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.     

制度差异、“一带一路”倡议与中国大型对外投资——基于投资边际、模式与成败的三重视角

本文基于2005—2016年中国609个企业2 440个大型对外投资微观样本，全面考察正式与非正式制度差异对企业投资边际、模式与成败的影响。研究发现：正式制度差异对投资规模化的影响不大，但显著影响投资多元化。企业为了避免正式制度差异带来的管理风险，倾向于选择全资股权结构，但企业选择绿地投资还是跨国并购并不受到正式制度差异的影响。相比之下，非正式制度差异的影响更为广泛。无论是正式制度差异还是非正式制度差异，均会显著增加企业投资的失败概率。进一步研究发现，“一带一路”倡议主要对正式制度差异存在优化互补效应。研究结论表明，夯实“一带一路”倡议，尤其大力发展与沿线国家的文化深度交流，有助于企业更快、更好地走出去。     

The impact on stock prices of deferral and elimination of internal control audit requirement for small firms

This paper examines the cumulative market reaction to the events related to deferral of internal control audit requirement under the Sarbanes-Oxley Act of 2002 and its elimination under the Dodd-Frank Act of 2010 for nonaccelerated filers (small firms). We document that small firms experienced negative cumulative abnormal returns around these events; and the differences between the cumulative abnormal returns for small firms and the two control groups (accelerated and large accelerated filers) were negative and significant at the 1% level. These results support the notion that market participants value the reliability of financial information irrespective of the firm size. Within the small firms, we find no firm characteristic significantly explains the market reaction to the events considered. That is, all small firms lost market value in reaction to the events that delayed and eliminated their internal control audit requirement.     

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.     

颠覆性技术识别与预测方法研究进展

为了把握颠覆性技术识别与预测方法研究进展，首先对颠覆性的内涵、特征和运行机制进行论述；然后，通过对已有文献进行系统梳理，将识别与预测方法分为5类，分别展开详述；最后，对各类方法进行比较分析。研究发现，已有颠覆性技术识别与预测方法仍处于发展的初期阶段，所有识别研究方法均围绕颠覆性技术的外部影响或内在特征中的某一方面展开，并未将二者有机结合起来。未来，应综合把握颠覆性技术内外部特征，构建全面、系统的识别与预测框架。     

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.