The market’s view on the probability of banking sector failure: cross-country comparisons

Hall and Miles (1990) suggest an approach of estimating default probabilities of banks using stock market information, and in this paper we apply an aggregated version of their approach to banking sectors around the world in both developed and emerging economies. We study the market’s assessment of the probability of systemic banking crises world wide over the last decade, including the Asian crisis 1997–1998. In addition, we investigate whether there is a relationship between the failure probability and institutional features of the actual banking sector. The quality of governance and the degree of law and order in a country is found to be significantly negatively related to the market based failure probabilities as is an explicit deposit insurance during periods of crisis.     

Are UK technology-based small firms still finance constrained?

This paper examines funding issues for UK Technology-based Small Firms (TBSFs). TBSFs are the cry of today and UK government like other countries is trying to increase the mass of TBSFs by various means. However, it is reported that TBSFs face many impediments to their successful development. Finance is believed as the main barrier at the early stages of TBSFs development. This paper is attempting to follow up and report if the funding situation for UK TBSFs has improved. The findings reported in this paper suggest that nearly 80% sample firms were finance constrained at the early stages of business development.     

不确定性与早期劳动合同 --契约理论的一个新发展

在劳动力市场上 ,劳动者和厂商未来的生产率都具有不确定性。早期劳动合同或签约由于一方面可以保证劳动者被排除在劳动力市场之外 ,同时也保证了厂商被排除在事后职位市场的风险之外 ,因而厂商和劳动者具有签订早期劳动合同的激励。尽管早期劳动合同或签约存在成本 ,但只要事前利益超过事后无效率的损失 ,那么早期劳动合同或签约就会发生。对于那些不能获得完全市场保险的人来说 ,签约是一种帕累托最优。早期劳动合同或签约是市场失灵的一种证明 ,早期劳动合同是一种不完全合同 ,也是一种非正式合同 ,它是对契约理论的一个发展。     

基于忠诚度的知识型员工流失预警管理研究

员工忠诚是员工对企业的行为忠诚与态度忠诚的有机统一.基于忠诚度的知识型员工流失预警指标包括社会环境、员工个人特性、企业状况、领导的影响力、报酬制度、员工在企业的状况等.根据这六个指标确定的指标权重建立的评价集,可计算出员工的忠诚度,从而对员工流失进行预警管理.     

Systemic event prediction by an aggregate early warning system: An application to the Czech Republic

This work develops an early warning framework for assessing systemic risks and predicting systemic events over a short horizon of six quarters and a long horizon of 12 quarters on a panel of 14 countries, both advanced and developing. First, we build a financial stress index to identify the starting dates of systemic financial crises for each country in the panel. Second, early warning indicators for the assessment and prediction of systemic risk are selected in a two-step approach; we find relevant prediction horizons for each indicator by a univariate logit model followed by the application of Bayesian model averaging to identify the most useful indicators. Finally, we observe the performance of the constructed EWS over both horizons on the Czech data and find that the model over the long horizon outperforms the EWS over the short horizon. For both horizons, out-of-sample probability estimates do not deviate substantially from their in-sample estimates, indicating a good out-of-sample performance for the Czech Republic.     

The puzzle of greenhouse gas footprints of oil abundance

The present inquiry lays a groundwork for the analysis of the net greenhouse gas (GHG) footprint of oil in the oil-abundant settings. To address the research question, the study puts forward a three-sector decision model, which provides a common ground for the assessment of the interaction of the structuralist and institutional factors influencing environmental pollution in the oil-reliant economies. The study shows that fossil-fuel abundance triggers forces, which induce diametrically opposed effects concerning atmospheric pollution. These are the rising carbon-intensive oil extraction and processing and fossil-fueled power generation versus shrinkage of the carbon-intensive manufacturing and growth of the low-carbon tertiarization. The theoretical analysis enables compartmentalization of the essential factors, which determine GHG emissions in the respective countries. To assess the significance of the proposed theoretical framework, the study employs multivariate panel co-integration techniques and two-stage fixed effects estimations for a dataset of 38 oil-producing countries for the time period between 1960 and 2018. In contrast to the existing literature, this study drives apart from the black box approaches that employ just one omnibus variable, per capita income.     

Hiring Older Employees: Do the Age Limits of Early Retirement and the Contribution Rates of Firms Matter?

We examine the effects of a Finnish pension reform on firms' incentives to hire older employees. The reform restricted the eligibility ages for early retirement and changed the size‐related contribution rates of firms. According to our theoretical model, the positive effect on the values of new hires extends to age groups younger than those directly affected by the reform, and the effects are strongest in the largest firms. These model predictions were confirmed in a difference‐in‐difference‐in‐differences analysis on the probability of the hiring of workers of different ages in firms of different sizes.     

Systemic financial risk early warning of financial market in China using Attention-LSTM model

We propose an Attention-LSTM neural network model to study the systemic risk early warning of China. Based on text mining, the network public opinion index is constructed and used as a training set to be incorporated into the early warning model to test the early warning effect. The results show that: (i) the network public opinion is the non-linear Granger causality of systemic risk. (ii) The Attention-LSTM neural network has strong generalization ability. Early warning effects have been significantly improved. (iii) Compared with the BP neural network model, the SVR model and the ARIMA model, the LSTM neural network early warning model has a higher accuracy rate, and its average prediction accuracy for systemic risk indicators has been improved over short, medium and long terms. When the attention mechanism is included in the LSTM, the Attention-LSTM neural network model is even more accurate in all the cases.     

The long-run reversal in the long run: Insights from two centuries of international equity returns

We perform the most comprehensive test of long-term reversal in national equity indices ever done. Having examined data from 71 countries for the years 1830 through 2019, we demonstrate a strong reversal pattern: the past long-term return negatively predicts future performance. The phenomenon is not subsumed by other established cross-sectional return patterns, including the value effect. The long-term reversal is robust to many considerations but highly unstable through time. Finally, our findings support the overreaction explanation of this anomaly.