证券市场中的直接贷款及其风险衡量

证券市场风险对出资人来说意味着预期回报甚至本金的减少或消失。这种风险对中国股市中的分散股民已领教深刻。然而，作为广大储户委托人的银行中介在对上市公司的直接贷款行动中也同样承受了市场风险，这种处于隐蔽状态的风险在一些上市公司退市后已具有显性特征。本文试图从理论上解释与商业银行相关的两种风险，即储蓄转化为金融资产的投入风险，银行向上市公司提供直接贷款所承受的金融资产回报风险，从而提出了规避风险的关系组合均衡和管理对策。     

房地产企业社会责任与融资约束关系研究

为探究社会责任对缓解房地产企业融资约束的作用,在分析房地产企业通过承担社会责任降低融资约束路径的基础上,以沪深两市房地产上市公司为样本,首先构建社会责任和融资约束的表征模型,其次按社会责任承担多少将房地产企业分组,并通过比较两种组别的房地产企业的融资约束,证实房地产企业履行社会责任对减轻融资约束的作用。研究发现,社会责任履行程度高的房地产企业面临更低的融资约束;同时,越是在经济发达地区,社会责任对房地产企业融资的帮助越明显。最后,提出增强房地产企业社会责任的建议,为其减少融资约束提供参考。     

Mayetic公司向市场推出价格最低的存贮空间

正当一些互联网大公司围绕电子信箱的存贮能力进行激烈竞争的时候，法国一家企业Mayetic公司也加入了竞争行列，并很快从竞争对手中脱颖而出：该公司基于MayeticVillage技术的存贮空间每个月每个G的价格仅为6．95美元。为什么这竟成了市场上的最低价格呢?回答很简单。一方面，市场上所提供的服务主要是网络信箱的服务。当然，这种服务能够满足用户目前的需要。但是，网上的信息越来越多，污染也越来越厉害，使得将来对信箱的管理越来越困难。而且，网络信箱的结构贫乏，只能对个别信件进行管理。     

房地产开发企业项目管理层执行力维度结构模型

编制了房地产开发企业项目管理层执行力调查问卷,通过对270份有效问卷随机分成的两个均等样本分别采用探索性因子分析和验证性因子分析方法.研究表明:房地产开发企业项目管理层执行力维度结构模型由领悟能力、计划能力、指挥能力、督导能力和创新能力等五维构成.     

区域旅游贸易引力模型的构建及实证分析——以南京市国内客源市场为例

旅游者是旅游活动的主体，分析客源市场，对提高区域旅游竞争力，增加客流量具有重要的现实意义。本文以南京市为例，应用贸易引力模型，引入新的变量，对小区域客源市场影响因素进行了回归分析，通过变量剔除，构建了适合分析区域旅游影响因素的模型。通过分析发现感知距离是对居民出游影响最大的因素，城镇居民出游率、人均GDP和基础设施接待能力差异等变量都对客流量有重要影响。     

虚拟现实技术在房地产销售领域的应用研究

本文介绍了我国房地产销售领域的现状以及逐渐理性回归的现实,描述了应用三维GIS技术、虚拟现实技术(Virtual Reality)、可视化技术(Visualization)、数据库等技术推出的一种虚拟现实环境下的新型推销展示房地产的系统--上海新体育城项目(房产版).由此拓展了房地产销售的途径,并为未来房地产行业增添了一种高效、生动、有说服力的营销策略.本项目基于DEM、DOM、DLG、DM四种数据源[]和VRMap及Visual Basic软件平台技术,以上海新体育城房产为案例,通过输入楼房的楼栋号、面积、价格、楼层等参敦实现有目的的查询,并可在查询结果中挑选房间进行漫游,使浏览者不必亲赴房间查看即可有身临其境的感觉.     

浅议我国"入世"后对国内建筑业的影响及其对策

本文简要论述了我国建筑市场将面临的新的发展机遇，分析了我国加入世界贸易组织后对建筑业的有利与不利因素，讨论了我国建筑业进入国际市场的前景，建议了应对策略和政府的政策导向。     

三叉神经慢性缩窄环术大鼠半月节射频热凝的实验研究

目的观察三叉神经慢性缩窄环术大鼠半月节射频热凝的大体及镜下改变，探讨射频热凝温度和时间与半月神经节损伤的关系。方法36只成年雄性SD大鼠，随机选取33只建立慢性缩窄性损伤三叉神经痛模型。建模成功2周后，随机分为3组：射频热凝组（R组，n=24），假手术纽（S组，n=6），热凝对照纽（C组，n=3）。经中颅窝开颅暴露半月神经节，R组行半月节穿刺热凝，根据温度、时间随机分为8个小组，每组3只，分别为：65℃120″、180″组，70℃120″、180″组，75℃120″、180″组，80℃120″、180″组。S组仅行半月节穿刺不热凝，按留针时间分为120″、180″两组；C组不行半月神经节穿刺及热凝。余3只SD大鼠作为正常对照组（N组）。R组及S组热凝或穿刺结束即取出大鼠半月神经节，C组、N组直接取出半月神经节，观察半月神经节大体变化，游标卡尺测量神经节毁损区直径，光镜下观察神经节细胞的变化。结果S组、C组与N组比较，大鼠半月神经节大体及光镜下均无明显变化。R组在65℃时大鼠半月节大体及镜下无明显损伤；70℃时开始在肉眼及镜下出现损伤；75℃、80℃时半月节则出现明显凝固区，毁损区直径测量较70℃组明显增大（P〈0．01），光镜下神经元细胞核出现不同程度的固缩改变。毁损温度越高、时间越长，损伤范围越大，程度越重。结论75℃、80℃射频热凝可导致神经节细胞明显变性，损伤程度及范围随温度升高、时间延长逐渐加重与增大。     

我国A股增发定价相对效率的实证分析

本文以我国A股市场2000年1月到2007年12月实施增发的109家上市公司为样本，对我国目前普遍采用的增发定价方式-累计投标询价的定价效率进行综合考察。以增发价格对上市公司内在特质信息和市场状况信息的反映充分程度来考察增发定价的相对价格效率。通过参数假设检验、多元回归分析等方法，检验增发定价的效率机制，找出影响增发定价效率的不利因素，以及增发新股合理定价应充分考虑的因素。     

西藏自治区旅游产业发展研究

本文从西藏自治区发展旅游产业面临的良好宏观环境入手，分别对西藏旅游资源开发潜力，旅游市场和旅游产业可持续发展这些热点问题进行了研究。     

Relationships between Australian real estate and stock market prices—a case of market inefficiency

This paper explores the relationship between the Australian real estate and equity market between 1980 and 1999. The results from this study show three specific outcomes that extend the current literature on real estate finance. First, it is shown that structural shifts in stock and property markets can lead to the emergence of an unstable linear relationship between these markets. That is, full‐sample results support bi‐directional Granger causality between equity and real estate returns, whereas when sub‐samples are chosen that account for structural shifts the results generally show that changes within stock market prices influence real estate market returns, but not vice versa. Second, the results also indicate that non‐linear causality tests show a strong unidirectional relationship running from the stock market to the real estate market. Finally, from this empirical evidence a trading strategy is developed which offers superior performance when compared to adopting a passive strategy for investing in Australian securitized property. These results appear to have important implications for managing property assets in the funds management industry and also for the pricing efficiency within the Australian property market. Copyright © 2002 John Wiley & Sons, Ltd.     

Forecasting Stock Market Volatility in Central and Eastern European Countries

In recent years, considerable attention has focused on modelling and forecasting stock market volatility. Stock market volatility matters because stock markets are an integral part of the financial architecture in market economies and play a key role in channelling funds from savers to investors. The focus of this paper is on forecasting stock market volatility in Central and East European (CEE) countries. The obvious question to pose, therefore, is how volatility can be forecast and whether one technique consistently outperforms other techniques. Over the years a variety of techniques have been developed, ranging from the relatively simple to the more complex conditional heteroscedastic models of the GARCH family. In this paper we test the predictive power of 12 models to forecast volatility in the CEE countries. Our results confirm that models which allow for asymmetric volatility consistently outperform all other models considered. Copyright © 2011 John Wiley & Sons, Ltd.     

The Role of Momentum,Sentiment, and Economic Fundamentals in Forecasting Bear Stock Market

This article examines the role of market momentum, investor sentiment, and economic fundamentals in forecasting bear stock market. We find strong evidence that bear stock market is predictable by market momentum and investor sentiment in full‐sample and out‐of‐sample analyses. Most economic fundamental variables lose their out‐of‐sample significance once we control for market momentum and investor sentiment. However, the inclusion of economic fundamentals can improve the economic value of the forecasting model in our trading experiments. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting house prices in OECD economies

In this paper, we forecast real house price growth of 16 OECD countries using information from domestic macroeconomic indicators and global measures of the housing market. Consistent with the findings for the US housing market, we find that the forecasts from an autoregressive model dominate the forecasts from the random walk model for most of the countries in our sample. More importantly, we find that the forecasts from a bivariate model that includes economically important domestic macroeconomic variables and two global indicators of the housing market significantly improve upon the univariate autoregressive model forecasts. Among all the variables, the mean square forecast error from the model with the country's domestic interest rates has the best performance for most of the countries. The country's income, industrial production, and stock markets are also found to have valuable information about the future movements in real house price growth. There is also some evidence supporting the influence of the global housing price growth in out‐of‐sample forecasting of real house price growth in these OECD countries.     

金融租赁在商业地产中的应用探究

为了解决我国宏观调控下商业地产项目的融资难问‘题,总结了金融租赁的特点和运作模式,多角度论证了金融租赁在商业地产项目中应用的适用性。借鉴金融租赁在房地产和其他行业的成功运用经验,结合商业地产项目的自身特点,对金融租赁应用于商业地产的操作方法进行创新设计,提出了金融租赁在商业地产项目中运用的具体操作模式。并结合我国政策和金融制度,针对应用中存在的问题和困难提出了相应的对策建议,以期拓宽商业地产项目的融资渠道,促进商业地产的良性发展。     

Forecasting volatility of emerging stock markets: linear versus non‐linear GARCH models

ARCH and GARCH models are substantially used for modelling volatility of time series data. It is proven by many studies that if variables are significantly skewed, linear versions of these models are not sufficient for both explaining the past volatility and forecasting the future volatility. In this paper, we compare the linear(GARCH(1,1)) and non‐linear(EGARCH) versions of GARCH model by using the monthly stock market returns of seven emerging countries from February 1988 to December 1996. We find that for emerging stock markets GARCH(1,1) model performs better than EGARCH model, even if stock market return series display skewed distributions. Copyright © 2000 John Wiley & Sons, Ltd.     

Long‐Run and Cyclical Dynamics in the US Stock Market

This paper uses fractional integration to examine the long‐run dynamics and cyclical structure of US inflation, real risk‐free rate, real stock returns, equity premium and price/dividend ratio, annually from 1871 to 2000. It implements a procedure which allows consideration of unit roots with possibly fractional orders of integration both at zero (long‐run) and cyclical frequencies. When focusing exclusively on the former, the estimated order of integration varies considerably, and non‐stationarity is found only for the price/dividend ratio. When the cyclical component is also taken into account, the series appear to be stationary but to exhibit long memory with respect to both components in almost all cases. The exception is the price/dividend ratio, whose order of integration is higher than 0.5 but smaller than 1 for the long‐run frequency, and is between 0 and 0.5 for the cyclical component. Also, mean reversion occurs in all cases. Finally, six different criteria are applied to compare the forecasting performance of the fractional (at both zero and cyclical frequencies) models with others based on fractional and integer differentiation only at the zero frequency. The results, based on a 15‐year horizon, show that the former outperforms the others in a number of cases. Copyright © 2013 John Wiley & Sons, Ltd.     

Stochastic Multivariate Mixture Covariance Model

Stochastic covariance models have been explored in recent research to model the interdependence of assets in financial time series. The approach uses a single stochastic model to capture such interdependence. However, it may be inappropriate to assume a single coherence structure at all time t. In this paper, we propose the use of a mixture of stochastic covariance models to generalize the approach and offer greater flexibility in real data applications. Parameter estimation is performed by Bayesian analysis with Markov chain Monte Carlo sampling schemes. We conduct a simulation study on three different model setups and evaluate the performance of estimation and model selection. We also apply our modeling methods to high‐frequency stock data from Hong Kong. Model selection favors a mixture rather than non‐mixture model. In a real data study, we demonstrate that the mixture model is able to identify structural changes in market risk, as evidenced by a drastic change in mixture proportions over time. Copyright © 2016 John Wiley & Sons, Ltd.     

International transmission mechanism of stock market movements: evidence from emerging equity markets

This paper investigates the transmission patterns of stock market movements between developed and emerging market economies by estimating a four‐variable VAR model. The underlying economic fundamentals and trade links are considered as possible determinants of differences in transmission patterns. The results of the impulse response functions and variance decompositions indicate that significant links exist between the stock markets of the USA and Mexico and weaker links between the markets of the USA, Argentina, and Brazil. Differences in the patterns of stock market responses are consistent with differences in trade flows. The response of emerging markets to a shock to the US market lasts longer than that of a developed market such as the UK. While no single emerging market can affect the US stock market, the combined effect of emerging markets on the US stock market is found to be statistically significant. These findings can be linked to differences in the speed of information processing and to the institutional structure governing the market. Overall the findings suggest that the transmission of stock market movements is in accord with underlying economic fundamentals rather than irrational contagion effects. Copyright © 2000 John Wiley & Sons, Ltd.     

The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.