Integrated system of occupational safety and health and fire protection of the fire rescue brigades members

The objective of the conducted research is the identification and determination of requirements of members of fire rescue brigades during operations in the conditions of high risk in order to minimize the possibilities for injury incidence during the intervention. The research is focused on examination, determination and identification of factors affecting the increasing number of occupational injuries of members of fire rescue brigades during interventions. Hypothetical framework of the research problem consists of general hypothesis and six special hypotheses. Results suggest that almost all respondents believe that their skills and abilities are applicable in the intervention phase, but less than a half believe that their skills are applicable in prevention phase. Two-thirds of respondents stated that in their organization they have support for further education and upgrading while a half of respondents stated that they need education concerning identification, assessment and management of risks that can lead to emergency situations.     

Linear programing models for portfolio optimization using a benchmark

We consider the problem of constructing a perturbed portfolio by utilizing a benchmark portfolio. We propose two computationally efficient portfolio optimization models, the mean-absolute deviation risk and the Dantzig-type, which can be solved using linear programing. These portfolio models push the existing benchmark toward the efficient frontier through sparse and stable asset selection. We implement these models on two benchmarks, a market index and the equally-weighted portfolio. We carry out an extensive out-of-sample analysis with 11 empirical datasets and simulated data. The proposed portfolios outperform the benchmark portfolio in various performance measures, including the mean return and Sharpe ratio.     

A Historical Review of Postponement Research

This historical review traces the development of postponement research starting in the fifties. The focus is on seven related themes: connecting form and time postponement, conceptual extensions to postponement, decoupling points, design for postponement, customization and mass customization, factors favoring postponement implementation, and postponement in global settings. Within these themes, the review includes concepts such as postponement costs, decoupling points, part commonality, and the impact of demand correlations on the benefits of postponement. The review suggests that substantial progress has been made in postponement research.     

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.     

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.     

Risk-managed industry momentum and momentum crashes

This paper investigates Barroso and Santa-Clara’s [J. Financ. Econ., 2008, 116, 111–120] risk-managed momentum strategy in an industry momentum setting. We investigate several traditional momentum strategies including that recently proposed by Novy-Marx [J. Financ. Econ., 2012, 103, 429–453]. We moreover examine the impact of different variance forecast horizons on average pay-offs and also Daniel and Moskowitz’s [J. Financ. Econ., 2016, 122, 221–247] optionality effects. Our results show in general that neither plain industry momentum strategies nor the risk-managed industry momentum strategies are subject to optionality effects, implying that these strategies have no time-varying beta. Moreover, the benefits of risk management are robust across volatility estimators, momentum strategies and subsamples. Finally, the ‘echo effect’ in industries is not robust in subsamples as the strategy works only during the most recent subsample.