基于实物期权的竞争与合作情形下战略投资决策分析

考虑市场中两家相同的企业，双方都拥有对某一新产品进行战略投资的机会，其中先投资的企业将获得先发优势．运用实物期权方法，从企业价值最大化的角度分别研究了两家企业在竞争与合作情形下均衡投资点的变化．结果表明，双方在合作情形下的均衡投资点优于在竞争情形下的均衡投资点，其原因是在竞争情形下企业双方对于先发优势的争夺使得均衡投资点从最优投资点前移，导致等待期权未被充分利用，造成了双方企业价值的损失．     

中国股票市场经济学实验分析

将经济系统的分析扩展到合作博弈领域内。引入经济学实验的方法，介绍了一次在合作博弈范畴内的经济学实验的过程及其主要结果。实验参与者的行为与理论预期存在着偏离，造成该差异的主要原因在于局中人对公平性的要求。通过引入Banzhaf-coleman势值的概念，将实验数据与Banzhaf-coleman势值及Shapley值对比，显示出实验结果与理论解有着较好的相容性。     

发行可转换债券对公司股票价格影响的实证研究

对我国上市公司发行可转换债券对股票价格的影响进行了实证分析,结果发现:上市公司发布发行可转换债券公告后,二级市场股票价格显著上升,说明投资者青睐可转换债券.回归研究表明,发行可转换债券宣告效应与发行公司的公司规模,可转换债券发行规模以及宣告期间重大事件的公布呈显著正相关.     

期权费用化小议

股票期权制度成为安然事件后会计界备受关注的一个新兴领域，本文通过对美国股票期权会计理论发展的分析．对我国股票期权制度在现阶段的实施提出了一些建议。     

论闽台经贸交流与合作的机遇与挑战

闽台经贸交流与合作对促进两岸联系，加快推进祖国和平统一有着重要的意义。立足于这一思路，探讨了在新形势下，闽台经贸交流与合作面临的机遇和挑战，提出了应采取的策略。     

基于多Agent的RoboCup仿真足球比赛通信

足球机器人比赛系统需要机器人团队的合作 .但RoboCup仿真组比赛所提供的通信系统模型是一个低带宽、不可靠通信系统 .为了更好的利用这有限的通信能力 ,提出了对应的解决通信问题的方法 .最后举例说明了若干个在团队合作中使用通信系统的应用 .     

校企合作卓越人才项目学生量化考评体系构建 ——以市场营销专业为例

校企合作卓越人才项目为应用型本科院校的转型发展提供了有效路径,卓越人才项目的开展对学校、企业、学生都大有益处,但卓越人才项目的考评体系不健全影响了项目的开展。通过对参与卓越人才项目的三方调研,对现有卓越人才项目考评体系存在的问题进行分析,借鉴台湾地区相关经验提出了新的卓越人才项目学生量化考评体系。     

基于金属磁记忆的高铁轮对早期故障检测研究

列车安全性检测是铁路机车车辆工程的重要研究课题。轮对是高速列车行走部的关键部件,传统检测模式只能发现宏观存在的缺陷,而不适用于运行过程中的早期缺陷的发现与状态跟踪。为了实现对早期故障的快速识别与轮对寿命预测,针对传统测试方法在早期检测与动态检测方面的不足,提出了全新的动态检测系统,以满足列车运行的安全要求,还对磁记忆信号与该材料所受载荷状态之间的关系进行了大量的试验研究。研究结果表明：在50~150 kN的载荷区间内,试件的磁记忆信号呈单调变化,变化趋势明显;对试件进行疲劳载荷后发现磁记忆信号的原始值及梯度、峭度等信号特征均有所变化,峭度变化较为明显,能够有效反应材料的疲劳状态。采用金属磁记忆技术进行轮对早期故障检测与动态检测,对于保证列车的运行安全具有重要的现实意义,可为无损检测领域的应用提供借鉴。     

民族地区旅游业人才校企合作培养模式构建——以大连民族大学旅游管理专业为例

针对民族地区旅游行业发展状况及对民族地区旅游人才的需求,梳理旅游管理专业存在的问题,分析民族地区旅游业新的发展机遇。提出统一认识合作共建、突出特色重视实践的校企合作理念,采取“双师型”团队建设、民族特色课程共建、专项对口服务拓展等措施,系统构建校企合作应用型人才培养模式,深化教育体制改革和专业深层建设。     

Statistical inference and modelling of momentum in stock prices

The following results are obtained, (i) It is possible to obtain a time series of market data {y(t)} in which the fluctuations in fundamental value have been compensated for. An objective test of the efficient market hypothesis (EMH), which would predict random correlations about a constant value, is thereby possible, (ii) A time series procedure can be used to determine the extent to which the differences in the data and the moving averages are significant. This provides a model of the form y(t)-y(t-l)=0.5{y(t- l)-y(t-2)}+ε(t)+0.8ε(r-1) where ε(t) is the error at time t, and the coefficients 0.5 and 0.8 are determined from the data. One concludes that today's price is not a random perturbation from yesterday's; rather, yesterday's rate of change is a significant predictor of today's rate of change. This confirms the concept of momentum that is crucial to market participants. (iii) The model provides out-of-sample predictions that can be tested statistically. (iv) The model and coefficients obtained in this way can be used to make predictions on laboratory experiments to establish an objective and quantitative link between the experiments and the market data. These methods circumvent the central difficulty in testing market data, namely, that changes in fundamentals obscure intrinsic trends and autocorrelations. This procedure is implemented by considering the ratio of two similar funds (Germany and Future Germany) with the same manager and performing a set of statistical tests that have excluded fluctuations in fundamental factors. For the entire data of the first 1149 days beginning with the introduction of the latter fund, a standard runs test indicates that the data is 29 standard deviations away from that which would be expected under a hypothesis of random fluctuations about the fundamental value. This and other tests provide strong evidence against the efficient market hypothesis and in favour of autocorrelations in the data. An ARIMA time series finds strong evidence (9.6 and 21.6 standard deviations in the two coefficients) that the data is described by a model that involves the first difference, indicating that momentum is the significant factor. The first quarter's data is used to make out-of-sample predictions for the second quarter with results that are significant to 3 standard deviations. Finally, the ARIMA model and coefficients are used to make predictions on laboratory experiments of Porter and Smith in which the intrinsic value is clear. The model's forecasts are decidedly more accurate than that of the null hypothesis of random fluctuations about the fundamental value.