Price–Dividend Ratios and Stock Price Predictability

A long‐standing puzzle to financial economists is the difficulty of outperforming the benchmark random walk model in out‐of‐sample contests. Using data from the USA over the period of 1872–2007, this paper re‐examines the out‐of‐sample predictability of real stock prices based on price–dividend (PD) ratios. The current research focuses on the significance of the time‐varying mean and nonlinear dynamics of PD ratios in the empirical analysis. Empirical results support the proposed nonlinear model of the PD ratio and the stationarity of the trend‐adjusted PD ratio. Furthermore, this paper rejects the non‐predictability hypothesis of stock prices statistically based on in‐ and out‐of‐sample tests and economically based on the criteria of expected real return per unit of risk. Copyright © 2011 John Wiley & Sons, Ltd.     

基于无条件分位回归的民营上市公司高管薪酬问题研究

高级管理人员的薪酬作为现代企业委托代理关系中的核心问题一直是社会的焦点.本文以我国民营上市公司为样本,首先研究了公司规模、每股收益、营业收入、流动负债和高管持股比例作用于高管薪酬不同分位点处的条件分位偏效应,结果表明它们在不同分位水平下的影响各异;然后为了获得这些因素的一般边际影响,利用无条件分位回归分析了它们的无条件分位偏效应,结果指出公司规模、每股收益和营业收入与各个分位水平的高管薪酬呈不同程度的正相关而流动负债则为负相关,高管持股比例与低分位水平的高管薪酬正相关而对中高分位水平的影响不显著;最后对相关结果进行了对比,发现每个影响因素的条件和无条件分位偏效应有所区别,并且无条件分位偏效应的解释意义更具有现实价值.     

目标变量不完全情形下的最小二乘回归插补

充分利用对辅助变量的回答所提供的变量信息，采用最小二乘方法，给出了目标变量不完全情形下对其的一种回归插补，并对回归差补的期望和方差进行了讨论。     

相关分析中有关问题的探讨

本文推导了相关系数与回归系数之间的关系，提出了描述变量之间相关程度的另一个新指标     

用细胞神经网络实现蔡氏混沌电路

研究了用细胞神经网络（ＣＮＮ）实现蔡氏电路的方法，把ＣＮＮ模型与蔡氏电路模型两者联系起来，并且只采用一般常用的运算放大器、电阻和电容即可方便地构成蔡氏电路，试验结果表明，电路原理简明，工作可靠，性能优良，有较大的实用价值。     

一类三阶非线性控制系统的概周期解

本文研究了一类三阶非线性控制系统（１）的一致概周期解的存在性，得到了简洁的充分条件，并对保证有一致概周期解存在的参数范围及其与系统系数间的关系作出了估计。     

IC中心设计的一种改进方法

本文利用回归模型作为ＩＣ的影象模型，按照控制变量Ｍｏｎｔｅ－Ｃａｒｌｏ分析方法进行ＩＣ成品率估计，将一维搜索法与随机逼近优化法相结合进行ＩＣ中心设计，实例证明了该方法的可行性和实用性。     

对外开放与沿边开放城市经济增长因素的实证检验--以东北地区为例

以东北地区四个沿边开放城市———满洲里、珲春、绥芬河、黑河为例,运用合成数据进行回归分析,检验了对外开放对沿边开放城市经济增长的作用.结果表明,对外开放是促进沿边开放城市经济增长的最重要因素,这与一般城市和沿海开放城市不同,且对外开放因素对沿边开放城市经济增长的贡献度小于对沿海开放城市经济增长的贡献度.     

Time series forecasting using robust regression

The method of ordinary least squares (OLS) and generalizations of it have been the mainstay of most forecasting methodologies for many years. It is well-known, however, that outliers or unusual values can have a large influence on least-squares estimators. Users of automatic forecasting packages, in particular, need to be aware of the influence that outlying data values can have on statistical analyses and forecasting results. Robust methods are available to modify least-squares procedures so that outliers have much less influence on the final estimates; yet these formal methods have not found their way into general forecasting procedures. This paper provides a case study in which classical least-square-estimation procedures are complemented with a robust alternative to enhance statistical fit criteria and improve forecasting performance. The study suggests that much can be gained in understanding the nature of outliers and their influence on forecasting performance by performing a robust regression in addition to OLS.