基于ANP法的股权激励模式选择研究

本文对企业如何科学合理地选择股权激励模式问题进行研究探讨。不仅考虑到以往研究中的企业特征因素,同时也考虑了激励对象特征因素和外部实施环境因素,建立股权激励模式选择指标体系,并分析各指标间的依存关系,基于此构建了ANP网络结构模型,并选取两个代表性样本企业进行算例验证和应用,最终使得不同企业可根据自身的实际情况选择合适的股权激励模式,验证了该模型的合理性与可行性。     

控股股东对上市公司股利分配倾向的影响及实证分析——来自沪市A股上市公司的经验证据

当前对股利政策的研究主要集中在产权对股利政策的影响、自由现金流对股利政策的影响、公司过渡投资行为对股利政策的影响以及宏观经济波动对股利政策的影响等,而缺乏控股股东对股利政策的影响研究.现实中,控股股东对股利政策具有较大的影响,围绕控股股东对上市公司股利分配倾向的影响进行深入分析.具体而言,以2013-2014年度上证A股数据,建立Log斌ic模型和多元线性回归模型分析控股股东的几方面特征对股利分配倾向产生的多种影响.研究从理论上客观地评价了控股股东对股利分配倾向的影响,现实中对维护中小股东权益有重要意义.     

基于熵权模糊综合评价模型的股票内在价值研究

针对股票内在价值评判方法中指标权重设定的主观性缺陷,提出在利用熵权确定各指标权重的基础上,运用模糊综合评价方法对股票会计信息的综合指标进行模糊处理,为投资者投资股票提供一种新的参考;并通过"一带一路"概念股中的五支工程基建行业类股票进行模拟实证分析,证明将会计信息进行相关量化处理,能够为投资者提供较为客观的选择,同时基于熵权的模糊综合评价模型在股票内在价值评价中具有可行性.     

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A Markov observation model with dividend is defined and the interpretation of the practical significance is given. We try to use an irreducible and homogeneous discrete-time Markov chain to modulate the inter-observation times and embed a dividend strategy. In the Markov observation model with dividend, a system of liner equations for the expected discounted value of dividends until ruin time is derived. Moreover, an explicit expression is obtained and proved. Finally, some interesting properties are illustrated by numerical analysis and by comparing with the complete compound binomial model with dividend.     

Existence and uniqueness of optimal dynamic pricing and advertising controls without concavity

We consider a pricing and advertising dynamic-optimization problem where the goodwill dynamics evolve à la Nerlove–Arrow. The firm maximizes its profit over a finite-planning horizon corresponding to the product’s lifespan, and it turns out that the Hamiltonian is non-concave. We show the existence and uniqueness of an optimal solution under some mild conditions.     

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.     

ON THE EXPECTED DISCOUNTED PENALTY FUNCTION IN A MARKOV-DEPENDENT RISK MODEL WITH CONSTANT DIVIDEND BARRIER

This article considers a Markov-dependent risk model with a constant dividend barrier. A system of integro-differential equations with boundary conditions satisfied by the expected discounted penalty function, with given initial environment state, is derived and solved. Explicit formulas for the discounted penalty function are obtained when the initial surplus is zero or when all the claim amount distributions are from rational family. In two state model, numerical illustrations with exponential claim amounts are given.     

鞅分析在具有红利支付的n次幂型欧式期权定价中的应用

本文探讨了鞅分析在具有红利支付的n次幂型欧式期权定价中的应用,即用鞅分析的技巧与方法研究了在标的资产服从分数布朗运动的条件下具有红利支付的n次幂型欧式期权定价问题,并获得了其公式。丰富了已有期权定价结果,使期权定价公式更有利于实际的应用。     

随机支付红利的跳-扩散模型的期权定价

假设股票随机支付红利,且红利的大小与支付红利时刻及股票价格有关,并假设股票价格过程服从跳—扩散模型(其中跳跃过程为Poisson过程)的条件下,建立了股票价格行为模型,应用保险精算法给出了欧式看涨和看跌期权的定价公式,推广了Merton关于期权定价的结果。     

三种生成神经网络拓扑结构的方法在股票、商品价格预测中的应用及结果比较

对比了三种不同神经网络模型的生成方式:传统神经网络生成模型,遗传算法训练神经网络模型,以及在第二种方式训练参数的基础上,再使用传统神经网络优化生成模型.论文使用上述三种方法对代表性股票和商品价格进行拟合并预测,通过预测结果准确性和稳定性的比较发现:引入遗传算法后的神经网络在样本内的拟合误差有所降低,而第三种方法在样本外有最低的预测误差和最优稳定性.