scholarly journals How Change in Industry Mix Can Improve the Financial Performance of Regional Economies: Evidence from the Portfolio Approach

2019 ◽  
Vol 67 (6) ◽  
pp. 1561-1575
Author(s):  
Marina Malkina
Keyword(s):  
Financial Risk ◽  
Optimization Problems ◽  
Return Level ◽  
Risk Level ◽  
Regional Economies ◽  
Regional Effect ◽  
Financial Return ◽  
Industrial Structures ◽  
Federal Districts ◽  
Portfolio Approach

The aim of the study is to adapt the portfolio approach to optimization of the industrial structures of regional economies and to assess its results. The research is based on data of the Russian regions and federal districts in 2004–2016. The ratio of a balanced financial result to gross regional product referred to as financial return, and its volatility, called financial risk, were used as target parameters of regional economies. The application of the portfolio approach allowed us to evaluate financial return and risk in the regions and districts and decompose them by industries. Further, we solved three optimization problems: maximization of financial return at a given risk level, minimization of risk at a given return level, maximization of the Arrow-Pratt risk aversion utility function, and assessed their gains. As a result, we found that all three optimizations were often accompanied by a certain re-specialization of regional economies, rather than an increase in the degree of their diversification, although in the regions the situation was significantly different. For the federal districts, we identified a cross-regional effect that neutralized financial volatility, which can be used in re-specialization of regions within districts. Ultimately, the features and limitations of the application of the portfolio approach to the management of industrial structures of regional economies were discussed.

scholarly journals Optimization of Financial Asset Neutrosophic Portfolios

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1162
Author(s):  
Marcel-Ioan Boloș ◽  
Ioana-Alexandra Bradea ◽  
Camelia Delcea
Keyword(s):  
Capital Market ◽  
Financial Risk ◽  
Financial Asset ◽  
Financial Assets ◽  
Portfolio Risk ◽  
Financial Return ◽  
Additional Information ◽  
Triangular Numbers ◽  
The Capital Market

The purpose of this paper was to model, with the help of neutrosophic fuzzy numbers, the optimal financial asset portfolios, offering additional information to those investing in the capital market. The optimal neutrosophic portfolios are those categories of portfolios consisting of two or more financial assets, modeled using neutrosophic triangular numbers, that allow for the determination of financial performance indicators, respectively the neutrosophic average, the neutrosophic risk, for each financial asset, and the neutrosophic covariance as well as the determination of the portfolio return, respectively of the portfolio risk. There are two essential conditions established by rational investors on the capital market to obtain an optimal financial assets portfolio, respectively by fixing the financial return at the estimated level as well as minimizing the risk of the financial assets neutrosophic portfolio. These conditions allowed us to compute the financial assets’ share in the total value of the neutrosophic portfolios, for which the financial return reaches the level set by investors and the financial risk has the minimum value. In financial terms, the financial assets’ share answers the legitimate question of rational investors in the capital market regarding the amount of money they must invest in compliance with the optimal conditions regarding the neutrosophic return and risk.

scholarly journals Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation

2019 ◽  
Author(s):  
Dawei Cheng ◽  
Yi Tu ◽  
Zhenwei Ma ◽  
Zhibin Niu ◽  
Liqing Zhang
Keyword(s):  
Default Risk ◽  
Financial Risk ◽  
Learning Strategy ◽  
High Order ◽  
Risk Level ◽  
Financial Domain ◽  
Loan Risk ◽  
Representation Method ◽  
Low Dimensional ◽  
Financial Regulatory

Assessing and predicting the default risk of networked-guarantee loans is critical for the commercial banks and financial regulatory authorities. The guarantee relationships between the loan companies are usually modeled as directed networks. Learning the informative low-dimensional representation of the networks is important for the default risk prediction of loan companies, even for the assessment of systematic financial risk level. In this paper, we propose a high-order graph attention representation method (HGAR) to learn the embedding of guarantee networks. Because this financial network is different from other complex networks, such as social, language, or citation networks, we set the binary roles of vertices and define high-order adjacent measures based on financial domain characteristics. We design objective functions in addition to a graph attention layer to capture the importance of nodes. We implement a productive learning strategy and prove that the complexity is near-linear with the number of edges, which could scale to large datasets. Extensive experiments demonstrate the superiority of our model over state-of-the-art method. We also evaluate the model in a real-world loan risk control system, and the results validate the effectiveness of our proposed approaches.

The Genetic Algorithm

2019 ◽  
pp. 154-178
Author(s):  
Burcu Adıguzel Mercangöz ◽  
Ergun Eroglu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Models ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Biological Evolution ◽  
Research Field ◽  
Risk Level ◽  
Important Research ◽  
Portfolio Optimization Problem

The portfolio optimization is an important research field of the financial sciences. In portfolio optimization problems, it is aimed to create portfolios by giving the best return at a certain risk level from the asset pool or by selecting assets that give the lowest risk at a certain level of return. The diversity of the portfolio gives opportunity to increase the return by minimizing the risk. As a powerful alternative to the mathematical models, heuristics is used widely to solve the portfolio optimization problems. The genetic algorithm (GA) is a technique that is inspired by the biological evolution. While this book considers the heuristics methods for the portfolio optimization problems, this chapter will give the implementing steps of the GA clearly and apply this method to a portfolio optimization problem in a basic example.

Are banks misled by leverage misestimate of Chinese listed companies?

2020 ◽  
Vol 11 (4) ◽  
pp. 507-535
Author(s):  
Zhenjie Wang ◽  
Zhuquan Wang ◽  
Xinhui Su
Keyword(s):  
Financial Risk ◽  
Negative Impact ◽  
Listed Companies ◽  
Risk Indicators ◽  
Risk Level ◽  
Short Term ◽  
Content Type ◽  
The Real ◽  
Market Shares ◽  
Traditional Research

Purpose The authors point out that the existing research confuses the operational liabilities formed based on the “transaction” relationship with the financial liabilities formed based on the “investment” relationship, which not only exaggerates the value of leverage but also underestimates the level of protection that companies provide for creditors alone. That is, the confusion of concepts not only triggers the problem of leverage misestimate but also triggers the short-term financial risk misestimate. The performance of “nominal leverage” and “nominal short-term solvency” based on total assets calculation cannot reflect the real leverage level and the real short-term financial risk of enterprises. Design/methodology/approach To distinguish the concepts of “assets” and “capital” and rationalize the relationship between “transactions” and “investments”, authors systematically design the “real leverage” indicators and “real short-term solvency” indicators, and measure the degree of misestimate of leverage and short-term financial risk indicators by traditional research. On this basis, this paper describes and analyses the trends of leveraged misestimate and short-term financial risk misestimate of listed companies in China and analyses which companies have more serious leverage misestimate. And it helps readers to form an objective understanding of the leveraged misestimate and short-term financial risk misestimate of listed companies in China. Findings Firstly, the overall high level of leverage of listed companies in China in the traditional sense is largely because of the misestimate of indicators. And this kind of misestimate is more serious among firms that have advantages in trading, such as state-owned enterprises and firms with higher market shares. Secondly, for most firms with normal solvency, traditional research systematically overestimated the negative impact of “nominal leverage” on financial risk indicators (represented by short-term solvency). The overestimation is significant in firms with serious leverage misestimate. Thirdly, indicators’ misestimate of the traditional research makes the banks cannot make effective credit decisions according to the firm's “real leverage” and “real short-term solvency”. Originality/value Firstly, clarify the differences between the concepts of “assets” and “capital”, and clarify the level of “real leverage” of listed companies in China, which is conducive to the process of “de-leveraging”. Secondly, revise the problem of misestimate of related indicators, so that financial institutions can clearly identify the true profitability and real risk level of the entity domain, and thus improve the effectiveness of credit decisions.

scholarly journals ETHICS, SOCIAL RESPONSIBILITY AND CORRUPTION AS RISK FACTORS

10.26458/1711 ◽  
2017 ◽  
Vol 17 (1) ◽  
pp. 17
Author(s):  
Alexandru GRIBINCEA
Keyword(s):  
Risk Factors ◽  
Social Responsibility ◽  
Financial Risk ◽  
Financial Structure ◽  
Insurance Company ◽  
Financial Return ◽  
Net Profit ◽  
The Cost

 The financial risk characterises the variability of net profit, subject to the financial structure of the insurance. The capital of the insurance company has two elements (the equity and the borrowed one) that differ fundamentally in the cost they generate. If the company uses loans, it will bear systematically the related financial expenses, too. Through its size and cost, indebtedness leads to the variation and changes the size of financial risk. Resorting to the debt is justified through the high remuneration of equity in relation to borrowed capital, thus increasing the financial return.  

scholarly journals Ecological Restrictions of Spatial Development in Practice of Russian Regions

2019 ◽  
pp. 32-42 ◽  
Author(s):  
E. A. Zhalsaraeva ◽  
A. V. Shangina ◽  
M. A. Dugarzhapova
Keyword(s):  
Economic Development ◽  
Mineral Resources ◽  
Spatial Development ◽  
Regional Economies ◽  
Russian Regions ◽  
Natural Systems ◽  
Social And Economic Development ◽  
Federal Districts ◽  
The Republic ◽  
Economic Balance

The article describes conditions of spatial development of Russian regions in view of ‘The Strategy of Spatial Development of the Russian Federation up to 2025’ adopted in February 2019. Today the social and economic development of regions has technogeneous character. The anthropogenic impact on nature and climate is increasing, the quality of mineral resources is deteriorating. In this connection ecologic-economic balance becomes an important component of spatial development. The authors provide systematization of factors determining the spatial economic development at the level of regions, including ecological restrictions. During the research they used general academic methods of quantitative and qualitative analysis, scientific abstraction, synthesis and comparison. The current ecologic-economic restrictions for regions with unique natural systems and objects were identified and studied. Focus was made on particularly protected natural territories, which form the basis of ecological framework of regions. Principle groups of factors of spatial development were identified and ecologic-economic restrictions of regional development were described by using two big federal districts – the Republic of Buryatia and the Altay Territory. The authors put forward the lines of spatial development of regional economies with regard to ecological restrictions.  

scholarly journals Bayesian inference for extreme value flood frequency analysis in Bangladesh using Hamiltonian Monte Carlo techniques

2019 ◽  
Vol 276 ◽  
pp. 04006
Author(s):  
Md Ashraful Alam ◽  
Craig Farnham ◽  
Kazuo Emura
Keyword(s):  
Monte Carlo ◽  
Flood Risk ◽  
Extreme Value ◽  
Return Level ◽  
Flood Frequency Analysis ◽  
Extreme Value Analysis ◽  
Risk Level ◽  
Mcmc Methods ◽  
Value Analysis ◽  
Hamiltonian Monte Carlo

In Bangladesh, major floods are frequent due to its unique geographic location. About one-fourth to one-third of the country is inundated by overflowing rivers during the monsoon season almost every year. Calculating the risk level of river discharge is important for making plans to protect the ecosystem and increasing crop and fish production. In recent years, several Bayesian Markov chain Monte Carlo (MCMC) methods have been proposed in extreme value analysis (EVA) for assessing the flood risk in a certain location. The Hamiltonian Monte Carlo (HMC) method was employed to obtain the approximations to the posterior marginal distribution of the Generalized Extreme Value (GEV) model by using annual maximum discharges in two major river basins in Bangladesh. The discharge records of the two largest branches of the Ganges-Brahmaputra-Meghna river system in Bangladesh for the past 42 years were analysed. To estimate flood risk, a return level with 95% confidence intervals (CI) has also been calculated. Results show that, the shape parameter of each station was greater than zero, which shows that heavy-tailed Frechet cases. One station, Bahadurabad, at Brahmaputra river basin estimated 141,387 m3s-1 with a 95% CI range of [112,636, 170,138] for 100-year return level and the 1000-year return level was 195,018 m3s-1 with a 95% CI of [122493, 267544]. The other station, Hardinge Bridge, at Ganges basin estimated 124,134 m3 s-1 with a 95% CI of [108,726, 139,543] for 100-year return level and the 1000-year return level was 170,537 m3s-1 with a 95% CI of [133,784, 207,289]. As Bangladesh is a flood prone country, the approach of Bayesian with HMC in EVA can help policy-makers to plan initiatives that could result in preventing damage to both lives and assets.

A deforming time approach to the treatment of risk in projects evaluation

2018 ◽  
Vol 19 (5) ◽  
pp. 548-563
Author(s):  
Salvador Cruz-Rambaud ◽  
Ana Maria Sanchez-Perez
Keyword(s):  
Alternative Model ◽  
Financial Risk ◽  
Western Europe ◽  
Function Analysis ◽  
Point Of View ◽  
Risk Level ◽  
Discount Function ◽  
Content Type ◽  
Project Assessment ◽  
Significant Difference

Purpose The purpose of the paper is to introduce a novel methodology to identify and quantify the difference of financial risks exhibited by listed and unlisted companies in their debt payments from an empirical point of view. Design/methodology/approach The paper attempts to establish the theoretical relationship between the agreed original periods and their corresponding periods of real payments. It is based on Krugman’s curve. This relationship has been implemented using data from listed and unlisted companies of Spain and from Western Europe countries (divided by companies, size and industry). Findings An alternative model has been implemented with the available information about listed and unlisted companies. There is not a significant difference in the financial risk level corresponding to listed and unlisted firms in Spain. Practical/implications The paper could provide a useful guidance in applying the risk in project assessment. Originality/value This paper provides a new methodology to reduce the subjectivity shown in the treatment of risk by traditional approaches. The method allows to including the financial risk in the time parameter of the discount function. Analysis of the delays in debt payments by both listed and unlisted companies; Alternative model able to describe the expected delays from the initial agreed period; Inclusion of the financial risk in the parameter “time” of a discount function.

scholarly journals Application of Markowitz model in analysing risk and return a case study of BSE stock

2012 ◽  
Vol 2 (1) ◽  
pp. 7-15
Author(s):  
Manas Pandey
Keyword(s):  
Optimization Problems ◽  
Real Life ◽  
Efficient Frontier ◽  
Risk Level ◽  
Risk Return ◽  
Real Life Data ◽  
Markowitz Portfolio ◽  
Efficient Portfolios ◽  
Data Subject

In this paper the optimal portfolio formation using real life data subject to two different constraint sets is attempted. It is a theoretical framework for the analysis of risk return choices. Decisions are based on the concept of efficient portfolios. Markowitz portfolio analysis gives as output an efficient frontier on which each portfolio is the highest return earning portfolio for a specified level of risk. The investors can reduce their risks and can maximize their return from the investment, The Markowitz portfolio selections were obtained by solving the portfolio optimization problems to get maximum total returns, constrained by minimum allowable risk level. Investors can get lot of information knowledge about how to invest when to invest and why to invest in the particular portfolio. It basically calculates the standard deviation and returns for each of the feasible portfolios and identifies the efficient frontier, the boundary of the feasible portfolios of increasing returns.

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