J. Risk Financial Manag., Volume 13, Issue 3 (March 2020) – 23 articles

The green bond market is attracting new issuers and a more diversified base of investors. However, the size of the green bond market remains small compared to the challenges it is meant to address and to the overall traditional bond market. This paper is based on a unique methodology combining an extensive literature review, market data analysis, and interviews with a large spectrum of green bond market participants. We identify the current barriers explaining the lack of scalability of the green bond market: a deficit of harmonized global standards; risks of greenwashing; the perception of higher costs for issuers; the lack of supply of green bonds for investors; and the overall infancy of the market. This paper makes several recommendations to overcome these obstacles and unlock the full potential of green bonds to finance sustainability goals. Full article

Bankruptcy prediction is always a topical issue. The activities of all business entities are directly or indirectly affected by various external and internal factors that may influence a company in insolvency and lead to bankruptcy. It is important to find a suitable tool to assess the future development of any company in the market. The objective of this paper is to create a model for predicting potential bankruptcy of companies using suitable classification methods, namely Support Vector Machine and artificial neural networks, and to evaluate the results of the methods used. The data (balance sheets and profit and loss accounts) of industrial companies operating in the Czech Republic for the last 5 marketing years were used. For the application of classification methods, TIBCO’s Statistica software, version 13, is used. In total, 6 models were created and subsequently compared with each other, while the most successful one applicable in practice is the model determined by the neural structure 2.MLP 22-9-2. The model of Support Vector Machine shows a relatively high accuracy, but it is not applicable in the structure of correct classifications. Full article

This work expands the literature on a less studied topic, the Chief Executive Officer (CEO) turnover in post-communist economies, analyzed during an unstable and ambiguous economic and financial environment. For the period 2005–2010, the results indicate the political inference in CEO turnover decision for the Romanian listed companies. In this period, with great turmoil in the economy determined by the financial crisis of 2008, we also find that CEO gender helps to explain the probability of changing the CEO. Moreover, this paper empirically tests if the financial and corporate governance determinants that are validated in the existing literature work for the Romanian listed companies. We reinforce that CEO turnover decision is negatively related to accounting-based performance. We find evidence of the “voting with their feet” behavior of institutional investors, and of the lack of Board of Directors monitoring. The CEO–Chairman duality and the controlling power of the largest shareholder act as entrenchment mechanisms. Full article

Assessment and estimation of bankruptcy risk is important for managers in decision making for improving a firm’s financial performance, but also important for investors that consider it prior to making investment decision in equity or bonds, creditors and company itself. The aim of this paper is to improve the knowledge of bankruptcy prediction of companies and to analyse the predictive capacity of factor analysis using as basis the discriminant analysis and the following five models for assessing bankruptcy risk: Altman, Conan and Holder, Tafler, Springate and Zmijewski. Stata software was used for studying the effect of performance over risk and bankruptcy scores were obtained by year of analysis and country. Data used for non-financial large companies from European Union were provided by Amadeus database for the period 2006–2015. In order to analyse the effects of risk score over firm performance, we have applied a dynamic panel-data estimation model, with Generalized Method of Moments (GMM) estimators to regress firm performance indicator over risk by year and we have used Tobit models to infer about the influence of company performance measures over general bankruptcy risk scores. The results show that the Principal Component Analysis (PCA) used to build a bankruptcy risk scored based on discriminant analysis indices is effective for determining the influence of corporate performance over risk. Full article

Consolidation in euro area banking has been the major trend post-crisis. Has it been accompanied by more or less competition? Has it led to more or less credit risk? In all or some countries? In this study, we examine the evolution of competition (through market power and concentration) and credit risk (through non-performing loans) in 2005–2017 across all euro area countries (EA-19), as well as core (EA-Co) and periphery (EA-Pe) countries separately. Using Theil inequality and convergence analysis, our results support the continued existence of fragmentation as well as of divergence within and/or between core and periphery with respect to competition and credit risk, especially post-crisis, in spite of some partial reintegration trends. Policy measures supporting faster convergence of our variables would be helpful in establishing a real banking union. Full article

In this paper, we use a rich dataset of several countries to analyze how sound political measures affect cross-border bank flows. Furthermore, our work is the first to comprehensively examine various components of political stability on the aforementioned subject using a larger sample than previous studies, and covering the period 1984–2013. Our paper will inform policy makers which particular aspects of political stability have a significant effect on cross-border bank flows and provide an outline on the favorable long term political and institutional development to increase such flows. We find that sound political measures—and therefore, higher political stability—increase cross-border bank flows, especially in advanced economies. Moreover, we find that in advanced economies, the political stability components; socioeconomic conditions, investment profile, corruption within the political system, religious tensions, ethnic tensions, and bureaucracy quality have a positive and close association with such bank flows. In our work, we also find that policies aiming to increase political stability have a stronger impact after the financial crisis of 2008, namely with regard to policies that affect socioeconomic conditions, investment profile, corruption within the political system and religious tensions. Full article

Against the backdrop of the COVID-19 outbreak, an emergency policy initiative called “Suspending Classes Without Stopping Learning” was launched by the Chinese government to continue teaching activities as schools across the country were closed to contain the virus. However, there is ambiguity and disagreement about what to teach, how to teach, the workload of teachers and students, the teaching environment, and the implications for education equity. Possible difficulties that the policy faces include: the weakness of the online teaching infrastructure, the inexperience of teachers (including unequal learning outcomes caused by teachers’ varied experience), the information gap, the complex environment at home, and so forth. To tackle the problems, we suggest that the government needs to further promote the construction of the educational information superhighway, consider equipping teachers and students with standardized home-based teaching/learning equipment, conduct online teacher training, include the development of massive online education in the national strategic plan, and support academic research into online education, especially education to help students with online learning difficulties. Full article

This paper discusses the relationship between stock market liquidity and corporate governance. Both concepts are widely investigated from different angles in the literature. It is generally agreed that they are related so that better corporate governance implies higher liquidity for shares of listed companies. However, the importance of good corporate governance for the market liquidity of the share will differ depending on the characteristics of the firm’s business. Good corporate governance will be particularly important in reducing agency problems in firms where the business is subject to a high degree of uncertainty. Proper corporate governance, in other words, matters most for firms where external assessment of the firm’s business prospects is difficult, while it is less important for value creation in firms where the business is easier to understand. Full article

The availability of better behavioral information about their customer portfolios holds the promise for different and more accurate pricing models for insurers. Changes in pricing, however, are always fraught with danger for insurers, as they enter long-term commitments with incomplete historical information. On the other hand, sharing personal information is still viewed with skepticism by consumers. Which type of personal information are consumers willing to share with insurers, and for what purpose? How would they like to be rewarded for this openness? For insurers, how will the transition shift their risk portfolios? This paper addresses these questions for auto insurance, particularly how the self-assessment of one’s driving style impacts this dynamic. In a survey of approximately 900 Swiss residents, we found that offering a compensation, especially premium discounts, but also services, significantly improves willingness to share information. Higher trust in insurance increases sharing. Women and younger people are more willing to share information. On the other hand, customers are less willing to disclose, to insurers, information not traditionally associated with insurance. The self-assessment of driving style also plays a significant role. More risk-averse driving styles are correlated with higher sharing. Conversely, riskier driving styles are correlated with lower sharing. This result is significant for insurers, as new data-driven pricing and services models should tend to attract less risky customer portfolios. Full article

As a rule, the economy regularly undergoes various phases, from a recession up to expansion. This paper is focused on models predicting corporate financial distress. Its aim is to analyze impact of individual phases of the economic cycle on final scores of the prediction models. The prediction models may be used for quick, inexpensive evaluation of a corporate financial situation leading to business risk mitigation. The research conducted is drawn from accounting data extracted from the prepaid corporate database, Albertina. The carried-out analysis also highlights and examines industry specifics; therefore, three industry branches are under examination. Enterprises falling under Manufacture of metal products, Machinery, and Construction are categorized into insolvent and healthy entities. In this study, 18 models are selected and then applied to the business data describing recession and expansion. The final scores achieved are summarized by the main descriptive statistics, such as mean, median, and trimmed mean, followed by the absolute difference comparing expansion and recession. The results confirm the expectations, assuming that final scores with higher values describe better corporate financial standing during the expansion phase. Similar results are achieved for both healthy and insolvent enterprises. The paper highlights exceptions and offers possible interpretations. As a conclusion, it is recommended that users need to respect the current phase of the economic cycle when interpreting particular results of the prediction models. Full article

In this paper, we modify Duan’s (1995) local risk-neutral valuation relationship (mLRNVR) for the GARCH option-pricing models. In our mLRNVR, the conditional variances under two measures are designed to be different and the variance process is more persistent in the risk-neutral measure than in the physical one, so that one is able to capture the variance risk premium. Empirical estimation exercises show that the GARCH option-pricing models under our mLRNVR are able to price the SPX one-month variance swap rate, i.e., the CBOE Volatility Index (VIX) accurately. Our research suggests that one should use our mLRNVR when pricing options with GARCH models. Full article

Nowadays, all sources in the reproduction process are easily substituted, thus the most important factors in reaching a competitive advantage are human resources. Talent management is the process oriented to enrich higher the ability of employers to increase their quality and productivity. Globalization has changed the structure of the companies in Slovakia, depending on the size of the company. This paper compares how the size of the company influences the main phases of the talent management process (strategy, identification, assessment, development, retaining). A scaled questionnaire was applied as a tool for data collection in 381 companies operating business in Slovakia. Questionnaire reliability was verified by Cronbach’s alpha. To verify the existence of statistically significant differences between individual groups of respondents, ANOVA was used. We found that the main differences between small and large companies were identified in the phases of talent identification and talent development. In bigger companies, management is more focused on HR plans that include talent identification and acquisition and have more possibilities to develop talented individuals. On the other side we could see that small companies were more successful in the process of retaining the talents. Talented people in small companies are more loyal to the employers and stay in the company for longer periods than talented individuals in large companies. Full article

We provide the analytical gradient of the full model likelihood for the Dynamic Conditional Correlation (DCC) specification by Engle (2002), the generalised version by Cappiello et al. (2006), and of the cDCC model by Aielli(2013). We discuss how the gradient might be further extended by introducing elements related to the conditional variance parameters, and discuss the issue arising from the estimation of constrained and/or reparametrised versions of the model. A computational simulation compares analytical versus numerical gradients, with a view to parameter estimation; we find that analytical differentiation yields more efficiency and improved accuracy. Full article

In 1983, Meese and Rogoff showed that traditional economic models developed since the 1970s do not perform better than the random walk in predicting out-of-sample exchange rates when using data obtained after the beginning of the floating rate system. Subsequently, whether traditional economical models can ever outperform the random walk in forecasting out-of-sample exchange rates has received scholarly attention. Recently, a combination of fundamental models with machine learning methodologies was found to outcompete the predictability of random walk (Amat et al. 2018). This paper focuses on combining modern machine learning methodologies with traditional economic models and examines whether such combinations can outperform the prediction performance of random walk without drift. More specifically, this paper applies the random forest, support vector machine, and neural network models to four fundamental theories (uncovered interest rate parity, purchase power parity, the monetary model, and the Taylor rule models). We performed a thorough robustness check using six government bonds with different maturities and four price indexes, which demonstrated the superior performance of fundamental models combined with modern machine learning in predicting future exchange rates in comparison with the results of random walk. These results were examined using a root mean squared error (RMSE) and a Diebold–Mariano (DM) test. The main findings are as follows. First, when comparing the performance of fundamental models combined with machine learning with the performance of random walk, the RMSE results show that the fundamental models with machine learning outperform the random walk. In the DM test, the results are mixed as most of the results show significantly different predictive accuracies compared with the random walk. Second, when comparing the performance of fundamental models combined with machine learning, the models using the producer price index (PPI) consistently show good predictability. Meanwhile, the consumer price index (CPI) appears to be comparatively poor in predicting exchange rate, based on its poor results in the RMSE test and the DM test. Full article

Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy. Full article

A novel coronavirus was reported to the World Health Organization (WHO) in China on 31 December 2019. The WHO named the disease COVID-19 on 11 February 2020. As of 26 February 2020, the disease has been detected on all continents, except for Antarctica. Daily updates on COVID-19 since early February 2020 have made headline news worldwide for much of 2020. This editorial evaluates risk management based on the Global Health Security (GHS) Index of global health security capabilities in 195 countries. The GHS Index lists the countries best prepared for an epidemic or pandemic. COVID-19 is compared with two related coronavirus epidemics, SARS and MERS, in terms of the number of reported human infections, deaths, countries, major country clusters, timelines, and the likelihood of discovering a safe, effective, and approved vaccine. Full article

In this paper, we propose a general family of Birnbaum–Saunders autoregressive conditional duration (BS-ACD) models based on generalized Birnbaum–Saunders (GBS) distributions, denoted by GBS-ACD. We further generalize these GBS-ACD models by using a Box-Cox transformation with a shape parameter $\lambda$ to the conditional median dynamics and an asymmetric response to shocks; this is denoted by GBS-AACD. We then carry out a Monte Carlo simulation study to evaluate the performance of the GBS-ACD models. Finally, an illustration of the proposed models is made by using New York stock exchange (NYSE) transaction data. Full article

Professional forecasters can rely on an econometric model to create their forecasts. It is usually unknown to what extent they adjust an econometric model-based forecast. In this paper we show, while making just two simple assumptions, that it is possible to estimate the persistence and variance of the deviation of their forecasts from forecasts from an econometric model. A key feature of the data that facilitates our estimates is that we have forecast updates for the same forecast target. An illustration to consensus forecasters who give forecasts for GDP growth, inflation and unemployment for a range of countries and years suggests that the more a forecaster deviates from a prediction from an econometric model, the less accurate are the forecasts. Full article

The development of Islamic banking continues to increase in many Muslim (majority) countries. Substituting interest with profit shares in the assets of a given Islamic bank as one of the bases of operation has many interesting implications, one of which is the need for more involved risk and return measures. In this paper, we take a balance sheet analysis-based approach to formulating profit in order to assess the performance of an Islamic bank. Then the implementation of this approach is demonstrated using data provided by Indonesia’s financial services authority, known as the OJK. We develop formulae for the calculation of profit share between funding and financing funds as well as the appropriate rates of return. The resulting figures are then used to construct statistical models for short-term forecasting of the volumes of funding fund from the depositors and financing fund for business people who need funds for their investment projects. The approach we develop is innovative for Islamic banks and would be a welcome addition to their performance assessment toolkit. One of the results of our model indicates an increasing pattern on the equivalent rates of returns for funding and financing funds every year, which is caused by the fact that the reported income from the financing fund seems to have been accumulated from the beginning until the end of year in the Islamic bank. Full article

Peering through the lenses of the strategic intent perspective and strategic fit paradigm, in this study, we seek to examine the contingent conditions under which emerging market multinational enterprises (EMNEs) with strategic asset seeking (SAS) intent can achieve improved innovation performance. We developed a contingency model of how the relationship between SAS intent and innovation performance is contingent on the moderating effects of firms’ innovation capability and institutional quality in the host country, as well as on the synergistic interaction of independent moderating effects from these two factors. We combined survey data from 320 Chinese MNEs with archival data to test our hypotheses. Our results show that SAS intent can lead to positive innovation performance when (a) the investing firm has developed high levels of innovation capability, and (b) synergistic interactions exist between institutional quality and firms’ innovation capability regarding their moderating effect on the SAS intent-innovation performance link. Full article

Since 2016, the United Nations Global Compact (UNGC), one of the most prominent worldwide corporate social responsibility and sustainability initiatives, has been linked to the Sustainable Development Goals (SDGs). However, despite the enormous scholarly interest in the UNGC since the very beginning, its impact on the integration of the SDGs into the business activities, risk management and reporting of its participants remains understudied. This paper examines support and action for the SDGs among companies from the Visegrad Four (V4) countries. It attempts to find out whether the recent UNGC efforts result in their mobilisation towards the SDGs’ implementation or merely creates a new space for instrumental adoption to improve image and reputation. The paper adopts qualitative content analysis of 42 Communications of Progress (COPs), submitted by 25 companies from the V4 in 2017–2019. The related self-assessments in the UNGC Participation Database were also used. It reveals that the companies obviously fulfil their obligation to report their activities related to SDGs but fail to provide relevant details. Moreover, divergences between the challenges faced by V4 countries and the priorities of the companies related to individual SDGs are also identified. This raises serious concerns about the UNGC’s practical effects. Full article

This paper focuses on three “safe haven” assets (gold, oil, and the Swiss Franc) and examines the impact of recent financial crises and some macroeconomic variables on their return co-movements during the last two decades. All financial crises produced significant increases in conditional correlations between these asset returns, thus revealing consistent portfolio shifts from more traditional towards safer financial instruments during turbulent periods. The world equity risk premium stands out as the most relevant macroeconomic variable affecting return co-movements, while economic policy uncertainty indicators also exerted significant effects. Overall, this evidence points out that gold, oil, and the Swiss currency played an important role in global investors’ portfolio allocation choices, and that these assets preserved their essential “safe haven” properties during the period examined. Full article

Crowdfunding has mostly been used to finance very unique projects. Recently, however, companies have begun using it to finance more traditional products where they compete against other sellers of similar products. Major crowdfunding platforms, Kickstarter and Indiegogo, as well as Amazon have launched several projects consistent with this trend. This paper offers a model where two competing firms can use crowdfunding prior to direct sales. The model provides several implications that have not yet been tested e.g., (1) Firms can use crowdfunding strategically to signal a high level of demand for their products; (2) (Reward-based) crowdfunding is procyclical; (3) A higher platform fee may lead to higher firm profits in equilibrium; (4) Competition increases the chances of using crowdfunding compared to the monopoly case; (5) A non-monotonic relationship exists between the risk of crowdfunding campaign failure and firm profit. Full article