Time and frequency domain causality Testing: The causal linkage between FDI and economic risk for the case of Turkey

This study aims to explore the causal relationship between economic risk and foreign direct investment (FDI) inflows for the case of Turkey. With the aim of establishing robust findings for the research in mind, both traditional and modern causality techniques are utilized; time domain Granger (1969, “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods.” Econometrica 37: 424–438.), Toda and Yamamoto (1995, “Statistical Inference in Vector Autoregressions with Possibly Integrated Processes.” Journal of Econometrics 66 (1–2): 225–250.), Fourier Toda-Yamamoto and frequency domain Breitung and Candelon (2006, “Testing for short- and long-run causality: A frequency-domain approach.” Journal of Econometrics 132 (2): 363–378.) spectral causality test. Our empirical findings reveal that; economic risk changes in Turkey significantly lead to changes in FDI inflows. However, there is no evidence of causality running from FDI to economic risk. The findings imply that economic risk is an essential determinant of FDI inflows in Turkey. Our findings are compatible with historical macroeconomic developments in Turkey and imply important policy implications. The results of this study can be generalized for other emerging economies that have similar macroeconomic environments, in order to create useful policy implications regarding FDI inflow.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

The equity impacts of bus rapid transit: A review of the evidence and implications for sustainable transport

The paper offers an analysis of empirical evidence on the equity impacts of operational Bus Rapid Transit (BRT) systems in the Global South. The focus is on vertical equity, i.e. whether BRT systems achieve progressive benefits for poorer segments of the population. Findings from Africa, Asia, and Latin America all suggest that BRT does offer significant benefits to low-income groups, in terms of travel time and cost savings, access enhancement, and safety and health benefits. However benefits are often skewed toward medium-income users and thus less progressive than they might be. Two primary reasons for this are insufficient spatial coverage and inappropriate fare policies. While many features of BRT potentially allow it to deliver pro-poor outcomes, such outcomes only materialize if BRT implementers pay specific and sustained attention to equity. The paper identifies key issues that need to be addressed to steer BRT implementation toward more socially sustainable outcomes—including better integration with other transit, paratransit, and nonmotorized transport services, and with the housing sector.     

Audit Firm Attributes and Auditor Litigation Risk

This study examines the association between auditors' litigation risk and audit firm attributes. Using professional liability insurance premiums as a proxy for auditors' litigation risk, we present evidence that the risk is lower in audit firms having: (1) separate non-audit and audit divisions; (2) a higher proportion of partners; and (3) a higher annual growth in number of CPAs employed. Additionally, we find that the risk is higher in audit firms having: (1) operating losses; and (2) high revenue growth. Our results are consistent with the idea that audit firms' financial condition and organizational structure affect their independence/ expertise, and, in turn, their litigation risk. Our results are broadly supportive of the PCAOB's (2015) and US Department of Treasury's (2008) views that investors, audit committees, management, and other regulators could benefit from having access to financial and organizational information about audit firms.     

Operational risk quantified with spectral risk measures: a refined closed-form approximation

The quantification of operational risk has become an important issue as a result of the new capital charges required by the Basel Capital Accord (Basel II) to cover the potential losses of this type of risk. In this paper, we investigate second-order approximation of operational risk quantified with spectral risk measures (OpSRMs) within the theory of second-order regular variation (2RV) and second-order subexponentiality. The result shows that asymptotically two cases (the fast convergence case and the slow convergence) arise depending on the range of the second-order parameter. We also show that the second-order approximation under 2RV is asymptotically equivalent to the slow convergence case. A number of Monte Carlo simulations for a range of empirically relevant frequency and severity distributions are employed to illustrate the performance of our second-order results. The simulation results indicate that our second-order approximations tend to reduce the estimation errors to a great degree, especially for the fast convergence case, and are able to capture the sub-extremal behavior of OpSRMs better than the first-order approximation. Our asymptotic results have implications for the regulation of financial institutions, and may provide further insights into the measurement and management of operational risk.     

A hybrid information approach to predict corporate credit risk

This study proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced‐form models to produce a model combination based on credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced‐form models contribute more in the pooled strategies for speculative‐grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names.     

The new ecological paradigm and responses to climate change in China

The purpose of this study was to develop and evaluate a Chinese-Mandarin version of the revised new ecological paradigm (NEP-R) scale. In a sample of 515 Mandarin-speaking Chinese nationals, we first assessed the factor structure and internal consistency of the NEP-R and assessed its validity by examining associations with global warming risk perceptions and mitigation behavior. Respondents completed the NEP-R scale, together with measures of risk perception and mitigation behavior. Exploratory and confirmatory factor analyses indicated that a two-factor solution, reflecting ecocentric and anthropocentric worldviews, best fit the data. Multi-group path analysis revealed that respondents with stronger ecocentric and weaker anthropocentric worldviews perceived more risks associated with global warming. In turn, respondents who perceived more risks reported engaging in more global warming mitigation behaviors. But importantly, the path between risk perceptions and behavior was significantly stronger for highly educated respondents than for less educated respondents, suggesting that education may represent an important strategy for bridging the gap between perceived risks about global warming and action.     

Unemployment insurance benefits and income smoothing

Labor unemployment insurance reduces unemployment concerns. We argue that these benefits moderate incentives to smooth earnings to reduce employees’ concerns about unemployment risk. Using exogenous variations in unemployment insurance benefits, we find evidence consistent with this argument. We also find that the link between unemployment insurance benefits and income smoothing is stronger when there is higher unemployment risk and when the firm is likely to employ more low-wage workers, who find unemployment insurance benefits especially useful. Our paper contributes to the literature by showing that public policy decisions such as unemployment insurance have significant, albeit probably unintended, externalities on corporate financial reporting.     

Economic policy uncertainty and stock price crash risk

This paper studies the impact of economic policy uncertainty on stock price crash risk using data from China. We develop a new index to measure Chinese economic policy uncertainty and find that economic policy uncertainty has a remarkable positive effect on stock price crash risk. However, the effect reverses later. The results also indicate that the positive effect of economic policy uncertainty on stock price crash risk is more prominent for state‐owned enterprises. Moreover, this effect is more prominent for firms with higher information asymmetry and firms with greater disagreement among investors, indicating that economic policy uncertainty affects crash risk through two mechanisms: managers’ concealment of bad news and investors’ heterogeneous beliefs.