J. Risk Financial Manag., Volume 17, Issue 5 (May 2024) – 16 articles

The expectile-based Value at Risk (EVaR) has gained popularity as it is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR). This paper applies the expectile regression approach to evaluate the EVaR of stock market indices of Australia and Japan. We use an expectile regression model that considers lagged returns and common risk factors to calculate the EVaR for each stock market and to evaluate the interdependence of downside risk between the two markets. Our findings suggest that both Australian and Japanese stock markets are affected by their past development and the international stock markets. Additionally, ASX 200 index has significant impact on Nikkei 225 in terms of downside tail risk, while the impact of Nikkei 225 on ASX is not significant. Full article

Changes in customer behaviors after the COVID-19 pandemic have encouraged the transformation of banking systems. Neobanks have emerged as an innovation and entered the banking system to compete with traditional banks by offering new customer experiences. Neobanks transform traditional banking products and services which are delivered through physical interactions into those delivered via digital channels. This paper analyzes traditional banks that have transformed into neobanks, specifically their efficiency after digital transformation. Efficiency was measured using Stochastic Frontier Analysis (SFA), as it is highly accurate in estimating efficiency scores. This study also used a Pooled Mean Group (PMG) estimation of the Panel ARDL (Autoregressive Distributed Lag), as this approach is useful for analyzing the relationship between variables in panel data, to investigate digital transformation as a determinant of neobanks’ efficiency and examine the existence of short-term and long-term relationships between digital transformation and efficiency. We found that the efficiency of neobanks increases after digital transformation. Furthermore, it can be concluded that digital transformation is a determinant of efficiency and that there is long-term relationship between digital transformation and efficiency. In the short term, digital transformation has a significant negative correlation with efficiency, but in the long term, it has a significant positive relationship; this is because the cost of digital transformation initially decreases the profit efficiency, but afterwards, it increases the efficiency. Full article

This study rigorously investigates the intricate dynamics between Corporate Social Responsibility (CSR), quantified through Environmental, Social, and Governance (ESG) scores, and financial performance (FP), measured via the return on assets (ROA) and return on equity (ROE), within the UK banking sector. Our analysis is based on a comprehensive dataset from Bloomberg. This research encapsulates data from 32 banks publicly listed on the London Stock Exchange over a six-year span from 2017 to 2022. Employing panel data regression models while controlling leverage and bank size, we delve into the relationship between banks’ CSR engagements, as reflected in their ESG scores, and their financial outcomes. Our findings indicate a negative correlation between the ESG score and both the ROA and ROE, suggesting that elevated CSR commitments may inversely impact short-term financial returns. This finding not only challenges prevailing narratives within the sector but also fosters a crucial discourse on the balance between ethical banking practices and profitability. The implications of this research study are manifold, extending to policymakers, banking executives, and investors, suggesting a revaluation of CSR strategies in alignment with long-term value creation and sustainable banking. This study not only enriches academic discourse on CSR within the financial sector but also serves as a beacon for future inquiries into the evolving landscape of responsible banking, advocating for a nuanced understanding of CSR’s role in shaping the financial and ethical contours of the banking industry. Full article

This study aims to empirically examine the relationship between cryptocurrency and various facets of the financial system. It seeks to provide a comprehensive understanding of how cryptocurrencies interact with, and influence, the stock market, the U.S. dollar’s strength, inflation rates, and traditional banking operations. This is carried out using linear regression models, Granger causality tests, case studies, including the collapse of the Futures Exchange (FTX), and the successful integration of Binance. The study unveiled a strong positive correlation between cryptocurrency market capitalization and key financial indicators like the Dow Jones Industrial Average, Consumer Price Index, and traditional banking operations. This indicates the growing significance of cryptocurrencies within the global financial landscape. However, a mild association was found with the U.S. dollar, suggesting a limited influence of cryptocurrencies on traditional fiat currencies currently. Despite certain limitations such as reliance on secondary data, methodological choices, and geographic focus, this research provides valuable insights for policymakers, financial industry stakeholders, and academic researchers, underlining the necessity for continued study into the complex interplay between cryptocurrencies and financial stability. Full article

A general methodology for loan amortization under arbitrary discount functions is discussed. It is shown that it is always possible to uniquely define a scheme for constructing the loan amortization schedule with an arbitrary assigned discount function. It is also shown that, even if the loan amortization is carried out from the sequence of principal payments and the sequence of accrued interest, the underlying discount function can be uniquely determined at the maturities corresponding to the installment payment dates. As a special case of the proposed approach, we derive the amortization method according to the law of simple interest. Full article

Public–Private Partnerships (PPPs) are formed to finance and deliver large infrastructural projects that may not be entirely feasible by governments alone. This study investigates the intricate role of financial risks, subsidies, and bidding criteria in the context of PPPs in India, and their relationship to the amount and extent of investments made by private partners. Studies have claimed that the success of PPP projects is determined by the type of funding, the nature of risk undertaken by investors, and the bidding criteria used by a government to attract investors. However, there is sparse literature on these variables impacting the private investment in these projects. Thus, in an attempt to address this gap, we collated data from the World Bank for a ten-year period (i.e., 2009 to 2019) for the study variables, and used regression to analyze the hypotheses, while adopting both SPSS 24 and PROCESS Macro. This study disapproved some commonly held notions of risk relationships, such as the government using “viability gap” funding to attract private investment, and that “leverage” does not moderate the relationship between risk assumed and private investment, thereby contributing to the literature on private investment in PPPs as impacted by several factors. This study is among the first to recognize and elaborate on financial risk relationships, specifically in the context of Indian PPPs. These findings are significant for both private and public participants in terms of financial considerations in PPP projects, especially within the ambits of emerging markets. Full article

In the more than 70 years since Markowitz introduced mean-variance optimization for portfolio construction, academics and practitioners have documented numerous weaknesses in the approach. In this paper, we propose two easily understandable improvements to mean-variance optimization in the context of multi-asset class portfolios, each of which provides less extreme and more stable portfolio weights. The first method sacrifices a small amount of expected optimality for reduced weight concentration, while the second method randomly resamples the available assets. Additionally, we develop a process for testing the performance of portfolio construction approaches on simulated data assuming variable degrees of forecasting skill. Finally, we show that the improved methods achieve better out-of-sample risk-adjusted returns than standard mean-variance optimization for realistic investor skill levels. Full article

Future index prices are viewed as a critical issue for any trader and investor. In the literature, various models have been developed for forecasting index prices. For example, the geometric Brownian motion (GBM) model is one of the most popular tools. This work examined four types of GBM models in terms of the presence of memory and the kind of volatility estimations. These models include the classical GBM model with memoryless and constant volatility assumptions, the SVGBM model with memoryless and stochastic volatility assumptions, the GFBM model with memory and constant volatility assumptions, and the SVGFBM model with memory and stochastic volatility assumptions. In this study, these models were utilized in an empirical study to forecast the future index price of the energy sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, the mean square error (MSE) and mean absolute percentage error (MAPE). The results show that the SVGFBM model demonstrates the highest accuracy, resulting in the lowest MSE and MAPE, while the GBM model was the least accurate of all the models under study. These results affirm the benefits of combining memory and stochastic volatility assumptions into the GBM model, which is also supported by the findings of numerous earlier studies. Furthermore, the findings of this study show that GFBM models are more accurate than GBM models, regardless of the type of volatility. Furthermore, under the same type of memory, the models with a stochastic volatility assumption are more accurate than the corresponding models with a constant volatility assumption. In general, all models considered in this work showed a high accuracy, with MAPE ≤ 10%. This indicates that these models can be applied in real financial environments. Based on the results of this empirical study, the future of the energy sector in Saudi Arabia is forecast to be predictable and stable, and we urge financial investors and stockholders to trade and invest in this sector. Full article

Migrant remittances have become significant in poverty alleviation and microeconomic development in low-income countries. However, the ease of conducting global migrant remittance transfers has also introduced the risk of misuse by terrorist organizations to quickly move and conceal operational funds, facilitating terrorism financing. This study aims to develop an unsupervised machine learning algorithm capable of detecting suspicious financial transactions associated with terrorist financing in migrant remittances. To achieve this goal, a structural equation model (SEM) and an outlier detection algorithm were developed to analyze and identify suspicious transactions among the financial activities of migrants residing in Belgium. The results show that the SEM model classifies a significantly high number of transactions as suspicious, making it prone to detecting false positives. Finally, the study developed an ensemble outlier detection algorithm that comprises an isolation forest (IF) and a local outlier factor (LOF) to detect suspicious transactions in the same dataset. The model performed exceptionally well, being able to detect over 90% of suspicious transactions. Full article

Research and development (R&D) is a significant driver of innovation that leads to superior performance. The present study attempts to examine the relationship between R&D intensity and a firm’s performance at both aggregate and industry levels in the emerging market of India using a battery of R&D intensity measures and stock market returns as a measure of a firm’s performance. The study was conducted on 1097 companies from six R&D-intensive industries. The Fama-French portfolio formation method was used to evaluate the stock market performance of R&D-intensive firms for both equal-weighted (EW) and value-weighted (VW) returns. The findings suggest that R&D intensity and stock returns show a positive relationship. A long–short strategy in R&D-intense firms has yielded 1.43% (t = 4.22) per month in the sample. In general, the results suggest an undervaluation of highly R&D-intensive firms that investors can exploit for above-average returns. The effect is not homogeneous across return schemes (equal-weighted and value-weighted) or across industries. R&D growth measures and R&D capital are not found to have significant impacts on stock returns. Both the market firm size and age are included as control variables, and the results reveal that the relationship is robust to these control variables. The sub-periods ranging from 2000 to 2007 and 2008 to 2019 have been considered in the present study and the results are consistent with the overall sample. The study fills the existing empirical void for R&D intensity and stock returns in relation to the emerging market of India. Full article

This study examines the dynamics between treasury and market capitalization in two Decentralized Autonomous Organization (DAO) projects: OlympusDAO and KlimaDAO. This research examines the relationship between market capitalization and treasuries in these projects using vector autoregression (VAR), Granger causality, and Vector Error Correction models (VECM), incorporating an exogenous variable to account for the comovement of decentralized finance assets. Additionally, a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is employed to assess the impact of carbon offset tokens on KlimaDAO’s market capitalization returns’ conditional variance. The findings suggest a connection between market capitalization and treasuries in the analyzed projects, underscoring the importance of the treasury and carbon offset tokens in impacting a DAO’s market capitalization and variance. Additionally, the results suggest significant implications for predictive modeling, highlighting the distinct behaviors observed in OlympusDAO and KlimaDAO. Investors and policymakers can leverage these results to refine investment strategies and adjust treasury allocation strategies to align with market trends. Furthermore, this study addresses the importance of responsible investing, advocating for including sustainable investment assets alongside a foundational framework for informed investment decisions and future studies in the field, offering novel insights into decentralized finance dynamics and tokenized assets’ role within the crypto-asset ecosystem. Full article

The influence of business incubation systems on family businesses in African economies has not been thoroughly investigated despite the potential contribution of family businesses to Africa’s economic expansion and the attainment of development goals outlined in the Africa Development Agenda 2063 and the Sustainable Development Goals. Therefore, this study investigates the potential benefits that family businesses in Africa can derive from engaging in business incubation. This study utilised an integrative literature review methodology to investigate the research question. Twenty-three peer-reviewed articles were systematically selected from the Scopus, Web of Science, and Google Scholar databases using the following combination of phrases: “family business” and either “business incubation” or “business incubator”. The findings suggest ways to create a mutually beneficial relationship between family businesses and business incubators to improve long-term sustainability, promote collaboration, facilitate knowledge transfer, and foster an entrepreneurial ecosystem. It also recognises challenges, such as cultural alignment in family businesses. Business incubators in Africa can improve the sustainability of family businesses, such as during the succession, by offering support, resources, and guidance. The South African experience is a role model for the rest of the continent, in this regard. Future research should broaden the sources beyond the three databases utilised, including non-peer-reviewed sources such as grey literature, and extend the focus beyond developing economies. Full article

This paper investigates the evolution of the Ukrainian stock market through an analysis of various data properties, including persistence, volatility, normality, and resistance to anomalies for the case of daily returns from the PFTS stock index spanning 1995–2022. Segmented into sub-periods, it aims to test the hypothesis that the market’s efficiency has increased over time. To do this different statistical techniques and methods are used, including R/S analysis, ANOVA analysis, regression analysis with dummy variables, t-tests, and others. The findings present a mixed picture: while volatility and persistence demonstrate a general decreasing trend, indicating a potential shift towards a more efficient market, normality tests reveal no discernible differences between analyzed periods. Similarly, the analysis of anomalies shows no specific trends in the market’s resilience to the day-of-the-week effect. Overall, the results suggest a lack of systematic changes in data properties in the Ukrainian stock market over time, possibly due to the country’s volatile conditions, including two revolutions, economic crises, the annexation of territories, and a Russian invasion leading to the largest war in Europe since WWII. The limited impact of reforms and changes justifies the need for continued market reform and evolution post-war. Full article

A farming household’s decision to continue producing agricultural commodities within the United States is influenced by a multitude of factors. Thus, this study seeks to examine whether the outstanding student loan balance of any member within a farming household may explain why the total number of acres devoted to the production of agriculture in the United States continues to decline. Panel data from the 2007–2009 Survey of Consumer Finances are analyzed via a fixed effect model to estimate the effect of outstanding student loan balances on farmland acreage owned, controlling for other factors like farm income, debt, and land prices. The results suggest that for each additional dollar of outstanding student loan debt, there is an associated decrease of 0.0064 acres in total farmland ownership. This suggests that student loan debt may also be a factor in the decline in real estate devoted to agriculture production. The estimated effect is both economically and statistically significant. This study contributes to the literature on the risks and constraints associated with farming households that own or seek to procure additional acres of agricultural producing real estate. Full article

Purpose: This study evaluates the impact of the water management policies of energy companies on their volatility interactions with energy commodities. Design/methodology: We tested for volatility transmissions between 66 energy funds and fossil-fuel commodities. After identifying possible integrations, we investigated whether water management policies, after controlling for other fund characteristics, impact the probability of integration. Results: Our findings indicate strong volatility transmission from oil prices to energy funds. However, a reverse of this information flow was not observed. From the perspective of natural gas, we found strong bi-directional integration with energy funds. When we analyzed the influence of fund characteristics on the previously established integrations, water management policies do not impact the probability of the integration of oil. However, these policies are shown to have a significant influence on integration with the natural gas market. Originality/value: While there are multiple studies that show the integration between energy companies and corresponding commodities, according to our knowledge, this is the first study that evaluates the significance of water management policies with respect to volatility integration. This study highlights the importance of water-related policies with respect to the susceptibility of energy firms to volatility contagion from the natural gas market. Full article

This study examines the impact of Internet Banking on banking profitability in Saudi Arabia in a sample of conventional and Islamic banks. The study uses Return on Assets (ROA) and Return on Equity (ROE) as key metrics to measure profitability in a sample of 10 Saudi conventional and Islamic banks observed over the 2013–2022 period. The used regression analysis reveals a significant effect of Internet Banking on the profitability of both conventional and Islamic banks, as indicated by the ROA and ROE metrics. These findings have implications that underscore the strategic importance of adopting Internet Banking, emphasizing its substantial contribution to the financial performance of both conventional and Islamic banks in the Saudi Arabian banking landscape. This study offers critical insights into the strategic significance of Internet Banking for Saudi Arabian banks’ profitability and future planning, in line with the 2030 Vision goals. This research also supports informed decision making in the digital era, emphasizing the pivotal role of Internet Banking in shaping the future of the Saudi Arabian banking industry. Full article