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The exchange of reciprocal and quantitative consideration between transaction parties rests on the independent intentions and judgments of potential benefit for each. A transaction consummated between independent parties should logically be beyond subsequent unilateral modification. And, because “duality” is inherent in every exchange-priced transaction, the account form follows the lead of transaction substance. Conditions precedent to consummation of a transaction is two affirmative value judgments by the parties as to the benefits expected.

Smart contracts are written in high-level programming languages, compiled into Ethereum Virtual Machine (EVM) bytecode, deployed onto blockchain systems and called with the corresponding address by transactions. The deployed smart contracts are immutable, even if there are bugs or vulnerabilities. Therefore, it is critical to verify smart contracts before deployment. This paper aims to help developers effectively and efficiently locate potential defects in smart contracts.

GethReplayer, a smart contract testing method based on transaction replay, is proposed. It constructs a parallel transaction execution environment with two virtual machines to compare the execution results. It uses the real existing transaction data on Ethereum and the source code of the tested smart contacts as inputs, conditionally substitutes the bytecode of the tested smart contract input into the testing EVM, and then monitors the environmental information to check the correctness of the contract.

Experiments verified that the proposed method is effective in smart contract testing. Virtual environmental information has a significant effect on the success of transaction replay, which is the basis for the performance of the method. The efficiency of error locating was approximately 14 times faster with the proposed method than without. In addition, the proposed method supports gas consumption analysis.

This paper addresses the difficulty that developers encounter in testing smart contracts before deployment and focuses on helping develop smart contracts with as few defects as possible. GethReplayer is expected to be an alternative solution for smart contract testing and provide inspiration for further research.

The Latin American merger and acquisition (M&A) market offers enormous opportunities. M&A activity in Argentina, Brazil, Chile, Mexico and Venezuela is examined and compared with M&A activity in the United States. Characteristics examined include the industries and form of ownership of the target firms, the types of transactions, and the home countries of the acquiring firms. Differences are found between characteristics of M&A activity in the Latin American countries and the United States. Differences are also found among the characteristics of M&A activity in the various Latin American countries. Implications for managers and areas in need of additional research are discussed.

Introduction Since the earliest transaction monitoring studies, researchers have encountered the boundaries that define transaction log analysis as a methodology for studying the use of online information retrieval systems. Because, among other reasons, transaction log databases contain relatively few fields and lack sufficient retrieval tools, students of transaction log data have begun to ask as many questions about what transaction logs cannot reveal as they have asked about what transaction logs can reveal. Researchers have conducted transaction monitoring studies to understand the objective phenomena embodied in this statement: “Library patrons enter searches into online information retrieval systems.” Transaction log data effectively describe what searches patrons enter and when they enter them, but they don't reflect, except through inference, who enters the searches, why they enter them, and how satisfied they are with their results.

The division of labor of e-commerce transaction system is an important research topic. However, there is a certain disparity between the current mainstream research model and the reality, which leads to a biased result. This paper aims to find the effects of transaction efficiency on the evolution of e-commerce transaction system’s division of labor and the relationships between the results and other parameters.

This paper puts forward a definition of transaction efficiency based on transaction services and establishes a model of middleman’s specialized production decision of transaction services on this basis.

The research results show that the transaction efficiency plays an important role on the change of middlemen’s division of labor level. The degree of economic specialization, price of commodities and transaction services and other associated factors also affect middlemen’s division of labor.

This paper is of great significance for evaluating the development level and forecast the development direction of e-commerce.

To provide a background for this special section on transaction log analysis, the following discussion proposes a definition of transaction log analysis and briefly introduces some of the issues involved in the methodology.

Taiwan OTC market is an electronic, order driven, call market. The purpose of this paper is to gain understanding of whether trade size or number of transaction provides more information on explaining price volatility and market liquidity in this market. The paper also aims to investigate how market condition can affect the relationship between information type and trading activities.

The paper uses data from the Taiwan OTC market to run the empirical tests. It divides firms into five size groups based on their market capitalization. Regression equations are run to test: whether number of transactions has a more significant impact on price volatility on the Taiwan OTC market; the impact of market information on number of transactions; the relative impact of firm specific and market information on number of transactions; and the impact of number of transaction of bid‐ask spread.

Findings show that the larger the number of transactions, the higher the price volatility. Smaller firms on the Taiwan OTC market are traded based on firm‐specific information. This relation is further affected by market trends. Especially for the larger firms, when the market is up and the amount of market information increases, number of transactions increases. When the market is down and the amount of market information increases, number of transactions decreases. Finally, it is found spread size is more likely to be influenced by number of transactions, instead of trade size. Overall, based on these empirical results, the information content of number of transactions seems to be higher than that of trade size in the Taiwan OTC market.

Investors now understand that number of transaction actually carry more information than trade size does.

The relation between market information and number of transaction, also that between market information and trade size is influenced by market condition. The paper fills a gap in the literature to show that market condition has an impact on the relation between information type and trader's behavior. A number of transactions are identified that provide more information than trade size does. It is also shown that market conditions can further affect the impact of information on trading activities.

The purpose of this paper is to present a scheduling model, scheduling algorithms, and formal model and analysis techniques for concurrency transaction in grid database environment.

Classical transaction models and scheduling algorithms developed for homogeneous distributed architecture will not work in the grid architecture and should be revisited for this new and evolving architecture. The conventional model is improved by three‐level transaction scheduling model and the scheduling algorithms for concurrency transaction is improved by considering transmission time of a transaction, user's priority, and the number of database sites accessed by the transaction as a priority of the transaction. Aiming at the problems of analysis and modeling of the transaction scheduling in grid database, colored dynamic time Petri nets (CDTPN) model are proposed. Then the reachability of the transaction scheduling model is analyzed.

The three‐level transaction scheduling model not only supports the autonomy of grid but also lightens the pressure of communication. Compared with classical transaction scheduling algorithms, the algorithms not only support the correctness of the data but also improve the effectiveness of the system. The CDTPN model is convenient for modeling and analyzing dynamic performance of grid transaction. Some important results such as abort‐ratio and turnover‐time are gotten by analyzing reachability of CDTPN.

The three‐level transaction scheduling model and improved scheduling algorithms with more complex priority are presented in the paper. The paper gives a CDTPN model for modeling transaction scheduling in grid database. In CDTPN model, the time interval of a transition is a function of tokens in input places of the transition.

This chapter introduces a risk control framework on credit card fraud instead of providing a solely binary classifier model. The anomaly detection approach is adopted to identify fraud events as the outliers of the reconstruction error of a trained autoencoder (AE). The trained AE shows fitness and robustness on the normal transactions and heterogeneous behavior on fraud activities. The cost of false-positive normal transactions is controlled, and the loss of false-negative frauds can be evaluated by the thresholds from the percentiles of reconstruction error of trained AE on normal transactions. To align the risk assessment of the economic and financial situation, the risk manager can adjust the threshold to meet the risk control requirements. Using the 95th percentile as the threshold, the rate of wrongly detecting normal transactions is controlled at 5% and the true positive rate is 86%. For the 99th percentile threshold, the well-controlled false positive rate is around 1% and 83% for the truly detecting fraud activities. The performance of a false positive rate and the true positive rate is competitive with other supervised learning algorithms.