There is plenty of evidence on the effect that equity analysts’ forecasts and recommendations have over investors’ decisions and stock prices. However, there is limited evidence on how analyst coverage affect managers’ behavior.
The debate over how equity analysts may affect managers’ behavior and potentially firm value revolves around two main ideas. On one hand, equity analysts may be seen as ‘external monitors’. Typically, analysts are highly trained accounting and finance professionals, with a substantial industry knowledge. According to this view, analyst play a very important role in capital markets, which is being ‘information intermediaries’. This role involves processing and disseminating information disclosed by companies in their financial statements (and other sources), and direct interactions with managers. One direct channel trough which analysts can ‘monitor’ managers are earnings announcement conference calls. During conference calls analysts can rise questions regarding different aspects of a firm’s financial reports casting doubt on earnings figures (if a concern exist). Other channels trough which analysts can express their concerns about the firms being covered are:
- Their own research reports distributed among clients.
- Changes in earnings forecasts and stock recommendations available to investors.
- Appearances in media (e.g. tv, newspapers).
Moreover, if analysts detect financial reporting irregularities they can act as ‘whistle blowers’ and help regulators to uncover cases of corporate fraud. Therefore, analyst coverage can discipline managerial misbehavior.
On the other hand, analyst coverage can induce pressure on managers to manipulate earnings. Empirical evidence indicates that firms failing to meet/beat earnings benchmarks suffer a significant decrease in stock prices. Because the evolution of stock prices may be tied to managers’ compensation and the likelihood of turnover, they face market pressures to meet analysts’ expectations about earnings. Moreover, survey evidence by Graham et al. (2005) indicates that managers are willing to manipulate earnings to avoid falling short of earnings benchmarks. Consequently, analyst coverage can foster managerial misbehavior.
To shed light on the above debate, you are required to use the given dataset to examine the
following research question:
Does analyst coverage affect earnings manipulation?
To answer the above research question bear in mind that evidence in prior literature indicate that analysts tend to cover:
- Larger firms and more transparent firms.
- Firms with better information environment.
- Firms with higher financial reporting quality.
- Firms that provide more voluntary disclosure.
- Firms with better performance.
The papers in the references are related to the project and may be helpful to you. You are encouraged to review more related literature for the assignment.
The dataset provided contains quarterly data over the period 2002-2017 for US publicly traded firms. Below is a summary of the variables included in the dataset and its definition.
|am||Accruals manipulation calculated as in Zang (2012).|
|big4||Indicator variable that equals 1 if the firm’s auditor is one of the Big 4.|
|btm||Book to market ratio.|
|cfv||Cash flow volatility.|
|coverage||Is natural logarithm of one plus the number of analyst following the company during the quarter.|
|cycle||Operating cycle measured as in Zang (2012).|
|exp||Average experience of analysts covering the firm.|
|firm id||Firm unique identifier.|
|inst||Shares held by institutional investors as a percentage of total outstanding shares as in Bushee (1998).|
|lev||Leverage, long-term debt divided by total assets.|
|noa||Net operating assets.|
|patent||Total number of patents granted during quarter t.|
|quarter||This variable identifies the fiscal year and quarter.|
|rm||Real earnings manipulation calculated as in Zang (2012).|
|roa||Return on assets.|
|shock||Indicator variable that equals 1 if the firm suffered a decrease in analyst coverage during quarter t that is exogenous to firm fundamentals.|
|sic2||Two-digits standard industry classification Code.|
|size||Natural logarithm of total assets.|
|top||Proportion of analyst covering the firm that work for a top brokerage firm.|
|trans||Shares held by transient institutional investors as a percentage of total outstanding shares as in Bushee (1998).|
In this research assignment you are required to do an empirical analysis on a research project using the dataset provided. You are allowed to discuss with your peers, but you will be assessed only on your own project reports and ‘Do-Files’. The marking is based on how well you will address the assigned research task in your project report. You have much room for developing your empirical research in a way that satisfactorily addresses the research questions (within the requirement of not exceeding 2500 words). This research assignment accounts for 40% of the final mark, and the submission deadline is 12th of April at 20:00, you should submit your research assignment report via my.wbs before the submission deadline.
Your project report should include the following sections:
- Literature Review/Hypothesis/Predictions
– Keep it short and clear, reference only those articles that help you develop the arguments of your hypothesis.
The maximum length of the report is 2500 words (aprox. 10 pages max!) (exclusive of embedded references or citations in the main body text, tables, diagrams, charts, figures, appendices, and bibliography). The body text of the project report should be double-spaced and formatted in 12-point font (Times New Roman). Footnotes, if any, should be single-spaced and formatted in 10.5-point font. Margins should be one inch from top, bottom, and sides. Cites and references should be made using the Harvard Referencing System/Style.
You will need to have access to STATA. The dataset provided is in STATA format, and you will have to provide the ‘Do-File’ containing the code that replicates the results in your research report.
For marking purposes both the ‘Do-File’ and the ‘Research Report’ will be considered. Make sure you provide enough explanations regarding your research choices both in the Do-File and in the research report.
The mark will be calculated over a total of 100 possible points.
Your work will be assessed on the basis of how well you address the assigned research question.
- Developing testable empirical predictions/hypothesis.
- The inspection of the dataset provided, and identification of potential data issues that might affect the subsequent analysis and how do you address any issue if present.
- The research design used to answer the research question.
- Choice of dependent variable (or variables)
- Choice main independent variable
- Choice of control variables
- Sample choices
- Control sample
- Robustness Checks/Additional analysis/cross-sectional tests/
- Econometric tests employed – Explanation of the research design choices.
- Presentation of the results.
- Description and interpretation of your empirical results.
- Statistical Interpretation
- Economicall Interpretation
- Conclusions drawn from your empirical analysis.
- Discussion of limitations and potential concerns related with the empirical analysis carried out.
- Replication of your results using the ‘Do-File’ that you should upload to wbs.
- Quality of your academic writing
- Use your own words
- Avoid cheating
- Avoid plagiarism
- Don’t copy/paste from anywhere (including this instructions) it will show up on the turnitin report, high levels of similarity may result on lower marks and/or cheating/plagiarism investigations
In evaluating all of the above the following elements will be considered:
Comprehension: Showing knowledge & understanding about the subject matter.
Analysis: Presenting logical arguments supported by evidence.
Critical Evaluation: Showing capacity for original thought by questioning relevant arguments and/or identifying their strengths and weaknesses.
Academic writing: Presenting a clear and structured assignment; use of relevant literature;academic honesty;referencing and citation.
Bushee, B. J., 1998. The influence of institutional investors on myopic R&D investment behavior. Accounting review 73, 305–333.
Dechow, P., Ge, W., Schrand, C., 2010. Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics 50, 344–401.
Graham, J. R., Harvey, C. R., Rajgopal, S., 2005. The economic implications of corporate financial reporting. Journal of Accounting and Economics 40, 3–73.
Healy, P. M., Palepu, K. G., 2001. Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature. Journal of accounting and economics 31, 405–440.
Irani, R. M., Oesch, D., 2016. Analyst coverage and real earnings management: Quasi-experimental evidence. Journal of Financial and Quantitative Analysis 51, 589–627.
Kothari, S., Leone, A. J., Wasley, C. E., 2005. Performance matched discretionary accrual measures. Journal of Accounting and Economics 39, 163–197.
Larson, C. R., Sloan, R., Zha Giedt, J., 2018. Defining, measuring, and modeling accruals: a guide for researchers. Review of Accounting Studies 23, 827–871.
Owens, E. L., Wu, J. S., Zimmerman, J., 2017. Idiosyncratic shocks to firm underlying economics and abnormal accruals. The Accounting Review 92, 183–219.
Roychowdhury, S., 2006. Earnings management through real activities manipulation. Journal of Accounting and Economics 42, 335–370.
Yu, F. F., 2008. Analyst coverage and earnings management. Journal of Financial Economics 88, 245–271.
Zang, A. Y., 2012. Evidence on the Trade-Off between Real Activities Manipulation and Accrual-
Based Earnings Management. The Accounting Review 87, 675–703.
 Do-files are required for replicability and transparency purposes (i.e., to avoid plagiarism and poor academic practices).