The effect of auditor's cooperation with independent financial advisors on the reliability of performance commitment-Based on the moderating effect of managerial overconfidence.

Based on the sample of major asset restructuring transactions of Shanghai and Shenzhen A-share listed companies from 2009 to 2018, this paper uses Logit model to examine the impact of auditors ' cooperation experience with independent financial advisors on the reliability of performance commitments, and to examine the moderating effect of management overconfidence on the relationship between the two. The results show that the cooperation experience between auditors and independent financial advisors is significantly positively correlated with the reliability of performance commitment, and the closer the cooperation experience is, the easier the performance commitment will be realized, this has played a financial intermediary team effect. Further analysis shows that in the context of management overconfidence, the positive relationship between financial intermediaries ' cooperation experience and the reliability of performance commitment is stronger. The research results enrich the research on the influencing factors of M & A performance commitment and the economic consequences of financial intermediaries ' cooperation, and help the transaction subjects to rationally view performance commitment and regulators to improve policies and regulations.

Виконання бюджету у сфері охорони здоров’я: тематичне дослідження: Україна: серія аналітичних записок.

Цей звіт був підготовлений на початку 2022 року і, отже, є узагальненою картиною виконання бюджету України, як це було напередодні початку війни. Знадобляться роки, щоб належним чином врахувати вплив війни на системи та процеси виконання бюджету на охорону здоров’я та оцінити, наскільки стійкими вони будуть після цього жахливого потрясіння. Однак швидкий моніторинг діяльності уряду через чотири місяці в нових реаліях дає обнадійливі сигнали про те, що багато, якщо не більшість інституційних рамок, про які йдеться в цьому звіті, є міцними й повністю функціональними.

Budget execution in health: case study: Ukraine. Health policy papers series

This report was finalized in early 2022 and is therefore a snapshot of Ukraine’s budget execution as it was just before the start of the war. It will take years to duly account for the impact of the war on the systems and processes of health budget implementation, and to assess how resiliently they will have emerged from this horrendous disruption. However, rapid monitoring of the government’s activities four months into the new reality is giving encouraging signals that much if not most of the institutional framework discussed in this report is holding strong and is fully functional.

Intelligent Financial Auditing Model Based on Deep Learning.

The entire auditing process is complicated and tedious and requires a lot of human resources. Therefore, the intelligent development of auditing is the general trend. In order to improve the audit quality, this paper establishes an intelligent financial audit model that can predict the audit opinion of the consolidated financial statements. This paper proposes an audit opinion prediction model based on the fusion of deep belief neural network (DBN) and long-short term memory (LSTM). First, an indicator system is established for audit opinions, and multiple financial parameters are used to describe possible audit opinions. On this basis, a DBN network is designed to complete deep feature extraction and used for LSTM training. According to the prediction model obtained by training, the subsequent audit opinion can be scientifically predicted. In the experiment, the method in this paper is tested based on financial audit related data sets and compared with the prediction results of traditional multilayer perceptron (MLP), convolutional neural network (CNN), and LSTM models. The results verify the validity and reliability of the model in this paper.

Does Signing Auditor's Reputational Promotion Ease the Financing Constraints of Audited Firms?

Based on the senior certified public accountants selected by the Chinese Institute of Certified Public Accountants and data drawn from China's A-share listed companies from 2014 to 2019, this study studies the influence mechanism of signing auditors' personal reputational promotion on corporate financing constraints. The results show that the improved reputation of signing auditors will help ease the financing constraints faced by companies. Moreover, compared with that of signing auditors from Big Four accounting firms, the improved reputation of signing auditors from non-Big Four firms has a more significant effect on alleviating the financing constraints of enterprises. In addition, private enterprises and small and medium-sized enterprises face more severe financing constraints than state-owned enterprises and large enterprises, and the reputational promotion of signing auditors can better alleviate the financing constraints of the former two types of enterprises. The research conclusions provide theoretical and data-driven support for constructing audit reputation mechanisms in China and improving the financing capabilities of enterprises.

Bayesian inference of local government audit outcomes.

The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with great success. This work presents the first application of a Bayesian inference approach to the problem of predicting the audit outcomes of financial statements of local government entities using financial ratios. Bayesian logistic regression (BLR) with automatic relevance determination (BLR-ARD) is applied to predict audit outcomes. The benefit of using BLR-ARD, instead of BLR without ARD, is that it allows one to automatically determine which input features are the most relevant for the task at hand, which is a critical aspect to consider when designing decision support systems. This work presents the first implementation of BLR-ARD trained with Separable Shadow Hamiltonian Hybrid Monte Carlo, No-U-Turn sampler, Metropolis Adjusted Langevin Algorithm and Metropolis-Hasting algorithms. Unlike the Gibbs sampling procedure that is typically employed in sampling from ARD models, in this work we jointly sample the parameters and the hyperparameters by putting a log normal prior on the hyperparameters. The analysis also shows that the repairs and maintenance as a percentage of total assets ratio, current ratio, debt to total operating revenue, net operating surplus margin and capital cost to total operating expenditure ratio are the important features when predicting local government audit outcomes using financial ratios. These results could be of use for auditors as focusing on these ratios could potentially speed up the detection of fraudulent behaviour in municipal entities, and improve the speed and quality of the overall audit.

Audit Data Analysis and Application Based on Correlation Analysis Algorithm.

Traditional audit data analysis algorithms have many shortcomings, such as the lack of means to mine the hidden audit clues behind the data, the difficulty of finding increasingly hidden cheating techniques caused by the electronic and networked environment, and the inability to solve the quality defects of the audited data. Correlation analysis algorithm in data mining technology is an effective means to obtain knowledge from massive data, which can complete, muffle, clean, and reduce defective data and then can analyze massive data and obtain audit trails under the guidance of expert experience or analysts. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the research status and significance of audit data analysis and application; elaborates the development background, current status, and future challenges of correlation analysis algorithm; introduces the methods and principles of data model and its conversion and audit model construction; conducts audit data collection and cleaning; implements audit data preprocessing and its algorithm description; performs audit data analysis based on correlation analysis algorithm; analyzes the hidden node activation value and audit rule extraction in correlation analysis algorithm; proposes the application of audit data based on correlation analysis algorithm; discusses the relationship between audit data quality and audit risk; and finally compares different data mining algorithms in audit data analysis. The findings demonstrate that by analyzing association rules, the correlation analysis algorithm can determine the significance of a huge quantity of audit data and characterise the degree to which linked events would occur concurrently or sequentially in a probabilistic manner. The correlation analysis algorithm first inputs the collected audit data through preprocessing module to filter out useless data and then organizes the obtained data into a format that can be recognized by data mining algorithm and executes the correlation analysis algorithm on the sorted data; finally, the obtained hidden data is divided into normal data and suspicious data by comparing it with the pattern in the rule base. The algorithm can conduct in-depth analysis and research on the company's accounting vouchers, account books, and a large number of financial accounting data and other data of various natures in the company's accounting vouchers; reveal its original characteristics and internal connections; and turn it into an audit. People need more direct and useful information. The study results of this paper provide a reference for further researches on audit data analysis and application based on correlation analysis algorithm.

Construction and Simulation of Financial Audit Model Based on Convolutional Neural Network.

Big data has brought a new round of information revolution. Faced with the goal of full coverage of audit and supervision, making full use of big data is the main method to promote the realization of the goal of full coverage of audit and supervision. Data analysis and utilization is an indispensable task of auditing. Actively exploring multidimensional and intelligent data analysis methods and developing big data audit cases are the new development direction of auditing. The convolutional neural network's excellent ability to extract data features well meets the relevant requirements of financial auditing. However, in practical applications, convolutional neural networks often encounter various problems such as disappearance of gradients and difficulty in convergence, which reduces its expected performance in financial audit applications. In order to make the performance of the financial audit model based on convolutional neural network more excellent, after summarizing the characteristics of genetic algorithm, this article applies genetic algorithm to the optimization of the convolutional neural network model. We applied genetic algorithm to optimize the initial weights of the convolutional neural network. The error sensitivity and learning rate changes of different hidden layers are discussed, the influence of different learning rates on the convergence speed of convolutional neural networks is analyzed, and the recognition performance of other algorithms on financial audit data sets is simulated and compared. We conducted experiments on the network structure and parameter optimization on the financial audit database. The results show that the recognition error rate of the convolutional neural network model with improved learning rate algorithm in the financial audit data set is lower than that of the multilayer feedforward network, so it has better performance.