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。     

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。     

前景价值能否预测股票收益？——基于定向增发的实证研究

基于前景理论,提出前景价值影响股票收益的研究假设,运用2006-2016年定向增发的相关数据,实证检验前景价值对增发股票收益的预测能力.结果表明:在定向增发实施前,投资者对定增股票的前景价值估值为负,且对股票收益有负向影响,当前景价值较高或收益较低时,前景价值对股票收益的预测能力相对较强,投资者和监管部门可运用前景价值作为决策变量,制定其投资组合方案和监管政策.     

有限注意、上市公司金融投资与股票回报率

我国上市公司参与金融投资活动十分普遍,这一行为极大地增加了公司估值的复杂性。当投资者存在有限注意时,金融投资收益很可能引起系统性的估值偏误。本文以A股非金融类上市公司为对象,考察金融投资收益对股票长期回报率的影响。研究发现,投资者倾向于高估金融投资盈利较高的股票,低估没有金融投资收益(金融投资亏损)的股票,构造对冲组合的超额回报高达每年12%。进一步地,这一估值偏误与投资者有限的信息处理能力、错误地选择估值基准以及忽视隐含信息相关。本文的发现对理解市场有效性、企业真实盈余管理以及影子银行监管具有重要参考价值。     

盈余公告后债券价格反应对股票未来回报的预测研究

本文研究了我国A股上市公司发布盈余公告后,债券价格反应对于关联股票未来回报的预测能力。研究发现,盈余公告前1日至后1日债券收益率的变动可以预测公告公布后窗口期为20日的股票持有超额累计回报;实证结果显示,这种预测能力不会因为盈余信息的好坏而存在显著的差异;最后基于似无相关模型SUR的检验后,发现上市公司的机构持股比例越低,债券价格反应对于股票回报的预测能力越强。本文研究表明,我国被富有经验的投资者所主导的债券市场中债券价格相对于股票价格会更迅速地吸收消化盈余公告信息。     

线上销售与未来股票收益

本文通过分析相关上市公司在电商平台的线上销售数据,发现线上销售增长可以预测未来股票收益。根据线上销售增长率构建投资组合可以获得月均1.27%的超额收益,经三因子、五因子模型调整后收益率分别为1.40%和1.35%,并且该超额收益在较长时间内不会逆转。横截面回归结果显示,线上销售增长与未来股票收益显著正相关,并在控制其他市场异象因子后仍然显著。此外,本文还发现线上销售数据的预测能力主要集中在投资者关注有限、线上销售占比高以及套利成本高的公司,其投资价值来源于对公司未来基本面信息的预测能力。进一步研究表明,同时利用线上销售指标和营业收入指标进行投资可以获得更高的超额收益。在考虑业绩预告和业绩快报对线上销售指标预测能力的潜在影响后,结果依然稳健。     

风格投资与收益协同性能够预测股票收益吗?——来自中国A股市场的经验证据

基于中国A股市场2006年1月到2014年12月的股票收益率数据,使用Fama-Macbeth回归和Jegadeesh-Titman的经典方法,对风格投资、收益协同性和股票收益预测能力之间的关系进行了研究。结果表明:风格组合存在显著的动量效应,并能够预测股票收益;个股与风格组合的收益协同性有助于增强预测能力,投资组合的协同性越高,未来收益越高。结论有助于投资者提高资产配置效率,对监管部门也有一定的参考价值。     

创业板IPO定价估值方法的探讨

2009年我国深圳创业板的推出对扶持国家的技术创新、调整产业结构有着深远的战略意义。怎样对具有"三高五新"特征的创业板IPO企业进行合理估值,合理确定其发行价格,一直受到多方的关注。股票的价格是其价值的体现,本文从公司内在价值角度出发,分析了目前常用的估值方法适用范围,探讨了适用于创业板特点的企业估值方法,并指出,剩余收益模型是创业板IPO定价估值中适用性较强的方法。     

分析师盈余预测准确性与投资评级的效率——基于中国证券市场的实证分析

本文以我国证券分析师(以下简称分析师)在2005-2010年发布的研究报告为对象,研究分析师盈余预测准确性与其投资评级效率之间的关系。本文的研究方法是,按照准确性对分析师盈余预测进行分组并根据组内的投资评级构造股票投资组合,比较各投资组合的月平均超额收益率的差异。研究发现,分析师盈余预测的准确性与其投资评级的效率正相关,盈余预测准确性最高组的买入-卖空投资组合的月平均超额收益比准确性最低组高出1.997%。我们的研究结果表明,准确的盈余预测信息有助于分析师做出更有价值的投资评级,同时,研究也初步揭开了分析师工作过程的"黑匣子",为未来深入研究分析师行为提供了基础。     

股权分置改革与股票市场价值相关性研究

本文使用剩余收益估值模型估计我国上市公司股票的内在价值,以股票内在价值为基础检验上市公司股权分置改革对股票价格和价值之间相关性的影响。选择已完成股权分置改革的上市公司为样本,研究表明股权分置改革完成后股票价格和价值之间相关性有显著的提高,业绩较差公司股价相对价值偏离程度得到一定程度的修正。股权分置改革在改善股票市场定价机制方面实现了管理层预期的效果。     

财务分析师盈利预测的投资价值:来自深沪A股市场的证据

本文研究了中国股票市场上财务分析师的盈利预测信息对投资者是否有价值。我们发现,可以利用公开的盈利预测制定可获利的套头交易策略。套头交易的回报率不仅在统计意义上显著大于零,而且在经济意义上也是显著的。这些结果不受不同的检验方法影响,也无法为我们所考虑到的风险因素所解释。本文的发现说明了中国的股票市场尚未达到Fama(1970)意义上的半强式有效,投资者在投资决策时可以利用分析师的盈利预测以提高其投资的回报。此外,本文的结果也有助于回答中国的财务分析师是否具有专业胜任能力这一颇有争议的问题。     

Dispersion in Analyst Forecasts and the Profitability of Earnings Momentum Strategies

This paper shows that the dispersion in analysts' consensus forecasts contains incremental information to predict future stock returns. Consistent with prior research, stock prices in the German market underreact to news about future earnings and drift in the direction suggested by analysts' forecasts revisions. Even higher abnormal returns can be achieved by applying such an earnings momentum strategy to stocks with a low dispersion in analyst forecasts. These results support one of the recent behavioural models in which investors underweight new evidence and conservatively update their beliefs in the right direction, but by too little in magnitude with respect to more objective information.     

Does the Stock Market Fully Appreciate the Implications of Leading Indicators for Future Earnings? Evidence from Order Backlog

Although leading indicators are becoming increasingly important for equity valuation, disclosures of such indicators suffer from the absence of GAAP related guidance on content and presentation. We explicitly examine (i) whether one leading indicator—order backlog—predicts future earnings, and (ii) whether market participants correctly incorporate such predictive ability in determining share prices. We find that the stock market overweights the contribution of order backlog in predicting future earnings, and a hedge strategy that exploits such overweighting generates significant future abnormal returns. However, such mispricing is not due to analysts' inability to incorporate order backlog into their earnings forecasts.     

The Value Relevance of Management Forecasts and Their Impact on Analysts' Forecasts: Empirical Evidence From Japan

A major financial disclosure feature in Japan is that stock exchanges require firms to provide next year's earnings forecasts. This study investigates the value relevance of Japanese management earnings forecasts and their impact on analysts' earnings forecasts. First, the value relevance of management forecasts is investigated using a valuation framework provided by Ohlson (2001 ), in which firm value is expressed as a function of book value, current earnings and next year's expected earnings. The analysis yields that of the three accounting variables examined, management forecasts have the highest correlation and incremental explanatory power with stock price.
Next, the impact of management forecasts on analysts' forecasts is examined. The results show that more than 90% of changes in analysts' forecasts are explained by management forecasts alone. Further analysis reveals that the heavy dependence of financial analysts on management forecasts in formulating their own forecasts may partially be attributed to the relatively high accuracy of management forecasts. At the same time, financial analysts also somewhat modify management forecasts when certain financial factors indicate that the credibility of management forecasts is in doubt.
Overall, this study presents empirical evidence that Japanese management forecasts provide useful information for the market and have a significant influence on analysts' forecasts.     

Consumption-based equity valuation

Using a CCAPM-based risk-adjustment model, we perform yearly valuations of a large sample of stocks listed on NYSE, AMEX, and NASDAQ over a 30-year period. The model differs from standard valuation models in the sense that it adjusts forecasted residual income for risk in the numerator rather than through a risk-adjusted cost of equity in the denominator. The risk adjustments are derived based on assumptions about the time-series properties of residual income returns and aggregate consumption rather than on historical stock returns. We compare the performance of the model with several implementations of standard valuation models, both in terms of median absolute valuation errors (MAVE) and in terms of excess returns on simple investment strategies based on the differences between model and market prices. The CCAPM-based valuation model yields a significantly lower MAVE than the best performing standard valuation model. Both types of models can identify investment strategies with subsequent excess returns. The CCAPM-based valuation model yields time-series of realized hedge returns with more and higher positive returns and fewer and less negative returns compared with the time-series of realized hedge returns based on the best performing standard valuation model for holding periods from 1 to 5 years. In a statistical test of 1-year-ahead excess return predictability based on the models’ implied pricing errors, the CCAPM-based valuation model is selected as the better model. Using the standard series of aggregate consumption and the nominal price index, a reasonable level of relative risk aversion, and calibrated growth rates in the continuing value at each valuation date, the CCAPM-based valuation model produces small risk adjustments to forecasted residual income and low continuing values. Compared with standard valuation models, it relies less on estimated parameters and speculative elements when aggregating residual earnings forecasts into a valuation.     

Understanding analysts' use of stock returns and other analysts' revisions when forecasting earnings

We investigate analysts' use of stock returns and other analysts' forecast revisions in revising their own forecasts after an earnings announcement. We find that analysts respond more strongly to these signals when the signals are more informative about future earnings changes. Although analysts underreact to these signals on average, we find that analysts who are most sensitive to signal informativeness achieve superior forecast accuracy relative to their peers and have a greater influence on the market. The results suggest that the ability to extract information from the actions of others serves as one source of analyst expertise.     

Using residual income to refine the relationship between earnings growth and stock returns

We use residual income (RI) to decompose earnings growth into growth in RI, growth in invested capital and other components and use this decomposition to explain stock returns. Our approach provides a significant increase in explanatory power vis-à-vis a regression of returns on levels and changes in earnings. While the market values growth in RI more than growth in invested capital, it still undervalues growth in RI and overvalues growth in invested capital. Earnings growth from growth in RI is more persistent, while earnings growth from growth in invested capital is more likely to reverse. Future returns are positively associated with growth in RI and negatively associated with growth in invested capital. A trading rule based on these findings generates significant hedge returns that persist after controlling for known risk factors. Hence, RI, a measure long recommended by accountants, allows investors to differentiate and evaluate different sources of earnings growth.     

Investor and (Value Line) Analyst Underreaction to Information about Future Earnings: The Corrective Role of Non-Earnings-Surprise Information

Prior research suggests that financial analysts' earnings forecasts and stock prices underreact to earnings news. This paper provides evidence that analysts and investors correct this underreaction in response to the next earnings announcement and to other (non-earnings-surprise) information available between earnings announcements. Our evidence also suggests that analysts and investors underreact to information reflected in analysts' earnings forecast revisions and that non-earnings-surprise information helps correct this underreaction as well. Controlling for corrective non-earnings-surprise information significantly increases estimates of the degree to which analysts' forecasting behavior can explain drifts in returns following both earnings announcements and analysts' earnings forecast revisions.     

证券分析师VS统计模型:证券分析师盈余预测的相对准确性及其决定因素

本文使用2005年35家券商对我国上市公司做出的每股盈余预测数据,考察了证券分析师盈余预测相对于统计模型的相对准确性及其决定因素。我们发现,我国证券分析师做出的盈余预测,同以年度历史数据为基础的统计模型得出的盈余预测相比,预测误差较小,证券分析师盈余预测具有一定的优势;但同某些以季度历史数据为基础的统计模型得出的盈余预测相比,预测误差较大,证券分析师盈余预测不具有优势。我们同时考察了决定证券分析师盈余预测相对准确性的决定因素。我们发现,公司每股盈余的波动性越大,公司上市越晚,跟踪公司的分析师越多,证券分析师的优势就越大。我们的研究对证券分析师以及投资者都有一定的启示作用。     

Tests of Market Reaction and Analysts' Forecast Revisions to the Disclosure of Improved Management Earnings Expectations: A Case of Concurrent Bad News Management Earnings Forecasts

This paper tests whether a negative stock market reaction, associated with a management forecast of near term bad earnings, is lessened by a concurrent management forecast of improved longer term earnings expectations. Stock market reactions depend on the creditability of management forecasts of improved earnings expectations. In this analysis, the authors examined market reactions around the time of management forecasts of bad earnings, with and without longer-term management forecasts of improved earnings expectations. The results show that the stock market reaction is significantly less negative when management forecasts of bad earnings are followed by management forecasts of improved long run earnings expectations than when management forecasts of bad earnings are not accompanied by management forecasts of improved earnings expectations. In addition, this paper examines financial analysts' reactions to management bad earnings forecasts and management forecasts of improved earnings expectations. The findings show that analysts react less negatively to management forecasts of improved earnings expectations than to management forecasts of bad earnings. An analysis of a sub-sample of observations shows that analysts consider management forecasts of improved earnings expectations to imply improved expected future performance, thus conveying that analysts give credence to management forecasts of improved earnings expectations. However, results show that the stock market and analysts are unable to distinguish management forecasts of improved earnings expectations that come true from management forecasts of improved earning expectations that do not come true.