明星基金溢价效应:“高技术”还是“好运气”?

本文研究我国股票型、偏股型开放式基金的评级与未来业绩的关系。根据晨星公司的基金评级数据,每月构造1星级至5星级的基金投资组合,并采用Carhart四因子模型对组合收益进行风险调整,研究发现5星级基金每年能获得2%的超额收益率,5星级与1星级基金的套利组合年超额收益为6%。此外,采用自助法对超额收益率的进一步检验,本文发现,明星基金溢价与基金经理的选股能力无关,与基金经理的好运气有关。     

分析师修正信息、基本面分析与未来股票收益北大核心CSSCISSCI

本文基于日历时间组合方法检验中国A股市场分析师修正信息的投资价值。研究发现,分析师发布的盈余预测修正和投资评级修正最具信息含量,投资者既未充分意识到两类修正的信息含量,也未充分意识到明星分析师和普通分析师能力的差别。根据分析师盈余预测修正和投资评级修正构建套利组合,能分别获得月均1.34%和0.92%的经三因子模型调整的超额收益。本文进一步指出,分析师修正信息的投资价值来源于其基本面分析的能力。分析师盈余预测修正和投资评级修正能够显著预测公司未来的盈利能力、标准化未预期盈余和盈余公告窗口期超额收益,并且套利组合的超额收益集中在盈余公告窗口期。本文的结论对于理解分析师在资本市场中发挥的作用、提高市场有效性具有重要意义。     

公募基金改善了市场定价效率吗?——持股基金质量与股票收益

由于忽略了基金管理能力差异,现有文献无法证明公募基金可以改善市场定价效率,即基金持有的股票未来收益率并未优于市场平均水平。本文以2005—2020年中国市场数据为基础,构建了股票的持股基金质量指标(而非权重),并研究了该指标对未来表现的预测能力。实证结果表明,持股基金质量较高的股票表现显著优于质量较低的股票,基于该指标构建的套利组合可以获得年化14%左右的超额收益,且该现象不能被基金持股权重、羊群效应引起的价格压力、系统性风险和其他定价异象所解释。本文发现我国公募基金持仓正向预测资产未来收益率的证据,可为相关监管政策制定和执行提供启示。     

上市公司综合盈利水平与股票收益

本文基于2000-2017年上市公司的财务及股票交易数据,研究了上市公司综合盈利水平与股票收益之间的关系。我们使用目前资产定价文献中较新的偏最小二乘法和组合预测法,从12个衡量公司盈利能力的指标中提取了一个测度上市公司综合盈利水平的指标。研究结果显示,上市公司综合盈利水平能够显著预测未来股票收益。使用单因子偏最小二乘法、取12个月斜率的平均值构造的综合盈利水平最有效,以其构建的多空对冲投资组合能产生15%的年平均收益,夏普比率达到0.75。与此对应,组合预测法提取的上市公司综合盈利水平的预测能力稍低,但依然显著。在控制了其他公司特征变量后,综合盈利水平对于股票收益的解释能力依然稳健。本文还从经济机制的角度出发,探讨了综合盈利水平对收益的预测来源。我们发现,上市公司综合盈利水平与股票预期回报的正向关系在投资摩擦更低的组中更高,而在错误定价程度更高的组通常更低。这些结果支持了基于投资摩擦的Q理论,而与行为金融的错误定价理论相悖。     

财务业绩、投资评级与股票收益

以Z证券公司2014年5月至7月间发布的208份投资评级报告为研究样本,实证检验了上市公司财务业绩、投资评级与股票收益之间的关系.结果表明:证券分析师更倾向于给予上市公司积极的投资评级,且随着证券分析师对上市公司投资评级积极程度的增强,目标价在其未来约定的期间内越发难以实现;若投资者严格遵循证券分析师的投资评级进行投资,总体上可以获得超过市场平均收益水平的超额收益;相对于单纯考虑财务业绩或事件的投资评级,综合财务业绩与事件共同影响的投资评级会带来更高的收益.     

市场特征、基本因素双重考虑下的β值估计与股票未来收益

本文从公司基本因素出发,通过与市场特征、历史β值结合构建线性回归模型,用于预测下一年的βf值,并进一步分析预测的βf值与市场表现的βm值之间的比值(βf/βm)与股票未来超额回报之间的关系。我们发现基于βf/βm高低分组的股票未来超额回报呈现一定的规律性,并且可被用来构建投资组合。在控制了投资规模、资产负债率、总资产增长率、销售增长率、市盈率等因素后,βf/βm对股票未来超额回报仍然具有显著预测作用。     

我国开放式股票型基金窗饰效应研究

以开放式股票型基金为研究对象实证研究我国基金市场的窗饰效应。首先,初步考察基金对股票的买卖倾向,发现基金在报告期末更加倾向于买进盈利股票,而卖出亏损股票的行为则不明显。之后构建一个＂买入-持有＂投资策略,比较该策略收益与基金实际收益,发现前者能够获得2.38%的平均超额收益,证明基金在报表中公布的投资组合与其平时表现存在显著差异,剔除其他一些可能导致基金投资组合调整的因素后,证明我国开放式股票型基金存在显著的窗饰效应,且成长型基金以及之前业绩较差、股票投资换手率较高的基金更可能出现窗饰效应。     

剩余收益模型与股票未来回报

本文采用Hou et al.(2012)公司基本面盈余预测模型并结合剩余收益模型对上市公司的内在价值进行估计,并分析内在价值与市价比率(V/P)与股票未来回报之间的关系。我们发现基于V/P分组的投资组合,在未来一至三年规模调整的持有超额回报套利分别达到15.2%、37.9%和55.9%;在控制了市账比等因素以后,V/P对股票未来回报仍然具有显著的预测作用。本文的研究克服了以往文献中运用证券分析师盈余预测进行剩余收益模型估值的内在局限,并提供了我国资本市场背景下切实可行的基于剩余收益模型估值的投资组合策略。     

投资者关注与股市收益 ——基于股吧数据挖掘的实证分析

采用股吧发帖量衡量个股投资者关注度,并构建了市场整体的关注度指标以及关注度风险因子,通过对市场整体以及异质性股票的实证检验,结果发现:(1)总体关注度指标只对当期市场收益率有影响,对未来预测能力的影响并不显著,同时投机性较强的股票对关注度指标的敏感性更高;(2)在控制了现有定价因子(MKT、SMB、HML、MOM、ILLIQ)后,关注度因子仍然能够获得显著的正阿拉法收益;(3)多因子回归结果显示关注度因子对投资组合收益率有显著的解释能力,说明关注度因子能够为现有的定价模型带来增量信息.总体来看,采用个股关注度简单加权的市场总体关注度指标并不能对市场收益率进行有效预测,但是采用分组排序法构造的关注度因子却对市场收益率具有较强的解释能力.     

影响股票收益的基本面因子略探——基于中小板上市公司的实证分析

选取公司规模、股价、股东人数变动、换手率、每股盈余、净资产收益率、毛利率、市盈率相对盈利增长比率及市净率等基本面因子作为影响股票收益的自变量,利用中小板上市公司2010年7月—2016年7月的季度数据进行了实证分析。结果表明:规模、股价、股东人数变动、换手率及毛利率五个指标对股票收益有显著影响;单从系数上看,规模的影响最大,其次分别依次是毛利率、换手率、价格、股东人数变动。采用简单赋值的方法,对这5个指标进行赋值,并选出得分靠前的股票进行等额投资,可以获得不错的收益;其中最优组合的阈值为N=16,该组合自2010年来获得了36.34%的年化收益率及2.6%的回撤深度。     

Market Segmentation and Cross‐predictability of Returns

We present evidence supporting the hypothesis that due to investor specialization and market segmentation, value‐relevant information diffuses gradually in financial markets. Using the stock market as our setting, we find that (i) stocks that are in economically related supplier and customer industries cross‐predict each other's returns, (ii) the magnitude of return cross‐predictability declines with the number of informed investors in the market as proxied by the level of analyst coverage and institutional ownership, and (iii) changes in the stock holdings of institutional investors mirror the model trading behavior of informed investors.     

卖空者与内幕交易——来自中国证券市场的证据

本文研究上市公司内部人减持、年报、诉讼、分析师评级、停复牌以及高送转等重大公告前后卖空交易行为的变化,系统考察卖空者是否参与内幕交易以及何种因素影响卖空者参与内幕交易,发现卖空率较高的股票具有较低的未来收益,表明卖空者拥有信息优势,属知情交易者;卖空者拥有非常精确的择时交易能力,在重大利空公告前显著增加卖空量,而在利好公告前则显著减少卖空头寸,表明卖空者作为知情交易者的信息优势源自内幕消息;公司内、外部投资者的信息不对称程度越低或公司所在地的法治水平越高,卖空者参与内幕交易的行为就越少。因此,监管机构应密切关注公司重大消息发布前后卖空量的异常变动,同时,完善信息披露规则、健全证券分析师制度并强化法律法规的执行力度,才能有效防范卖空者参与内幕交易。     

Stock price reactions to earnings announcements: evidence from Chinese markets

We examine the stock price reactions to changes in earnings per share (EPS) in the Chinese stock markets. We find that domestic A-share investors do not correctly anticipate the changes in earnings and fail to adjust new earnngs information quickly, but international B-share investors can predict earnings changes better than A-share investors. As a result, abnormal returns (ARs) can be obtained by trading on the earnings information, but for A shares only. An explanation is that most A-share holders are individuals with short-term investment horizon while most B-share holders are large institutions that trade on more detailed and accurate financial information not immediately available to A-share holders.     

The complementary relationship between financial and non-financial information in the biotechnology industry and the degree of investor sophistication

We examine whether financial and non-financial variables, separately and in tandem, are value relevant in explaining market returns, equity values and the degree of investment by sophisticated investors for a sample of drug development companies. Patent counts, number of collaborations and probability-adjusted portfolios of drugs under development are the non-financial information metrics used in this study. Earnings are the main financial information variable. We show that news about these non-financial measures is significantly associated with abnormal returns. We also find that earnings are value relevant in explaining cumulative abnormal returns and equity prices around earnings announcement dates despite the fact that R&D expenditures are large and usually expensed as incurred. We further show that non-financial information is value relevant in explaining annual returns, equity prices and degree of investment by (long-horizon) sophisticated investors. Moreover, non-financial variables are value relevant after controlling for financial variables suggesting that the two types of variables are complements.     

短期羊群行为的影响因素与价格效应——基于高频数据的实证检验

通过创新性地使用日内高频交易数据对A股市场中的羊群行为进行研究,本文发现:(1)羊群行为具有短期脆弱性特征,随着度量频率的提高,羊群行为的程度严格递增。(2)信息不对称程度、机构投资者比例、股票规模等因素,会显著影响短期羊群行为程度。(3)短期羊群行为会伴随着明显的价格反转:短期买入(卖出)羊群行为后,股票的超额收益显著为负(正),并且短期羊群行为越显著,价格反转的程度越大。(4)价格反转效应存在不对称性:规模越大、交易越活跃的股票,短期买入羊群行为的价格反转越明显,而短期卖出羊群行为的价格反转越不明显。     

Striking oil: Another puzzle?

Changes in oil prices predict stock market returns worldwide. We find significant predictability in both developed and emerging markets. These results cannot be explained by time-varying risk premia as oil price changes also significantly predict negative excess returns. Investors seem to underreact to information in the price of oil. A rise in oil prices drastically lowers future stock returns. Consistent with the hypothesis of a delayed reaction by investors, the relation between monthly stock returns and lagged monthly oil price changes strengthens once we introduce lags of several trading days between monthly stock returns and lagged monthly oil price changes.     

Investor sentiment and return predictability of disagreement

This study examines the influence of investor sentiment on the relationship between disagreement among investors and future stock market returns. We find that the relationship between disagreement and future stock market returns time-varies with the degree of investor sentiment. Higher disagreement among investors’ opinions predicts significantly lower future stock market returns during high-sentiment periods, but it has no significant effect on future stock market returns during low-sentiment periods. Our findings imply that investor sentiment is related to several causes of short-sale impediments suggested in the previous literature on investor sentiment, and that the stock return predictability of disagreement is driven by investor sentiment. We demonstrate that investor sentiment has a significant impact on the stock market return predictability of disagreement through in-sample and out-of-sample analyses. In addition, the profitability of our suggested trading strategy exploiting disagreement and investor sentiment level confirms the economic significance of incorporating investor sentiment into the relationship between disagreement among investors and future stock market returns.     

Predictability of Stock Returns: Robustness and Economic Significance

This article examines the robustness of the evidence on predictability of U.S. stock returns, and addresses the issue of whether this predictability could have been historically exploited by investors to earn profits in excess of a buy-and-hold strategy in the market index. We find that the predictive power of various economic factors over stock returns changes through time and tends to vary with the volatility of returns. The degree to which stock returns were predictable seemed quite low during the relatively calm markets in the 1960s, but increased to a level where, net of transaction costs, it could have been exploited by investors in the volatile markets of the 1970s.     

有效预警上市公司违规的递延所得税异动指标和人工智能模型

2000-2017年,3434家A股上市公司样本中的47.79%至少有一次违规记录,每年平均有17%的公司违规,而监管机构平均需要2.7年查证并通告违规行为。我们用当年数据构建递延所得税异动指标,可有效预判违规,并发现监管机构未能识别这一指标的警示作用,实际激励了违规公司通过操纵递延所得税提高财务指标以规避稽查。进一步构建决策树模型,对违规事件实现样本外精准判别。本文揭示了A股公司大面积违规而稽查过程冗长迟缓这一重要现象,并分析了违规机制,指出了所得税数据在稽查违规中可以发挥的预警作用,为监管者和投资者提供有效预警上市公司违规行为的新指标和方法。     

The effect of option transaction costs on informed trading in the options market around earnings announcements

We investigate the effect of option market transaction costs (a form of market imperfection) on the ability of option implied volatility-based measures to predict future stock returns and volatility around quarterly earnings announcements. We find that the predictability is significantly stronger for firms with lower option relative bid-ask spreads. The effect is more pronounced around positive rather than negative earnings news. We find no significant effect of option transaction costs around randomly chosen dates when there is no clustering of major information events. Trading strategies based on option market predictors and transaction costs earn monthly abnormal returns of 1.39% to 1.91%.