Media Coverage and Sustainable Stock Returns: Evidence from China
Round 1
Reviewer 1 Report
In this paper the authors try to measure the relevance of media coverage in several variables of stock markets. Despite the interesting of the thematic, the paper fails in some points. For example, it starts with the title, which does not match with the studied issues. Although, the most critical issue is the fail in the methodology. The authors retrieve data for several stocks during several time periods, which is consistent with the use of panel data. This should be the used methodology, and not the traditional regressions, which could create several problems in the results.
Author Response
Dear Editor and Reviewer：
We are grateful for the opportunity to revise and resubmit this manuscript. We have tried to address the reviewer’s concerns to the fullest extent possible. In response to specific feedbacks, we conducted careful modification and clarification. We believe that this revised manuscript is stronger as changes made in response to the feedbacks and hope you agree with this. For your convenience, the reviewer’s comments are reproduced with our high lightened responses following the comments. To keep the revised manuscript clearly readable, we highlight our major revisions and additions in colored texts, instead of keeping track of all minor changes of words or deletions at the same time. Please refer to the revised manuscript as well as our responses to the reviewer reports for the details.
Again, we wish to thank the great help from the reviewers as well as the kind assistance from the editor.
Reviewer 1：
Comment1: In this paper the authors try to measure the relevance of media coverage in several variables of stock markets. Despite the interesting of the thematic, the paper fails in some points. For example, it starts with the title, which does not match with the studied issues.
Reply: Thank you very much for the valuable and useful comments. We could like to make some clarifications as follows to help our results and methodology better understood:
In this research, we explore the relationship between media coverage and stock returns using monthly data of news reports from major Chinese newspapers. We find that stocks with higher media coverage in current month persistently have higher stock returns in the following months of the year. The results are subjected to robustness checks.
Stock price is changing in every minute, and one year holding period has been long enough for most investors. Our paper verify that the excess return from buying stocks with higher media coverage in current month could sustain for one year. That’s why we call it “sustainable stock returns” in the title. Meanwhile, this result provides important investment strategy implications for investors.
We could change our title to “Media coverage and persistent stock returns: Evidence from China”, if necessary, but we prefer keeping the current title to fit the journal’s scope better if the editor and the reviewer allow it.
Comment 2: Although, the most critical issue is the fail in the methodology. The authors retrieve data for several stocks during several time periods, which is consistent with the use of panel data. This should be the used methodology, and not the traditional regressions, which could create several problems in the results.
Reply: Thanks for your comments. We agree with the reviewer that our sample is indeed a panel data. That’s why we did not use traditional OLS regression methodology. Instead, we employed fama-macbeth model. This model is also a typical methodology to deal with panel data. The basic idea of this methodology is to run cross-sectional regressions for each time period and then take the average of the regression coefficients of all time periods as the final result. Comparing to OLS and other traditional regressions, Fama-macbeth model can better control time fixed effects in panel data. This methodology is one of the best options to investigate cross-sectional patterns when there are multiple time periods. In this paper, we mainly focus on the cross-sectional differences in stock returns among stocks with different degrees of media coverage, and thus we employ the methodology proposed by Fama and Macbeth.
Author Response File: 
Author Response.docx
Reviewer 2 Report
In the research the authors explore the relationship between media coverage and stock returns using monthly data of news reports from major Chinese newspapers. The authors show that stocks with higher media coverage in current month persistently have higher stock returns in the following months of the year. The results are subjected to robustness checks.
In my opinion, the article is interesting and well structured. However, some aspects of the work need to be improved.
1) Describe better the theoretical setting of the univariate analysis.
2) In Table 3, enter the thousands separator everywhere. Also in the other tables, where it is necessary, enter the thousands separator.
3) In the lines 376-377, the authors write: “Using data of news reports from China's major newspapers during the period between 2001 and 2011, …”. Can you extend the analysis to the following years?
4) With reference to the political implications of the research, it is important to expand the bibliography of the article. In fact, especially with reference to the political implications, the issues of risk analysis and evaluation and selection of investment projects are important. These are issues on which it is appropriate to give some calls. In this regard, I suggest reading and considering for references:
- Nesticò A., He S., De Mare G., Benintendi R., Maselli G., The ALARP Principle in the Cost-Benefit Analysis for the Acceptability of Investment Risk. Sustainability 2018, 10(12):4668; doi:10.3390/su10124668. MDPI AG, Basel, Switzerland.
- Nesticò A. (2018), Risk-Analysis Techniques for the Economic Evaluation of Investment Projects. In: Mondini G., Fattinnanzi E., Oppio A., Bottero M., Stanghellini S. (eds) Integrated Evaluation for the Management of Contemporary Cities. SIEV 2016. Green Energy and Technology, Part F8, pp. 617-629. DOI: 10.1007/978-3-319-78271-3_49. Springer, Cham.
5) It is useful to re-read the paper, to eliminate small errors in writing. For example:
Line 44, 195, 210 …: position the note well
Line 82: add space after the point
Line 109: add space after [9]
Line 166: add space after negotiations and after [18]
Author Response
Dear Editor and Reviewer：
We are grateful for the opportunity to revise and resubmit this manuscript. We have tried to address the reviewer’s concerns to the fullest extent possible. In response to specific feedbacks, we conducted careful modification and clarification. We believe that this revised manuscript is stronger as changes made in response to the feedbacks and hope you agree with this. For your convenience, the reviewer’s comments are reproduced with our high lightened responses following the comments. To keep the revised manuscript clearly readable, we highlight our major revisions and additions in colored texts, instead of keeping track of all minor changes of words or deletions at the same time. Please refer to the revised manuscript as well as our responses to the reviewer reports for the details.
Again, we wish to thank the great help from the reviewers as well as the kind assistance from the editor.
Reviewer 2:
In the research the authors explore the relationship between media coverage and stock returns using monthly data of news reports from major Chinese newspapers. The authors show that stocks with higher media coverage in current month persistently have higher stock returns in the following months of the year. The results are subjected to robustness checks.
In my opinion, the article is interesting and well structured. However, some aspects of the work need to be improved.
Comment 1: Describe better the theoretical setting of the univariate analysis.
Reply: Thanks for your comments. We have now described the univariate analysis more clearly. In the revised version, we have emphasized that we classify stocks into different groups on a monthly basis. Besides, we have provided more theoretical implications for the univariate analysis results by adding the following statements:
“These preliminary statistical results are just opposite to the “rick compensation theory” proposed by Fang and Peress [2], who argue that no or low media coverage stocks ask for higher risk premiums and thus yield higher returns. Instead, the above univariate analysis results are consistent with the “attention-driven buying” theory proposed by Barber and Odean [4] , who argue that investors tend to buy stocks that draw their attention. Stocks with higher media coverage induce investors’ attention more easily, leading to a higher buying pressure and thus higher stock returns.”
Comment 2: In Table 3, enter the thousands separator everywhere. Also in the other tables, where it is necessary, enter the thousands separator.
Reply: Thanks for your kind reminding. We have now added the thousands separator everywhere necessary.
Comment 3: In the lines 376-377, the authors write: “Using data of news reports from China's major newspapers during the period between 2001 and 2011, …”. Can you extend the analysis to the following years?
Reply: Thanks for your suggestion. Unfortunately, our measure of investor attention, Baidu Search Index is only available before 2011. Baidu stopped providing this data to public since 2012. It’s not possible to update the data of investor attention. That’s why we had to limit our analysis up to 2011.
Comment 4: With reference to the political implications of the research, it is important to expand the bibliography of the article. In fact, especially with reference to the political implications, the issues of risk analysis and evaluation and selection of investment projects are important. These are issues on which it is appropriate to give some calls. In this regard, I suggest reading and considering for references:
- Nesticò A., He S., De Mare G., Benintendi R., Maselli G., The ALARP Principle in the Cost-Benefit Analysis for the Acceptability of Investment Risk. Sustainability 2018, 10(12):4668; doi:10.3390/su10124668. MDPI AG, Basel, Switzerland.
- Nesticò A. (2018), Risk-Analysis Techniques for the Economic Evaluation of Investment Projects. In: Mondini G., Fattinnanzi E., Oppio A., Bottero M., Stanghellini S. (eds) Integrated Evaluation for the Management of Contemporary Cities. SIEV 2016. Green Energy and Technology, Part F8, pp. 617-629. DOI: 10.1007/978-3-319-78271-3_49. Springer, Cham.
Reply: Thanks for your comment. We have carefully considered your suggestion. However, the main implication of this paper is to suggest investors take advantage of the media coverage information to build up an investment strategy generating persistent stock returns. Political implications are beyond the scope our research. We have read the references suggested by the reviewer in details. We found that these references are mainly about corporations’ investment projects instead of investors’ investment strategy in stock market. Since they are two distinct research areas, we find it really difficult to incorporate the above references into our paper. We are sorry for this, but we hope the editor and the reviewer could understand our situation. However, following the reviewer’s suggestion to expand the bibliography of the article, we have added some closely related references to our topic in this revised version.
Comment 5: It is useful to re-read the paper, to eliminate small errors in writing. For example:
Line 44, 195, 210 …: position the note well
Line 82: add space after the point
Line 109: add space after [9]
Line 166: add space after negotiations and after [18]
Reply: Thank you very much for your careful checking. We have now incorporated the notes into the main text of the paper and corrected all the above errors as well as other similar errors in our revised version.
Author Response File: Author Response.docx
Reviewer 3 Report
Authors aim to study the effect of media coverage on stock returns for A-shares Chinese stock markets. In general, they indicate the positive effect of media coverage, confirming to a certain extent the "attention-driven" effect of media coverage.
The paper is well written, the aims and scope of research within the proper confinements, without promising too much or less compared to what it provides. Also, the structure is good, facilitating reader's easy "walk-through" the topics developed.
On the other hand, I believe that method adopted and implemented, even though looking fine, is very basic and without the necessary rigour to provide really robust results. It seems like that the ideal approach is an event study analysis, but I am not sure authors employ this approach (the refer to returns as the dependent variable, but to me it is not clear how they come up with this variable). The also use simple cross-sectional regressions, which potentially could be fine.
Robustness checks has to do basically with different specifications, but I would like to see more on cross-sectional effects; maybe some interaction terms indicating the macroeconomic effects to to the markets etc would further enhance the quality of findings.
Moreover, the analysis is slightly weak, as there is not real cross market or cross country analysis here to beef up the findings provided for the Chinese market. I would suggest a comparison of these results with similar studies for markets sharing similar characteristics with the Chinese stock market.
Given the number of explanatory variables, a general-to-specific approach could also be a useful alternative empirical approach here.
Overall, a nice paper, with some potential but still needs work to come up to the necessary level for publication.
Author Response
Dear Editor and Reviewer：
We are grateful for the opportunity to revise and resubmit this manuscript. We have tried to address the reviewer’s concerns to the fullest extent possible. In response to specific feedbacks, we conducted careful modification and clarification. We believe that this revised manuscript is stronger as changes made in response to the feedbacks and hope you agree with this. For your convenience, the reviewer’s comments are reproduced with our high lightened responses following the comments. To keep the revised manuscript clearly readable, we highlight our major revisions and additions in colored texts, instead of keeping track of all minor changes of words or deletions at the same time. Please refer to the revised manuscript as well as our responses to the reviewer reports for the details.
Again, we wish to thank the great help from the reviewers as well as the kind assistance from the editor.
Reviewer 3:
Authors aim to study the effect of media coverage on stock returns for A-shares Chinese stock markets. In general, they indicate the positive effect of media coverage, confirming to a certain extent the "attention-driven" effect of media coverage.
The paper is well written, the aims and scope of research within the proper confinements, without promising too much or less compared to what it provides. Also, the structure is good, facilitating reader's easy "walk-through" the topics developed.
Comment 1: On the other hand, I believe that method adopted and implemented, even though looking fine, is very basic and without the necessary rigour to provide really robust results. It seems like that the ideal approach is an event study analysis, but I am not sure authors employ this approach (the refer to returns as the dependent variable, but to me it is not clear how they come up with this variable). The also use simple cross-sectional regressions, which potentially could be fine.
Reply：Thanks for your comment. In this paper, we focus on studying how stocks with different media coverage differ in their monthly returns in the following months during a year. The main dependent variables is monthly stock return. We use two indicators to measure each month’s stock return. The first indicator is the monthly stock holding period raw return, while the second indicator is the monthly DGTW excess return. We employ the approach of Daniel, Grinblatt, Titman, and Wermers [23] to construct monthly DGTW abnormal returns. Specifically, we firstly sort stocks into 125 portfolios based on the quintiles of firms’ market value, book-to-market radio and average monthly returns in the preceding year, and then we use the average return of each portfolio as a benchmark. We calculate DGTW excess return by the difference between the raw monthly return and its benchmark. The definitions of the above two indicators of the dependent variable was clearly described in Table 1. In the revised version, we also add descriptions on indicators of dependent variables in the main text for readers’ convenience.
We did not use traditional OLS regression methodology to test the above issue. Instead, we employed Fama-Macbeth model. This model is one of the typical methodologies to deal with panel data. The basic idea of this methodology is to run cross-sectional regressions for each time period and then take the average of the regression coefficients of all time periods as the final result. Comparing to OLS and other traditional regressions, Fama-Macbeth model can better control time fixed effects in panel data. This methodology is one of the best options to investigate cross-sectional patterns when there are multiple time periods. In this paper, we mainly focus on the cross-sectional differences in stock returns among stocks with different degrees of media coverage, and thus we employ the methodology proposed by Fama and Macbeth.
We also carefully considered the reviewer’s suggestions on using event study approach. However, it is not applicable in our research for two reasons. Firstly, we are trying to find out the general relation between media coverage and monthly stock returns, instead of the short-term market reactions to some specific big news. Secondly, the preconditions to use event study approach is not satisfied in our research. event study approach requires that there is a unique event day and there no other events happening around the event day. However, there are different media reports on stocks almost every day, making it impossible to satisfy the above requirements. Therefore, we did not use event study approach, but use Fama and Macbeth’s approach instead.
For this clarification, we have also added some explanations in the main text of our revised version.
Comment 2: Robustness checks has to do basically with different specifications, but I would like to see more on cross-sectional effects; maybe some interaction terms indicating the macroeconomic effects to the markets etc would further enhance the quality of findings.
Reply: Thanks for your great suggestion! We have now made an additional test in Table 9 in the revised version to enhance the quality of our main findings. According to existing literatures [27] [28] [29], growth stocks with higher price to equity ratio tend to have attract more investor attention. To verify the validity of “attention-driven buying” theory to explain our main empirical results, we further test how the effect of media coverage on stock returns differ in stocks with different implied growth level measured by price to equity ratio. Consistent with our expectation, the regression results in Table 9 in the revised version show that the positive effect of current month’s media coverage on stock returns in the following months significantly increase with the level of price to equity ratio.
Table 9. Additional Test: Media Coverage, Implied Growth and Stock Returns.
DGTW [t+1] | DGTW [t+2] | DGTW [t+3, t+12] | Stkret [t+1] | Stkret [t+2] | Stkret [t+3,t+12] | |
Media | -0.281** | -0.066 | 0.095 | -0.418*** | -0.034 | -1.349*** |
(-2.47) | (-0.60) | (0.21) | (-3.33) | (-0.29) | (-2.79) | |
Media*Growth | 0.144*** | 0.101*** | 0.528*** | 0.194*** | 0.112*** | 1.021*** |
(3.46) | (2.62) | (3.84) | (4.42) | (2.71) | (6.37) | |
Growth | 0.869*** | 0.761*** | 2.624*** | 0.962*** | 0.853*** | 2.556*** |
(12.53) | (11.26) | (9.63) | (11.68) | (10.45) | (8.18) | |
Asset | 0.011 | -0.054 | -0.743*** | -0.009 | -0.139 | -1.004*** |
(0.18) | (-0.86) | (-4.98) | (-0.09) | (-1.34) | (-2.77) | |
BM | 3.181*** | 2.822*** | 9.473*** | 3.879*** | 3.513*** | 13.201*** |
(11.13) | (9.34) | (9.04) | (11.43) | (10.67) | (11.62) | |
⊿Turnover | -0.514*** | -0.250*** | -0.211 | -0.775*** | -0.289*** | -0.135 |
(-5.84) | (-2.83) | (-0.83) | (-6.83) | (-2.94) | (-0.54) | |
Analyst | 0.364*** | 0.329*** | 2.062*** | 0.437*** | 0.372*** | 1.743*** |
(2.78) | (3.46) | (6.39) | (3.50) | (3.20) | (4.66) | |
Cprice | -0.204 | -0.146 | -0.554 | -0.533* | -0.276 | -1.728* |
(-0.91) | (-0.64) | (-0.79) | (-1.68) | (-0.86) | (-1.91) | |
Pret12 | -0.077*** | -0.030 | -0.373*** | -0.062 | -0.048 | -0.419*** |
(-2.73) | (-1.06) | (-4.27) | (-1.65) | (-1.28) | (-4.28) | |
Amihud’s Liquidity | 13.059*** | 4.992* | 5.501 | 13.993** | 8.654** | 29.237*** |
(2.90) | (1.71) | (0.77) | (2.35) | (2.41) | (3.44) | |
Volatility | -0.338*** | -0.171* | -1.130*** | -0.338** | -0.148 | -0.872*** |
(-3.49) | (-1.72) | (-4.48) | (-2.46) | (-1.15) | (-3.01) | |
Constant | 0.720 | 2.773* | 20.523*** | 3.238 | 5.866** | 49.525*** |
(0.47) | (1.75) | (4.33) | (1.10) | (2.02) | (5.25) | |
Industry | Yes | Yes | Yes | Yes | Yes | Yes |
Obs. | 128846 | 127030 | 108569 | 128846 | 127030 | 108569 |
R2 | 0.142 | 0.137 | 0.150 | 0.220 | 0.204 | 0.218 |
Growth represents the implied growth measured by price to equity ratio in the current month. We employ the cross-sectional regression method proposed by Fama and MacBeth (1973) in table 4. T-values are in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
Comment 3: Moreover, the analysis is slightly weak, as there is not real cross market or cross country analysis here to beef up the findings provided for the Chinese market. I would suggest a comparison of these results with similar studies for markets sharing similar characteristics with the Chinese stock market.
Reply: Thanks for your comment. As introduced in the paper, Fang and Peress [2] have already investigated the cross-sectional relation between media coverage and expected stock returns in the stock market of the united states, a representative of the developed stock market. Chinese stock marekt is a representative of the emerging stock market. One of the main purposes of this paper is to have a comparison beween our results in the stock market of China with Fang and Peress's result in the stock market of the united states. We did find an opposites result. While Fang and Peress found that higher media coverage yield lower stock returns in the united states’ stock market, we show that stocks with higher media coverage persistently have higher returns in Chinese stock market. Our results have important implications for investors in Chinese stock market, one of the most important capital market in the world.
However, due to the restrictions on data availability, it is difficult for us now to extend our studies to other emerging stock markets. We would like to leave the tests on other emrging stock marekts for future research.
Comment 4: Given the number of explanatory variables, a general-to-specific approach could also be a useful alternative empirical approach here.
Reply: Thanks for your suggestion. We would like to make a clarification that the main explanatory variable of this paper is media coverage and the other independent variables are just control variables. We focus on studying the general relation between media coverage and monthly stock returns, and our results have important implications for investors. We are sorry that A general-to-specific approach might be beyond the aim and scope of this paper. Thanks for your understanding.
Comment 5: Overall, a nice paper, with some potential but still needs work to come up to the necessary level for publication.
Reply: Thanks for all your comments and suggestions. We have tried our best to address your concerns, respond to your comments and make revisions according to your suggestions. We sincerely hope that you would accept it for publication. Thanks again!
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
I had read carefully all the explanations given by the authors, regarding to all the comments of the reviewers. In the issues raised by myself, I would like to say that I am satisfied with the answers. The most severe issue I put was about the used methodology, which I have criticized unfairly (I misunderstood it). So, this is not a problem. I just would like to see in the paper, with a slight reference in the introduction but with a deeper reflection in the conclusion, about the limitation of the use data just for 2011. The motives are clear and explained in the text, but authors should explain what could change (or not) since that moment, and how this limitation could have impact on market agents.
Author Response
Reviewer Comment: I had read carefully all the explanations given by the authors, regarding to all the comments of the reviewers. In the issues raised by myself, I would like to say that I am satisfied with the answers. The most severe issue I put was about the used methodology, which I have criticized unfairly (I misunderstood it). So, this is not a problem. I just would like to see in the paper, with a slight reference in the introduction but with a deeper reflection in conclusion, about the limitation of the use data just for 2011. The motives are clear and explained in the text, but authors should explain what could change (or not) since that moment, and how this limitation could have an impact on market agents.
Reply： Thanks for your good suggestion. In this final revised version, we mentioned the limitations of data and explained the detailed reason for this limitation in section “3.data “ instead of in the section of “1.introduction” , so that readers could better understand the main ideas of our paper in the introduction part without the interference of detailed data issues in the very beginning. In the “conclusion” section, we further explained the possible impact of this limitation of data and proposed possible directions for future research.
Author Response File: Author Response.docx
Reviewer 3 Report
I am happy that authors took into consideration some of the comments and they improved their paper.
Author Response
Thank you very much for your appreciation and support of our revised paper. We would also like to thank again for your earlier comments and suggestions which helped us a lot to improve the paper.
