中国证券市场与国际主要市场相关性的行业特征分析

通过对中国证券市场中主要行业与香港、美国和日本市场之间相关性的现状和变化趋势进行比较分析,研究结果表明:首先,尽管中国证券市场中各行业的国际相关性水平相对较低,但从总体上有增强的趋势;其次,不同行业国际相关性的差异化程度在逐步扩大,显示国际经济和金融因素对中国经济不同产业的影响差异性正在日益增加;再次,不同行业国际相关性的增长率也存在差异,金融和基础材料等行业的国际相关性具有相对较高的增长率.     

边疆民族地区人口结构预测及优化对策研究——以云南省为例

人口结构问题对边疆民族地区社会经济发展的制约作用越来越明显。对人口结构进行预测,把握人口结构发展趋势,制定人口结构对策对社会经济发展具有重要的理论和实践意义。优化云南人口结构的对策是：调整生育政策;发挥年龄结构优势使劳动适龄人口充分就业;利用主体功能区划进行人口布局的合理规划。     

中国与东盟五国股市投资组合的风险和收益——基于MV和M-LPM模型的实证研究

选取2003年1月至2011年12月间中国和东盟五国股市的市场指数数据样本,分别基于MV和M-LPM模型构造等权重组合、最小方差组合和最大夏普比率组合,比较不同模型和不同投资组合的风险和收益率,实证结果表明构造中国与东盟五国股市投资组合与仅投资国内A股相比可以显著提高风险收益率。     

县(市)级土地利用总体规划中耕地需求量预测方法及其应用

科学准确地预测耕地需求量,是制定县(市)级土地利用总体规划的基础.文章根据中国南方某县1996-2005年来的耕地变更调查统计数据,分别采用趋势分析法、回归分析法、指数平滑法、GM(1,1)模型法和灰色-马尔柯夫链模型的方法,对该地区耕地总量进行了模拟和预测.不同方法的预测结果表明,基于灰色-马尔柯夫链方法的预测结果更加准确可靠,更有利于土地管理决策者的经济决策行为.在当前耕地利用方式的背景下,灰色-马尔柯夫链模型可以作为县(市)级土地利用总体规划中耕地需求量预测方法的一种好选择.     

基于IAHP的流程重组预警指标体系设计

根据业务流程重组预警的实际需要,结合业务流程重组评价指标,构建了涵盖流程效率、资源利用率、流程成本及顾客满意度四个方面的业务流程重组预警的指标体系。最后用改进的层次分析法(IAHP法),对该指标体系进行了评价,得出了各指标对业务流程重组影响的权重。     

大股东资金占用与审计质量的实证分析

运用logistic多元回归方法,设置了两个考察变量和十一个控制变量,检验了上市公司大股东资金占用与审计质量成反比的假设。     

Can (unusual) weather conditions in New York predict South African stock returns?

This paper investigates the explanatory power of certain weather variables, measured as deviations from their monthly averages, in a leading international financial trading centre, i.e., New York, for South African stock returns, over the daily period January 2nd, 1973 to December, 31, 2015. The empirical results highlight that these unusual deviations of weather variables have a statistically significant negative effect on the stock returns in South Africa, indicating that unusual weather conditions in New York can be used to predict South African stock returns, which otherwise seems to be highly unpredictable. In fact, a forecasting exercise recommends that a trading rule that considers those weather variables through a GARCH modelling approach seems to outperform the random walk model and thus beat the market.     

On the volatility spillover between lslamic and conventional stock markets: A quantile regression analysis

This paper aims at analyzing the degree and structure of interdependencies in terms of volatility (transmission, contagion) between Islamic and conventional stock markets on calm periods and at times of financial fragility and crisis. We focused on the recent financial instability periods and used the Quantile Regression-based GARCH model. Main results lead to very interesting conclusions. First, it has been found that Islamic stock markets are not totally immune to the global financial crisis. Second, a very strong interdependence is sensed from the conventional to the Islamic stock markets, especially, from the conventional developed markets to the Islamic Emerging and Arab markets and to the Islamic developed markets. Finally, it has been proved that the interdependencies from conventional to Islamic markets are propagated between Islamic markets. Our findings suggest that the Islamic finance industry does not seem able to provide cushion against economic and financial shocks that affect conventional markets.     

The impact of FIFA’s official announcements on the stock market of Qatar: The case of the 2022 World Cup

This study examines the impact of the FIFA’s official announcements on Doha Stock Exchange (DSE) of Qatar with respect to the 2022 World Cup. Using the abnormal unsystematic volatility method of Hilliard and Savickas (2002), our empirical findings reveal that the DSE market is sensitive to FIFA’s announcements about the 2022 World Cup. We find that four out of six FIFA announcements act as primary drivers to the DSE market volatility. The significant reactions of the DSE market to these announcements unveil the investors’ sentiments about the fate of the governmental and private expenditures on medium- and long-term projects undertaken in anticipation of hosting the 2022 World Cup. The results have some implications to investors in this newly emerging market related to this global sporting event. Any future announcements, good or bad, are likely to impact share prices in DSE market and trigger portfolio reallocation by local and international investors, leading to increased volatility.     

Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks

Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. ( 2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of “new age” classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that “new age” classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).