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An audit of a contract is a type of review that takes place intending to ascertain whether one or both parties of a contract have complied with the provisions of a contract or not. New contract risks and volumes in various businesses have led to increased audit requirements in terms of ability, efficiency, and sophistication. Standard contract auditing can be described as tedious, manual, imprecise, and time-consuming. This research work also introduces the Contract Audit Automation System, which has incorporated the use of a neural network. In this study, we introduce a new beetle swarm-optimized adaptive long and short-term memory model (BSO-ALSTM), which can improve the efficiency of contract auditing. Data about contracts are collected from different companies including in the fields of finance and purchasing. The proposed methodology involves further analysis of the contract text and extraction of relevant phrases through natural language processing (NLP). Tokenization was used in analyzing the data to help isolate important aspects of the data. The proposed method is compared with other traditional algorithms to analyze its performance. The results attest that the proposed method has offered enhancements in audit speed and accuracy as compared to other algorithms. This research focuses on ways through which the technologies can be adapted to support contract management and auditing, a solution that can be replicated and adapted to meet different industry requirements.

The recent stimulating proposal of a "Gauge Theory of Finance" by Ilinsky et al. is connected here with traditional approaches. First, the derivation of the log-normal distribution is shown to be equivalent both in information and mathematical content to the simpler and well-known derivation, dating back from Bachelier and Samuelson. Similarly, the re-derivation of Black–Scholes equation is shown equivalent to the standard one because the limit of no uncertainty is equivalent to the standard risk-free replication argument. Both re-derivations of the log-normality and Black–Scholes result do not provide a test of the theory because it is not uniquely specified in the limits where these results apply. Third, the choice of the exponential form a la Boltzmann, of the weight of a given market configuration, is a key postulate that requires justification. In addition, the "Gauge Theory of Finance" seems to lead to "virtual" arbitrage opportunities for a pure Markov random walk market when there should be none. These remarks are offered in the hope to improve the formulation of the "Gauge Theory of Finance" into a coherent and useful framework.

This paper uses an economic growth model with a financial sector to examine the conditions under which financial liberalization is desirable from the perspective of a policymaker who cares about the well-being of domestic households. Financial liberalization raises the rate of financial innovation and the efficiency of financial intermediation, but typically causes large foreign firms from leading-edge countries to displace smaller and less efficient domestic firms. Profits are repatriated abroad instead of accruing to domestic households as dividends. This trade-off is further complicated when we allow financial firms to hire talented foreigners once liberalization has taken place. Simulations of the model indicate that the case for financial liberalization is stronger when agents are more impatient, spillover effects of current financial innovation on future innovation are larger, financial innovations are more susceptible to congestion in their use, and when knowledge diffuses more quickly from foreign to domestic workers.

The global financial crisis of 2008 challenges some relevant aspects of macroeconomic theory such as the neutrality of money. This paper shows that this neutrality is based on the unrealistic assumption of perfect competition. Relaxing this alone (without time lags, price rigidities, menu costs and other frictions) makes money no longer necessarily neutral and hence makes financial crises and institutions much more important. The presence of increasing returns to scale at the firm level and to specialization at the economy level due to the division of labor also makes finance much more important than suggested by traditional economics.

Discriminant Analysis (DA) is a statistical tool that can predict the group membership of a newly sampled observation. Sueyoshi (European Journal of Operational Research, 115 (1999) 564; 131 (2001) 324; 152 (2004) 45) and Sueyoshi and Kirihara (International Journal of Systems Science, 29 (1998) 1249) have recently proposed a new type of nonparametric DA approach that provides a set of weights of a linear discriminant function, consequently yielding an evaluation score for the determination of group membership. The nonparametric DA is referred to as "Data Envelopment Analysis-Discriminant Analysis (DEA-DA)," because it maintains its discriminant capabilities by incorporating the nonparametric feature of DEA into DA. In this study, a use of two statistical tests is proposed for DEA-DA and its discriminant capability is compared with DEA from a perspective of financial analysis.

Financial Ratio Analysis is newly proposed to examine the financial performance of the American power/energy industry. The new approach compares the financial performances of 147 non-default firms with those of 24 default firms in the US power/energy market. The proposed approach is a new type of nonparametric discriminant analysis that provides a set of weights of a linear discriminant function, consequently yielding an evaluation score for group membership. Such weight estimates, along with an evaluation score, of the discriminant function provide a total financial evaluation measure, based upon which we can determine the financial performance of the power/energy firms. This empirical study informs that both leverage (debt) and profitability (returns on equity) are important financial factors in terms of avoiding corporate distress or bankruptcy. The empirical results obtained from the American power/energy industry are further extended to the international comparison of other major industrial nations including Japan and the European nations. The international comparison concludes that Japanese electric power firms have enough managerial and financial capabilities even if the American financial standard is hypothetically introduced into the evaluation of their financial performances. However, the empirical results also indicate that the Japanese power industry performs barely above the American standard. Thus, corporate leaders in the Japanese power industry need to pay more serious attention to their corporate finances and financial strategies. Such financial perspective will be increasingly important along with the current deregulation policy of the Japanese government.

Software development with its unique characteristics having knowledge-intensive and human-oriented aspects and complex domains, challenges organizations. The timely outcomes with high quality and desired cost that directly affect customer satisfaction have an important place in many organizations, including banks. In the last decade, as an emerging technique for business processes management, process mining has been applied in many domains, including manufacturing, supply chain, government, healthcare, and software engineering. There are limited number of studies on process mining techniques carried out for the software process, especially in the banking sector. A lack of tool infrastructure enabling to run the entire software development process and the challenges in integrating processed data from separated varying tools and assets complicate the use of process mining for software processes. This paper aims to identify the improvement points in the software development process of the Kuveyt Turk Participation Bank in Turkey through the surfacing actions. The findings and results are gathered by the application of process mining techniques of bupaR, and evaluation is provided by experts in the bank. After that, the relevant process improvements are identified. The results of this paper show that using process mining provides the organization with beneficial results, in particular, and a comprehensive view of the end-to-end Software Development Life Cycle (SDLC) processes.

The purpose of this paper is to improve the financial management efficiency of large enterprises and enhance the overall operation vitality of enterprises. First, the connotation and characteristics of enterprise resource planning (ERP) are analyzed, and the financial ERP system is established. Then, the relevant dynamic models of nonlinear systems are classified and their characteristics are analyzed. Moreover, the system model of enterprise financial risk management is constructed based on the key success factors of project implementation risk and control flow chart of project life cycle. Finally, based on MATLAB software, $Z$ large enterprise is taken as an example to evaluate the implementation effect of analytical hierarchy process (AHP) algorithm and back propagation neural network (BPNN) algorithm in ERP system. The results reveal that compared with 2019, the capital concentration in 2020 increases by 8%, the operating cost decreases by 23.6%, and the expense reimbursement process time decreases from 60–80 days to about 6 days. The expected output and assessment result of AHP are 6.912 and 6.823, respectively, and the error between them is 0.0196. The expected output and assessment result of BPNN are 6.798 and 6.675, respectively, and the error between them is 0.0121. The error value of BPNN in ERP implementation effect assessment is less than that of AHP, which indicates that the assessment effect of BPNN is better than that of AHP.

In today’s sophisticated global marketplace, supply chains are complex nonlinear systems in the presence of different types of uncertainties, including supply-demand and delivery uncertainties. Though up to now, some features of these systems are studied, there are still many aspects of these systems which need more attention. This necessitates more research studies on these systems. Hence, in this study, we propose a variable-order fractional supply chain network. The dynamic of the system is investigated using the Lyapunov exponent and bifurcation diagram. It is demonstrated that a minor change in the system’s fractional-derivative may dramatically affect its behavior. Then, distributed consensus tracking of the multi-agent network is studied. To this end, a control technique based on the sliding concept and Chebyshev neural network estimator is offered. The system’s stability is demonstrated using the Lyapunov stability theorem and Barbalat’s lemma. Finally, through numerical results, the proposed controller’s excellent performance for distributed consensus tracking of multi-agent supply chain network is demonstrated.

This paper shows how Fuzzy Set Theory can be used in investment analysis when, as usual, these investments are developed under uncertainty, i.e. the investor has only subjective estimates based on his experience or knowledge about the future cash-flows of the investments, the discount rate, etc. In particular, we will develop basic concepts for investment analysis as the Net Present Value and the Internal Rate of Return by assuming that the initial data are fuzzy numbers. Later we will analyse how to rank investments and how to select the tangible investment portfolios when the magnitudes are estimated subjectively by comparing fuzzy numbers and with possibilistic mathematical programming.

Small businesses are critical to improving economic development in rural areas of South Africa. However, rural entrepreneurs are still faced with challenges and problems which make the success of small businesses, especially in rural areas, uncertain. This paper investigates business environmental, financial and infrastructural factors that influence the success or otherwise of Small and Medium Enterprises (SMEs) in rural areas. Primary data was collected in five rural areas of KwaZulu-Natal (KZN) from a sample of 374 business owners/managers, with respondents completing a questionnaire. Access to finance and skills shortages were the factors that most significantly differentiated between more successful and less successful rural businesses in KZN. The majority of respondents indicated that poor roads/transport and access to electricity were major problems.

Social capital and access to finance have been identified as key resources that influence the growth of small firms however, these variables have rarely been studied. This paper, therefore, examines the relationship between social capital and firm growth with access to finance as a moderating role. 250 small firms in the Kumasi Metropolis in Ghana were used for the study. Structural Equation Modelling using Partial Least Square (PLS) was used to analyze the data collected using area sampling. The results indicated that social capital does not directly influence firm growth. In addition, access to finance does not moderate the relationship between social capital and firm growth. However, a positive relationship was found between social capital and access to finance. Access to finance and firm growth, though significant, had a negative relationship. It is recommended that since social capital influences the capability to access finance, entrepreneurs should be encouraged to build more relationships within their networks. Moreover, government agencies and financial institutions should devise strategies that will reduce the interest rates so that though these small firms in Ghana can access finance, the high interest rates will not erode the gains they may achieve in the long run.

At present, the financial situation of China’s supply chain finance is still relatively unstable, and there are still some problems between supply chain enterprises and banks such as asymmetric information, insufficient model innovation and high operational risks. Based on this, this paper proposes and constructs a risk control model of financial big data analysis based on collaborative filtering algorithm. The purpose of this study is to realize the resource integration of supply chain enterprises and optimize the logistics chain, financial chain and information chain through the analysis of financial big data based on collaborative filtering algorithm, provide quality services for supply chain enterprises and good support for solving the financing problems of small and medium-sized enterprises. In order to verify the feasibility of the model, an experimental analysis is carried out. The experimental results show that this model has good scalability and operability, and the algorithm itself also has good scalability. The results of empirical analysis further verify that the design method in this paper has a good recommendation effect in terms of matching degree and user satisfaction. Compared with other risk control models, it is more practical and feasible. This research has certain practical significance for the financial management of supply chain enterprises.

We use fuzzy logic engineering tools to detect human behavior in the finance arena, specifically in the technical analysis field. Since technical analysis theory consists of indicators used by experts to evaluate stock prices, the new proposed method maps these indicators into new inputs that can be fed into a fuzzy logic system. This system can create an optimum computerized model to evaluate stock price movement. This method relies on human psychology to predict human behavior when certain price movements or certain price formations occur. The success of the system is measured by comparing system output versus stock price movement. The new stock evaluation method is proven to exceed market performance and it can be an excellent tool in the technical analysis field. The flexibility of the system is also demonstrated.

Despite the widespread realization that financial models for contingent claim pricing, asset allocation and risk management depend critically on their underlying assumptions, the vast majority of financial models are based on single probability measures. In such models, asset prices are assumed to be random, but asset price probabilities are assumed to be known with certainty, an obviously false assumption.

We explore practical methods to specify collections of probability measures for an assortment of important financial problems; we provide practical methods to solve the robust financial optimization problems that arise and, in the process, discover "dangerous" measures.

The drawdown duration, which measures the time elapsed since the portfolio obtained its maximum value, is an important criterion in active portfolio management for institutional investors. Although several optimization models exist for controlling portfolio drawdown magnitude (i.e. the percentage drop in portfolio value from its latest peak value), developing similar models for the drawdown duration has received minimal attention in the literature. Therefore, this paper develops a family of models for optimizing average, maximum and tail drawdown duration formulated as mixed-integer linear programming (MILP) problems, allowing the utilization of powerful solvers to identify optimal asset portfolios. We apply the developed models to real data on historical returns to compare their performance against traditional and drawdown-based portfolio selection models. The results indicate that the developed models lead to decrease in drawdown duration levels both in in-sample and out-of-sample tests. The constructed efficient frontiers also show a clear trade-off between minimizing drawdown duration and maximizing expected returns.

Statistical dynamics of financial systems is investigated, based on a model of a randomly coupled equation system driven by a stochastic Langevin force. It is found that in a stable regime the noise power spectrum of the system is 1/f-like: ∝ ω^{- 3/2} (where ω is the frequency), that the autocorrelation function of the increments of the variables (returns of prices) is negative and follows the power law: ∝ - τ^{- 3/2} (where τ is the delay), and that the stochastic drift of the variables (prices, exchange rates) is subdiffusive: ∝ t^H (where t is the time, H ≈ 1/4 is the Hurst, or self-similarity, exponent). These dependencies correspond to those calculated from historical $/EURO exchange rates.

Given the economy's complex behavior and sudden transitions as evidenced in the 2007–2008 crisis, agent-based models are widely considered a promising alternative to current macroeconomic research dominated by DSGE models. Their failure is commonly interpreted as a failure to incorporate heterogeneous interacting agents. This paper explains that complex behavior and sudden transitions also arise from the economy's financial structure as reflected in its balance sheets, not just from heterogeneous interacting agents. It introduces "flow-of-funds" or "accounting" models, which were pre eminent in successful anticipations of the recent crisis. In illustration, a simple balance sheet model of the economy is developed to demonstrate that non-linear behavior and sudden transition may arise from the economy's balance sheet structure, even without any micro-foundations. The paper concludes by discussing one recent example of combining flow-of-funds and agent-based models. This appears a promising avenue for future research.

We present a brief discussion on the impact of information technology on problems from finance and economics. Then, an overview is given upon the papers that are included in the special issue.

Amid the pandemic infection, people are bound to use contactless mobile payment (M-Payment) services. M-Payment is a payment method using an application in a mobile device, such as a mobile phone, and gadget. Owing to the convenience, reliability and contact-free feature of M-Payment, it has been diffusely adopted to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating the stabilization of the social economy. Consequently, it has become one of the day’s most important topics. Therefore, the purpose of this study is to provide a systematic literature review (SLR) on the applications of M-payment services in financial strategies, focusing on the pandemic crisis. 19 papers were collected and divided into three groups for further analysis. The results showed that M-Payments applications in financial strategies during the pandemic crisis could help reduce the spread of infection risks by hastening the transition to touchless habits.