上市公司壳资源价值的实证分析

壳资源是当前中国资本市场的热点。我们考察了2001,3,1-2002/7/30的26只在上海证券交易所交易的ST股票,利用CAPM模型,分析ST股票与市场综合收益,得出ST股票的超额回报,从而反映控制上市公司或是企业控制上市公司的大股东们为了保持壳资源而注入上市企业的优良资产的价值。     

贝塔系数是风险的正确度量吗

在经典的投资组合理论中 ,假设所有资产的报酬率服从对数正态分布 ,因而只需要用收益的方差来度量风险就足够了 ,忽略了偏度的影响。资产收益的分布往往不是对称的 ,偏度是客观存在的 ,而且投资者具有正偏度的爱好。所以必须用方差和偏度来共同度量投资的风险 ,在这种情况下 ,贝塔系数不再是风险的正确度量 ,采用有效的修正方法 ,可以用来对资产进行正确的定价     

证券市场异象的行为金融学探讨及对我国的启示

自 2 0世纪 80年代以来证券市场涌现了许多与有效市场理论相悖的市场异象 ,而利用最近兴起的行为金融学则可通过心理学与人类行为学等研究对这些市场异象作出与以往不同的新角度的探讨。且这对于在我国这一并不完全理性市场中的机构投资者有一定的投资策略启示。     

机构投资对我国资本市场资产价格影响的实证研究

本文以1999年3月到2011年3月的沪深主板上市公司月收益率为样本,通过定义机构投资者的投资策略,对加入机构投资者影响后的CAPM扩展模型进行实证研究,发现扩展模型消除了经典CAPM中存在的异方差性,且不存在一阶自相关性;资产组合的收益率同市场组合收益率呈反比关系,与基准投资组合收益率呈正比关系;模型拟合度有了很大的改善。机构投资对我国资本市场资产价格产生了积极的影响。因此,我国资本市场应该大力发展机构投资者,机构投资者本身也应该朝着健康的方向发展。     

基于稳态分布的资本资产定价模型上海股市实证分析

运用BS估计和OLS估计对资本资产定价模型在我国证券市场的应用进行了实证分析,分析结果显示,运用OLS方法对建立在资产收益为正态分布基础上的传统资本资产定价模型与运用BS方法对资产收益为α稳态分布的资本资产定价模型的贝塔系数估计值存在较大的差异,而BS估计方法明显比OLS估计方法更有效率。     

When bad news is good news: Geopolitical risk and the cross-section of emerging market stock returns

Using a news-based gauge of geopolitical risk, we study its role in asset pricing in global emerging markets. We find that changes in risk positively predict future stock returns. The countries with the highest increase in geopolitical uncertainty outperform their counterparts with the lowest change by up to 1% per month. The anomaly is not explained by other established asset pricing effects and remains robust to many considerations. We link the observed phenomenon with investor overreaction to geopolitical news driven by the availability bias.     

Sentiment-scaled CAPM and market mispricing

This study explores the conditional version of the capital asset pricing model on sentiment to provide a behavioural intuition behind the value premium and market mispricing. We find betas (β) and the market risk premium to vary over time across different sentiment indices and portfolios. More importantly, the state β derived from this sentiment-scaled model provides a behavioural explanation of the value premium and a set of anomalies driven by mispricing. Different from the static β–return relation that gives a flat security market line, we document upward security market lines when plotting portfolio returns against their state βs and portfolios with higher state βs earn higher returns.     

Time-series and cross-sectional momentum in anomaly returns

We find strong evidence of time-series and cross-sectional momentum in the long–short returns of a comprehensive sample of anomalies. Strategies that exploit such persistence deliver significant abnormal returns that are robust to the stock momentum effect, cannot be explained by traditional asset-pricing models, and are more pronounced when arbitrage capital is scarcer or market liquidity is lower. Momentum in anomaly returns dissipates but does not reverse, in the long-run. Our findings are consistent with limits-to-arbitrage and slow-moving capital causing mispricing to persist. Supporting this explanation, we find that both the level and persistence of anomaly returns are positively related to idiosyncratic volatility.     

Aggregate volatility expectations and threshold CAPM

We propose a volatility-based capital asset pricing model (V-CAPM) in which asset betas change discretely with respect to changes in investors’ expectations regarding near-term aggregate volatility. Using a novel measure to proxy uncertainty about expected changes in aggregate volatility, i.e. monthly range of the VIX index (RVIX), we find that portfolio betas change significantly when uncertainty about aggregate volatility expectations is beyond a certain threshold level. Due to changes in their market betas, small and value stocks are perceived as riskier than their big and growth counterparts in bad times, when uncertainty about aggregate volatility expectations is high. The proposed model yields a positive and significant market risk premium during periods when investors do not expect significant uncertainty in near-term aggregate volatility. Our findings support a volatility-based time-varying risk explanation.