On the Nonlinear Predictability of Stock Returns Using Financial and Economic Variables

In a recent article by Qi, neural networks trained by Bayesian regularization were used to predict excess returns on the S&P 500. The article concluded that the switching portfolio based on the recursive neural-network forecasts generates higher accumulated wealth with lower risks than that based on linear regression. Unfortunately, attempts to replicate the results were unsuccessful. Replicated results using the same software, approach and data detailed by Qi indicate that, in fact, the switching portfolio based on the recursive neural-network forecasts generates lower accumulated wealth with higher risks than that based on linear regression.     

A segmented regime-switching model with its application to stock market indices

This paper evaluates the ability of a Markov regime-switching log-normal (RSLN) model to capture the time-varying features of stock return and volatility. The model displays a better ability to depict a fat tail distribution as compared with using a log-normal model, which means that the RSLN model can describe observed market behavior better. Our major objective is to explore the capability of the model to capture stock market behavior over time. By analyzing the behavior of calibrated regime-switching parameters over different lengths of time intervals, the change-point concept is introduced and an algorithm is proposed for identifying the change-points in the series corresponding to the times when there are changes in parameter estimates. This algorithm for identifying change-points is tested on the Standard and Poor's 500 monthly index data from 1971 to 2008, and the Nikkei 225 monthly index data from 1984 to 2008. It is evident that the change-points we identify match the big events observed in the US stock market and the Japan stock market (e.g., the October 1987 stock market crash), and that the segmentations of stock index series, which are defined as the periods between change-points, match the observed bear–bull market phases.     

金融监管与银行业证券业分业经营

:本文从金融监管的角度 ,针对全能银行制的弊端并结合中国的国情 ,提出中国目前应采取银行业证券业分业经营的模式 ,认为实行商业银行与投资银行分离制有利于维护金融市场的稳定 ,保护投资者、银行自身及存款人的利益     

反馈交易规则与股票收益自相关实证分析

研究了反馈交易规则与上证综合指数日收益自相关之间的联系，样本取自上证指数１９９２年９月２９日至１９９７年９月３０日的日收益率数据。此外，文中采用ＧＡＲＣＨ（１，１）处理收益波动的异方差性，模型参数采用极大似然估计。实证模型还考虑了非同步交易引起的工序列相关以及反馈交易的非对称性。结果表明，除以往文献涉及的正序列相关外，正反馈交易将导致收益负的序列自相关，且相关系数绝对值随波动增大而增大，从而整个收益表现出随波动变化的序列相关。     

对保险公司上市之思考

改革开放以来,我国保险业得到了快速发展。随着业务规模的扩大及加入世贸组织后与外资保险机构竞争的加剧,我国保险业迫切需要开拓新的融资渠道。发行上市是我国保险业和证券市场发展的双重需要。但目前条件尚未成熟,还需保险公司、保监会、证监会等各方共同努力,尽早推出我国股市的“保险板块”。     

上市公司融资偏好的内部治理机制研究

面对弱效率的资本市场,如何有效地解决国内上市公司在融资选择时对股权融资的过度偏好成为实务和理论界关注的一个重要问题。本文认为除了现行研究所提出的完善资本市场的规范性和有效性,加强上市公司的外部调控和管制以外,还有一个很重要的方面就是从微观的公司治理角度来分析上市公司的融资行为。在这一思路的基础上,本文利用一个多任务委托代理模型来解释影响企业融资方式的内部因素,并从公司治理结构的角度来提出完善融资激励和约束机制的要素。     

中国股票市场多重分形游走及其预测

股票价格波动规律的研究是预测的基础,多重分形过程是迄今为止最为符合价格波动特性的模型。本文验证了中国股票市场的多重分形游走,并根据多重分形过程的局部尺度特性和多尺度相关性建立了小波和神经网络相结合的股票价格预测模型。实证研究结果表明,本文模型预测精度较由其他模型得到的预测精度明显提高。     

粗集与神经网络相结合的股票价格预测模型

粗集和神经网络结合反映了人类智能的定性和定量、清晰和隐含、串行和并行相互交叉混合的常规思维机理。本文建立这样一种混合杂交模型用于股票价格波动趋势的预测,通过粗集对数据的二维约简预处理消除了样本中的噪声和冗余,在提高神经网络预测精度的同时降低了学习负担。为了获得最优的预测精度,本文还利用遗传算法进行属性离散化和网络学习。通过对上证综指的实证研究表明,这种混合杂交模型的性能明显优于BP和GA神经网络模型。     

布莱克─肖尔斯期权定价模型在公司价值评估中的应用

公司股票、债券在本质上可看成是基于公司资产的期权,因而可用期权定价方法对其价值进行评估。本文介绍了最典型的期权定价模型─布莱克一肖尔斯期权定价模型,分析了该模型在公司证券价值评估中的应用。     

试论我国股份公司理财行为矛盾与矛盾关系的调整方法

股份公司两权分离后，在具有相对独立经济利益的所有者集团和经营者集团之间形成了一种集中体现在公司理财行为之中的利益矛盾，这种矛盾导致了公司理财行为矛盾，损害了所有者集团的利益。本文通过对我国股份公司理财行为矛盾现状和成因的分析，认为解决理财矛盾的基本方法应是制订一种能使两大利益集团的利益有机结合起来的、对经营者业绩实施科学考核的考核指标。