Content
1 Value Relevance and Information Content (expectation models based on small windows/event studies) ...................................................... 2
1.1 (1) Ball and Brown (1968) – Value relevance and aggregate beliefs ....................................................................................................... 2
1.2 (2) Landsman and Maydew (2002) – Information content and individual beliefs .................................................................................. 3
2 Financial Reporting Quality ............................................................................................................................................................................... 4
2.1 (3) Dechow, Sloan, Sweeney (1995) – Detecting Earnings Management (Discretionary Accruals Models → Expectation Model) ......... 4
2.2 (4) Dechow and Dichev (2002) – The role of Accrual Estimation Errors .................................................................................................. 5
2.3 (5) Burgstahler and Dichev (1997) – Earnings Management to Avoid Earnings Decreases and Losses ................................................... 6
3 Financial Accounting Conservatism ................................................................................................................................................................... 7
3.1 (6) Basu (1997) – The conservatism principle and the asymmetric timeliness of earnings ..................................................................... 7
3.2 (7) Ball and Shivakumar (2005) – Earnings Quality in private firms: comparative loss recognition timeliness ........................................ 8
4 Standards: IFRS .................................................................................................................................................................................................. 9
4.1 (8) Barth, Landsman and Lang (2008) Voluntary adoption of IFRS and Financial Accounting Quality .................................................... 9
4.2 (9) Christensen, Hail, Leuz (2013) Mandatory IFRS Reporting and Changes in Enforcement ................................................................ 11
5 Standards: Auditing ......................................................................................................................................................................................... 12
5.1 (10) Minnis (201) – The value of financial statement verification in debt financing: evidence from US private firms .......................... 12
5.2 (11) Francis and Yu (2009) – Big 4 office size and audit quality ............................................................................................................. 13
6 Combining Human and Artificial Intelligence In Decision-Making ................................................................................................................... 14
6.1 (12) Dietvorst, Simmons and Massey (2015) – Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them ........... 14
6.2 (13) Commerford, Dennis, Joe and Wang (2021) – Complex Estimates and Auditor Reliance on Artificial Intelligence ....................... 15
7 How to review a paper .................................................................................................................................................................................... 16
7.1 (14) How to review an accounting paper ............................................................................................................................................... 16
7.2 (15) Du, Erkens and Xu (2020) – Gender Differences in Monitoring and the Leadership Gap ............................................................... 17
8 Decision Influencing ad facilitating roles of accounting .................................................................................................................................. 18
8.1 (16) Chen and Sandino (2012) – Can Wages Buy Honesty? The Relationship Between Relative Wages and Employee Theft .............. 18
8.2 (17) Cases-Arce et al (2017) – The Impact of Forward-Looking Metrics on Employee Decision-Making: Case of CLV .......................... 20
9 Investor Attention to Information ................................................................................................................................................................... 22
9.1 (18) Asay, Elliot and Rennekamp (2017) – Disclosure Readability and the Sensitivity of investors’ valuation to outside information . 22
9.2 (19) Cardinaels, Hollander and White (2019) – Investor attention to information ............................................................................... 23
10 Corporate Governance and CEO Power...................................................................................................................................................... 25
10.1 (20) Bruynseels and Cardinaels (2014) – The Audit Committee: Management Watchdog or Personal Friend of the CEO ................... 25
10.2 (21) Faulkender and Yang (2010) – Governance and CEO power .......................................................................................................... 26
11 Corporate Governance and CEO Power...................................................................................................................................................... 28
11.1 (22) Abernethy, Bouwens and Kroos (2017) – Organizational Identity and Earnings Management ...................................................... 28
11.2 (23) Grabner (2014) – Incentive System Design in Creativity-Dependent Firms.................................................................................... 30
Problem
Aim
Hypotheses
Method
Results
Conclusion
Limitations
1
,1 Value Relevance and Information Content (expectation models based on
small windows/event studies)
1.1 (1) Ball and Brown (1968) – Value relevance and aggregate beliefs
➔ Started empirical research in accounting
RQ: Are accounting numbers useful to investors?
Problem: Accounting used to be highly theory based, focused on better capturing
economic performance of a firm. These models were completely analytical. But solely
focusing on numbers does not imply anything with respect to “meaning” and are
therefore of doubtful utility.
Aim: Ball & Brown emphasized reports as being information sources and whether these reports effect decision making. With this RQ, they were one
of the first to have a user’s perspective on accounting.
Paper looks at both content (information relevancy) and timing (accounting reports are backward looking) of usefulness.
Method:
Unexpected Earnings (UE) is used to pull out the information part → Firm specific news
Expectation model: UE(NI) = A(NI) – E(NI) → this, actual – expected = unexpected, which provides information.
o Here, they use Naïve model (NI last year is Expected NI this year). You do not take other events
into account that might have happened between the years.
o Regression model: regress change in earnings on the change of the aggregate earnings in the
market. ∆𝑁𝐼 − (𝑎1 + 𝑎2∆𝑀) (M = market average NI). The error term in the paper is the
actual income change minus expected income change. This error term is the positive or
negative forecast error (good vs bad news)
➔ The error term is the new information conveyed by the present income number
Unexpected Returns (UR) is used to see the Stock Market Reaction to firm specific
news.
This reaction highlights the decision of investors, thus whether investors behave differently on
new firm specific news. Since stock prices, and therefore rates of returns from holding stocks,
tend to move together.
→ for the individual firm, its contents and timing of specific information should be assessed
relative to changes in the rate of return in the firm’s stocks net of market-wide effects.
They regress the stock return of the firm on the return in the market.
(they use an equally weighted index, to make comparison between small and large firms equal,
which in this case Is an disadvantage. But back in the day they had to do it by hand thus this it to make it easier for them)
Good news = income above expectations (u > 0). Earnings announcement should be accompanies with positive stock
return residuals (“abnormal returns”/ vjm) → v jm is the stock return residual from firm j in month m.
Bad news = income below expectations (u < 0). Negative stock return residuals should be expected around the
announcement of the bad news.
Data from 1946-1966 to estimate unexpected ni and return regressions.Event window was -11, +6
Results: you have two portfolios. And the lines represent the monthly average of the bad vs good news companies
within the portfolios. On average, you see from the start that the earnings news build up/build down to the date of
earnings announcement month (EA-date).
→ Green line shows when it is 100% timely, it instantly reacts to the announcement. This is not the case, so there is a
low timeliness of earnings information.
Conclusion: key take away is that only 10% of income information has not been anticipated till the
reporting month (meaning, 90% is anticipated). This is due to the low timeliness of earnings
information.
This paper addresses the value relevance aspect. To what extent do accounting returns correlate
with market returns? This is called value relevance, the relation between the stock price and
earnings. Earnings and stock returns might actually be correlated according to this study. Information
content looks at the day or days surrounding the announcement. Drift shows the late processing of
information.
2
, 1.2 (2) Landsman and Maydew (2002) – Information content and individual beliefs
Replication study of Beaver (1968) results:
Problem: Previous paper investigated whether earnings releases are useful to investors.
However, it might be that earnings measures are too noisy that investors do not respond.
Since a lot of times the measurement error of earnings was too large. Moreover, it is
replicated since in the meantime there were indications that the information content
declined over time, thus it is updated. (economy changed to more Int. assets)
Aim: looks whether investors react to earnings. Moreover, by the time they replicated the
study, more information sources are used that contain the same information but are more
timely.
Hypotheses:
H1: price-based hypothesis → if the price moves, on average, market participants have revised their beliefs, the variability of price changes would
increase. (Beaver: see that heartbeat goes up in the week of the EA, hardly any response before nor delayed response)
H2: volume-based hypothesis → if more individuals revise their beliefs, there will be more shares trades in a period of an Earnings Announcement
(Beaver: slightly lower volume before EA, and stark spike in week of EA that immediately trails off after).
Method: information content study, based on the date of earnings announcement → “Small window information content event study” → UoA is
deaily/weekly. Key difference with beaver is interval in days instead of weeks.
→ they use this spike on the day before, on the day and after the day, the aggregate of that spike. And try to explain cross-sectional variation in that
spike over time.
Abnormal trading value (AVOL)
using AVOL means that they look at the spike, relative to the average volume in the weeks before the announcements. by
scaling with standard deviation, abnormal volume formula controls for changes in normal, non-announcement period volume across time. this is
necessary because trading volume increased approximately tenfold during their sample period (to make the observations in the cross-sectional test
comparable)
In this regression, if coefficient of the time dummy is positive and significant, it means that the information content of earnings increased over-time
linearly.
→ key take away here: it is that the time variable shows that information content of earnings using abnormal volume, increased over time. we
thought that value relevance would increase due to economic changes by using more intangibles. BUT that is not what we see
Abnormal stock price volatility (AVAR).
this is calculated fo day t, t=-1, 0, +1 relative to announcement day 0.
Results:
Conclusion:
Limitations: cannot rule out the possibility that other explanations not related to earnings management could account for increase in information
content.
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