Financial Risk Estimation with KNN Classification Algorithm on Determined Financial Ratios

The study of the financial imbalances of companies is a common topic for academics and practitioners because bankruptcy affects financial stability and modifies the investors' behavior. Since the 1960s, financial ratios have been used as diagnostic tools and also as independent variables within models aimed at quantifying firms' financial risk (e.g., Altman's Z-Score). In parallel, the strategic theory has developed theoretical constructs to explain why competitiveness is empirically heterogeneous. The resource-based view argues that companies can outperform rivals if they manage scarce, expensive, and hard-to-imitate resources. Ultimately, outperformers should be able to avoid (or overcome) financial imbalances. This chapter intends to analyze whether IT resources modify firm performance and financial risk. To do that, the authors collected data from a random sample of Galician SMEs, combining questionnaires, focused interviews, and public financial data. Hypotheses are explored by applying parametric statistical methods.

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Financial Risk Estimation in Conditions of Stochastic Uncertainties

Lecture Notes in Computational Intelligence and Decision Making - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-3-030-82014-5_1 ◽

2021 ◽

pp. 3-24

Author(s):

Oleksandr Trofymchuk ◽

Peter Bidyuk ◽

Irina Kalinina ◽

Aleksandr Gozhyj

Keyword(s):

Financial Risk ◽

Risk Estimation ◽

Stochastic Uncertainties

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Influencing Factors and Forecasting Statistics of Enterprise Market Sales Based on Big Data and Intelligent IoT

Wireless Communications and Mobile Computing ◽

10.1155/2021/9981581 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Zhen Guo ◽

Tao Zou

Keyword(s):

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Classification Algorithm ◽

Processing Method ◽

Enterprise Management ◽

Training Process ◽

Data Processing Method ◽

Knn Classification ◽

Positive Effect

With the acceleration of economic development, enterprise management is facing more severe challenges. Big data analysis based on the intelligent Internet of Things (IoT) has a positive effect on the development of enterprise management and can make up for the shortcomings of enterprise management. In this paper, we develop a big data processing method based on intelligent IoT which can mine the factors that affect the company’s market sales from the collected data. Then, we propose a KNN classification algorithm based on overlapping k -means clustering. This algorithm adds a training process to the traditional KNN algorithm, which can accurately classify data and greatly improve the efficiency of the classification algorithm. Numerical analysis results prove the effectiveness of the proposed algorithm.

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PROCESS-EVENT APPROACH FOR OPERATIONAL RISK ESTIMATION

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2019vol2.4041 ◽

2019 ◽

Vol 2 ◽

pp. 70

Author(s):

Vasily Karasev ◽

Ekaterina Karaseva

Keyword(s):

Financial Risk ◽

Business Processes ◽

Process Mining ◽

Quantitative Estimation ◽

Risk Estimation ◽

Main Idea ◽

Operational Risk ◽

Economic Capital ◽

Advantages And Disadvantages ◽

Process Event

Authors propose a new process-event approach for quantitative estimation of operational risk in a bank and calculation the amount of economic capital in dynamics. The proposed approach, according to the Basel II Capital Accord, belongs to the category of “advanced methods”. Operational risk is not financial risk and appeared in unfavorable events mainly. A number of business processes are performed in banking activity. Every business process contains a set of routines (operations), which can be interrupted by operational risk events with certain losses. The main idea of the approach is to describe processes as chains of casual events instead of a traditional graphic description as diagrams. Authors introduce new concepts: an elementary process event, a chain of process events, a time diagram of enterprise’s event flow, and build logical and probabilistic risk models. Methods and formulas for calculation the current and integrated operational risk in dynamics, the amount of economic capital, upper and lower limits of reservation, are given. The value of integrated operational risk can be used as a rating of the current reliability of the bank. The paper outlines the advantages and disadvantages of proposed process-event approach. Research results can be implemented as analytical tool in risk management technology, “process mining” technology and in bank intelligent management systems.

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