Financial Risk

A study of 63 Toronto restaurants found that certain attributes or amenities had a statistically significant effect on average check, as well as customers' ratings of decor and service quality. The features tested were offering catering, a dress code, late-night dining, live entertainment, outside seating, parking, smoking, takeout, and some form of internet activity. Specifically, a dress code boosted check averages and improved ratings for decor, service, and food quality. Offering parking also boosted check averages. In contrast, offering takeout depressed check averages and reduced the ratings for decor. Offering a late-night menu had a significant negative effect on perceptions of food and service quality. The restaurant features offered most frequently were catering, takeout, and a smoking section.

In this paper, the time–frequency dependency of political risk as well as economic and financial risks is explored in Venezuela using quarterly data from 1984Q1 to 2018Q4. The present study uses the wavelet coherence technique, which allows the investigation
of both the long and short-term causal relationships between political risk and economic and financial risks in Venezuela. The findings of this study indicate that: (i)
significant vulnerabilities in political risk, economic risk, and financial risk are observed at different time periods and different frequency levels; (ii) political risk has a strong power for explaining economic risk from 1995 to 2005 in the long run, while between 1984 and 2010, economic risk and political risk are positively correlated at different frequency levels; (iii) in the long run, changes in political risk significantly lead to changes in financial risk in Venezuela.

The study focused on the impact of the short-term and long-term financial risk on systematic risks through analyzing 120 corporations listed in the international and emerging stock exchange markets of the United States, Germany, South Korea, and Egypt, (30 from each country). The variability in common stock's systematic risks was explained by 93.58% according to short-and long-term financial risk under two control variables which are market capitalization of the corporation and the efficiency of the stock exchange. When our results were compared to those of Hamada, 1972, Lee and Jang, 2007, and Alaghi, 2011, the study found that short-term financial risk increased which was explained by common stock's systematic risk. Finally, the study found a positive relationship between each quick ratio & total debt to equity on one hand and a common stock's systematic risk on another hand. JEL classification numbers: G32

Assessment of BOT project financial risk is generally performed by combining Monte Carlo simulation with discounted cash flow analysis. The outcomes of this risk assessment depend, to a significant extent, upon the total project uncertainty, which aggregates aleatory and epistemic uncertainties of key risk variables. Unlike aleatory uncertainty, modelling epistemic uncertainty is a rather difficult endeavour. In fact, BOT epistemic uncertainty may vary according to the significant information disclosed during the concession period. Two properties generally characterize the stochastic behaviour of the uncertainty of BOT epistemic variables: (1) the learning property and (2) the increasing uncertainty property. A new family of Markovian processes, the Martingale variance model and the general variance model, are proposed as an alternative modelling tool for BOT risk variables. Unlike current stochastic models, the proposed models can be adapted to incorporate a risk analyst's view of properties (1) and (2). A case study, a hypothetical BOT transportation project, illustrates that failing to properly model a project's epistemic uncertainty may lead to a biased estimate of the project's financial risk. The variance models may support, guide and extend the thinking process of risk analysts who face the challenging task of representing subjective assessments of key risk factors.

Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. In this study, we try to use a triple-phase neural network ensemble technique to design a credit risk evaluation system to discriminate good creditors from bad ones. In this model, many diverse neural network models are first created. Then an uncorrelation maximization algorithm is used to select the appropriate ensemble members. Finally, a reliability-based method is used for neural network ensemble. For further illustration, a publicly credit dataset is used to test the effectiveness of the proposed neural ensemble model.

The up growth of business rivalry at the consumer and services markets make many of the suppliers grant delayed or deferred payments to their customers and act as creditors who thus accept credit, exchange and interest risks. It results in the suppliers’ floating assets withdrawal and has a negative impact upon their financial statements showings. Factoring is one of the

The purpose of this study is to explore the extent to which individuals' knowledge of retirement planning, future time perspective, and financial risk tolerance influence retirement saving practices. A total of 270 young working adults participated in the study. Regression analyses reveal that each of the three variables is predictive of saving practices, and they interact with one another as well. From an applied perspective, the findings suggest that counseling and intervention efforts aimed at pro-moting retirement saving should differentially target individuals on the basis of these three psycho-logical dimensions.

This article argues that any ecological finance theory devised to fit the Sustainable Development Goals (SDGs) needs a paradigm shift in the morphology of randomness underlying financial risk modelling, by integrating the characteristics of "nature" and sustainability into the modelling carried out. It extends the common diagnosis of the 2008 financial crisis with considerations on the morphology of randomness and the reasons why neoclassical finance theory is not sustainable from this perspective. It argues that the main problem with unsustainable neoclassical finance risk modelling is its underlying morphology of randomness that creates a dangerous risk culture. It presents Leibniz's principle of continuity and Quetelet's theory of average as cornerstones of classical risk culture in finance, acting as a mental model for financial experts and practitioners. It links the notion of sustainability with the morphology of randomness and presents a possible alternative approach to financial risk modelling defined by rough randomness. If morphology of randomness in nature is properly described by fractal and multifractal methods, hence ecological finance theory has to include fractal properties into financial risk models. The conclusion proposes a new agenda for future research.

Islamic banking is interest free banking which makes it necessary for Islamic banks to take active part in the operations of the business i.e. share profits as well as losses. Banks including Islamic banks prefer to take minimum risk. On the surface, it may seem that Islamic banks face more risk and hence will have more volatile or even negative returns on their assets.

This paper analyzes the risk management procedures of Islamic banks by giving a differential analysis of risk management discussing only the unique characteristics of risk management in Islamic Banking. The usual credit assessment procedures and BASEL are not discussed. This paper looks at the comparative performance of Islamic banks and conventional banks by using ROE as the benchmark.