Transaction Level Research Articles

Shadow economy in agriculture and specifics of its measurement

Despite the extensive list of works devoted to the analysis of the shadow economy, the issue of determining its scale at the regional level and a separate type of activity (according to the All-Russian Classifier OKVED 2. has not been resolved. This work aims at filling this gap by showing existing methods of accounting for the shadow economy in agriculture. The aim is to identify the scale of the shadow economy in agriculture at the regional level in Russia. The novelty lies in the development and application of a methodology for assessing the shadow economy in agriculture in the Russian regions. The paper uses economic and statistical, calculation, cartographic methods. The paper forms empirical basis on the official statistical data. As a result of using the authors’ methodology, the study finds that in the absolute majority of regions there is a moderate level of shadow transactions of legal entities in section A according to the All-Russian Classifier. Moreover, the level of differentiation is low. Only in Chukotka Autonomous Okrug, Chelyabinsk, Bryansk, Lipetsk regions, Mari El Republic there is a higher level of shadow transactions. In general, in 2017–2022 there is a trend towards a reduction in the share of shadow transactions of legal entities in the gross value added (GVA) of section A “Agriculture, forestry, hunting, fishing and fish farming” in the regions of Russia. It is largely achieved thanks to the introduction of federal government information systems that make shadow turnover difficult. At the same time, when interpreting the results, it is important to make an adjustment for that our methodology may underestimate the scale of the shadow economy, since it does not consider agricultural organizations that are small business entities, as well as peasant farms and individual entrepreneurs.

Horizontal acquisitions of growth potential

Abstract We analyze the operating performance of horizontal acquisitions between firms that differ in their growth potential: (I) low-potential firms that buy targets with high potential; and (II) high-potential firms that buy low-potential targets. These two types of acquisition have been the topic of research at three levels of analysis: the industry, the firm, and the transaction. At the industry level, incumbents in the core and innovative startups in the periphery are presumed to have complementary assets managed more effectively through co-ownership. In this vein, studies have focused chiefly on incumbents buying startups. We redress this imbalance. At the firm level, research on real options for growth has examined how multi-business firms exploit their relatedness in entry and exit decisions. Here, we study growth potential in terms of real options in horizontal deals and analyze their effect on post-acquisition performance. At the transaction level, a number of studies have focused on post-acquisition organizational structure and the transfer of knowledge between incumbents and startups. We expand this research by estimating the presumed flow of growth potential capabilities in both High–Low and Low–High deals. Our method uses a difference-in-differences fixed effect model design with Coarsened Exact Matching and Kernel-weighted Propensity Score Matching among comparable firm pairs to measure synergy and to control for endogeneity and portfolio effects using data on U.S. firms over 30 years. We also use a novel accounting adjustment formula to calculate comparable post-M&A operating performance.

Data Deduplication Based on Content Locality of Transactions to Enhance Blockchain Scalability

Blockchain is a promising infrastructure for the internet and digital economy, but it has serious scalability problems, that is, long block synchronization time and high storage cost. Conventional coarse-grained data deduplication schemes (block or file level) are proved to be ineffective on improving the scalability of blockchains. Based on comprehensive analysis on typical blockchain workloads, we propose two new locality concepts (economic and argument locality) and a novel fine-grained data deduplication scheme (transaction level) named Alias-Chain. Specifically, Alias-Chain replaces frequently used data, for example, smart contract arguments, with much shorter aliases to reduce the block sizes, which results in both shorter synchronization time and lower storage cost. Furthermore, to solve the potential consistency issue in Alias-Chain, we propose two complementary techniques: one is generating aliases from history blocks with high consistency, and the other is speeding up the generation of aliases via a specific algorithm. Our simulation results show: (1) the average transfer and SC-call transaction (a transaction used to call the smart contracts in the blockchain) sizes can be significantly reduced by up to 11.03% and 79.44% in native Ethereum, and up to 39.29% and 81.84% in Ethereum optimized by state-of-the-art techniques; and (2) the two complementary techniques well address the inconsistency risk with very limited impact on the benefit of Alias-Chain. Prototyping-based experiments are further conducted on a testbed consisting of up to 3200 miners. The results demonstrate the effectiveness and efficiency of Alias-Chain on reducing block synchronization time and storage cost under typical real-world workloads.

A Note on the Determinants of IFRS Policy Choice When Accounting for Non‐controlling Interest and Goodwill

This paper provides evidence on the choices made by European firms when measuring non‐controlling interest (NCI) and goodwill. Since 2009, IFRS 3 allows measurement of NCI either at fair value (full goodwill method) or at the proportionate share of net assets (partial goodwill method). IFRS 3 allows this policy choice on a per transaction basis. Our data comprise 188 hand‐collected firm choices in connection with business combinations with remaining NCI, between 2010 and 2016. We use the theoretical framework developed by Stadler and Nobes (2014) explaining differences in firms’ choices of overt accounting options by country, industry, and firm‐specific topic factors. Based on univariate and multivariate analyses, we find that transaction‐specific and firm‐specific topic factors influence accounting policy choice, whereas country and industry factors do not. The choice has different effects on financial leverage and operating returns, which inform firm‐level preferences. Regarding choices made at the transaction level, we find acquirers with an intention to buy additional shares from non‐controlling shareholders tend to prefer the full goodwill method. Thus, the choice of accounting method conveys information to users about future additional acquisitions and has implications for standard setters, indicating that offering choices at transaction level may increase the forward‐looking content of financial statements.

A systematic review of the extant literature on the Tanzanian e-government services for economic development

This paper investigates the existing Tanzanian e-government services for economic development through a systematic review of the literature. The main objective is to examine researchers’ articles on e-government services towards achieving the Tanzanian economic growth as well as the current issues and trends on e-government globally. Specifically, this study aims to answer three research questions on: what are the functional e-government services in Tanzania? What is the level of Tanzanian e-government service performance? And what are the factors influencing Tanzanian e-government service performance for economic development? A systematic literature search was conducted on four aggregate data sources, which included Google Scholar, ResearchGate, CORE, and Refseek. A combination of search key terms was employed to obtain the data sources from 2015 to 2022. One hundred and five articles were found by the combined search and screened, resulting to eighteen articles that were recommended for the study. Content and Microsoft Excel computer application were used to analyse the screened data sources. The findings reveal that, a number of functional services is offered and considered potential for revenue collection and contribute to the nation's economic development. Nevertheless, Tanzania has improved and dynamically advanced to the transaction level of e-government implementation where the majority of the citizens interact for services and information. However, factors related to the activeness of the platforms, accessibility, usability, infrastructure, security issues, privacy, and technical support services among others, were identified to influence the performance of Tanzanian e-government services and thus call attention to the government in addressing these issues.

Use of e-Wallets and Their Impact on SME Business Transactions

Currently, worldwide there is an accelerated digital transformation, which will continue in time with the same trend, becoming an everyday part of human life. The appearance of covid-19 has had a strong impact on several aspects of life, especially in the way of acquiring products and/or services, which, due to the fear of receiving money from a person infected with the virus, led people to look for other options to carry out their economic transactions without physical contact, such as the use of electronic wallets. However, many entrepreneurs still do not use this type of technology in their businesses. The purpose of this research is to determine the impact of the use of electronic wallets in the commercial transactions of SMEs. To this end, we worked with entrepreneurs in the town of Otuzco in the La Libertad Region of Peru. It was observed that they only carried out transactions using the cash collection and payment method and did not use technological tools such as electronic wallets. This is applied research with a quasi-experimental design, cross-sectional and quantitative approach, with a sample of 88 SMEs divided into a control group (23 SMEs) and an experimental group (65 SMEs). The survey was used as a data collection technique, and a program was developed to promote the use of electronic wallets in the entrepreneurs of the city of Otuzco, obtaining as a result a positive impact on the commercial transactions of these SMEs. The results show that 96% of the merchants were reluctant to use electronic wallets for fear of being affected by computer crimes. After the training, 98% of the merchants monitored recognized the importance of their use in their regular commercial transactions. In conclusion, it was found that training on the use of electronic wallets and continuous monitoring have a positive impact on the level of commercial transactions, boosting the competitiveness of businesses by increasing sales, streamlining operations, and reducing financial costs. The research is of interest because it shows the importance of disseminating programs in the use of technological tools such as digital wallets in small businesses to achieve a positive impact on the improvement of their economic activities.

Prevention of credit card fraud transaction using GA feature selection for web-based application

Credit card fraud (CCF) is a regular event that generates financial losses. A considerable share of the significantly increased volume of internet transactions is made with credit cards. CCF detection programmes are consequently highly prioritised by banks and other financial organisations. These fraudulent transactions can come in a wide variety of formats and categories. To maintain data integrity, financial institutions support digital transactions. One of the most popular ways to pay the products and services can be done by both online and offline by using a credit card. Thus, there is a higher possibility of fraud during these financial transactions. This informs programmers to the requirement for a reliable technique for identifying successful fraud. Credit card users and businesses that accept credit cards have recently had to contend with the serious issue of CCF. Application-level frauds and transaction level frauds are the two categories into which CCF controlled frauds are divided. Therefore, utilizing genetic algorithm (GA) feature selection for web-based applications, it is advised to use this strategy as a method for the prevention of CCF transaction. This method's performance is evaluated based on a number of factors, including accuracy, recall, and specificity.

Fast Loosely-Timed Deep Neural Network Models with Accurate Memory Contention

The emergence of data-intensive applications, such as Deep Neural Networks (DNN), exacerbates the well-known memory bottleneck in computer systems and demands early attention in the design flow. Electronic System-Level (ESL) design using SystemC Transaction Level Modeling (TLM) enables effective performance estimation, design space exploration (DSE), and gradual refinement. However, memory contention is often only detectable after detailed TLM-2.0 approximately-timed or cycle-accurate RTL models are developed. A memory bottleneck detected at such a late stage can severely limit the available design choices or even require costly redesign. In this work, we propose a novel TLM-2.0 loosely-timed contention-aware (LT-CA) modeling style that offers high-speed simulation close to traditional loosely-timed (LT) models, yet shows the same accuracy for memory contention as low-level approximately-timed (AT) models. Thus, our proposed LT-CA modeling breaks the speed/accuracy tradeoff between regular LT and AT models and offers fast and accurate observation and visualization of memory contention. Our extensible SystemC model generator automatically produces desired TLM-1 and TLM-2.0 models from a DNN architecture description for design space exploration focusing on memory contention. We demonstrate our approach with a real-world industry-strength DNN application, GoogLeNet. The experimental results show that the proposed LT-CA modeling is 46× faster in simulation than equivalent AT models with an average error of less than 1% in simulated time. Early detection of memory contentions also suggests that local memories close to computing cores can eliminate memory contention in such applications.

A method for allocating demand response benefits considering contribution degree

To enhance the operational level of demand response transactions in the new power system and meet the requirements for refined management of demand response transactions, this paper proposes a novel method for allocation of demand response benefits. This method introduces the concept of contribution degree, and based on considering the contribution degree of agent users, it achieves a refined allocation of demand response benefits. This approach enables precise evaluation of agent users' participation in demand response, effectively enhancing their enthusiasm to engage in demand response, and promoting the level of market-oriented transactions in demand response. Practical applications have demonstrated the accuracy and effectiveness of this scheme in market-oriented demand response transactions.

The Effects of Transit Systems on International Trade

Abstract In this paper, we estimate the trade effects of a transit system upgrading that streamlines border processing in developing countries. Our empirical approach combines transaction-level export data from El Salvador with unique data that distinguishes export flows that were processed on the transit system. Our results indicate that the new transit system lowered regulatory border costs and raised exports. At the low end, our back-of-the-envelope estimate of the return to investment is US$ 3-to-1. This evidence informs a policy covered by the 2013 WTO Agreement of Trade Facilitation.

Evaluation of the professional development stake model for geography teachers in Jambi city

The research aims to assess the Continuous Professional Development (CPD) of geography teachers, addressing: 1) the CPD profile at each level of the stake evaluation model and 2) recommendations based on the Minister of State Regulation Utilization of State Apparatus and Bureaucratic Reform Number 16 of 2009, regarding the Teacher's Functional Department and Credit Score Article 11, which mandates teachers to engage in self-development activities, scientific publications, and innovative works over the past 10 years (2012-2022). The research employs a quantitative descriptive approach utilizing the Stake Model Evaluation, covering the antecedent (planning), transaction (process), and outcomes (results) levels. Primary data collection involves questionnaires supplemented by observations, interviews, and documentation as needed. Data analysis utilizes a percentage approach at each level. Research respondents, selected through disproportionate Random Sampling, include 19 schools in Jambi City, comprising a total of 32 Geography Teachers. Data presentation follows a quantitative descriptive approach. The research findings indicate that: 1) the CPD profile of geography teachers at the antecedent (planning) level is in the good category (79%), the transaction (process) level is also in the good category (84%), while the outcomes (results) level is in the less category (13%). 2) Recommendations at the antecedent level include engaging in enrichment activities. Transaction-level recommendations suggest improving digital skills and supplementing infrastructure facilities with CPD-related books. Outcome-level recommendations propose that the Education Department implement continuous teacher construction. Schools are encouraged to manage teacher's academic qualifications. Geography teachers are urged to contribute to educational works.

Outlier Detection in Auditing: Integrating Unsupervised Learning within a Multilevel Framework for General Ledger Analysis

ABSTRACT Auditors traditionally use sampling techniques to examine general ledger (GL) data, which suffer from sampling risks. Hence, recent research proposes full-population testing techniques, such as suspicion scoring, which rely on auditors’ judgment to recognize possible risk factors and develop corresponding risk filters to identify abnormal transactions. Thus, when auditors miss potential problems, the related transactions are not likely to be identified. This paper uses unsupervised outlier detection methods, which require no prior knowledge about outliers in a dataset, to identify outliers in GL data and tests whether auditors can gain new insights from those identified outliers. A framework called the Multilevel Outlier Detection Framework (MODF) is proposed to identify outliers at the transaction level, account level, and combination-by-variable level. Experiments with one real and one synthetic GL dataset demonstrate that the MODF can help auditors to gain new insights about GL data. Data Availability: The real dataset used in the experiment is not publicly available due to privacy policies. JEL Classifications: M410, M42.

Reputation System as a Digital Socio-Economic Institution: Willingness to Sell in Online Markets

This study examines the features and big data consisting of digital trace records from economic actions within Bukalapak's reputation system and their impact on the willingness to sell (WTS) electronic products. Digital mixed content analysis was used to analyze the rapidly evolving digital-social-based economic institution infrastructure. Data were collected, analyzed, and visualized using Python and analyzed using the concepts of new economic institutions and WTS. The findings indicate that the reputation system is a formal element comprising algorithms, rules, scripts, and patterns. This system facilitates social processes through feedback mechanisms in product descriptions, comments, reviews, ratings, and badges, which generate informal elements such as norms, trust, and symbolic values. These two elements are interconnected into an institutional framework whose function is to ensure that the market, characterized by high levels of anonymous transactions, uncertainty, and asymmetric information, can remain stable and meet the expectations of the actors involved.

Comparison of Sales From Vending Machines With 4 Different Food and Beverage Messages

Point-of-sale food messaging can encourage healthier purchases, but no studies have directly compared multiple interventions in the field. To examine which of 4 food and beverage messages would increase healthier vending machine purchases. This randomized trial assessed 13 months (February 1, 2019, to February 29, 2020) of vending sales data from 267 machines and 1065 customer purchase assessments from vending machines on government property in Philadelphia, Pennsylvania. Data analysis was performed from March 5, 2020, to November 8, 2022. Study interventions were 4 food and beverage messaging systems: (1) beverage tax posters encouraging healthy choices because of the Philadelphia tax on sweetened drinks; (2) green labels for healthy products; (3) traffic light labels: green (healthy), yellow (moderately healthy), or red (unhealthy); or (4) physical activity equivalent labels (minutes of activity to metabolize product calories). Sales data were analyzed separately for beverages and snacks. The main outcomes analyzed at the transaction level were calories sold and the health status (using traffic light criteria) of each item sold. Additional outcomes were analyzed at the monthly machine level: total units sold, calories sold, and units of each health status sold. The customer purchase assessment outcome was calories purchased per vending trip. Monthly sales data came from 150 beverage and 117 snack vending machines, whereas 1065 customers (558 [52%] male) contributed purchase assessment data. Traffic light labels led to a 30% decrease in the mean monthly number of unhealthy beverages sold (mean ratio [MR], 0.70; 95% CI, 0.55-0.88) compared with beverage tax posters. Physical activity labels led to a 34% (MR, 0.66; 95% CI, 0.51-0.87) reduction in the number of unhealthy beverages sold at the machine level and 35% (MR, 0.65; 95% CI, 0.50-0.86) reduction in mean calories sold. Traffic light labels also led to a 30-calorie reduction (b = -30.46; 95% CI, -49.36 to -11.56) per customer trip in the customer purchase analyses compared to physical activity labels. There were very few significant differences for snack machines. In this 13-month randomized trial of 267 vending machines, the traffic light and physical activity labels encouraged healthier beverage purchases, but no change in snack sales, compared with a beverage tax poster. Corporations and governments should consider such labeling approaches to promote healthier beverage choices. ClinicalTrials.gov Identifier: NCT06260176.

Earnings Management via Not-Wholly-Owned Subsidiaries

We investigate an unexplored mechanism of earnings management: income shifting from not-wholly-owned subsidiaries to help the parent company avoid losses at the expense of subsidiaries. Consolidated net income attributable to the parent company (i.e., net income) increases through this mechanism, as the parent company enjoys the full amount of the shifted earnings rather than sharing them with minority investors. We design an empirical model to directly estimate the amount of income shifted from subsidiaries to parent firms. Employing this measure, we find that firms opportunistically decrease earnings of their not-wholly-owned subsidiaries to manage net income upward to avoid losses. The results are stronger for firms with high noncontrolling ownership, firms with large subsidiaries, firms with strong influence over not-wholly-owned subsidiaries, and firms with a high level of related-party transactions. Our results are robust to alternative research designs, including controls for within-firm variations, alternative earnings thresholds, propensity score matching, and entropy balancing techniques. Our mechanism of earnings management is generalizable to other earnings management scenarios, such as share pledging. This paper was accepted by Brian Bushee, accounting. Funding: X. Zhang thanks the National Natural Science Foundation of China [Grant 72102243] for financial support. M. Luo thank the financial support from National Natural Science Foundation of China [Grant 71840011], and the Research Center for Digital Financial Assets at School of Economics and Management of Tsinghua University. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03090 .

Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model

The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.

Comparative Analysis of Predictive Models Using Synthetic Data for Financial Fraud Detection

This paper conducts a comparative analysis of predictive models in financial fraud detection using synthetic data. Financial fraud is an adversarial problem that is becoming increasingly common with technological advancement; fraudsters use techniques such as social engineering to lure victims into releasing confidential information for nefarious purposes. One of the types of fraud this research aims to tackle is detecting fraudulent transactions processed on customers' accounts as customers are usually responsible for bearing the costs associated with fraud. The study examines machine learning techniques applied to mitigate fraud in financial transactions under controlled five scenarios which include unusual transaction type, account behaviour profiling, and temporal correlation of transactions, time based velocity checks and 1 hour window analysis. A synthetic dataset comprising 2,549,085 transactions categorized into fraud and non fraud classes (849,695 fraudulent transactions and 1,699,390 non fraudulent transactions) was utilized for analysis. Transaction level features were employed to evaluate the performance of various machine learning classifiers and Deep Learning Algorithms. The classifiers examined include SVM, Decision Trees, Logistic Regression, Gradient Boosting, LSTM, Deep Neural Network, CNN, and Random Forest. Among these, the Random Forest Classifier demonstrates superior performance, achieving an accuracy rate of 99.18%. The evaluation primarily focuses on comparing the performance of classifiers using the confusion matrix as the primary evaluation metric. The findings indicate that the Random Forest Classifier, Decision Trees and Gradient Boosting are suitable classifiers for detecting financial fraud.

Learning to Use Trade Preferences: A Firm and Transaction Level Analysis of the EU–South Korea FTA

Abstract This article examines imports by Swedish firms and the utilization of the tariff preferences offered by the EU–South Korea Free Trade Agreement. To benefit from tariff preferences, the importer must make a formal request to use the preferences and also document the origin of the imported products (with a certificate of origin provided by the foreign exporter). This may be costly, and some importers choose to pay import tariffs even when tariff preferences are available. Hence, the preferences are not fully utilized. Using a detailed firm–transaction level data set on Swedish imports from South Korea, we analyse the determinants of preference utilization and how firms learn to use preferences. The results show that preference utilization is strongly correlated with potential duty savings, which depend on the preference margin and the size of the import transaction. From a learning perspective, we find that preference utilization is closely related to the number of import transactions undertaken by the firm, suggesting a learning-by-doing mechanism. The length of time the firm has been involved in importing activities plays a smaller role.

Impact of Outside Blockholders Over Link between Independent Directors and the Normal and Abnormal Related Party Transactions

This study examines the effect of independent directors on related sales and related purchases using the normal and abnormal values from 2003 to 2022 of the total sample of 13,789 firms representing 89% of the total Taiwanese listed firms. As a second tier of independent external mechanisms, impact of outside blockholders, on the association between independent directors and related party transactions is examined. This study hypothesizes that the level of related party transactions varies with independent directors. Further, the connection between related party transactions and independent directors varies with the impact of outside blockholders. This study finds that a greater number of independent directors is significantly linked with lesser levels of normal related sales, lower levels of normal related purchases, and higher levels of abnormal related purchases. Thus, the hypotheses are confirmed. This study also finds that the extent of impact of outside blockholders supports a lower level of normal related purchases but higher abnormal related purchases, thus, the hypotheses are supported. Implications include that firms should strengthen their monitoring mechanisms to oversee both normal and abnormal related transactions, emphasizing the importance of regular board reviews. Additionally, the influence of outside blockholders on purchasing decisions underscores the need for companies to engage with these stakeholders to align strategies with shareholder interests.

Simulation of discrete control systems with parallelism of behavior

Objectives. The problem of functional verification of control devices with respect to their design specification is considered. When solving the problems of implementing and testing of discrete systems, one has to deal with the presence of parallelism in the behavior of interacting control objects, which is also displayed in the assignment for designing control systems. The aim of the work is to develop a method for simulating descriptions of such systems, which allows their behavior testing dynamically on the area limited by their possible functioning.Methods. The paper considers a class of control systems with parallelism of the processes occurring in them, which permits linearization of their execution. To specify the behavior of such control systems, it is proposed to use the PRALU language of parallel control algorithms, which is based on Petri nets and which allows to order events occurring during the device operation. An object-oriented approach to simulation of the description of the control algorithm at the transaction level is proposed. For this purpose, a TLM (Transaction-Level Modeling) model has been developed for describing the devices with behavior parallelism in PRALU language. The transaction level model describes a system as a set of interacting processes that run in parallel and determine the behavior of the system over time.Results. The key concepts of the TLM model for simulating the descriptions of control algorithms in the PRALU language are defined: data structure, transactions, processes, and a barrier mechanism for synchronization of parallel processes. A method is proposed for transforming the description of an algorithm in the language into a TLM model, which is based on the representation of language operations as compositions of elementary operations that are performed sequentially. The set of these operations forms the basis for the algorithmic decomposition of a parallel algorithm in PRALU language into intermediate language program that is executed strictly sequentially. Translators of this program into the Verilog and C languages have been developed, the results of their compilation are simulators of the behavior of control system.Conclusion. The proposed simulation method can be used to create a test bench for functional verification of the circuit implementation of control devices with behavior parallelism. In this case, test sequences for verifying the circuit implementation can be generated dynamically – in the process of simulating the description of the algorithm in the PRALU language directly the control device or system, which include the control algorithm and the algorithms of controlled objects behavior.