Financial Risk Profile Research Articles

Assessing stochastic dominance of downside and upside financial risk profiles using the block maxima method in extreme value theory

This paper aims to assess the stochastic dominance of the extreme downside (negative return) and upside (positive return) risk profiles of three US stock market indices, namely NASDAQ Composite, S&P 500 and Dow Jones Industrial Average (DJIA) based on the block maxima method in extreme value theory. The extreme downside and upside risk profiles were developed using two datasets of 360 monthly minimum and maximum daily log returns respectively (from January 1992 to December 2021). Extreme losses beyond the 80th percentile (corresponding to a tail probability of less than 0.2) of the theoretical extreme risk profiles were adopted to investigate stochastic dominance. Pairwise comparisons show that the DJIA stochastically dominates the other two indices in both extreme negative and positive returns. Moreover, the extreme upside risk profile of the DJIA stochastically dominates its extreme downside risk profile. The paper finds that investment in short positions (encountering upside risk) provides the least extreme risk compared with long positions (encountering downside risk) for the DJIA, as well as both short and long positions for the S&P 500 and NASDAQ Composite.

Probability of Default. A Machine Learning Approach for Non-Financial Companies

This paper aims to enhance credit risk assessment for non-financial companies in Romania by developing a machine learning (ML) model to estimate the probability of default. Utilizing an extensive set of microeconomic data, including financial statements, loan-level data from the Credit Risk Register, shareholder structure, export and import activities, and external debt, the model provides a comprehensive analysis of a company’s financial health and risk profile. The ML model employs logistic regression for classification, with 80% of the data used for training and 20% for validation. The model’s performance was evaluated using the receiver operating characteristic curve and confusion matrix, demonstrating an accuracy of 88%. Further validation through point-in-time estimation confirmed the model’s stability. The study is limited by the relatively low number of defaulting companies in the sample and the unique economic disruptions of 2020 due to the COVID-19 pandemic. To account for these factors, a Random Under Sample Boosted Trees approach is employed, which improves the model’s ability to distinguish between defaulted and non-defaulted debtors. Despite these limitations, the research concludes that integrating extensive financial data and advanced ML techniques have the potential to markedly enhance credit risk assessment, providing a reliable tool for financial institutions to manage credit risk effectively. Future improvements could address data imbalance and incorporate more diverse economic conditions to enhance predictive power for defaulting companies.

Quantifying the Impact of Environmental Regulations on Financial Risk Profiles in the Metals and Mining Industry using Machine Learning Techniques

This white paper examines the influence of environmental regulations and sustainable practices on the financial risk profiles of metals and mining companies. It specifically looks at the costs of compliance and the potential for growth driven by innovation. By incorporating environmental variables into a machine learning-driven framework for evaluating financial risk, we measure the impact of adherence to regulations on financial outcomes and pinpoint prospects for sustainable expansion.

A Comparative Study on Relation Between ROE and Standard Deviation of Public Sector Company National Stock Exchange

This study investigates the relationship between Return on Equity (ROE) and Standard Deviation (SD) of public sector companies listed on the National Stock Exchange (NSE). ROE serves as a measure of profitability, reflecting a company's ability to generate earnings from shareholders' equity, while SD represents the volatility or risk associated with the stock returns of a company. Understanding the interplay between these two metrics is crucial for investors seeking to optimize their investment portfolios by balancing risk and return. Over a five-year period, this comparative study analyzes the ROE and SD of public sector companies on the NSE, aiming to uncover any significant correlations between these metrics. By conducting statistical analysis, including correlation and regression analysis, we seek to identify patterns and trends that can provide valuable insights into the financial performance and risk profiles of public sector companies. The study is motivated by the need to offer empirical evidence and insights that can assist investors in making informed decisions regarding their investments in public sector companies on the NSE. By examining the relationship between ROE and SD, we aim to provide investors with a deeper understanding of the risk-return trade off inherent in these companies, thus enabling them to better manage their investment strategies and portfolios. Through the findings of this study, investors can gain insights into the financial health and risk characteristics of public sector companies on the NSE, facilitating more informed investment decisions. Additionally, the study contributes to the existing body of research on financial performance and risk management in the context of government-owned enterprises, offering valuable implications for both investors and policymakers.

Comparative Analysis of Risk and Return of Manufacturing Companies: A Five-Year Study-(2019-2023)

The study employs both quantitative and qualitative research approaches to assess the historical performance of a selected group of Manufacturing companies in terms of risk-adjusted returns. In order to evaluate the risk and return profiles of Manufacturing companies, a number of financial measures are looked at, such as ROI, beta coefficients, and stock price volatility. To further assist investors, financial experts, and policymakers, the study also investigates the connection between economic and financial variables and stock returns. The research process includes gathering information from primary and secondary sources, choosing Manufacturing firms according to predetermined standards, and using statistical analysis tools like regression and correlation analysis. The results underscore the variation in the financial performance and risk profiles of Manufacturing firms, underscoring the significance of making well-informed investment decisions tactics and market forecasts in the ever-changing manufacturing industry. For investors looking to maximise their investment returns inside the Manufacturing Companies, the findings provide practical insights.

Comparative Analysis of Risk and Return of IT Companies

The study focuses on evaluating the historical performance of selected IT companies in terms of risk-adjusted returns, utilizing both quantitative and qualitative research methods. Various financial metrics, including stock price volatility, beta coefficients, and return on investment (ROI), are examined to assess the risk and return profiles of IT companies. Additionally, the study explores the relationship between economic and financial indicators and stock returns to provide insights for investors, financial analysts, and policymakers. The research methodology involves data collection from primary and secondary sources, selection of IT companies based on specific criteria, and statistical analysis techniques such as regression analysis and correlation analysis. The findings highlight the diversity in financial performance and risk profiles among IT companies, emphasizing the importance of informed decision-making for investment strategies and market predictions in the dynamic landscape of the IT sector. The findings offer actionable insights for investors seeking to optimize their investment outcomes within the IT Companies

Accounting, reporting and verification of impact: Implications for sustainability: A commentary on Adams et al. (2024) “Styles of verification and the pursuit of organisational repair: The case of social impact”

Accounting and the accountancy profession sit at a consequential crossroads as the realities of planetary boundaries manifest through climate change, biodiversity loss and soil depletion, forcing societies and economic systems to transform. Alongside the effects sustainability has on corporations’ financial performance, future outlook and risk profile, accounting and reporting need to focus on externalities, with the aim of shedding light on the impacts firm activities have on society and the natural environment. Drawing on Adams et al. (2023), this essay continues the discussion on sustainability, impact and verification, calling for attention to the styles of verification prioritized by current accounting infrastructures. With the interlinkages between verification, reporting and accounting, the elements and types of information we emphasize in verification have implications for corporate activities and priorities and, therefore, for their negative social and environmental impacts, which we as societies ought to reduce urgently. While challenging, the terrain of accounting, reporting and verifying of impact offers opportunities for the accounting profession and for institutions to serve the public interest by helping societies transform. This may, however, require them to transform themselves.

An Empirical Analysis of the Dynamics Influencing Bank Capital Structure in Africa

Financial institutions, particularly banks, have long grappled with the dilemma of structuring their capital optimally. This process, commonly referred to as capital structure decision-making, is of paramount importance, especially within the financial services sector, where strict regulations are imposed by reserve and central banks in alignment with global Basel guidelines. This study unveils the key factors that determine the capital structure choices of African banks, using panel data encompassing 45 listed banks across six nations that had embraced the Basel III Accord spanning the years 2010 to 2019. The study used the system-generalised moment methods (sys-GMM) estimator to fit the formulated panel data regression model. The study findings showed positive associations between ZSCORE, an indicator of bank financial stability, and net interest margin ratio (NIMR) with bank leverage (TCTE). In addition, the results revealed positive correlations between earnings volatility (EV), profitability (P), and risk (R) with bank leverage (TDCE). This suggests that profitable banks are inclined to favour debt financing, a phenomenon driven by their ability to comfortably service debt obligations with free cash flows. This study’s overarching conclusion underscores the dominant influence of the Liquidity Coverage Ratio (LCR) on African bank capital structures. Whether assessing traditional or Basel III-prescribed measures of bank leverage, LCR consistently emerged as the primary determinant. This finding is of significant relevance to bank executives and regulators, offering them essential insights for informed decision-making by considering striking a balance between equity and debt financing based on financial stability, profitability, and risk profiles.

A study on predictive analysis and rules for international financial risk considering debt crisis

Abstract This paper first establishes a system of international financial risk early warning indicators, constructs a VAR forecasting model for international financial risk, and uses the ADF test to identify the best amount of lags for the model before doing a smoothness test on the variables. The lagged values are used as independent variables to establish equations to describe the dynamic relationship between multiple variables. Then the model is tested for cointegration and Grange causality. The VAR model is then used to estimate the financial risk profile from the standpoint of a global economy and to examine the extent to which the debt crisis has affected international financial risk. The results show that at the 5% significant level, national debt expansion is the cause of asset price volatility, macroeconomic volatility, and debt risk. The forecasted inflation rate for 2023-2025 is 4.18, output gap is 4.94, and the policy interest rate is 4.18 for emerging Asian countries and regions.

Board Size and Leverage in Indonesian’s Pharmaceutical Companies. The Moderating Role of Profitability

This empirical study examines the relationship between board size, leverage, and profitability in the pharmaceutical sector. Using a sample of pharmaceutical companies from 2019 to 2022, the study reveals that board size has a significant positive impact on leverage. Additionally, profitability acts as a moderator in the relationship between board size and leverage. The results indicate a significant negative relationship between board size and leverage when considering profitability, suggesting that larger boards in pharmaceutical companies are associated with lower levels of leverage. These findings contribute to the existing literature on corporate governance and financial performance, highlighting the significance of board size in shaping leverage decisions and influencing profitability. The study suggests that larger boards in the pharmaceutical sector exercise caution in managing debt, leveraging their enhanced monitoring capabilities, diverse expertise, and increased accountability. Policymakers, industry practitioners, and investors can use these insights to make informed decisions regarding board composition, financing strategies, and evaluating the financial performance and risk profiles of pharmaceutical companies.

A model for wealth management through standardized financial advisory practices in Nigeria

This paper proposes a model for wealth management through standardized financial advisory practices in Nigeria, aimed at improving financial literacy, investment decisions, and long-term wealth creation. In the context of Nigeria’s evolving financial sector, the need for a standardized approach to financial advisory services has become essential to bridge gaps in wealth management and improve the financial well-being of individuals and institutions. The model focuses on establishing a framework that standardizes financial advisory services, ensuring consistency, transparency, and accountability across the financial services industry. Key components include developing regulatory guidelines for financial advisors, implementing standardized tools for risk assessment and portfolio management, and promoting adherence to ethical practices. This framework also emphasizes the importance of financial literacy as a foundational element in wealth management. By improving public knowledge on investment options, risk tolerance, and long-term financial planning, individuals can make more informed decisions, reducing exposure to fraudulent schemes and unregulated advisors. Additionally, the model proposes the development of a national certification program for financial advisors to ensure high standards of practice. Another aspect of the model involves leveraging technology to provide personalized financial solutions. Digital platforms, powered by artificial intelligence, will offer customized investment strategies tailored to individual financial goals and risk profiles. These platforms will also improve accessibility to advisory services, especially for underserved populations. The expected outcomes of this model include a more resilient financial services industry, increased consumer trust in financial advisory services, and broader participation in investment activities. In the long term, standardized financial advisory practices will lead to better wealth distribution and economic growth in Nigeria. In conclusion, the proposed model for wealth management through standardized financial advisory practices provides a comprehensive solution to enhance financial decision-making and wealth creation in Nigeria, fostering sustainable economic development.

Corporate environmental responsibility and default risk: Evidence from China

We use a novel and comprehensive dataset to investigate the influence of corporate environmental responsibility on the default risk of listed firms in China. We document a significant and negative impact of environmental performance on a firm's default risk. We use instrumental variables regressions and placebo tests to address endogeneity concerns and document consistent results. Further, we conduct cross-sectional analysis and find a strong negative influence of environmental performance on default risk in firms with high systematic risk, weak fundamentals, severe pollution, and high energy consumption, supporting volatility and environmental regulation uncertainty channels. Our findings reveal the externality of corporate environmental responsibility on a firm's financial risk profile.

Drought Shocks and Gearing Impacts on the Profitability of Sheep Farming

The resilience and profitability of livestock production in many countries can be impacted by shocks, such as drought and market shifts, especially under high debt levels. For farmers to remain profitable through such uncertainty, there is a need to understand and predict a farming business’s ability to withstand and recover from such shocks. This research demonstrates the use of biophysical modelling linked with copula and Monte Carlo simulation techniques to predict the risks faced by a typical wool and meat lamb enterprise in South-Eastern Australia, given the financial impacts of different debt levels on a farming business’s profitability and growth in net wealth. The study tested five starting gearing scenarios, i.e., debt to equity (D:E) ratios to define a farm’s financial risk profiles, given weather and price variations over time. Farms with higher gearing are increasingly worse off, highlighting the implications of debt accumulating over time due to drought shocks. In addition to business risk, financial risk should be included in the analyses and planning of farm production to identify optimal management strategies better. The methods described in this paper enable the extension of production simulation to include the farmer’s management information to determine financial risk profiles and guide decision making for improved business resilience.

Evaluating risks-based communities of Mafia companies: a complex networks perspective

This paper presents a data-driven complex network approach, to show similarities and differences—in terms of financial risks—between the companies involved in organized crime businesses and those who are not. At this aim, we construct and explore two networks under the assumption that highly connected companies hold similar financial risk profiles of large entity. Companies risk profiles are captured by a statistically consistent overall risk indicator, which is obtained by suitably aggregating four financial risk ratios. The community structures of the networks are analyzed under a statistical perspective, by implementing a rank-size analysis and by investigating the features of their distributions through entropic comparisons. The theoretical model is empirically validated through a high quality dataset of Italian companies. Results highlights remarkable differences between the considered sets of companies, with a higher heterogeneity and a general higher risk profiles in companies traceable back to a crime organization environment.

FINANCIAL LITERACY AND RISK PROFILE: AN EXTENSIVE OBSERVATION ON BANK EMPLOYEES

People with a high level of financial literacy tend to have better financial management skills to realize their financial well-being through effective financial decisions including investing according to their risk profile. The banking industry is an industry that has the highest inclusive level selected because it can represent financial literacy conditions. On the other hand, the gap between financial inclusion and financial literacy leads to a large number of investment (illegal) cases and complaints to regulators. The purpose of this research is to find out the level of financial literacy and type of risk profile, factors that affect it with bank employees in Bandung as research objects. The sampling technique used is a non-probability sampling technique that is purposive sampling with a total of 408 respondents. Data collection is through online questionnaires. There are three sections questionnaire, demographic factors, financial literacy, and risk profile. The data processing techniques used are descriptive statistical analysis and multiple regressions. The results showed that bank employees in Bandung had financial literacy indexes categorized as “medium” or “sufficient” (66.7%) with a risk profile index of “moderate” type (60%). Demographic factors that affect financial literacy are age, education level, and organizational position. While the factor that affects the risk profile is age and gender. Research has also revealed a strong correlation between financial literacy and risk profile.

Financial Performance of Renewable and Fossil Power Sources in India

This paper seeks to study and compare the historical and present-day financial performance and risk profile of the renewable energy and fossil fuel power sectors. Our findings are as follows. First, renewable energy power portfolios have historically shown more attractive investment characteristics including, on average, 12% higher annual returns, 20% lower annual volatility and 61% higher risk-adjusted returns. Second, investors perceive renewable energy power investments to be less risky than fossil fuel power investments, with the expected returns on debt to the fossil fuel power sector is at least 80 basis points higher than for expected returns on debt for the renewable energy power sector. Third, the main risk factors driving the risk perception of both renewable energy and fossil fuels are counterparty, grid and financial risks; counterparty risk is the most significant risk by far, followed by grid risk and then financial sector risk. Our findings have significant implications for investments in these technologies in India.

Maximizing performance with an eye on the finances: a chance-constrained model for football transfer market decisions

Composing a team of professional players is among the most crucial decisions in association football. Nevertheless, transfer market decisions are often based on myopic objectives and are questionable from a financial point of view. This paper introduces a chance-constrained model to provide analytic support to club managers during transfer windows. The model seeks a top-performing team while adapting to different budgets and financial-risk profiles. In addition, it provides a new rating system that is able to numerically reflect the on-field performance of football players and thus contribute to an objective assessment of football players. The model and rating system are tested on a case study based on real market data. The data from the case study are available online for the benefit of future research.

Precision variable rate nitrogen for dryland farming on waterlogging Riverine Plains of Southeast Australia?

Precision agriculture, using satellite navigation, has grown in popularity around the world. Because of its practical uses for land preparation, sowing, nutrient applications, pest (weed, pathogen, invertebrate) management, stress sensing and harvest recording, many major equipment manufacturers include precision variable rate capability as standard fittings. GIS (Geographic Information System) maps may include georeferenced soil chemistry information and detailed historical harvest yield data. We integrate such data to examine Precision Variable Rate Nitrogen (PVRN) applications with whole-farm management information and rainfall records in a district where waterlogging frequently reduces crop yields.Given wide variations in growing season rainfalls (GSR) and soils in the district, we test year-to-year stability of rainfed crop-yield rankings over time on 90x90m GIS grid-areas in large paddocks (over 100 ha). Variations in historical yield-quartile rankings of grid-areas across GSR levels over time are observed; some areas yield best at some GSR levels but not others, such that the best-yielding part of a paddock one year may be poorest in the next.We answer the question: “Why would a farmer in this district choose to apply a uniform moderate rate of N to a paddock at sowing even though in possession of precision variable rate-capable (PVR) equipment, georeferenced electromagnetic conductance (EM38) data and crop-yield map data for that paddock in many past seasons?” We show that soil conditions in the study district challenge the economic value of PVRN versus uniform rates in farming systems prone to waterlogging.If full-season GSR were reliably predictable early in the season, applications of N could be based on a rule calling for 40 kg/ha N/t of attainable yield at that GSR and grid-area EM38 level, minus sampled soil-N. Unfortunately, GSR is notoriously unpredictable.We simulate whole-farm financial risk profiles (CDFs of simulated decadal cash margins with varying prices and yields, minus all variable, fixed and capital costs) assuming moderate uniform N rates, as practiced in the study area, on two model farms; one with low and one with high-fixed-costs, given historical variations in GSR and prices. Assuming PVRN requires annual geo-referenced soil nutrient sampling of each hectare, these added costs could be covered by a 1% increase in yields across all wheat and canola crops or a 7% decrease in applied N. We cannot reject the null hypothesis that PVRN is no more profitable than uniform applications in this district. Near-real-time NDVI may lower the cost of PVRN for late applications.

Does learning by observing matter for M&As? International evidence from the insurance industry

ABSTRACT This paper examines the role of learning by observing in insurance mergers and acquisitions (M&As) by employing an international sample across 18 countries spanning the years 1990 to 2014. The empirical results support a view of semi-strong market efficiency: the long-run improvements in financial performance and risk profiles are significantly related to past M&As in the insurance industry. Moreover, we find evidence of more accurate stock market predictions of long-run financial performance and risk profiles if there are more recent insurance merger activities. This paper provides a new explanation for the controversy observed in the failure of insurance M&As to create value.

What soil information do crop advisors use to develop nitrogen fertilizer recommendations for grain growers in New South Wales, Australia?

AbstractThe Australian grains industry relies on mineralized nitrogen (N) from soil organic matter and plant residues, but fertilizer N is increasingly needed to optimize yields. Most farmers are guided on N fertilizer requirements by commercial crop advisors. We surveyed (n = 132) and interviewed (n = 11) New South Wales grains advisors to gauge the usage of soil process understanding, soil data and decision support systems (DSSs) when developing N recommendations. Soil moisture at sowing, seasonal forecasts, crop rotation, soil mineral N, financial risk profiles and paddock history were all used to prepare N fertilizer advice, but stored soil moisture was most important. Farmer confidence in soil N testing was low due to high spatial variability. Most advisors calculated N fertilizer required for yields within 10%–15% of crop potential, but clients’ attitude to financial risk guided final N recommendations. Conservative growers preferred a low input system, while more reliable rainfall or greater reliance on stored soil water led growers to apply higher N rates to maximize long‐term profits. Advisors preferred “rules‐of‐thumb,” simple DSSs and knowledge of crop growth, to elaborate DSSs requiring detailed inputs and soil characterization. Few used in‐crop N sensing. N decision methodologies need to be updated to account for changes in soil fertility, cropping systems and farming practices. New research is needed to answer practical questions regarding soil N mineralization and N losses associated with alternative N application practices and extreme weather events. Training of new advisors in N processes and DSS use needs to be ongoing.