Investment Risk Analysis Research Articles

Navigating investment risks mode and transmission: a study of China’s participation in the Belt and Road Initiative through the lens of risk society

ABSTRACT The world is experiencing a period of heightened complexity and uncertainty, where risks are interconnected and extend beyond traditional boundaries. This paper delves into the effective identification of investment risks for China’s involvement in the Belt and Road Initiative (BRI), utilizing methodologies rooted in complex networks and Minimum Spanning Tree. It establishes an indicator system for foreign direct investment (FDI) risks across four dimensions: economic, political, social, and bilateral relations, while also developing a risk identification model grounded in complex network theory. The empirical analysis draws from data spanning 2010 to 2019, encompassing 103 co-building countries of the BRI. Key findings include: (1) Political risk emerges as pivotal when Chinese enterprises invest in co-building countries of the BRI. (2) Examining individual country investment risks reveals an increased transmissibility of China’s FDI risks. Notably, Indonesia, Brazil, and Bangladesh showcase internally complex investment risk environments. (3) Analysis of investment risks between countries and regions indicates a rise in risk transmission paths since 2010. Particularly, the South Asian region stands out for harbouring the longest transmission path. This study advances theoretical and empirical understanding of effective identification of China’s overseas investment risks, offering valuable insights for the development of global risk society governance.

Financing of the company's investment activity in the face of growing risks

The article examines the impact of globalization and economic reforms on enterprise management, focusing on the need to modernize production facilities, automate processes and use information technology. The role of investment as a key tool for promoting economic growth of enterprises, increasing their competitiveness and adaptation to new challenges of the globalized economy is considered. Particular attention is paid to the analysis of investment risks, their classification and methods of minimization, which allows enterprises to reduce negative impacts in the implementation of investment projects. Effective management of investment risks and determination of optimal investment strategies are critical factors for attracting new investments and achieving sustainable economic growth. The legal framework governing investment activity in Ukraine is defined, and approaches to investment management in accordance with international financial reporting standards are summarized. Thus, investment activity is considered not only as a source of economic benefit for enterprises, but also as a factor contributing to the economic stability of the national economy.

Analysis of Financial Investment Risk and Preventive Measures

With the development of economic cycle, financial investment has become one of the key exploration directions of enterprises. Scientific financial investment can help enterprises increase the value of assets and expand the scale of operation. However, due to the increasingly complex economic market, financial investment risks are more complex. Based on this, this paper takes the risks faced by enterprises in the process of financial investment as the research basis, sorts out the causes of risks, and puts forward targeted preventive measures, aiming at providing support for enterprises to stabilize operation and obtain more investment returns.

Reducing the negative impact of wildfire disasters through multi-scenario risk analysis of land investment

Natural meteorological and climatological hazards and vulnerable vegetation are frequent triggers of natural disasters, such as wildfires. These cause loss of life and significant economic damage, likely to increase due to climate and socio-economic changes. The increase in wildfires in recent decades has affected the decision-making of technocrats and policy experts and created the need for more sophisticated risk management. This paper develops a theoretical framework for reducing the economic impact of wildfire risk on forests. Also provides insight into land use decisions regarding losses that this increased risk of forest land management can cause. The impact of wildfire risk on economic land value is evaluated by comparing three scenarios, i.e. the deterministic risk-adjusted land expectation value with its risk-free counterparts calculated assuming the stationary risk and predicted increasing risk as time goes by. Also, for this purpose, we evaluate the economically optimal length rotation period pine stands and the risk premium. Fire risk is described as the probability of pine stands destruction due to their age and meteorological conditions in the experimental area Záhorská lowland (western Slovakia). Our findings highlight the importance of alerting forest policymakers to carefully incorporate assessment of the increasing risk of random natural disasters due to climate change into sustainable forest management. The study's results will also help current or future forest owners estimate and evaluate the economic/investing risk when buying or selling forest land.

Sustainable Information System for Enhancing Virtual Company Resilience Through Machine Learning in Smart City Socio-Economic Scenarios

Abstract This paper introduces an innovative framework for the management of virtual companies in smart urban environments, with an emphasis on socio-economic resilience facilitated by Sustainable Information Systems. The system aims to equip virtual enterprises in smart cities with tools for robust operations amid socio-economic challenges. Its effectiveness is evidenced by improvements in investment risk assessment, business process simulation, and HR project management, enhancing efficiency and foresight. A key feature is predictive analytics for crisis demand forecasting, enabling swift market adjustments and strategic inventory management. It also helps identify alternative clients and suppliers, ensuring business continuity. Integrating machine learning and augmented reality, the system supports automation and strategic decision-making, significantly benefiting the e-commerce sector by addressing fluctuating demand, supply chain issues, and market adaptations during crises. The Sustainable Information System for Virtual Company Management in Smart Cities offers crucial support for e-businesses facing these socio-economic challenges, facilitating their navigation through turbulent times. Its meticulously designed architecture and functionalities make it a powerful instrument for assisting virtual companies in crisis conditions, fostering their sustainable growth within the socio-economic framework of smart urban settings. Comparative studies with existing models underscore this system’s superior efficiency and holistic approach, highlighting its contribution to enhancing the operational efficiency of virtual companies by 95%, reducing the time needed for critical activities like investment risk analysis and business process simulation, and bolstering the socio-economic resilience of smart cities against crises

Literature Study of Stock Investment Risk Analysis for Beginner Investors

Awareness of investment has been owned by all parties, including young people who unfortunately still do not understand the risks of investment. This can certainly have a negative impact that is far from the expectations or objectives of the investment itself. This article was compiled using a literature review based on the Google Scholar database for the period 2014-2024, with one of the objectives to study the risks often experienced by novice investors. The results of the literature review show that investment decisions whose decision making is ideally rational, but often become irrational and this condition is very relevant to various theories such as the theory of Efficient Market Hypothesis (EMH), the theory of Behavioral Finance, the Theory of Planned Behavior.

Systemic Risk Analysis of Safety, Progress, and Investment in the Construction of a Water Transfer Project and the Importance of Common Cause Failure

Safety, progress, and investment risks are correlated during the construction period of large-scale water transfer projects. However, previous studies have only considered individual risk factors, overlooking the potential systemic risk posed to safety, progress, and investment, as well as any underlying common cause failures. Since traditional risk analysis methods are ill-suited to addressing common cause failure, this paper’s objective was to establish a comprehensive evaluation index framework and to identify the basic events of common cause failure. To do that, we developed a risk analysis method that models common cause failure based on a Bayesian network for assessing that systemic risk. The Henan Section of the Yangtze-to-Huaihe River Water Diversion Project in China was then used as a case study. The results show that a variety of common cause failure events, such as epidemic disease, design alteration, lagged approval process, heavy rain in the flood season, renewal material and failing equipment, construction accidents, and external interference, can significantly impact the safety, progress, and investment systemic risk. Design alteration poses the greatest risk, with renewal material and failing equipment exerting the strongest influence among all common cause failure events. It is also possible to elucidate the predominant causal chains; specifically, the contributing influence of each basic failure event to the systemic risk can be clarified by adjusting their respective initial state. The failure of renewal material and failing equipment was found to significantly increase the safety risk. This study effectively simulated the complex causal relationships and uncertainties of pertinent risk factors, thereby enhancing our understanding of the systemic risk associated with safety, progress, and investment in large-scale water transfer projects.

Deep Learning for Investment Risk Analysis, Expected Return and Stock Market Prediction

Ability For analyze prediction mark something share going up or down as well as level possible risk _ taken into account , much needed stock investors . In study this done analysis risk and correlation between share with calculate daily returns use method moving averages (MA). Besides that with a dataset of 4 stocks (Apple, Google, Microsoft and Amazon) also performed prediction mark stock in period time next (future), with use neural network method (deep learning) Long Short Term Memory (LSTM) model. Result of programming in python language in the form of a number of visualization easy graph _ reading information . Changes in sales volume share No happen in a manner significant , though from term MA chart short and long to four share tend experience decline price since month January to May 2022. Correlation highest 75% between shares of Google and Microsoft, and the lowest 60% between Apple and Google shares. From analysis risk and expected return, obtained results shares Amazon has risk the highest (0.022059) and the lowest expected return (-0.000003). Apple shares with medium risk ( 0.019926 ) and the highest expected return (0.001283). While 2 shares finally Google and Microsoft have risk small below 0.018 with expected return at the level are respectively 0.001019 and 0.000784. predictions time future with the LSTM model, indicating Apple , Google and Amazon will experience increase price stock , meanwhile decline price share will happened at Microsoft.

Investment risk analysis of electricity generation from vinasse biodigestion in the Brazilian Regulated Market

This paper presents a methodology of investment risk analysis of the electricity generation by using biogas from biodigestion of sugarcane vinasse produced in sugarcane agroindustries. The methodology to be presented uses econometric models to generate synthetic time series of ethanol production, which is used to estimate the production of vinasse, biogas and electricity. The economic viability analysis will be done from the evaluation of the probability distribution functions of Net Present Value, Modified Internal Rate of Return and Discounted Payback, by considering that the electricity produced is commercialized in the Brazilian Regulated Contracting Environment. A case study is carried out using real data from a sugarcane plant located in the Midwest region of Brazil. The results indicate the economic viability of the project implementation.

Risk Analysis of Financial Investment in Enterprise Financial Management in the Information Age

Abstract This paper introduces the GAS framework into the research problem of financial asset risk in the financial management of enterprises in the information age, gives the basic expression of the GAS framework model, improves based on the basic expression, and combines it with the traditional GARCH model to construct the GAS-GARCH-sst model. After that, the study of financial investment risk is extended further by constructing the GAS-factor Copula model, and when the parameters of the model are extrapolated, a two-step estimation method is used, i.e., the parameters of the marginal distribution and the parameters of the Copula model are extrapolated separately. Finally, with the help of the GAS-Factor Copula model, the financial investment risk analysis of ML Company can shows that the return on net assets, return on total assets and cost-expense margin of ML Company are lower than the industry average since 2020, and the growth rate of operating income has changed greatly, ranging from -354.88% to 54.3%. Cost-expense margin has been declining consistently since 50.1% in 2017, while cash inflow in investing activities is only 1.9%-17.57% of cash outflow in 2016-2021. This indicates that ML is facing the challenge of financial risk in various financial investments in the information age.

Инвестиционное развитие сельского хозяйства и его инвестиционная привлекательность в условиях современных вызовов и угроз

The article is devoted to the issues of investment development of agriculture and its investment attractiveness. The authors consider the main elements of the economic mechanism that influence investment activity in agriculture. The article also identifies the need to assess the investment attractiveness of the agricultural sector based on the analysis of investment potential and risks in order to develop solutions in the formation of investment agricultural policy and attract potential investors. Based on the assessment of the level of investment attractiveness in the context of the subjects of the Russian Federation and federal districts, the authors propose measures and mechanisms to increase investment potential in agriculture.

Financial investment risk analysis and countermeasures research based on CVaR-GARCH model

Abstract In this paper, based on the vector autoregressive algorithm, the conditional value-at-risk algorithm is used to compute the optimal portfolio, and the mean-CVaR model oriented to portfolio optimization is established based on the mean-variance model. To explain the volatility accumulation characteristics of financial asset return series, the autoregressive conditional heteroskedasticity model with CvaR is designed. After the design and optimization of the algorithm are completed, the daily closing price of a commodity futures contract, CSI 300 and the stock situation of different companies, and the daily closing index data of the financial index of a city are collected, and the three major groups of data are tested for the model of this paper. The results show that the maximum value of the CVaR-GARCH model at 95% and 90% confidence levels is generally 4000~6000, which is higher than that of the VaR model alone at 1000~2000. The difference between the actual loss and the loss predicted by the model in this paper is 88.618~279.181. The number of failures at 90% confidence level is 44. The number of failures at 95% confidence level is 31, while the number of failures at 99% confidence level is 1,000~1,000. The number of failures at 99% confidence level is 1,000~1,000. Number of times is 31. At a 99% confidence level, the number of failures is 11. This results in a generalized failure rate of 6.03%. Investors can make program adjustments with more accuracy due to the model in this paper’s ability to predict stock market risk.

Valuation Analysis of Lithium Battery Industry: Evidence from Kedali

In order to solve the problems of energy security and environmental pollution, governments are promoting the development of new energy industries to ensure the sustainable development of the human world. Among the new energy idustries, lithium batteries, as an important part of new energy vehicles, have widely used in recent years with the rise in sales of new energy vehicles. In China, with the increasing competition in the industry, various companies have different performances, and their business conditions affect the development of the entire new energy industry. Therefore, it is very necessary to build a reasonable value evaluation system for lithium battery companies to help related enterprises develop healthily and continue to operate, and market investors better understand the industry situation to make bright investment decisions. Based on the FCFF model, this paper analyzes the valuation of Kedali Company (one of the representative company in China's lithium battery industry), compares the intrinsic value of Kedali with the market value, and comes to the conclusion that the current stock price of Kedali is slightly undervalued. Finally, based on the conclusion, the investment risk analysis is presenetd for market investors, offering them some useful investment decisions.

BASIC APPROACHES TO INVESTMENT RISKS MODELING

The paper highlights the need for analysis and assessment of enterprise investment risks in conditions of uncertainty and suggests the use of a neuro-fuzzy investment risk assessment model.

Integrated techno-economic, investment risk and life cycle analysis of Indian lignocellulosic biomass valorisation via co-gasification and syngas fermentation

The current work presents techno-economic and life cycle assessment of a co-gasification- fermentation hybrid model of bio-ethanol production with recycle and recovery of different resources. The model is also equipped with waste heat recovery from the hot syngas and power generation from unconverted syngas. The co-gasification process is based on Gibbs’ free energy minimization and syngas fermentation follows chemical equilibrium. With the objective of studying the economic feasibility and environmental sustainability of the proposed model, techno-economic assessment followed by financial risk analysis and life cycle analysis has been conducted. On the basis of 1 kg product bio-ethanol the results of the life cycle analysis, the present bio-ethanol production process have been compared to those of 1st generation ethanol production and biomass to bio-ethanol conversion via sugar platform. The present process is found to be economically viable with a net present value, internal rate of return and payback period of 18.7 M$, 13.33% and 6.7 years respectively. Monte Carlo simulation of financial risk has been modelled to predict the uncertainty over net present value and 81.8% is the probability of getting a non-negative net present value. Life cycle analysis suggest that the presented process emits 1.23 kg less CO2 per kg of bio-ethanol compared to the conventional bio-ethanol production process from biomass.

Comparative Analysis of Investment Risk and Return in Crypto Currency, LQ-45 Index Shares, Exchange Rates and Gold during the Covid-19 Pandemic

The purpose of this study is to determine the return and risk conditions between investments in the form of crypto currency, LQ-45 Index shares, Exchange Rates and Gold. The research period was carried out during the Covid-19 pandemic, namely from March 2020 to June 2023. The results showed that investing with Crypto Currency provided the highest return as well as risk compared to other investments studied in this study. The research results also show that there are no differences between the four investments studied. The suggestion for this research is investors should be careful in choosing the type of investment because each investment has different risks and returns and to expand the variables and research period.

Fuzzy Comprehensive Evaluation Model of Project Investment Risk Based on Computer Vision Technology

With the development of the economy and real-time embedded systems and the progress of science and technology, people’s economic income forms have undergone tremendous changes, and the concept of financial management has become clearer in people’s property income arrangements. Project investment is one of the most popular financial management methods in the era of big data. Both large enterprise groups and individual petty bourgeoisie groups have begun to pay attention to the risks and benefits brought by the new financial management method of project investment. This paper’s goal is to develop a fuzzy comprehensive evaluation (FCE) model for project investment risk based on computer vision technology and explore the application of computer vision technology in project investment risk evaluation. This article first uses a real-time embedded system to understand the basic process of project investment and select 10 investment experts for risk assessment, risks, and causes of the risks through literature research and case analysis. Then, this paper establishes a model of fuzzy comprehensive evaluation of project investment risk through computer vision technology, real-time embedded systems, and neural network models in big data and artificial intelligence technology to realize the analysis and prediction of project investment risk. The fuzzy comprehensive evaluation method and analytic hierarchy process (AHP) are used in this evaluation model to evaluate and forecast project investment risks. In addition, this paper also trains and tests the risk evaluation model of this research through the support vector machine classification algorithm, the real-time embedded system, and the average random consistency index. The research shows that the fuzzy comprehensive evaluation model of this study has higher accuracy for project investment risk evaluation than other risk evaluation methods. For example, for the investment risk of chemical fiber projects, this research model evaluated the factors such as organization, management, technology, and economy and found that the risks were all higher than 21.36%, which concluded that the overall investment risk of chemical fiber projects was relatively high.

Analysis of Investment Risk and Share Return in Coal Mining Companies Listed on IDX for The Period 2019-2021

This research is to determine the level of investment risk and stock returns as well as grouping efficient shares and inefficient shares of coal mining companies through the Capital Asset Pricing Model (CAPM) method for the 2019-2021 period. The research method used is descriptive method with a quantitative approach, while the data analysis method uses the Capital Asset Pricing Model (CAPM) method. The results showed that there were 8 shares of coal mining companies with a beta (systematic risk) value of greater than 1 (β > 1), and 16 shares of companies with a beta of less than 1 (β < 1. The highest RI was found in HRUM shares with a rate of return of 0.07650 or 7.65%, while the lowest Ri is found in BOSS shares with an average rate of return of -0.06000 or - 6%. The highest E(Ri) value is found in DOID shares with a value of 0.00177 or 0.18%, while the lowest E(Ri) is found in GEMS companies with a value of 0.00126 or 0.13%. There are 16 shares of coal mining companies which are classified as efficient shares consisting of shares of ADRO, BSSR, BYAN, DOID, DSSA, FIRE, GEMS, iHRUM, INDY, iITMG, MBAP, MYOH, iPTRO, SMMT, iTOBA, and BRMS, as well as 8 inefficient stocks namely ARII, BOSS, BUMI, DEWA, ​​GTBO, KKGI, PTBA, and SMRU.

Risk analysis and preventive measures of local government investment and financing platform companies

District B is the central location of the economy of Shenzhen and Hong Kong, in the 1990s the urban infrastructure construction operation and maintenance are carried out by the government, the project construction funds are government financial funds, with the rapid increase in the economic level of the district, the residents' living needs for infrastructure needs increased, the limited government financial support in District B is difficult to promote the development and construction of urban infrastructure high-speed in all aspects. The government gradually tried to promote the reform of the investment and financing system mechanism, set up an investment and financing platform, tried to promote the construction of the investment and financing system by taking the government's financial capital as the lead and using bank credit funds and social capital to participate, set up a number of local investment and financing platforms, such as construction investment companies and investment management companies, and took the initiative to undertake the government-led infrastructure and construction of major industrial projects, prompting the investment and financing platform to enter the This has led to the fast track of development of investment and financing platforms.

Financial Investment Risk Analysis in the Post-Epidemic Era

This paper investigates the correlation between the persistent impact of the epidemic on investment risk in the post-epidemic period and the response measures. A high correlation is found between market performance and policies introduced due to the ongoing impact of the epidemic. Further validation is provided by using the CSI 300 as a reference portfolio and by selecting ten stocks from the CSI 300 in the capital markets services sector to calculate the correlation coefficient between the latter and the former. It is found that placing assets in capital market services companies leads to lower investment risk, and the correlation between investment risk and the ongoing impact of the epidemic is moderately low. However, placing assets in capital markets services firms is not a better choice currently if investors want to obtain a higher return on their assets.