Transaction Time Research Articles

Supporting management decisions for M&A transactions based on the strategic allocation of intangible assets

In the context of mergers and acquisitions (M&A), management decisions regarding asset allocation play a key role in determining the strategic value of intangible assets. This study investigates the allocation of such assets, particularly goodwill, in relation to enterprise value on balance sheets across global M&A transactions within the B2C sector from 2000 to 2021. Utilizing data from the Markables database, which includes 543 transactions, this study presents robust and quantile regression analyses to effectively address challenges arising from non-normally distributed data. The findings underscore a significant correlation between the strategic allocation of intangible assets, especially goodwill, and enterprise value, highlighting their essential role in reflecting future earning potential and growth prospects. Additionally, the study reveals specific factors, including transaction type (asset vs. share deals) and timing (transaction year), that influence these asset allocation decisions. These insights are critical for enhancing management decisions in valuation and strategic financial planning during M&A. By elucidating these dynamics, this paper significantly contributes to the literature on management accounting and corporate finance, offering a granular understanding of the valuation of intangible assets in business combinations. Acknowledgment This study emerged from a cooperative project between the University of Applied Sciences Kufstein (Austria) and the University of Economics in Bratislava (Slovakia) 2023-05-15-003 “Enhancing long-term business value towards environmentally and socially sustainable economy,” which was funded by the performance committee of the Austria-Slovakia Action initiative.

Secure traceability mechanism of green electricity based on smart contracts and provenance model

Green and low-carbon development is an important part of global sustainable development. Green power trading provides strong support and assurance for promoting green and low-carbon development. Due to the long cycle of green power data chains and their susceptibility to malicious tampering, the integrity and traceability of data are difficult to guarantee. Therefore, this paper first proposes a security provenance model with enhanced relations based on the core structure of PROV and blockchain technology, which can securely capture provenance records, use the transfer time and number of transactions between various links in the traceability network as reasoning clues, realize the correlation tracing of the green electricity transfer process. Under the model, a traceability mechanism of green electricity is designed based on smart contracts. Trustworthy green electricity data collection is achieved through data filling and data verification techniques. Traceability query technique is adopted to achieve trustworthy traceability of green electricity. And the effectiveness of the proposed solution is demonstrated through simulation experiments.

Blockchain-Based Banking System

Abstract: The emergence of blockchain technology has revolutionized the traditional banking system by providing a decentralized, secure, and transparent platform for financial transactions. This research paper explores the feasibility and benefits of implementing a blockchain-based banking system. By examining current challenges within the traditional banking sector, such as lengthy transaction times, high fees, and lack of transparency, this paper argues that blockchain technology can offer a viable solution. The paper also evaluates the potential risks and regulatory concerns associated with blockchain-based banking systems, and proposes strategies to mitigate these challenges. This research paper aims to highlight the transformative potential of blockchain technology in revolutionizing the banking sector and improving financial services for both consumers and institutions.

Do corporations benefit from divesting to private equity acquirers? An empirical investigation

AbstractResearch SummaryFrom the perspective of the divesting firm, do divestitures to private equity (PE) acquirers perform differently from divestitures to corporate acquirers? If so, why? This question‐based, empirical study shows that on average, divestitures to PE acquirers correlate with lower divesting firms' shareholder returns than divestitures to corporate acquirers. The study explores whether these lower returns when divesting to PE acquirers are explained by the differences in PE acquirers' distinct value creation strategies when it comes to target selection, ownership, or transaction timing. The results reveal that divesting firms' lower shareholder returns when divesting to PE acquirers are more likely correlated with differences in value creation by PE acquirers due to their distinct ownership and transaction timing strategies, but not their selection strategies.Managerial SummaryPrivate equity (PE) firms are prominent buyers of corporate divestitures, and PE firms' strategies for creating value when acquiring divested businesses tend to differ from those of corporate buyers. Yet the performance implications, from the perspective of the divesting firm, of divesting a business to a PE acquirer versus a corporate acquirer are not clear. In this study, I explore the differences in returns to firms divesting to PE acquirers versus those divesting to corporate acquirers. First, on average, divesting firms' returns are lower when divesting to PE acquirers. Second, these lower returns are more likely to occur when the PE acquirer may be expecting to create less value, or when firms choose to divest at a suboptimal time.

Transaction Method of Warehouse Sharing Platform Based on Blockchain Technology

With the continuous development of big data and blockchain technology, there are more applications of warehousing sharing platform, and warehousing transaction method has become the research focus. The original barter method can not solve the problem of accurate warehousing transactions, and the calculation accuracy of warehousing transactions is poor. Therefore, this paper proposes a warehouse transaction model based on blockchain technology, and comprehensively analyzes the form and accuracy of warehouse transactions. Firstly, the warehouse trading platform is used to count the transaction data and transaction methods, and the transaction forms and results are judged according to the warehouse characteristics, and irrelevant transaction information is abandoned. Then, according to the change rate of transaction data and transaction mode, the results are calculated, and compared with the actual transaction situation, and the parameters and indicators of transaction calculation are adjusted. MATLAB simulation test analysis shows that blockchain calculation method can improve the accuracy of warehousing transactions, and the accuracy rate reaches 95.3%. According to different transaction contents, the platform and form are judged, and the transaction time is calculated. It is found that the blockchain calculation method can meet the needs of warehousing transactions.

Data acquisition and application of internet of things in financial management

With the rapid development of information technology, Internet of Things technology has become a hot topic in today’s society. This study aims to explore how IoT technology impacts various aspects of financial management, in particular how it changes the collection, processing, and parsing of financial data. Through in-depth literature review, questionnaire survey and experimental design, the research found that IoT technology has a significant positive effect on sales, transaction times and inventory control. But at the same time, data security, privacy concerns and initial technology investment are also challenges that enterprises need to consider when adopting these technologies. Overall, IoT technology offers tremendous opportunities for financial management, but businesses must fully assess the potential risks and benefits when implementing it.

Bridging Distances: The Impact of ICT on Diaspora Connectivity and Cultural Exchange

This paper examines the transformative role of Information and Communication Technologies (ICT) in enhancing diaspora connectivity, cultural preservation, and economic contributions to homelands. Through quantitative and qualitative analyses, it delves into various ICT toolsincluding social media platforms, mobile applications, online forums, and virtual cultural festivalsand their pivotal role in sustaining and strengthening the ties of diaspora communities with their countries of origin. It also explores the revolutionized remittance landscape, crowdfunding for development projects, and the facilitation of diaspora entrepreneurship and investment through digital platforms. Employing models like the Network Analysis Model, Remittance Flow Model, and Event Participation Model, the study provides insights into the increased frequency and depth of communication, significant reductions in remittance transaction costs and times, and the enhanced engagement in cultural and economic activities. The findings highlight ICT's capacity not only to maintain but also to enrich the cultural, social, and economic fabric of diaspora communities, underscoring its vital role in promoting global connectivity, cultural exchange, and economic development.

The Startup Analysis Canvas: Smartfill RFID System

Abstract: In today's fast-paced world, convenience and efficiency are paramount, extending to mundane tasks like refueling vehicles. Traditional gas station payment methods involvingcash or credit cards can be cumbersome and time-consuming. Radio Frequency Identification (RFID) technology offers a promising solution to expedite gas station transactions, enhancing the customer experience. An RFID-based payment system leveragestags affixed to vehicles or issued as cards. Upon pulling up to an RFID-enabled pump, the reader automatically identifies the customer's vehicle or card, retrieving linked account information. The customer simply authorizes fueling, and the designated amount is seamlessly deducted from their account. This eliminates the need to exit the vehicle, searchfor wallets or cards, and swipe or insert them at the pump, significantly reducing transaction times. The benefits of an RFID-based payment system are manifold. First and foremost, it offers unparalleled convenience. Customers can streamline the fueling process without fumbling for cards or cash, leading to a more pleasant and time-efficient experience.Furthermore, RFID technology translates to shorter queues and improved customersatisfaction at gas stations. Reduced transaction times at pumps lead to faster throughput,benefiting both customers and gas station operators. Security is another significant advantage of RFID-based payments. Unlike traditional methods susceptible to fraud, RFID tags are tamper-proof and difficult to replicate. This significantly reduces the risk of unauthorized transactions and fraudulent activity, enhancing financial security for both customers and gas station businesses. Additionally, RFID technology offers potential for integration with loyalty programs. The system can be designed to automatically identify customers and credit their accounts with loyalty points based on their purchases. This fosters customer engagement and incentivizes repeat business, creating a win-win situation for both gas stations and their patrons. While RFID-based payment systems hold immense promise, there are challenges to consider. Implementation costs associated with equipping gas stations with RFID readers and potentially issuing tags to customers need to be carefully evaluated. Additionally, customer adoption is a crucial factor.Encouraging widespread acceptance of this new technology may require educational campaigns to highlight the benefits and dispel any misconceptions

A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

Research on automatic pricing and replenishment strategy of vegetable commodities based on ARIMA algorithm

In just a few years' time, supermarkets have transformed from a novelty into a mainstream channel that unifies the retail world. Since superstores sell vegetables with a wide variety of types, different origins, and shorter freshness periods, the incoming transaction time of vegetables is often in the early morning, so superstores usually replenish their stocks on a daily basis according to the historical sales and demand of each commodity. First, this paper visualizes the historical data of vegetable commodity purchasing and pricing in superstores in order to observe the distribution characteristics and trends, and then analyzes the correlation between categories or individual products using statistical methods such as correlation factors and covariance. To predict replenishment and pricing strategies for the coming week, this paper analyzes the relationship between overall sales and cost-plus pricing for each vegetable category, using given sales data to establish the relationship between sales volume and price for each category. Based on the ARIMA predicted sales model and given costs, the optimal replenishment quantities and pricing strategies were determined using an optimization algorithm.

Synergizing Intelligence: Revolutionizing Supply Chains with Blockchain and AI

In the evolving landscape of supply chain management, the integration of blockchain technology and artificial intelligence (AI) stands as a beacon of innovation, promising to address the perennial challenges of efficiency, transparency, and reliability. This paper presents a comprehensive exploration of how AI can revolutionize blockchain supply chains, offering a synthesis of current research, methodologies, and case studies that highlight the transformative potential of this synergy. The supply chain, a complex network that underpins global trade, is often beleaguered by inefficiencies and vulnerabilities. Blockchain technology, with its decentralized and immutable ledger, has emerged as a solution to enhance traceability and trust. However, it is the infusion of AI that has the potential to catalyze a paradigm shift in supply chain management. AI’s capabilities in data analytics, machine learning, and autonomous decision-making can optimize logistics, predict trends, and automate tasks, thereby elevating the blockchain beyond its current utility. This research adopts a mixed-methods approach, drawing on qualitative insights from industry experts and quantitative data from performance metrics to assess the impact of AI on blockchain supply chains. Through a series of case studies, the paper illustrates the practical applications and challenges of this integration, providing a nuanced understanding of its implications. The findings reveal that AI significantly enhances the efficiency and accuracy of blockchain supply chains, leading to improvements in transaction times, data verification processes, and overall supply chain performance. The discussion delves into the strategic advantages of this integration, such as improved compliance and ethical supply chain practices, while also acknowledging the limitations and challenges that organizations must navigate. In conclusion, the paper posits that the convergence of AI and blockchain holds great promise for the future of supply chains. It offers a roadmap for practitioners looking to harness these technologies and sets forth directions for future research, particularly in the development of sophisticated AI algorithms tailored for blockchain applications and the long-term economic impacts on supply chain management. The study contributes to the broader field by providing empirical evidence and a new perspective on the potential of AI to create more resilient, efficient, and transparent supply networks..

Efficient and secure confidential transaction scheme based on commitment and aggregated zero-knowledge proofs

ABSTRACT As a distributed ledger technology, blockchain has broad applications in many areas such as finance, agriculture, and contract signing due to its advantages of being tamperproof and difficult to forge. However, the open nature of blockchain also introduces severe privacy issues, currently cryptocurrency privacy protection solutions under account-based models cannot balance the internal verification time and confidentiality of transactions. In order to improve the confidentiality and efficiency of transactions under the account-based model, this paper proposes an access control anonymous payment scheme based on homomorphic commitment and aggregated zero-knowledge proofs, focusing on the realization of a one-to-many anonymous transfer function, one-to-one transfer function, deposit function and withdraw function, to ensure the privacy of a transaction data while reducing verification time and Gas costs. We evaluated our approach on a proof-of-concept implementation by generating Solidity contracts and implemented some interesting contracts. The experimental results show that this scheme not only consumes lower gas, but also reduces the internal time during transaction generation. In addition, our solution has established a one to many transfer transaction scheme, and the proof verification time after aggregation is constant. Thus the efficiency of this scheme is particularly well suited for the digital finance scenarios.

Smart Contract and IPFS Decentralized Storage for Halal Certification Process

The halal industry today has achieved rapid development. Halal product is mandatory for Muslims and a big business for Indonesia. For others, it affirms the product's quality assurance and becomes a trending lifestyle. The product owner must submit an application and undergo several processes to obtain a halal certificate. However, there are challenges in the certification process and documentation. The proposed system automates the flow between certification processes through digital signing and stores the certificate and fatwa file. The study investigates the utilization of blockchain to manage the process and the integration of decentralized storage (IPFS) to store the digital version of the fatwa and certificate. A smart contract is designed and deployed on the Ethereum blockchain, and the transaction time and cost are analyzed. A smart contract enforces that certain actions are executed once the required conditions are fulfilled. The proposed system would cost 24.6 USD and require 227 seconds on average for the system setup. Each submission requires 9.86 USD and takes 92 seconds on average. Verification is free, and the average result can be obtained in one second. The appointed officer sets each entity to interact with the contract, and the digital documents (fatwa and certificate) are available online using IPFS. Progress of the certification is transparent to the public, increasing the public's trust. The study demonstrates a smart contract's capability to manage a product's certification process.

Ensemble intelligence algorithms and soil environmental quality to model economic quantity of land resource allocation and spatial inequality

With the increasing concern on soil pollution in context of land market reform, it’s an emerging topic to discuss whether soil pollution can cause land economic value substantially depreciated at geospatial scale. This study proposed a data science approach by synthesizing machine learning and deep learning algorithms to establish the land economic equivalent model (LEEM) with best prediction ability. The results indicated that Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) performed well in estimating residential/commercial and industrial land value, respectively. Spatial variation of soil environment quality indeed largely affected economic productivity of residential/commercial land with contribution of 13.82%. For industrial land, population density was strongly related to the land price (11.77%), of which importance score was much higher than other 10 variables except distance to center business district (CBD, 28.75%) and transaction time (28.61%). Another important finding was that soil protection measures could generate extra benefits equivalent with a mean of 22.46% and 28.92% for residential/commercial and industrial land value in current status, respectively, and high-value areas could also be predicted for effective decisions on land allocation and trading. In terms of sustainable land management, the regression tree-based LEEM is an effective tool in assisting decision makers with urban land reclamation, planning and pricing.

Computation offloading in NOMA-MEC-enabled aerial-vehicular networks exploiting mmWave capabilities

In recent years, there has been a significant interest in ubiquitous coverage, high data rate connectivity, and mobile edge computing (MEC) as crucial services within the future sixth-generation (6G) wireless networks. These services are regarded as essential components, exemplifying the advancements anticipated in 6G technology. Nevertheless, the successful implementation of these services in MEC-enabled vehicular networks significantly relies on the availability of robust network coverage and telecommunication framework. Unfortunately, in far-flung and isolated regions, such framework is often lacking, posing significant challenges in achieving uninterrupted connectivity, comprehensive coverage, and efficient computation offloading. Targeting the aforementioned horizon, in this paper, an uplink non-orthogonal multiple access (NOMA)-MEC-enabled aerial-vehicular network operating at millimeter wave (mmWave) is proposed in which the ground vehicles are provided edge computing services by an aerial autonomous vehicle, i.e., a high-altitude platform (HAP). We present a system model in which a HAP equipped with MEC servers offers computation offloading capabilities to a vehicular network. Our main objective is to minimize the transaction time of a NOMA cluster offloading its data to MEC servers located at the HAP. We devise a dual-layer optimization scheme for optimizing the transmission power and computational resource allocation by using the Lagrange multipliers method and then attain convergence by implementing a sub-gradient approach. We further extend our work by proposing a data-aware NOMA clustering scheme. The simulation results demonstrate the efficacy of our proposed approach, showing a notable reduction in the transaction time in comparison to the baseline scheme. The optimal power allocation enhances the data rates which subsequently reduces the transmission time, and the optimal cores assignment effectively minimizes the computation time. Additionally, the data-aware NOMA clustering scheme shows promising results by enhancing the system effective throughput and the spectral efficiency in comparison to the conventional NOMA clustering.

The role of cities in cross-border mergers and acquisitions — Evidence from China

This study explores the impact of an international friendship city, serving as diplomatic relations between cities, on the transaction time and equity ownership choice in cross-border mergers and acquisitions (CBMAs). Analyzing data from Chinese firms spanning the period 2001 to 2019, we discover a positive and significant correlation between the establishment of an international friendship city and shorter CBMAs duration and higher CBMAs share of equity. Further evidence suggests that the effect is more pronounced (a) in target countries that keep cooperative partnerships with China, (b) countries along the Belt and Road, and (c) state-owned enterprises (SOEs) acquirers. In addition, we identify effects at the provincial level, demonstrating a correlation between the number of international friendship cities and positive outcomes. Mechanism tests show that the subnational-level friendship relationship consolidates cultural exchange and lessens institutional barriers, thereby reducing the completion time and raising the ownership of acquirers. Drawing on political perspective, our results provide new insights into the important role of friendly diplomatic relations to cross-border investment for enterprises internationalization.

Multiscale Feature Fusion and Graph Convolutional Network for Detecting Ethereum Phishing Scams

With the emergence of blockchain technology, the cryptocurrency market has experienced significant growth in recent years, simultaneously fostering environments conducive to cybercrimes such as phishing scams. Phishing scams on blockchain platforms like Ethereum have become a grave economic threat. Consequently, there is a pressing demand for effective detection mechanisms for these phishing activities to establish a secure financial transaction environment. However, existing methods typically utilize only the most recent transaction record when constructing features, resulting in the loss of vast amounts of transaction data and failing to adequately reflect the characteristics of nodes. Addressing this need, this study introduces a multiscale feature fusion approach integrated with a graph convolutional network model to detect phishing scams on Ethereum. A node basic feature set comprising 12 features is initially designed based on the Ethereum transaction dataset in the basic feature module. Subsequently, in the edge embedding representation module, all transaction times and amounts between two nodes are sorted, and a gate recurrent unit (GRU) neural network is employed to capture the temporal features within this transaction sequence, generating a fixed-length edge embedding representation from variable-length input. In the time trading feature module, attention weights are allocated to all embedding representations surrounding a node, aggregating the edge embedding representations and structural relationships into the node. Finally, combining basic and time trading features of the node, graph convolutional networks (GCNs), SAGEConv, and graph attention networks (GATs) are utilized to classify phishing nodes. The performance of these three graph convolution-based deep learning models is validated on a real Ethereum phishing scam dataset, demonstrating commendable efficiency. Among these, SAGEConv achieves an F1-score of 0.958, an AUC-ROC value of 0.956, and an AUC-PR value of 0.949, outperforming existing methods and baseline models.

BLOCKCHAIN, A STRATEGIC TECHNOLOGY TO BE ADOPTED BY PUBLIC AND PRIVATE COMPANIES FOR THE BENEFIT OF BRAZILIAN SOCIETY

Purpose: Highlight the strategies used by organizations when using blockchain, the problems and difficulties they face, and how the use of blockchain technologies can help public and private Brazilian companies, understanding their concepts and importance. Theoretical Framework: Blockchain technology emerges to allow transactions to be more secure and transparent and is very important for organizations to make their processes more agile. However, to implement it, it is important to use strategies. Design/Methodology/Approach: Important aspects for the implementation and use of blockchain by organizations, challenges, benefits, essentiality, and the need to obtain this knowledge were analyzed, therefore, four public and private Brazilian companies were analyzed, where a bibliographic review was used and, therefore, exploratory research. Findings: Strategies were identified, such as storing and managing data with blockchain and using cryptocurrencies as a form of payment. The challenges to be overcome are usability, scalability, environmental impact, security, and crime. Public and private companies benefit from lower costs, agility, efficiency, shorter transaction times, customer attraction and greater engagement. Research, Practical & Social Implications: The research was relevant, and the objectives were met, the implications identified proved to be beneficial for companies and society. Originality/Value: The use of blockchain by organizations to address problems and increase business scalability is essential, as it brings greater capacity to control operations in an integrated manner with better communication and greater transparency towards society.

코로나 19 이후 개인투자자의 투자패턴에 대한 연구

Purpose – This research investigates the trading patterns of individual investors participating in the KOSPI stock market during and after the COVID-19 Pandemic, thereby explaining the indisputably low performance compared with institutional and foreign investors. Design/Methodology/Approach – Daily KOSPI stock market data was used, and the data period ranges from 2018 to 2023. The total period is divided into 12 sub-periods. For each sub-period, 25 stock-groups were generated by the magnitude of net purchases of individual investors at 4% intervals. Periodic information on returns, volatilities, and the net purchases of institutional and foreign investors were also utilized in the analysis. Findings – The sub-period analysis shows that the ratio of individual investors’ net purchases was negatively correlated with periodic cumulated returns, and the ratio of institutional and foreign investors’ net purchases with a strong statistical significance. The total period analysis also confirms the negative relationship between the ratio of individual investors’ net purchases and periodic cumulative returns, and shows a positive relationship between the ratio of individual investors’ net purchases and the change in volatilities. It was also revealed that the low performance of individual investors results from the choice of transaction timing, not from the choice of stocks. Research Implications – Empirical results imply that individual investors need to be cautious in determining the timing of transactions, thus utilizing the negative relation between the ratio of individual investors’ net purchases and the ratio of institutional and foreign investors’ net purchases merits consideration to enhance the profitability of individual investor portfolios.

Enhancing blockchain scalability with snake optimization algorithm: a novel approach

Scalability remains a critical challenge for blockchain technology, limiting its potential for widespread adoption in high-demand transactional systems. This paper proposes an innovative solution to this challenge by applying the Snake Optimization Algorithm (SOA) to a blockchain framework, aimed at enhancing transaction throughput and reducing latency. A thorough literature review contextualizes our work within the current state of blockchain scalability efforts. We introduce a methodology that integrates SOA into the transaction validation process of a blockchain network. The effectiveness of this approach is empirically evaluated by comparing transaction processing times before and after the implementation of SOA. The results show a substantial reduction in latency, with the optimized system achieving lower average transaction times across various transaction volumes. Notably, the latency for processing batches of 10 and 100 transactions decreased from 30.29 ms to 155.66 ms–0.42 ms and 0.37 ms, respectively, post optimization. These findings indicate that SOA is exceptionally efficient in batch transaction scenarios, presenting an inverse scalability behavior that defies typical system performance degradation with increased load. Our research contributes a significant advancement in blockchain scalability, with implications for the development of more efficient and adaptable blockchain systems suitable for high throughput enterprise applications.