Investment Risk Research Articles

Dirichlet Integral and Its Generalization by Different Methods

Dirichlet integral, as the integration of sine function over x from positive infinity to 0, or negative infinity, is known as one of the most important concepts in mathematical analysis. In probability and statistics, Dirichlet integral is used in calculating expectation and variance. In physics, Dirichlet integral is used in describing the movement of a charged particle in specific electromagnetic fields, and the wave function of particles in quantum physics. In computer science, Dirichlet integral is used in the optimization of neural network. In economics and management, Dirichlet integral is used in the optimization of production and distribution, the estimation of the risk and return in investment. In this paper, Dirichlet Integral and its generalization are studied. This paper provides several methods including residue theorem and Feynman’s trick to determine the value of Dirichlet integral. Meanwhile, this paper extends Dirichlet integral to the case of n -th power and deduces a general solution by gamma function for the integral.

Wellbeing at the Edges of Ownership: The Impacts of Housing Tenure, Social Capital, and Institutions

This paper presents a three-country analysis of the relationship between housing, indicators of social capital, and life satisfaction (a proxy for wellbeing) using cross-sectional data from national surveys in Australia, the UK and the US. Addressing a key policy debate we construct, for each jurisdiction, models of life satisfaction as a function of housing and social capital, with a view to better targeting policy interventions to achieve wellbeing outcomes. First, we show that the wellbeing premium associated with homeownership remains resilient despite housing market instability early in the 21st century. Second, we find that while social capital is an important independent positive influence on life satisfaction, its effect is weaker than that of housing tenure. Third, we include two measures of housing risk (repayment risk and investment risk). These position mortgagors on a continuum across the edges of owner-occupation, placing them closer to, or further from, the ‘ideal’ of outright ownership. The impact of these measures varies across the three countries, raising questions about the way life satisfaction at the edges of ownership may be shaped by institutional forces.

METODE MEAN-SEMIVARIANCE DALAM PEMBENTUKAN PORTOFOLIO REKSA DANA SAHAM TERBAIK BAROMETER BAREKSA

The most popular investment product today is mutual funds. One of the factors in selecting mutual funds is the low investment risk. This risk can be minimized again by combining several assets and then compiling them into a portfolio. The portfolio formation method used in this study is the Mean-Semivariance method and the Sharpe Index to calculate portfolio performance. The data used is the daily net asset value per unit of Mandiri Investa Atraktif Syariah, Rencana Cerdas, Manulife Saham SMC Plus and BNP Paribas Solaris for the period January 3 2022 to March 9 2023, Jakarta Composite Index (IHSG) data as a benchmark and BI-7 Day Reverse Repo Rate as a riskfree interest rate. The minimum risk of the portfolio will be achieved by compiling a portfolio of two mutual funds, namely Mandiri Investa Atraktif Syariah with a weight of 92,50% and the Smart Plan with a weight of 7,50% with an expected return rate of 0,00019 and a risk of 0,000000663. The Sharpe Index is 0.08971 indicating that the portfolio formed has good performance and can be used as a choice of investment assets by investors.

The Dual Leverage and Mechanism of ESG Information Disclosure on Investor Behavior

Abstract: With the global emphasis on sustainable development, ESG information disclosure has become a key factor for investors to assess corporate value and risk. The academic value of this paper lies in an in-depth analysis of the reverse effect, which is not mentioned in most literature. It stands from the perspective of investors' investment purposes and interests, the attitudes of long-term investors who desire sustainable development and short-term investors who only care about immediate returns in a short period when facing ESG information disclosure. On the other hand, there are negative impacts on investors due to reasons such as government policy standards or the level of corporate ESG information disclosure itself, which may also prevent investors from achieving their expected goals, leading to investment risks and ultimately investors may flow out of the company. At the same time, some external factors can also lead to a dual leverage effect, such as the economic slowdown caused by the pandemic.

Investment Feasibility Analysis Using Discounted Cash Flow Valuation Method (Case Study: Gold Mine in Pit A PT XYZ)

Investment in the mining sector carries a high risk, so a comprehensive financial evaluation is required to ensure project feasibility. This study assesses the financial feasibility of gold mining investment in Pit A, PT XYZ, for the period 2025 to 2036. The purpose of this study is to evaluate the feasibility of the project through the analysis of key financial indicators such as Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period (PBP) using the Discounted Cash Flow (DCF) method. The DCF method was chosen due to its ability to project future cash flows and incorporate them into a comprehensive assessment of the potential benefits and risks of the project. In addition, sensitivity analysis was conducted to see the effect of changes in important variables such as revenue, cost of goods sold (COGS), and capital expenditure (capex) on the financial results of the project. The results show that the project is financially viable, with a positive NPV of US$404.16 million, an IRR of 52.10%, and a PBP of 3.32 years, all of which indicate a strong return on investment. Further sensitivity analysis shows that the project remains robust under various scenarios of changes in key variables. This study contributes to the investment evaluation literature in the mining sector by offering a more integrated approach to addressing investment uncertainty and risk through the combined use of the DCF method and sensitivity analysis.

Particle swarm optimization with historical return decay enhances cooperation in public goods games with investment risks

In the realm of spatial public goods games, heterogeneous investment has emerged as a potent mechanism to enhance cooperative behavior. Savvy investors dynamically adjust their investment levels to optimize returns based on environmental conditions and personal circumstances. This study delves into the evolution of cooperation within a spatial public goods game framework, characterized by heterogeneous investment and associated risks, conducted on a square lattice. We introduce the investors (I) with heterogeneous investment, who updates his investment amount according to the current environment of the game and his own historical experience. We introduce a particle swarm optimization algorithm with decaying historical returns to fine-tune the investment levels of investors. Furthermore, a risk mechanism inspired by the ultimatum game is integrated into the model. This paper investigates the impact of the risk threshold θ on cooperative behavior and examines the influence of the risk decay factor α on cooperation. Additionally, it analyzes the investment behavior of investors in scenarios where two types of investors coexist. Finally, this study explores the effects of the historical best payoff decay factor β and the self-learning rate c1 on cooperative behavior. This research contributes to a nuanced understanding of heterogeneous investment behaviors in public goods games under specified risk mechanisms, providing novel insights into the intricate dynamics of cooperation in complex systems.

Strategic Investment and Industry Risk Dynamics

Abstract This paper characterizes how firms’ strategic interaction in product markets affects the industry dynamics of investment and expected returns. In imperfectly competitive industries, a firm’s exposure to systematic risk is affected by both its own investment strategy and the investment strategies of its peers, so that the dynamics of its expected returns depend on the intraindustry value spread. In the model and the data, firms’ betas and returns correlate more positively in industries with low value spread, low dispersion in operating markups, and low concentration. (JEL G12, G31)

Modeling risky portfolios with guaranteed maximize returns under a patent race

This paper studies the patent race problem of communication enterprises investing in communication technologies, and constructs a portfolio optimization model which considers the expected returns, investment risks, and replacement costs, in order to achieve the dual goals of maximizing the net investment income of backward enterprises and minimizing the expected investment risk. Through numerical experimental analysis, the optimal investment portfolio strategy under different risk levels and the impact of different risk levels on the net income of lagging company are obtained. The research results show that due to the backward research in the first stage of the backward enterprises, when their own investment decision-making power is relatively high, they can focus on the development of self-interested key technology areas in order to achieve the victory of the patent race.

How do Carbon Emissions Spillovers Reshape Metal Market Dynamics? Time– Frequency Insights on Precious and Non-precious Metals

Global disruptions, such as health crises and geopolitical tensions, significantly impact both climate and commodity dynamics. This study, well-grounded on environmental finance theory, input–output modelling, socio-transition philosophy and behavioural finance perspectives, explores the evolving interactions between carbon emissions (CE) and metal markets during COVID-19 pandemic and Russia–Ukraine war (RUW). The study uses time-varying parameter vector autoregressive model (TVP-VAR) technique to evaluate time and frequency varying connectedness between CE and metal markets from 3 January 2020 to 28 June 2024. During the COVID-19 pandemic, initial connectedness among selected markets peaked at 85%, averaging 46%, highlighting a significant CE–metal nexus that necessitates strategic responses. In the RUW period, connectedness averaged 47.82%. CE influence metal markets primarily in the short term. Wavelet coherence analysis reveals that palladium and platinum are highly sensitive to CE over the long term, while gold and silver may serve as effective diversifiers and hedges against carbon-related risks in metal investments. The study is relevant for investors in the metal sector with environmental considerations. JEL Classifications: G110, Q430, L720

A comprehensive framework for evaluating and predicting investment risks in renewable energy projects: a case study of Maynak hydropower station

ABSTRACT This study introduces a holistic analysis framework designed to evaluate and predict the investment risks associated with foreign renewable energy initiatives. This framework aims to reduce the uncertainties inherent in renewable energy projects. To achieve this, we utilize the variable weight matter-element extension model to establish the project’s fundamental reliability function. We then refine this model by incorporating evidence theory to assess the project’s risk level and derive precise risk index measurements. We utilize the GM model to forecast potential future risks associated with the project. In addition, a case study is presented on the risk assessment and prediction for the Maynak Hydropower Station. Our findings reveals that the project encountered significant investment risks in 2008, 2014, 2020, and 2022. Key risk indicators include political turmoil，policy change，imperfect legislation，cultural risk，exchange rate changes，technical risks and management risks. Furthermore, the investment risk level declined from 2023 to 2027, with our risk measurement outcomes closely aligning with actual conditions, thereby validating the effectiveness and applicability of our model.

The Impact of Investor Psychology on Investment Decisions

This article aims to explore the significant impact of investor psychology on investment decisions, taking overconfidence and risk tolerance theories as examples to analyze their influence on decision-making. The examination of the investment psychology during the Chinas 2018 A-share crash highlights that investors are susceptible to psychological biases and emotional reactions in the face of market changes. These biases include representativeness bias, where investors may rely too heavily on stereotypes, leading to misjudgments; panic, which can trigger impulsive and irrational selling; overconfidence, causing investors to overestimate their abilities and knowledge; and catastrophic thinking, where investors focus excessively on potential worst-case outcomes. These psychological factors contribute to increased market volatility and can lead to irrational judgments and behaviors among investors. Such behavior is detrimental to the market's stable development, as it can result in mispricing of assets and increased risk of misinvestment. The article emphasizes the critical role of investor psychology in rational decision-making and market efficiency. By recognizing the personal and environmental drivers of these biases, investors can adopt strategies to counteract their influence. This includes improving self-awareness, seeking a variety of perspectives, and employing tools to regulate emotional reactions to market shifts. Policymakers can further contribute by fostering market transparency and educating investors, which can diminish the impact of psychological biases on investment choices. In conclusion, a concerted effort to comprehend and tackle investor psychology is essential for cultivating a more stable and efficient investment landscape.

The Influence Of Profitability, Solvency And Asset Management On The Rate Of Return On Stock Investment

This research examines the influence of profitability, solvency and asset management on the rate of return on stock investment. This research is based on agency theory and signal theory, which shows how a company's financial performance can influence investment decisions. The analysis was carried out using secondary data from the financial reports of companies listed on the Indonesia Stock Exchange (BEI) in 2022 and 2023, with a sample of companies included in the IDX80 index. The research method used is associative quantitative, which aims to examine the relationship between independent variables (profitability, solvency and asset management) and the dependent variable (rate of return on stock investment). The analysis results show that profitability has a negative influence on the rate of return on stock investment, while solvency and asset management have a positive influence. Descriptive statistical tests, classical assumption tests, and multiple linear regression were used to analyze the data. Profitability is measured using Return on Equity (ROE), solvency is measured by Debt to Equity Ratio (DER), and asset management is measured by Total Asset Turnover. The conclusion of this research emphasizes the importance of good financial performance in increasing stock returns and reducing risks for investors. These findings are relevant for investors and managers in making investment decisions and company management.

Clean Energy Challenges and Innovation Opportunities in Kazakhstan

Abstract Kazakhstan has pledged to transition to a low-carbon economy by implementing national policies and strategies that promote clean energy innovation. However, Kazakhstan is still falling short of its expected targets for energy transition, and there is a lack of knowledge regarding the country's challenges and opportunities for clean energy development. Towards this end, the current study identifies and assesses the enablers and barriers related to clean energy innovation in Kazakhstan. Using the combination of SWOT analysis, survey data from 41 experts and the DEMATEL decision support tool, we evaluated the key factors affecting Kazakhstan's clean energy innovation and their implications for energy transition. Assessment results show that the immature business environment, underpinned by technological, institutional, and socioeconomic factors, is perceived as a high-impact constraint for clean energy innovation and green finance deployment in Kazakhstan. Skilled labour shortages, high reliance on hydrocarbons and low retail energy prices are significant challenges to Kazakhstan's clean energy innovation. The low-profit margin and high investment risk in clean energy projects are identified as transition barriers in the power and energy-intensive industries. In contrast, Kazakhstan's endowments of resources critical for developing clean energy technologies (rare earth metals, uranium, gas) and the potential of low-carbon investments (e.g. carbon storage) are perceived as prominent enablers of clean energy innovation. Results are consistent across expert subgroups (academia, industry, NGOs, etc). Findings call for policy support to modern and attractive business environments, capacity, and human capital development. The findings can provide helpful insights for countries in Central Asia and beyond with similar socioeconomic structures that aim for a timely energy transition.

Beyond Toy Story: Pixar's Business and Marketing Strategies for Enduring Success in Entertainment

As the animated film industry continues to grow, many companies are faced with a lack of content innovation and character innovation, and some companies continue to experience a decline in market competitiveness as well as an increase in investment risk as a result. However, Pixar Animation Studios, a subsidiary of the Disney Group, has created a series of classics through its compelling storytelling and superior animation techniques, which have not only won wide acclaim from audiences around the world, but also continue to set box office records. These achievements perfectly reflect Pixar’s innovation and market leadership in the animation industry. This study examines how Pixar continues to engage a new generation of audiences through unique storytelling and innovative strategies, as well as addressing some of the creative challenges it may face. The importance of the study is demonstrated by understanding the significant role that innovation has played in the development of the animated film company and how it has helped the company to address potential issues and challenges by approaching them from different innovative perspectives. Using a case study approach, this study assesses Pixar’s market performance, innovation strategies and potential for developing future growth through data analysis and literature research. The study finds that Pixar has successfully maintained its innovation and leadership in the animated film industry through technological innovation, creative marketing, cross-border collaborations, and adherence to originality, resulting in a series of award-winning productions such as “Coco” and “The Lion King”.

Decoding Market Efficiency: A Multifractal Approach to Financial Market Dynamics

Financial markets were traditionally analyzed using the Efficient Market Hypothesis (EMH), which argued that asset prices fully reflected all available information. This led to the belief that price movements were random, making it impossible to consistently outperform the market. However, emerging research had highlighted that markets could exhibit multifractality, where inefficiencies existed across different time scales, challenging the traditional EMH framework. Our study built on these insights by investigating the market efficiency of several asset classes, including stock market indices, cryptocurrencies (Bitcoin, Ethereum, USDT, and Binance), commodities (gold and crude oil), fixed income (U.S. 10-Year & 30-Year Treasury Bonds), and foreign exchange (EUR/USD & GBP/USD). Utilizing the Multifractal Detrended Fluctuation Analysis (MFDFA) method, we analyzed the multifractal properties and efficiency of each asset class. The results revealed that traditional assets exhibited monofractality, adhering more closely to the EMH. In contrast, cryptocurrencies displayed significant inefficiencies, likely due to their volatility, market structure, and susceptibility to external shocks. The findings provided valuable insights into portfolio diversification and risk management, suggesting that inefficiencies in certain asset classes might present both opportunities and risks for investors.

Empirical Study on BYD Stock Price Prediction Based on LSTM Model

This study employs Long Short-Term Memory (LSTM) neural network models for stock price prediction. LSTM models excel in managing nonlinearities and time-series dependencies in data, thereby enhancing the accuracy of stock price forecasts, reducing investment risks, and increasing returns. Investors can reasonably use the LSTM model to predict stock prices and obtain greater returns. The research utilizes eight years of historical data from BYD, including opening prices, closing prices, highest prices, lowest prices, and trading volumes, as training data. By adjusting various hyperparameters such as epochs, learning rates, and training-test ratios, the study analyzes their impact on the predictive accuracy of the model and identifies optimal configurations through comparative experimental methods. We found that the best parameters were when epoch number was 25, training ratio was 90:10 (minimum mean square error was 0.00175572) and 4 years of training data was selected as the training set (minimum mean square error was 0.00146477).

Research on Quantitative Investment Strategies of Securities Investment Funds

This paper investigates the quantitative investment strategies of securities investment funds, aiming to explore their applications in financial markets and their impact on investment decision-making. Through a review of relevant literature and empirical data, the paper first outlines the basic concepts of quantitative investing and its importance in securities investment funds. Then, it discusses various quantitative strategies, including multi-factor models and machine learning algorithms, and validates their effectiveness through case studies. The research finds that quantitative investing can enhance the scientific and accurate nature of investment decisions while effectively reducing investment risks. Finally, the paper provides suggestions for optimizing quantitative strategies and discusses future trends, offering practical references for the investment industry.

Research on investment and financing risk problems and countermeasures of PPP construction projects

This study analyzes financing costs, capital liquidity, policy changes and contract execution risks in PPP project investment and financing, and puts forward strategies such as optimizing financing platforms, reducing costs, strengthening regulations, regulatory coordination and improving prevention capabilities. By exploring the causes and effects of risks, combined with domestic and foreign cases to verify the effectiveness of the strategy. This study enriches the theory of PPP risk management, provides guidance for the practical operation of the project, and promotes the healthy development of the PPP project.

ESTRATÉGIAS FINANCEIRAS INTEGRADAS: MÉTODOS DE GESTÃO, ANÁLISE DE INVESTIMENTOS, AVALIAÇÃO DE RISCOS E TOMADA DE DECISÃO PARA A OTIMIZAÇÃO DE RECURSOS E MAXIMIZAÇÃO DE VALOR

This study explores integrated financial strategies as an essential approach to optimizing resources and maximizing value in organizations. The research analyzes financial management methods, investment analysis techniques and risk assessment approaches, highlighting the importance of strategic decision-making in complex environments. Integrated financial management allows for a more efficient allocation of resources, risk reduction and more informed decisions, providing a significant competitive advantage in the market. In addition, the integration of different functional areas improves organizational agility and the ability to adapt to constant market changes. The results suggest that companies that adopt these strategies achieve significant improvements in resource allocation, risk management and strategic decision-making. They are thus better placed to face contemporary challenges and guarantee long-term sustainability, ensuring their growth and success in a dynamic economic scenario.

Exploring the socioeconomic factors affecting green cities development

PurposeThis paper explored the socioeconomic factors affecting green cities development. Extant literature have highlighted green cities as a major path towards sustainability in the construction industry but very little is known on the socioeconomic aspect of green cities and its bid in promoting sustainability in the construction industry; hence, the premise of this study which highlights the socioeconomic factors affecting green cities development in Ghana.Design/methodology/approachA comprehensive literature review was conducted to identify the socioeconomic factors affecting green cities. A quantitative research strategy was adopted to collect primary data from respondents who have the requisite understanding and knowledge in green cities using questionnaires. The data gathered was then analysed using descriptive statistics and exploratory factor analysis viz principal component analysis.FindingsThe socioeconomic factors affecting green city development comprised: Green support mechanisms (i.e. innovation and technology, green city planning (urban planning), stakeholder engagement, awareness, city planning (transportation) and environmental regulations); green inhibitors (i.e. population, culture, housing and policy implementation); green market and finance (i.e. digital finance, green market mechanism, green investment finance, risks and uncertainties, income levels of clients). It was evident that socioeconomic factors are significant to the development of green cities in Ghana and hence policy makers and various stakeholders should prioritize socioeconomic factors in the bid to achieve sustainability through green cities in the construction industry.Originality/valueThis paper presents a foremost and comprehensive study on the socioeconomic factors affecting green cities in Ghana. The study results showed that even though the path to sustainability in green cities has pivoted mainly on environmental factors, socioeconomic factors are also significant to green city development, hence, policy makers and the construction industry should keenly consider the socioeconomic factors affecting green city development in the bid towards sustainability for cities.