Dynamic Q-Theory with Agency Investment Frictions and Cross-Sectional Stock Returns

2015 ◽  
Author(s):  
Lei Mao ◽  
Mike Qinghao Mao ◽  
K. C. John Wei
Keyword(s):  
Stock Returns ◽  
Cross Sectional ◽  
Q Theory

Freedom Indices and Capital Asset Pricing Model: Some Malaysian Evidence

2020 ◽  
Vol 1 (1) ◽  
pp. 1-21
Author(s):  
Ying Tay Lee ◽  
Devinaga Rasiah ◽  
Ming Ming Lai
Keyword(s):  
Human Rights ◽  
Asset Pricing ◽  
Stock Returns ◽  
Firm Size ◽  
Explanatory Power ◽  
Least Square ◽  
Press Freedom ◽  
Capital Asset Pricing ◽  
Capital Asset ◽  
Cross Sectional

Human rights and fundamental freedoms such as economic, political, and press freedoms vary widely from country to country. It creates opportunity and risk in investment decisions. Thus, this study is carried out to examine if the explanatory power of the model for capital asset pricing could be improved when these human rights movement indices are included in the model. The sample for this study comprises of 495 stocks listed in Bursa Malaysia, covering the sampling period from 2003 to 2013. The model applied in this study employed the pooled ordinary least square regression estimation. In addition, the robustness of the model is tested by using firm size as a controlled variable. The findings show that market beta as well as the economic and press freedom indices could explain the cross-sectional stock returns of the Malaysian stock market. By controlling the firm size, it adds marginally to the explanation of the extended CAP model which incorporated economic, political, and press freedom indices.

Analysts’ Forecasts Bias and Cross-Sectional Stock Returns

2012 ◽  
Author(s):  
Tingting Liu
Keyword(s):  
Stock Returns ◽  
Cross Sectional

Manager Uncertainty and Cross-Sectional Stock Returns

2020 ◽  
Author(s):  
Tengfei Zhang
Keyword(s):  
Stock Returns ◽  
Cross Sectional

Can network structure predict cross-sectional stock returns? Evidence from co-attention networks in China

2019 ◽  
pp. 101422 ◽  
Author(s):  
Xi Chen ◽  
Wuyue Shangguan ◽  
Yanchu Liu ◽  
Shichao Wang
Keyword(s):  
Stock Returns ◽  
Network Structure ◽  
Cross Sectional ◽  
Attention Networks

Information risk, stock returns, and asset pricing: Evidence from China

2017 ◽  
Vol 30 (4) ◽  
pp. 379-394 ◽  
Author(s):  
Raheel Safdar ◽  
Chen Yan
Keyword(s):  
Stock Returns ◽  
Factor Model ◽  
Empirical Literature ◽  
Firm Level ◽  
Cross Sectional ◽  
Market Pricing ◽  
Content Type ◽  
Information Risk ◽  
Accruals Quality ◽  
Three Factor

Purpose This study aims to investigate information risk in relation to stock returns of a firm and whether information risk is priced in China. Design/methodology/approach The authors used accruals quality (AQ) as their measure of information risk and performed Fama-Macbeth regressions to investigate association of AQ with future realized stock returns. Moreover, two-stage cross-sectional regression analysis was performed, both at firm level and at portfolio level, to test if the AQ factor is priced in China in addition to existing factors in the Fama French three-factor model. Findings The authors found poor AQ being associated with higher future realized stock returns. Moreover, they found evidence of market pricing of AQ in addition to existing factors in the Fama French three-factor model. Further, subsample analysis revealed that investors value AQ more in non-state owned enterprises than in state owned enterprises. Research limitations/implications The study sample comprises A-shares only and the generalization of the findings is limited by the peculiar institutional and economic setup in China. Originality/value This study contributes to market-based accounting literature by providing further insight into how and if investors value information risk, and it seeks to fill gap in empirical literature by providing evidence from the Chinese capital market.

Regulation and information asymmetry

2008 ◽  
Vol 16 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Robyn McLaughlin ◽  
Assem Safieddine
Keyword(s):  
Information Asymmetry ◽  
Stock Returns ◽  
Operating Performance ◽  
Cost Benefit ◽  
Future Research ◽  
Information Asymmetries ◽  
Cross Sectional ◽  
Industrial Firms ◽  
Content Type ◽  
Regulated Utilities

PurposeThis paper seeks to examine the potential for regulation to reduce information asymmetries between firm insiders and outside investors.Design/methodology/approachExtensive prior research has established that there are substantial effects of information asymmetry in seasoned equity offers (SEOs). The paper tests for a mitigating effect of regulation on such information asymmetries by examining differences in long‐run operating performance, changes in that performance, and announcement‐period stock returns between unregulated industrial firms and regulated utilities that issue seasoned equity. The authors also segment the samples by firm size, since smaller firms are likely to have greater asymmetries.FindingsConsistent with regulated utility firms having lower levels of information asymmetry, they have superior changes in abnormal operating performance than industrial firms pre‐ to post‐issue and their announcement period returns are significantly less negative. These findings are most pronounced for the smallest firms, firms likely to have the greatest information asymmetries and where regulation could have its greatest effect.Research limitations/implicationsThe paper does not examine costs of regulation. Thus, future research could seek to measure the cost/benefit trade‐off of regulation in reducing information asymmetry. Also, future research could examine cross‐sectional differences between different industries and regulated utilities.Practical implicationsRegulation reduces information asymmetry. Thus, regulation or mandated disclosure may be appropriate in industries/markets where information asymmetry is severe.Originality/valueThis paper is the first to compare the operating performance of regulated and unregulated SEO firms.

Does uncertainty boost overconfidence? The case of financial analysts' forecasts

2014 ◽  
Vol 40 (3) ◽  
pp. 300-324 ◽  
Author(s):  
Véronique Bessière ◽  
Taoufik Elkemali
Keyword(s):  
Stock Returns ◽  
Private Information ◽  
High Tech ◽  
Cross Sectional ◽  
Forecast Dispersion ◽  
Content Type ◽  
The Public ◽  
Public Announcement ◽  
Before And After ◽  
Sectional Analysis

Purpose – This article aims to examine the link between uncertainty and analysts' reaction to earnings announcements for a sample of European firms during the period 1997-2007. In the same way as Daniel et al., the authors posit that overconfidence leads to an overreaction to private information followed by an underreaction when the information becomes public. Design/methodology/approach – In this study, the authors test analysts' overconfidence through the overreaction preceding a public announcement followed by an underreaction after the announcement. If overconfidence occurs, over- and underreactions should be, respectively, observed before and after the public announcement. If uncertainty boosts overconfidence, the authors predict that these two combined misreactions should be stronger when uncertainty is higher. Uncertainty is defined according to technology intensity, and separate two types of firms: high-tech or low-tech. The authors use a sample of European firms during the period 1997-2007. Findings – The results support the overconfidence hypothesis. The authors jointly observe the two phenomena of under- and overreaction. Overreaction occurs when the information has not yet been made public and disappears just after public release. The results also show that both effects are more important for the high-tech subsample. For robustness, the authors sort the sample using analyst forecast dispersion as a proxy for uncertainty and obtain similar results. The authors also document that the high-tech stocks crash in 2000-2001 moderated the overconfidence of analysts, which then strongly declined during the post-crash period. Originality/value – This study offers interesting insights in two ways. First, in the area of financial markets, it provides a test of a major over- and underreaction model and implements it to analysts' reactions through their revisions (versus investors' reactions through stock returns). Second, in a broader way, it deals with the link between uncertainty and biases. The results are consistent with the experimental evidence and extend it to a cross-sectional analysis that reinforces it as pointed out by Kumar.

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