Analysis of Financial Risk Early Warning Systems of High-Tech Enterprises under Big Data Framework

With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.

Archetypal Analysis and DEA Model, Their Application on Financial Data and Visualization with PHATE

One of the goals of macroeconomic analysis is to rank and segment enterprises described by many financial indicators. The segmentation can be used for investment strategies or risk evaluation. The aim of this research was to distinguish groups of similar objects and visualize the results in a low dimensional space. In order to obtain clusters of similar objects, the authors applied a DEA BCC model and archetypal analysis for a set of companies described by financial indicators and listed on the Warsaw Stock Exchange. The authors showed that both methods give consistent results. To get a better insight into the data structure as well as a visualization of the similarities between objects, the authors used a new approach called the PHATE algorithm. It allowed the results of DEA and archetypal analysis to be visualized in a low dimensional space.

Analysis of Gayo wine-coffee processing facility development

The Indonesian plantation sector plays an important role in the overall national economy. The development of coffee processing methods into a variety of products, including the relatively new product Gayo wine coffee, will provide added value for farmers. However, the main question that is often asked about relatively new ventures is how viable they are. Thus, a techno-economic analysis of Gayo wine-coffee processing facility development was aimed to answer how viable is the business to ensure sustainability. The results of this research are expected to add scientific information regarding the feasibility study of developing a sustainable Gayo wine-coffee processing facility. The research was conducted in Aceh Tengah District by conducting a comparative study to a micro-scale wine coffee producer in the location, the Syukran Kopi Wine processing facility. Analysis of the facility development was more focused on the technological and financial aspects. The research results considered that Aceh Tengah District is very suitable as a location for a wine coffee factory to minimize the purchasing costs. Moreover, the process of wine coffee production is more difficult than the process of common coffee production. However, the Gayo wine-coffee processing facility uses semi-modern technology like the common coffee processing facility. Besides, all financial indicators meet the financial feasibility criteria for five years economic life of the project. Sensitivity analysis also shows that in both scenarios, all financial indicators still meet the financial feasibility criteria, although decreasing the benefit make a higher impact on most values of the investment criteria than increasing the operational cost.

Smart Design of Green Hydrogen Facilities: A Digital Twin-driven approach

This work studies the potentials of Digital Twin solutions for the design of competitive and reliable green hydrogen facilities. A digital twin based on stochastic simulations is proposed to address the uncertainties associated with investment and operating costs, to increase confidence and stimulate investments. Several input assumptions are involved (i.e., capital and operational costs, energy consumption, available energy, among others) to analyse their influence on financial indicators. A set of facility designs with equipment redundancy, and thus different system availabilities, was proposed. Monte Carlo simulation method is chosen to propagate uncertainties onto the project bankability assessment. By applying the proposed methodology, the opportunity index and internal rate of return (IRR) are calculated. A sensibility analysis is also carried out. The simulations illustrate that the design of a facility can be optimized to achieve higher profits, based on a trade-off between investment and availability. This study concludes that digital twin solutions are an opportunity for reducing the uncertainties associated with green hydrogen facility design. Improvements to the proposed model can be achieved by performing a refined simulation, in relation to the calculation of system availability and maintenance costs.

Changes in the Financial System of Vas County at the Beginning of the 20th Century

The purpose of the study. To showcase the growth of the region’s savings banks network during the period of Hungary’s capitalist development between the turn of the century and the World War I. Applied methods. Primarily data published in the financial almanac ‘Magyar Compass’ and newspaper articles of the time were sourced for the purposes of the study. Of financial indicators, balance sheet total and aggregate cash turnover figures were used. The study presents the accessible data of all the savings banks in operation at the time. Outcomes. By the end of 1912, various types of financial services had already been available in 28 Vas County settlements with 53 savings banks operating in the county. Their number was augmented by five branches and two affiliates. The savings banks furthermore established 17 disbursement points. This network of financial institutions was complemented by the Austro-Hungarian Bank (Osztrák-Magyar Bank, OMB) and two discount houses operating in Szombathely. The market district of Szombathely covered the whole county. While several larger microregional money markets were created, significant amounts were repatriated by Hungarians emigrating to America. Amidst all these changes, two banks’ bankruptcies made it known nationwide that the development of the local financial network was not without its failures.

Practical application of the CCB model in Czechia

This research aimed to present a new bankruptcy prediction model and apply this original prediction method in practice. The Come Clean Bankruptcy (or CCB) model uses relevant financial indicators and ratios to detect the signs of impending financial distress in time so that the management can take appropriate measures to avoid it. The model was applied to the data reported by 199 entities operating in the textile/clothing industry in the Czech Republic. Analyzing data reported for the previous seven years enabled us to predict which companies are more likely to end in a difficult financial situation. Afterward, comparing these predictions with the actual development of those companies in 2013-2020 serves to verify the efficacy and usability of the model to corporate reality. The research has shown that companies that went bankrupt in the analyzed period represented only a fraction of the data set (roughly 4.5%). Despite the small number of financial failures occurring during the analyzed period, the CCB model could detect impending bankruptcy in one-third of the cases.

Selected financial indicators as a tool for optimizing local government units management

Finances of each organization are the basic security of its existence and the main indicator of its profitability. In local government units public finances, despite many similarities, are governed by different rules than economic entities, if only because local governments have systemically guaranteed income in the form of state grants and subsidies. New and modern methods of financial management appear in self-government financial management. We can notice it both in the scope of sources of investment financing and the scope of information and data justifying or negating financial decisions and, consequently, local government management. Financial indicators as tools supporting the decision-making process of governing bodies are an important factor in decision-making, including the implementation of public tasks, satisfaction of social needs and expectations. Thanks to indicators local governments have analytical information, useful in the process of management taking into account social expectations comparable to other units. The indicators show where a given self-government unit is in relation to its competitors, allow for a comparable evaluation of the achievable results and for a social evaluation covering the expectations and needs of the community's inhabitants. It should be noted that the indicators are the effect of using data, which are very abundant on the local government and business market. We often deal with metadata covering the whole country and regions and their sources are more or less reliable. Reliability of sources is extremely important in public perception. It is guaranteed by institutions representing information collected on the basis of systematic reports, obtained in a uniform space for all local government units. Both data and indicators calculated on their basis, as well as reliable rankings presented, are tools used by mayors of communes, local communities, more and more often government institutions, funds, agencies and media presenting more and less reliable rankings.

Research on a Dynamic Evaluation of Financial Risks of Intellectual Property Pledge Financing of High-Tech Enterprises

Intellectual property pledge financing is effective in alleviating the financing problems of scientific and technological enterprises to a certain extent. However, compared with traditional loans, intellectual property pledge financing is at greater risk with an eye to the particularity of intellectual property pledges. Therefore, it is very important to evaluate the risk of intellectual property pledge financing. This article first outlines the information entropy method, functionalizing indicator data and weight data, and applies the combination of the weight function and indicator function in dynamic evaluation function, to get the final evaluation result. Second, taking four high-tech listed companies on the growth enterprises market (GEM) as example, an evaluation indicator system has been constructed with 16 relevant financial indicators from 2015 to 2019. Then, based on the dynamic function method, we can construct the evaluation function to describe the change trend of the financial indicators of enterprises with a comparison of the advantages and disadvantages of the financial indicators of enterprises. Finally, this article has made a comprehensively comparative analysis on the dynamic evaluation, and the financial risk of intellectual property pledge financing of high-tech enterprises has been done with the combination of the dynamic evaluation curve with the optimal time weight determined by the maximum entropy method.