers directly or through electronic market. Uncertain demands are described as a scenario set with certain probability;, the supply chain operation model is constructed by using the robust optimization method based on scenario analyses. The operation model we proposed is a multi-objective programming problem satisfying several conflict objectives, such as meeting the demands of all customers, minimizing the system cost, the availabilities of suppliers＇ capacities not below a certain level, and robustness of decision to uncertain demands. The results of numerical examples proved that the solution of the model is most conservative; however, it can ensure the robustness of the operation of the supply chain effectively.

中国电子产业:发展原因和制约因素(英文)

The development of China’s electronics industry can be attributed to supportive governmentpolicies, strong domestic market demand and the opportunities provided by the internationaltransfer of technology in the electronic manufacturing sector. China’s enterprises can onlycontinue to develop through technological upgrading due to the lack of core technologiesof their own. Since this process is still ongoing, China has not become a competitive countryin the field of electronic technology. As a country in transition, government initiatives designedto encourage the development of the electronics industry have also undergone changes inline with economic institutional reform. Direct government intervention policies have beengradually replaced by indirect regulatory policies and the market is playing a more andmore important role in the electronics industry.