Transaction Characteristics Research Articles

CTRF: Ethereum-Based Ponzi Contract Identification

In recent years, blockchain technology has been developing rapidly. More and more traditional industries are using blockchain as a platform for information storage and financial transactions, mainly because of its new characteristics of non-tamperability and decentralization compared with the traditional systems. As a representative of blockchain 2.0, Ethereum has gained popularity upon its introduction. However, because of the anonymity of blockchain, Ethereum has also attracted the attention of some unscrupulous people. Currently, millions of contracts are deployed on Ethereum, many of which are fraudulent contracts deployed by unscrupulous people for profit, and these contracts are causing huge losses to investors worldwide. Ponzi contracts are typical of these contracts, which mainly reward the funds invested by later investors to early investors, and later investors will have no gain. However, although there are some studies for identifying Ponzi contracts on Ethereum, there is some room for progress in the research. Therefore, we propose a method to detect Ponzi scheme contracts on Ethereum-CTRF. This method forms a dataset by extracting the word features and sequence features of the smart contract’s code and the features of transactions. The dataset is divided into a training set and a test set. Oversampling is performed on the training set to deal with the problem of positive and negative sample imbalance. Finally, the model is trained on the training set and tested on the test set. The experimental results show that the model has significantly improved recall compared with existing Ponzi contract detection methods.

Growing cotton to produce food: Unravelling interactions between value chains in southern Mali

AbstractMotivationMost transaction cost economic frameworks, commonly used to examine and explain the co‐ordination of agricultural transactions, use a linear approach for a single product transaction. This ignores the concurrence of multiple transactions by smallholder farmers in developing countries.PurposeThis study aims to understand co‐ordination among multiple product transactions by smallholder farmers and to identify ways to remove impediments to market participation. It develops an adapted transaction cost framework, considering contract types and forms of market participation as building blocks for co‐ordination structures.The framework was applied to explain co‐ordination structures between smallholders and buyers of cotton and cereals in southern Mali.Methods and approachTo make the framework operational, we did the following: (1) selected transaction characteristics; (2) elaborated benchmarks to describe the intensity of transactions; (3) identified co‐ordination structures; and (4) scored the intensity of transactions. Both quantitative and qualitative data were collected.FindingsThe majority of farmers grew cotton and sold it to a parastatal company, the sole buyer, that also supported the provision of inputs. Inputs were used to grow not only cotton, but also cereals. Most farmers sold cereals on spot markets to collectors and traders.Using different structures allowed smallholders to obtain inputs and services, to pursue different income sources over the year, and to balance flexibility and security.Policy implicationsCollective organizations of smallholder farmers should be supported to improve their financial and managerial capacities to allow them to co‐ordinate better with buyers and input suppliers.Institutional innovations to better balance risks for smallholders and buyers deserve consideration. These innovations include crop insurance, long‐term credit, and warehouse receipts.

Sales and Self: The Noneconomic Value of Selling the Fruits of One's Labor

A core assumption across many disciplines is that producers enter market exchange relationships for economic reasons. This research examines an overlooked factor; namely, the socioemotional benefits of selling the fruits of one's labor. Specifically, the authors find that individuals selling their products interpret sales as a signal from the market that serves as a source of self-validation, thus increasing their happiness above and beyond any monetary rewards from those sales. This effect highlights an information asymmetry that is opposite to what is found in traditional signaling theory. That is, the authors find that customers have information about product quality that they signal to the producer, validating the producer's skill level. Furthermore, the sales-as-signal effect is moderated by characteristics of the purchase transaction that determine the signal strength of sales: The effect is attenuated when product choice does not reflect a deliberate decision and is amplified when buyers incur higher monetary costs. In addition, sales have a stronger effect on happiness than alternative, nonmonetary forms of market signals such as likes. Finally, the sales-as-signal effect is more pronounced when individuals sell their self-made (vs. other-made) products and affects individuals’ happiness beyond the happiness gained from producing.

Consumers’ Perceived Trust Evaluation of Cross-border E-commerce Platforms in the Context of Socialization

In the current online shopping environment, the virtual nature of transactions and the characteristics of trans-temporal transactions make them subject to certain uncertainties and risks. Trust perception is the main means and way to establish a trust relationship between cross-border e-commerce and consumers.. This article analyzes the characteristics of consumers and cross-border e-commerce to build a consumer perception trust evaluation index system from three aspects, and applies the AHP method to determine the final index system. The evaluation results of the K-means algorithm are more objective and can be applied to the problem of consumer perception trust evaluation of cross-border e-commerce. The evaluation results can be used for cross-border e-commerce to intuitively understand consumers’ trust in themselves, and then improve their shortcomings in a targeted manner.

Criminal Procedure and Forensic Features of Recording Transactions in the Blockchain Network as Evidence in a Criminal Case

Criminal Procedure and Forensic Features of Recording Transactions in the Blockchain Network as Evidence in a Criminal Case

Detection and analysis of atypical stock transactions with possible misuse of insider information and market manipulation: methods and models

The article presents the results of the application of machine learning methods to identify customers prone to the use of insider information and market manipulation. The feature space was formed taking into account the specifics of the subject area. Cluster analysis techniques were used to markup data and to construct a training sample to identify features of anomalous objects. The key distinguishing features of atypical transactions were obtained and analyzed, an “atypical investor profile” was created. Ensemble algorithms of machine learning were used to build models for identifying transactions with possible misuse of insider information and signs of market manipulation. The practical significance of the study lies in the fact that the use of models will allow to effectively and timely identify atypical transactions, as well as unscrupulous investors involved in the misuse of insider information and market manipulation. The proposed approaches can be applied both in the work of the Bank of Russia and in the brokers’ work, who should independently monitor the doubtful operations of their clients.

Fake transactions in Russian law: history and modernity

This article examines the phenomenon of a fake transaction. The author shows the origin of this institution in the decisions of the civil cassation Department of the Governing Senate in the Russian Empire. In particular, it is shown that initially such decisions were about circumventing the law, then the idea of fictitious (imaginary) transactions was formulated, and only then were the imaginary and fake transactions differentiated. The Soviet approach was initially based on the division of the imaginary and feigned transaction with the indication reactions a fake transactions: nullity, but with the ability to save to cover its legal. The author analyzes the current legislation, judicial practice. The aim of the study is the determination of aggravating circumstances sham transactions, based on the legislative model of sham transactions, including the value at which regulatory provisions it is understood in the legal positions of the Constitutional Court of the Russian Federation, of the Supreme Court of the Russian Federation, courts of arbitration and courts of general jurisdiction, given the domestic doctrine and the experience of legal regulation. Based on the results of the study, the author formulated the main elements that form the model of a fake transaction in the law of the Russian Federation, including the qualifying features of a fake transaction.

Real-Trading-Oriented Price Prediction With Explainable Multiobjective Optimization in Quantitative Trading

Price prediction using machine learning is a heated topic and an effective task in quantitative trading. However, the objectives that existing researches focus on are typically naive and can hardly express the diverse properties in real trading, by using which the results of price prediction cannot precisely reflect the returns in real trading. To alleviate this problem, we first formulate the characteristics of transactions and then propose a multi-objective method under a real-trading-oriented perspective, where a tree model inducing explainable trading characteristics is proposed. With lots of considerations of real trading characteristics, the results of price prediction can better obtain the properties in trading, and especially perform quite well in real trading. Multiple experiments on the Chinese Index Future Market display the effectiveness of the proposed model, where the performance of real trading has reached the industry-leading level. In particular, the difference between model prediction and returns in real trading can be diminished.

Credit Risk Modelling of Mortgage Loans in the Supervisory Stress Test of the Magyar Nemzeti Bank

The study aims to develop a model that can estimate potential credit risk losses for housing and home equity loans using both macro and micro data, can be applied uniformly to all banks and takes into account the new accounting standards (IFRS 9). The model is based on a deal-level database for several Hungarian credit institutions, covering an entire business cycle (2004–2018). It uses economic indicators that strengthen risk sensitivity while also including transaction characteristics that mitigate procyclicality. Modelling in a two-step process allows risk groups to be created during forecasting in accordance with various credit characteristics. The results show that the evolution of employment has a stronger effect on riskier groups which potentially have only ad-hoc employment, while net wealth was not even among the explanatory variables for the group containing the best debtors, who presumably rely more on stable earned income.

Financial Crime In Digital Payments

Digital payments are proliferating along with a massive and rapid digital transformation. However, the characteristics of transactions using digital payments, which are real-time, not face-to-face, and borderless create potential risks for financial crimes, including, Money Laundering and Funding Terrorism. The potential for abuse occurs in the registered and licensed digital payments sector and illegal digital payments that are not registered with the Bank Indonesia. Undoubtedly, this condition can threaten economic stability and financial system integrity. This article seeks to identify the potential for digital payment use for financial crime and construct a legal framework to prevent the misuse of FinTech for financial crime in Indonesia. This type of research is legal research. The research method used was a statutory comparative approach. The legal materials used were primary and secondary legal materials. The findings have been analyzed using qualitative data analysis techniques. The results of the study show that several cases of terrorism financing have been proven to have used FinTech digital payments as a means of online funding. In this regard, to maintain the integrity of the financial system and strengthen the government’s control functions, a comprehensive legal framework is needed through the establishment of Law on FinTech.

Meaning making of mentorship for the tuition-free student

PurposeThe study aimed to explore how student recipients of a full-tuition scholarship envision, define and experience mentorship and the types of relationships they have and expect from mentors. The study adds to the growing body of literature on mentorship as supplemental support for college student success.Design/methodology/approachSemi-structured interviews of 20 first-year college students in the Mid-West United States were collected as a part of a more extensive mixed-methods study. The authors used a four-phase process to refine, derive meaning and develop themes. Kegan's orders of consciousness explain how students make meaning of mentorship.FindingsStudents described mentoring as a service that could provide specific transactional features. Ten participants were unable to acknowledge a mentoring relationship at all, despite describing mentoring experiences and opportunities. Students often align with Kegan's second order, which focuses on self and valuing transactional, short-term relationships. Adjusting approaches to explaining mentorship and the value of building relationships appear to be an opportunity for research and practice.Originality/valueThis study illustrates an apparent disconnect between the intent of mentorship and the experiences of mentees. The students' experiences add a valuable perspective that supports the need to further refine mentoring practices in meaningful ways to impact student success, persistence and retention.

Drivers of Individual Credit Risk of Retail Customers—A Case Study on the Example of the Polish Cooperative Banking Sector

The main aim of the research was to determine the key factors determining the level of credit risk of individual clients (clients in the form of natural persons, excluding companies) on the example of Polish cooperative banks according to the following features: transaction characteristics, socio-demographic characteristics of the customer, the customer’s financial situation, the customer’s history of cooperation with the cooperative bank where they applied for a loan, and the customer’s history of cooperation with other financial institutions. For the research gathered data from 1000 credit applications submitted by individual customers when applying for a credit in five different cooperative banks were used for the analyses. To assess the credit risk of retail clients we use logit regression models, and additionally, score cards were calculated. The results of the research indicate that among the factors with high predictive power there were the features characterizing the client’s history of cooperation with the cooperative bank, where they applied for a loan. It may mean that when assessing credit risk related to financing individual customers, cooperative banks due to their local character, have an advantage over other financial institutions.

Handgun purchasing characteristics and firearm suicide risk: a nested case\u2013control study

BackgroundFirearms are the most lethal method of suicide and account for approximately half of all suicide deaths nationwide. We describe associations between firearm purchasing characteristics and firearm suicide.MethodsData on all legal handgun transactions in California from 1996 to 2015 were obtained from the California Department of Justice Dealer’s Record of Sale database. Handgun purchasers were linked to mortality data to identify those who died between 1996 and 2015. To account for variation in timing and duration of observation time, analyses were stratified by birth cohort. The primary analysis focused on those aged 21–25 in 1996. A secondary analysis tested associations among those aged 50–54 in 1996. Using incidence density sampling, purchasers who died by firearm suicide (cases) were each gender-matched to 5 purchasers (controls) who remained at risk at the case’s time of death. We examined the characteristics of purchasers and transactions, focusing on the transaction closest in time to the case’s death. Data were analyzed with conditional logistic regression.ResultsThere were 390 firearm suicides among the younger cohort and 512 firearm suicides among the older cohort. Across both cohorts, older age at first purchase and the purchase of a revolver were associated with greater risk of firearm suicide. For example, among the younger cohort, those who purchased a revolver versus semiautomatic pistol had 1.78 times the risk of firearm suicide (95% CI 1.32, 2.40) in multivariable models. Other associations varied across cohorts, suggesting cohort or age effects in purchasing patterns.ConclusionsFindings add to the evidence on firearm suicide risk and may help inform prevention strategies and future research.

The concept and essence of electronic transactions in the civil law of Ukraine

This research is devoted to the analysis of the legal regulation of electronic transactions in the civil law of Ukraine, the disclosure of problematic issues related to the practical application of the Law of Ukraine "On Electronic Commerce".
 Within the framework of this article, a theoretical and applied study of various approaches of scientists was carried out, as well as the position of the legislator regarding the understanding of the concept and essence of an electronic transaction through its comparison with traditional approaches to understanding the concept and essence of a transaction. The analysis of the Law of Ukraine "On Electronic Commerce" was carried out, and it was found that the latter defines the organizational and legal basis for activities in the field of electronic commerce in Ukraine, establishes the procedure for making electronic transactions using information and telecommunication systems and determines the rights and obligations of participants in relations in the field of electronic commerce.
 To achieve this goal, the author applied methods characteristic of legal science. The study was conducted out using the dialectical method of cognition of legal reality, which provided an opportunity to analyze the essence of electronic transactions as a variety of legal facts affecting the dynamics of legal obligations in the civil law system of Ukraine. Whereas the use of the formal-legal method made it possible to analyze the regulations related to the regulation of electronic transactions, which contributed to the proper disclosure of the research objectives.
 Based on the study, the authors conclude that electronic transactions, like transactions in general, are a kind of legal facts acting in the form of an act of strong-willed lawful behavior aimed at establishing, changing or terminating civil rights and obligations. It has been established that a characteristic feature of electronic transactions is that they are carried out using information and telecommunication systems. It was determined that a characteristic feature of electronic transactions is interactivity, efficiency and multimedia.

A data grouping model based on cache transaction for unstructured data storage systems

Cache prefetching technology has become the mainstream data access optimization strategy in the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships, which can result in a heavy computational burden for the existing prefetching model and lead to severe degradation in the performance of data access. We propose cache-transaction-based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency. Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state-of-the-art methods, our algorithm achieves an average increase in the cache hit rate of 12% on the MSR dataset with small cache size (0.001% of all the data), which in turn reduces the number of data I/O accesses by 50% when the cache size is less than 0.008% of all the data.

From Smart Contract Litigation to Blockchain Arbitration, a New Decentralized Approach Leading Towards the Blockchain Arbitral Order

Abstract Blockchain dispute resolution has led the crypto economy to the surge of a new form of dispute resolution: blockchain arbitration. Resolving disputes on-chain is becoming necessary as national and international legal frameworks are not adapted to the characteristics of blockchain transactions. More importantly, the legality of smart contracts is highly debated under various national laws. This new sui generis form of arbitration should not be assimilated with traditional arbitration. Indeed, blockchain arbitration might not fit within the traditional international arbitration framework, and it does not have to. Because blockchain arbitration operates as an oracle, the decision from the jurors automatically triggers or modifies the smart contract. Through the blockchain technology, arbitration has reached the possibility to automatically enforce arbitral awards with no need to rely on state courts’ authority. Observing the legal theories of international arbitration, blockchain arbitration enshrines the representation of delocalized arbitration, but simultaneously leaves room for a new representation of arbitration: decentralized arbitration. In the next decades, we will see the surge of the blockchain arbitral order, an independent legal order anchored in the Lex Cryptographia. This essay advocates for the recognition of the blockchain arbitral legal order and tries to draw its contours.

Spatial Differentiation and Influencing Factors of Second-Hand Housing Prices: A Case Study of Binhu New District, Hefei City, Anhui Province, China

The multiscale geographic weighted regression (MGWR) model obtains different influence scales of various variables better than the classical geographic weighted regression (GWR) model. This paper studies the price characteristics of second-hand residential transactions in Binhu New District taking advantage of the hedonic price model and MGWR model and draws the following conclusions. (1) There are obvious spatial positive correlation and spatial heterogeneity in the price of second-hand housing in Binhu New District. (2) The number of bedrooms, area, age of the house, and the distance to the nearest school have small effect on the scale, so they have strong spatial heterogeneity. The decoration status and floor are global scale variables, and their spatial heterogeneity is weak. (3) The number of bedrooms, orientation, decoration status, floor, and building structure all positively affect house prices, while area, house age, the distance to the nearest subway station, and the distance to the nearest school negatively affect house prices. Among all factors, the distance to the nearest school is the most important factor affecting house prices, followed by the number of bedrooms and then followed by the distance to the nearest subway station and area, while the orientation, floor, building structure, and decoration conditions have less impact, and the house age has the weakest impact.

Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques

Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.

Loss given default in SME leasing

Leasing provides a fundamental source of firm funding, especially for small and medium-sized enterprises. A crucial difference from loans and bonds is that the lessor retains ownership rights of the leased asset during the lease term. This facilitates the asset utilization and work-out process and leads to higher liquidation proceeds. Hence, previous findings on the loan and bond loss given default (LGD) are not transferable to the leasing industry. Our analysis is based on a very granular data set covering a great variety of information on the lessee, the leased asset, as well as contractual and transactional characteristics. We examine novel LGD determinants such as an external credit rating, the lessee’s limited liability, and the number of leased assets and collaterals. Moreover, new results on previously explored factors question earlier findings, for example, on the lease contract type. Most importantly, as proposed by Miller and Töws (J Bank Finance 91:189–201, 2018), we analyze two different LGDs, one based on the asset utilization proceeds, the other on repayments. Our results clearly indicate the crucial importance of this separation when analyzing the drivers of the leasing LGD in detail because several determinants affect these LGDs in different ways. Our study assists both lessors and regulators in assessing the effective risk of lease contracts and enables lessors to enhance their risk management and work-out processes.

On the Ratio of Decisions, Deals and Consents

Introduction. Сivil legislation has been supplemented with norms regulating relations regarding the adoption of decisions by civil law communities, which are the reason for the emergence of civil rights and obligations. Nevertheless, there is still uncertainty in science about whether decisions can be considered independent legal facts or whether they are a kind of transactions or consents. Theoretical Basis. Methods. The article is based on an analysis of the norms of positive law, primarily – Civil Code of Russian Federation. Using the comparative method, common and distinctive features of decision, transaction and consent institutions are identified. The method of classification and systematization is used to describe them. Results. As a result, the author comes to the conclusion about the different nature of the studied institutions. The will of the party to the transaction is aimed at generating legal consequences for themselves personally. These consequences occur as a result of the transaction. A member of the community, giving his vote, forms a decision that will have consequences not for him personally. In addition, the vote may end with a decision that the community member did not expect. The will of the party to the transaction is absolute. The party itself determines whether to enter into a relationship or not, what the terms of the transaction will be, and so on. At the same time, the will of the participant in determining the final type of decision is very limited. As a rule, voting is carried out on pre-defined questions, and the answer to them is limited to the answers «yes» or «no». Common to transactions and decisions is the possibility provided for by law of calling them invalid. However, both the grounds and consequences of invalidity differ. Unlike transactions, decisions are not subject to convalidation, but their flaws can be corrected by repeated adoption. Decisions also differ from consents. The consents is aimed only at transactions, whereas the decisions have a much greater field of action, including the transaction. Consent – are acts of attached will and the decisions – are act of the consolidated will. Discussion and Conclusion. The decision is a special and independent legal fact, which differs from transactions and consents in its subject composition, the nature of the expressed will and its direction, as well as the consequences of invalidity and the possibility of healing.