一口价的信息与信誉机制——基于网上拍卖数据的实证研究

经典拍卖理论无法理解网络拍卖中的一口价拍卖,已有文献从风险态度和交易成本的角度给出了解释,但却难以解释"一口价"设定比例的横截面差异。利用网站大样本数据,本文对已有理论进行了检验,证实了风险态度和交易成本的作用。然而控制这些因素之后,卖家信用水平仍然对一口价的设定和执行具有显著的影响,一口价对于买家估价和出价的参考作用同样显著,而且效果与卖家的信用水平正相关,从而验证了一口价的信息与信誉机制。     

A buyer-seller watermarking protocol for digital secondary market

In the digital right management value chain, digital watermarking technology plays a very important role in digital product’s security, especially on its usage tracking and copyrights infringement authentication. However, watermark procedures can only effectively support copyright protection processes if they are applied as part of an appropriate watermark protocol. In this regard, a number of watermark protocols have been proposed in the literature and have been shown to facilitate the use of digital watermarking technology as copyright protection. One example of such protocols is the anonymous buyer-seller watermarking protocol. Although there are a number of protocols that have been proposed in the literature and provide suitable solutions, they are mainly designed as a watermarking protocol for the first-hand market and are unsuitable for second-hand transactions. As the complexity of online transaction increases, so does the size of the digital second-hand market. In this paper, we present a new buyer-seller watermark protocol that addresses the needs of customer’s rights problem in the digital secondary market. The proposed protocol consists of five sub-protocols that cover the registration process, watermarking process for the first, second and third-hand transactions as well as the identification & arbitration processes. This paper provides analysis that compares the proposed protocols with existing state-of-the-arts and shows that it has met not only all the buyer’s and seller’s requirements in the traditional sense but also accommodates the same requirements in the secondary market.     

面向排名预测的上市公司股价收益研究

对于在深圳证券交易所上市的公司,通过分析和挖掘其季度报表或者相关交易网站中的数据,提取到排名预测任务中相关的数据特征以及通过爬虫获得的文本特征,成功构建了公司每股收益预测排名的模型,实现了对股价收益排名的合理预测.实验结果表明,我们的提出的模型能够有效的提高股价排名预测任务的性能,其中SPRP-Random Forests模型在NDCG@10评价指标中可以达到0.9583.在为股民选择股票,公司经营模式调整等方面具有一定的实用价值.     

Examining business value of customer relationship management systems: IT usage and two-stage model perspectives

Empirical evidence for the business value of customer relationship management (CRM) systems remains unsolid in IS studies. This study proposes a new model for CRM value according to IT/IS usage theory and “two-stage model.” Empirical tests show that operational benefits of CRM are reflected in firms’ high revenue per employee, which leads to high profitability; strategic benefits of CRM are reflected in firms’ high customer satisfaction, which leads to high profitability and market valuation. Firm size positively moderates the operational and strategic benefits of CRM, while the industry’s product differentiation level negatively moderates operational and strategic benefits of CRM.     

The consignment stock of inventories in coordinated model with generalized policy

In this paper, coordination between a single vendor (or manufacturer) and a buyer (or retailer) via the delivery schedule in a production and distribution system is presented. A continuous deterministic model with centralized decision process is developed. To satisfy the buyer’s demands, the product is delivered in discrete batches from the vendor’s stock to the buyer’s stock and all shipments are realized instantaneously. A more general type of consignment stock (CS) policies for the vendor–buyer integrated production–distribution model is analyzed. Our model does not require equal in size shipments. The inventory patterns and the cost structure of production distribution cycles (PDC) are described in different scenarios. A comparative study of the results shows that the generalized CS policies perform better. Considering CS-policies Braglia and Zavanella (2003) ask about a possibility of cost reduction by delaying a number of late deliveries. Unfortunately, a negative answer was given by Zanoni and Grubbström (2004). We verify this problem to obtain a positive answer in more general setting. A solution procedure is developed to find optimal generalized CS-policy for the problems with nonequal and equal in size deliveries. Optimal solutions are found and illustrated with numerical examples.     

基于动机分析的区块链数字货币异常交易行为识别方法

当前区块链数字货币被众多恶意交易者利用,导致了"粉尘"注入、"空投"操作、勒索、骗局等一系列异常交易行为.因此,研究区块链数字货币异常交易行为的识别方法对于规范交易行为、保障网络空间安全具有重要意义.在众多区块链数字货币中,比特币市值超过所有区块链数字货币市值和的一半,具有高代表性.比特币系统的用户数量多、交易规模大、...     

Sustainable market valuation of buildings by the single-valued neutrosophic MAMVA method

Traditionally, the real estate asset assessment is performed by experienced valuators, who take into account its economic, social, physical and locational aspects. Nowadays, the construction industry is becoming more and more influenced by the sustainability requirements. Therefore, the inclusion of the sustainability evaluation into real estate asset valuation is of utmost importance. The Neutrosophic Multi-Attribute Market Value Assessment (MAMVA) method developed by the authors of this article handles market value calculations by solving multiple criteria assessment problems, and the initial information vagueness is modelled explicitly. The supplementary novelty of the present paper is the inclusion of the sustainability aspects into the real estate market valuation. The sustainable market valuation of Croydon University Hospital (Emergency Department) is performed as the case study to present numerical capabilities of the proposed approach. Our research findings suggest that neutrosophic MAMVA is a rational approach for calculations of property market valuation and might be suitable for application worldwide.     

Network Topology and Systemically Important Firms in the Interfirm Credit Network

In a transaction between two firms, the buyer is expected to pay the seller with cash; however, due to the buyer’s lack of liquidity, an account receivable might be provided instead. Accounts Receivable Financing (ARF) is a type of trade credit that facilitates Korean interfirm transactions. Under ARF, the obligation to reimburse the bank is imputed to the seller if the buyer does not repay the loan to the bank. When the buyer confronts a liquidity shortage and fails to repay the trade credits, unfortunately, the shock is not limited to damage to only the seller because it propagates throughout the interfirm network. The shock is easily propagated because it is highly likely that the seller would also be unable to repay obligations to its own sellers. Since annual interfirm networks follow a scale-free network model, a single liquidity shock triggers a systemic risk. The insolvency of topologically more central buyer firms is shown to pose a higher potential risk to the financial market. In contrast, the firm size is not significantly related to potential risk. Therefore, firms with high centrality should be closely monitored to prevent insolvency crisis and propagation of the liquidity shock in the interfirm network.     

The design and evaluation of an intelligent sales agent for online persuasion and negotiation

Online purchases from e-stores are getting popular among Internet users. Although many e-commerce activities, such as auctions, bargaining, and recommendation services are available, most e-stores lack clerk-like mechanisms to persuade potential buyers into buying products and to bargain with them for making a good deal. The objectives of this research are to design a lab prototype of a sales agent with persuasion and negotiation capabilities and to evaluate its effectiveness as a virtual clerk in an e-store. The prototypical intelligent sales agent (ISA) is equipped with reinforcement learning capabilities and an abstract argument framework. We conduct both laboratory and Internet experiments to assess ISA’s performance. The experimental results reveal that an e-store embedded within such a sales agent can improve a seller’s surplus and increase a buyer’s product valuation, willingness-to-pay, and satisfaction with the e-store.     

An agent-based system for bilateral contracts of energy

In this paper, an agent-based system for bilateral contracts of energy is proposed. The generating companies submit their offers to the demand companies. The demand companies also submit their bids to the generators. Each load or generator’s agent wants to match with an opponent, which offers the most valuable proposal. However, the problem of simultaneous decision-making causes decision conflicts among the agents. To overcome this conflict, we assume loads as the leaders and generators as the followers. We use Stackelberg game to match the seller and buyer agents. The negotiation process between a buyer and its potential seller will determine the power price between them. This process is carried out through a proposed combined time-behavioral protocol (TBP). With negligible changes in around the agreed price, this protocol can reduce the negotiation time considerably. After successful negotiation, the seller and buyer agents could sign a bilateral contract of energy if the market conditions allow it. The applicability of the proposed method is illustrated through a case study.     

Filtering unfair ratings from dishonest advisors in multi-criteria e-markets: a biclustering-based approach

In multiagent e-markets, trust between interaction partners (buying agents and selling agents) is vital for any transaction to be successful. Given the difficulty for a buyer to directly judge the quality (trustworthiness) of a seller for a transaction, a buyer also seeks opinions from other buyers (called advisors) in the marketplace to determine the seller’s trustworthiness. However, advisors may act dishonestly by conveying misleading information about the seller. We propose a novel approach to identify such dishonest advisors, while evaluating a seller’s trustworthiness on multiple criteria. It is based on a biclustering method which clusters honest advisors on different criteria. Correlation between advisors’ ratings to various criteria is used as additional information to accurately filter dishonest advisors. A transitive mechanism is also employed in the biclustering process to cope with rating sparsity. Further, we introduce a parallelization technique to reduce the time complexity involved in the biclustering process. Detailed experiments in simulated environments demonstrate the robustness of the proposed approach against strategic attacks from dishonest advisors. Evaluation on three real datasets confirms the effectiveness of our approach in real environments.     

关联规则挖掘在市场监管工作中的应用

关联规则是为了挖掘出隐藏在数据中的相互关系,找出所有能把一组事件或数据项与另一组事件或数据项联系起来的规则,从而辅助决策者进行决策。结合市场监督管理部门监管数据的实际情况,抽取市场主体部分基本信息和监管部门录入的违规、违法数据生成违规违法事务数据库,再将事务数据库转换为布尔矩阵,采用基于向量内积的关联规则挖掘方法生成频繁项集,进行关联规则挖掘。实验结果表明,该方法能够快速、准确地挖掘出相应的关联规则,符合市场监管部门日常工作的实际情况,对实际工作具有一定的指导意义。     

电子商务中信用值的计算方法的改进与研究

电子商务作为一种全新的商务运作模式,信任与信用问题一直都是其的核心问题.在传统的商务贸易中,买卖双方的信义由纸质合同来约束,但是在虚拟的电子商务交易中,这种方式显然是不可行的。因此就需要一套合理的信任与信用评价机制来给卖家的信用进行量化,方便买家的选择。由于现有的信任模型都过于简单,无法完整反应信任主体的信任风险,因此以淘宝网为例,本文提出在电子商务中把商品的价格因素也作为计算因子参与到信任与信用的量化中,并证明了这个问题是多项式时间可解的。     

Optimal versioning strategy for information products with behavior-based utility function of heterogeneous customers

This paper reconsiders two fundamental assumptions (i.e., on the quality of information product and the self-selection behavior of customers) that decide the optimality of versioning strategy (or vertically differentiated product line) for information products. The quality of an information product is clarified in terms of functional and nonfunctional features. The customers? behavior of self-selection among multiple versions of an information product is examined, and the disability of linear valuation function for exactly capturing customers? valuation on information products is clarified. The required quality is introduced to depict a customer?s requirement on the quality of an information product, and a behavior-based utility function is thus defined, where a customer has a marginal decrease (different from the constant marginal in linear valuation function) on the valuation of versions with quality levels higher than the required, but the valuation of a customer diminishes quickly on versions with quality levels lower than the required. Then, a bilevel programming model is built to represent the task for optimizing the strategy of versioning an information product, with the monopolist as the leader and all customers as followers. Optimal quality levels and prices for multiple versions are obtained for a given number of versions. To deal with the nonlinearity and multimodality of this model due to correlated decision variables, a steady-state evolutionary algorithm, hybridized with the local search method (called the hybrid steady-state evolutionary algorithms), is developed to improve the global optimality of versioning schemes. Numerical experiments are conducted on the bilevel programming model with specific parameterization by using the hybrid algorithm. Experimental results verify the optimality of multi-version strategy and reveal various facets of its property. With the behavior-based valuation function of heterogeneous customers, the multi-version strategy is superior to the one-version scheme. The total profit increases logarithmically with the maximal number of versions, which means that the introduction of a new lower-quality version contributes less to the total profit of a monopolist. Furthermore, when a lower quality level version is offered to the market, higher quality versions are priced higher than they were in previous versioning schemes, indicating that a multi-version scheme can make more detailed segmentation of the market. Therefore, a monopolist is able to gain greater revenue via price discrimination on heterogeneous customers through vertically differentiating information products.     

Vulnerability Bazaar

A pure vulnerability market is one in which each discrete vulnerability is a unit of trade with a price assigned to it by the buyer, seller, and demand. In such a market, exclusivity of knowledge is a key factor in overall value, thus when a vulnerability becomes public knowledge, it loses its value. Other factors also come into play, such as the affected product's popularity, the vulnerability's security impact, and the exploit's ease and efficacy. Vulnerabilities in this market retain their peak value when very few people know about them; value decreases through events such as vendor notification, information leaks, independent rediscovery, or accidental discovery of the vulnerability due to attack activity in the wild. Because it's difficult to certify and appraise information exclusivity, many buyers contractually obligate vulnerability reporters to exclusivity agreements to ensure that their information is exclusive to the best of their knowledge. Very few buyers are interested in nonexclusive information.     

系统信用事件与信用违约互换估值研究

本文以系统信用事件为载体,研究了双边交易对手风险下信用违约互换(credit default swap,CDS)的估值,研究表明:1)完备的信用事件组将形成一个信用风险系统,并可作为CDS估值的基础;2)在CDS估值中,买方违约的可能性是不可以忽略的.如果忽略,将产生一个错误的定价,并且这个错误的定价将低于真实价值而使信用保护的卖方受到损失;3)CDS交易中的替换成本是不可以忽略的,由于替换成本的存在,CDS合约的价值会发生超常变化,其变化幅度取决于合约当前的市场价格;4)CDS合约价格对参考资产信用价差十分敏感,信用价差的变化,将会显著改变合约的价格.     

Minimizing Market Risks Through Customer Integration in New Product Development: Learning from Bad Practice

Customer integration into the innovation process is about to become a best practice. The lead‐user approach has proven to be especially valuable when reducing discontinuous innovation's market risk. Since the theory of customer integration still lacks a concept and processes, this article illustrates how companies can be helped from a practice perspective to implement customer integration and maximize market safety. Triggered by the results of an in‐depth case study, we adapted Lettl's explorative model of customers’ contribution to the new product development (NPD) process, which was originally developed for the medical technology industry, to engineering companies.     

Modeling patent legal value by Extension Neural Network

This study aimed at the basis of patent law and proposed a revolutionary valuation model for the monetary legal value of patents. The damage award of a patent infringement lawsuit was deemed to be the legal value of a patent. Sixty-five effective samples were extracted from 4289 patent related lawsuits retrieved in US district courts of Delaware, California and Texas. Seventeen quantitative patent indicators for describing dimensions of patents were summarized. The Extension Neural Network incorporated with the factor analysis was applied to construct the valuation model of patent indicators and damage awards. The proposed valuation model was validated to have the predictive power by error analysis and was accommodated to valuate the possible damage award or to negotiate the settlement fee in disputing patent infringement suits. It also contributed to patent transaction deal, patent licensing, hypothecation of intangible assets, and shareholding by patent-based technologies, etc.     

Optimal online pricing with network externalities

We study the optimal pricing strategy for profit maximization in presence of network externalities where a decision to buy a product depends on the price offered to the buyer and also on the set of her friends who have already bought that product. We model the network influences by a weighted graph where the utility of each buyer is the sum of her initial value on the product, and the linearly additive influence from her friends. We assume that the buyers arrive online and the seller should offer a price to each buyer when she enters the market. We also take into account the manufacturing cost. In this paper, we first assume that the monopolist defines a unique price for the product and commits to it for all buyers. In this case, we present an FPTAS algorithm that approximates the optimal price with a high probability. We also prove that finding the optimum price is NP-hard. Second, we consider a market with positive network externalities and assume that the monopolist could offer a private price to each customer. We prove that this problem is also hard to approximate for linear influences. On the positive side, we present a polynomial time algorithm for the problem when influences are symmetric. At last, we show that the seller has more ability to extract influences with price discrimination.