Abstract
Since 2001, the number of financial statement line items forecasted by analysts and managers that I/B/E/S and FactSet capture in their data feeds has soared. Using this new data, we find that 13 item surprises—11 income statement-based and 2 cash flow statement-based analyst and management guidance surprises—reliably explain firms’ signed earnings announcement returns. No balance sheet or expense surprises are significant. The most important surprises are (i) one-quarter-ahead sales guidance surprise, (ii) analyst sales surprise, (iii) annual Street earnings guidance surprise, and (iv) analyst Street earnings surprise. We also find that the adjusted R2s of our multivariate regressions are three times higher than the adjusted R2s of univariate Street earnings surprise regressions, and that the four most important surprises account for approximately half of this increase in explanatory power.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data are available from the sources cited in the text.
Change history
19 August 2021
A Correction to this paper has been published: https://doi.org/10.1007/s11142-021-09616-6
Notes
The notion of Street earnings surprise was introduced into the academic literature by Bradshaw and Sloan (2002).
FactSet is a multinational financial data and software company that was founded in 1977 and went public in 1996. I/B/E/S (Institutional Brokers’ Estimates System) was founded by Lynch, Jones & Ryan and Technimetrics and began collecting earnings estimates for US companies in 1976. Barra bought I/B/E/S in 1993, then sold it to Primark in 1995. Thomson Financial (now Thomson Reuters) bought Primark in 2000. We focus on FactSet and I/B/E/S because they are the largest providers of analyst forecast and management guidance online data feeds to US capital markets.
A key part of FactSet’s strategy has been to combine with its own databases the disparate databases of many smaller data vendors that it has acquired. See https://en.wikipedia.org/wiki/FactSet.
The FactSet and I/B/E/S analyst forecast data feeds differ in how the data are collected. I/B/E/S data are supplied to I/B/E/S by analysts, while FactSet’s data are primarily gathered manually from analysts’ PDF reports by FactSet employees. This means that the databases are subject to different sources of bias and/or error. I/B/E/S history data constitute at root a voluntary disclosure that for a variety of strategic or other reasons may not exactly reflect the contents of analysts’ PDF reports or full Excel-based financial models. However, the strengths of the I/B/E/S approach are that there is less ambiguity about what analysts are forecasting (since they supply information directly to I/B/E/S in a standardized manner) and that analysts can supply I/B/E/S with better information than they disclose in their PDF reports. In contrast, since FactSet estimates are manually extracted from analysts’ reports, analysts are not able to choose to supply different information in their reports versus their database feeds. Potentially offsetting this advantage is the risk that FactSet employees may misinterpret analysts’ PDFs and/or incorrectly enter the data they contain.
We do not undertake separate analysis on FactSet and I/B/E/S data, because each dataset has been built up over time as FactSet and Thomson Reuters have acquired smaller data providers.
In an untabulated analysis, we include KPI surprises in our annual regressions of earnings announcement returns on analyst forecast and guidance surprises. We find no significant improvement in the overall explanatory power (i.e., adjusted R2) of the model. There are no changes in our top four ranked surprises. The 13 non-KPI surprises with average t-statistics above 1.95 between 2008 and 2016 remain significant. We find 1 KPI, same-store sales for the retail industry, that has an average t-statistic above 1.95 during the 2008–2016 period. Same-store sales data are available for approximately 50–80 firms per quarter.
We require that the analyst consensus period begin no earlier than the first day of the quarter forecasted and no later than the earnings announcement date, and that the earnings announcement date be within 150 days of the quarter-end.
Of the non-KPI item observations that we use in our analysis, 60% are from FactSet and 40% are from I/B/E/S. All KPI items are from FactSet because we did not have access to I/B/E/S KPIs.
Three Items are omitted from appendix 1 because we require item surprises to be available for at least 5% of our sample firms in a given year. The Items that do not meet that threshold in any year are ITEM 5 – “Deferred Revenue – Short Term,” ITEM 7 – “Deferred Revenue – Long Term,” and ITEM14 – “Stock Option Expense.”
We caution against reading figure 1 as suggesting that FactSet adds very little beyond I/B/E/S, or vice versa. This is because we use historical data feeds as of early 2017, and both FactSet and I/B/E/S regularly add to their data feeds forecasts that were available in real time from other vendors’ data feeds but not from their own.
Chuk et al. (2013) hand-collect the actual provision of management forecasts. They find an increase in management forecasts between 1997 and 2001 (implementation of Regulation FD) but a relatively flat or declining trend between 2001 and 2007.
We base our assessment of significance on t-statistics because in panel B the correlation between t-statistics and incremental R2 is 0.98.
Guidance firms are those with at least one guidance surprise available at the earnings announcement. High-revenue-growth firms are those whose prior-quarter sales are ≥10% higher than their sales four quarters earlier. Data on sales growth are not available for all firms in our sample. Street-profit firms are those for which the Street earnings announced at the earnings announcement are >0.
The items that Value Line Investment Survey has created and maintained in its Estimates and Projections File, a commercially available database, are sales, earnings, dividends, CAPEX, operating margin, depreciation, income tax rate, working capital, long-term debt, return on equity, and return on total capital. We choose to use the IUF data feed in our analysis for three reasons. First, Value Line’s forecasts are for annual periods, not quarterly periods (e.g., current-year EPS, or one-year-ahead sales revenue). There are therefore no quarterly line item surprises to calculate at a firm’s first-, second-, or third-quarter earnings announcements. Second, each of Value Line’s 1700 stocks has its forecasts updated on a set schedule only every 13 weeks. Value Line’s forecasts are therefore likely staler than those of FactSet and I/B/E/S. Third, quantitative equity hedge funds trade far more on quarterly signals than on annual signals. This makes FactSet’s and I/B/E/S’s continuously updated, online one-quarter-ahead consensus analyst forecast data feeds much more appealing to them than Value Line’s annual horizon forecasts.
Revenue forecasts: Bradshaw et al. (2018), Clark and Elgers (1973), Ertimur et al. (2003), Ertimur et al. (2011), Jegadeesh and Livnat (2006), Jones (2007), Keung (2010), Rees and Sivaramakrishnan (2007), Schreuder and Klaassen (1984), Swanson et al. (1985), and Trueman et al. (2001). Cash flow forecasts: Brown and Christensen (2014), Call et al. (2009, 2013), DeFond and Hung (2003), DeFond et al. (2007), Givoly et al. (2009), McInnis and Collins (2011), Mohanram (2014), and Radhakrishnan and Wu (2014). We identified four papers outside of the top five accounting journals: Brown et al. (2013), Lerman et al. (2007), Pae and Yoon (2011) and Yoon and Pae (2013). We also identified two recent working papers: Calegari and Eames (2016) and Ohlson et al. (2016). Also, Givoly et al. (2019) explore the information content of I/B/E/S’s KPI analyst forecasts, which we do not include in our study.
References
Amiram, D., Z. Bozanic, M. Bradshaw, and O. Rozenbaum. 2018. Selective information channels for investment research. Working paper, Columbia University.
Ball, R., and P. Brown. 1968. An empirical evaluation of accounting income numbers. Journal of Accounting Research 6: 159–178.
Beaver, W. 1968. The information content of annual earnings announcements. Journal of Accounting Research 6: 67–92.
Beaver, W.H., R. Clark, and W.F. Wright. 1979. The association between unsystematic security returns and the magnitude of earnings forecast errors. Journal of Accounting Research 17 (2): 316–340.
Beaver, W.H., M.F. McNichols, and Z.Z. Wang. 2017. Increased information content of earnings announcements in the 21st century: an empirical investigation. Working paper.
Beaver, W.H., M.F. McNichols, and Z.Z. Wang. 2018. The information content of earnings announcements: new insights from intertemporal and crosssectional behavior. Review of Accounting Studies 23 (1):95-135.
Bradshaw, M.T., T.E. Christensen, K.H. Gee, and B.C. Whipple. 2018. Analysts’ GAAP earnings forecasts and their implications for accounting research. Journal of Accounting and Economics 66: 46–66.
Bradshaw, M.T., and R.G. Sloan. 2002. GAAP versus the street: An empirical assessment of two alternative definitions of earnings. Journal of Accounting Research 40 (1): 41–66.
Brown, L.D., K. Huang, and A.S. Pinello. 2013. To beat or not to beat? The importance of analysts’ cash flow forecasts. Review of Quantitative Finance and Accounting 41 (4): 723–752.
Brown, N.C., and T.E. Christensen. 2014. The quality of street cash flow from operations. Review of Accounting Studies 19: 913–954.
Calegari, M., and M. Eames. 2016. Analysts’ earnings surprise components and future earnings performance. Working paper, Santa Clara University.
Call, A.C., S. Chen, and Y.H. Tong. 2009. Are earnings forecasts more accurate when accompanied by cash flow forecasts? Review of Accounting Studies 14: 358–391.
Call, A.C., S. Chen, and Y.H. Tong. 2013. Are analysts’ cash flow forecasts naïve extensions of their own earnings forecasts? Contemporary Accounting Research 30 (2): 438–465.
Chuk, E., D. Matsumoto, and G.S. Miller. 2013. Assessing methods of identifying management forecasts: CIG vs. researcher collected. Journal of Accounting and Economics 55: 23–42.
Clark, J.J., and P. Elgers. 1973. Forecasted income statements: An investor perspective. The Accounting Review 48 (4): 668–678.
DeFond, M., and M. Hung. 2003. An empirical analysis of analysts’ cash flow forecasts. Journal of Accounting and Economics 35: 73–100.
DeFond, M., M. Hung, and R. Trezevant. 2007. Investor protection and the information content of annual earnings announcements: International evidence. Journal of Accounting and Economics 43 (1): 37–67.
Ertimur, Y., J. Livnat, and M. Martikainen. 2003. Differential market reactions to revenue and expense surprises. Review of Accounting Studies 8: 185–211.
Ertimur, Y., W.J. Mayew, and M. Stubben. 2011. Analyst reputation and the issuance of disaggregated earnings forecasts to I/B/E/S. Review of Accounting Studies 16: 29–58.
Francis, J., K. Schipper, and L. Vincent. 2002. Expanded disclosures and the increased usefulness of earnings announcements. The Accounting Review (77) 3: 515–546.
Givoly, D., C. Hayn, and R. Lehavy. 2009. The quality of analysts’ cash flow forecasts. The Accounting Review 84 (6): 1877–1911.
Givoly, D., Y. Li, B. Lourie, and A. Nekrasov. 2019. Key performance indicators as supplements to earnings: Incremental informativeness, demand factors, measurement issues, and the properties of their forecasts. Review of Accounting Studies: Forthcoming.
Jegadeesh, N., and J. Livnat. 2006. Revenue surprises and stock returns. Journal of Accounting and Economics 41 (1–2): 147–171.
Jones, D.A. 2007. Voluntary disclosure in R&D-intensive industries. Contemporary Accounting Research 24 (2): 489–522.
Keung, E.C. 2010. Do supplementary sales forecasts increase the credibility of financial analysts’ earnings forecasts? The Accounting Review 85 (6): 2047–2074.
Lerman, A., J. Livnat, and R. Mendenhall. 2007. Double surprise into higher future returns. Financial Analysts Journal 63(4, July/Aug.): 63–71.
Lev, B. 1989. On the usefulness of earnings and earnings estimates—Lessons and directions from two decades of empirical research. Journal of Accounting Research 27: 153–192.
Lev, B., and F. Gu. 2016. The end of accounting and the path forward for investors and managers. Hoboken: John Wiley & Sons, Inc..
Lundholm, R., and R.G. Sloan. 2013. Equity valuation and analysis with eVal. 3rd ed. New York: McGraw-Hill.
McInnis, J., and D.W. Collins. 2011. The effect of cash flow forecasts on accrual quality and benchmark beating. Journal of Accounting and Economics 51 (3): 219–239.
Mohanram, P.S. 2014. Analysts’ cash flow forecasts and the decline of the accruals anomaly. Contemporary Accounting Research 31 (1): 1143–1170.
Ohlson, J., C. S. A. Cheng, and K. C. K. Chu. 2016. Analyst forecasts: Sales and profit margins. Working paper, Hong Kong Polytechnic University.
Pae, J., and S. Yoon. 2011. Determinants of analysts’ cash flow forecast accuracy. Journal of Accounting, Auditing and Finance 27 (1): 123–144.
Penman, S.H. 2012. Financial statement analysis and security valuation. 5th ed. New York: McGraw-Hill/Irwin.
Radhakrishnan, S., and S.-L. Wu. 2014. Analysts’ cash flow forecasts and accrual mispricing. Contemporary Accounting Research 31 (4): 1191–1219.
Rees, L., and K. Sivaramakrishnan. 2007. The effect of meeting or beating revenue forecasts on the association between quarterly returns and earnings forecast errors. Contemporary Accounting Research 24 (1): 259–290.
Rogers, J.L., and A. Van Buskirk. 2013. Bundled forecasts in empirical accounting research. Journal of Accounting and Economics 55: 43–65.
Schreuder, H., and, J. Klaassen. 1984. Confidential revenue and profit forecasts by management and financial analysts: Evidence from the Netherlands. The Accounting Review 59(1): 64–77.
Swanson, E.P., W.T. Shearon, and L.R. Thomas. 1985. Predicting current cost operating profit using component models incorporating analysts’ forecasts. The Accounting Review 60 (4): 681–691.
Trueman, B., M.H.F. Wong, and X.-J. Zhang. 2001. Back to basics: Forecasting the revenues of internet firms. Review of Accounting Studies 6: 305–329.
Yoon, C.-Y., and J. Pae. 2013. Estimation and prediction tests of cash flow forecast accuracy. Journal of Forecasting 32 (3): 215–225.
Acknowledgment
We appreciate the comments of Mark Bradshaw, Ole-Kristian Hope, Morten Jensen (Discussant), Peter Joos, Bjorn Jorgensen, Jim Ryans, Richard Sloan, and workshop participants at the Aarhus University, London Business School, IESE, the Third Annual Scandinavian Accounting Research Conference, and UNC–Chapel Hill.
Code availability
Not applicable.
Funding
Alastair Lawrence thanks the University of California at Berkeley Hellman Family Faculty Fund Award for funding support.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interest
Not applicable.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1
Listing of the full set of non-KPI data items in the FactSet, I/B/E/S, and union of I/B/E/S and FactSet (IUF) data feeds. FactSet Measures are listed in the left-hand section, along with their FactSet codes and the number of firm-quarters for which there is a consensus surprise for each Measure (n = 57). The middle section presents I/B/E/S Measures along with their I/B/E/S codes and the number of firm-quarters for which there is a consensus surprise for each Measure (n = 18). In the right-hand section, the FactSet and I/B/E/S Measures are manually consolidated into 26 unique financial statement Items, and we report the number of firm-quarters for which there is a consensus surprise for each Item. We exclude Items that are not well populated, which we define as being present for less than 5% of analyst-covered firms in any given year.
FACTSET MEASURE | FACTSET CODEI | # firm-quarters in Sample | I/B/E/S MEASURE | I/B/E/S CODE | # firm-quarters in Sample | UNION OF FACTSET AND I/B/E/S | # firm-quarters in Sample | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
CATEGORY | ITEM # | ITEM NAME | |||||||||
1 | Inventories | INVENTORIES | 11,451 | BS | ITEM 1 | Inventories | 11,451 | ||||
2 | Current Assets | CURRENT_ASSETS | 18,953 | BS | ITEM 2 | Current Assets | 18,953 | ||||
3 | Total Goodwill | GW_TOT | 25,834 | BS | ITEM 3 | Total Goodwill | 25,834 | ||||
4 | Total Assets | ASSETS | 50,043 | BS | ITEM 4 | Total Assets | 50,043 | ||||
5 | Current Liabilities | CURRENT_LIABILITIES | 19,046 | BS | ITEM 6 | Current Liabilities | 19,046 | ||||
6 | Net Debt | NDT | 26,640 | 1 | Net Debt | NDT | 47,921 | BS | ITEM 8 | Debt | 53,622 |
7 | Total Debt | TOTAL_DEBT | 9746 | ||||||||
8 | Shareholder’s Equity | SH_EQUITY | 55,490 | BS | ITEM 9 | Shareholder’s Equity | 55,490 | ||||
9 | Book Value Per Share | BPS | 44,187 | 2 | Book Value Per Share | BPS | 83,149 | BS | ITEM 10 | Book Value Per Share | 88,072 |
10 | Tangible Book Value Per Share | BPS_TANG | 16,871 | ||||||||
11 | Sales | SALES | 161,459 | 3 | Revenue (Non Per Share) | SAL | 223,003 | IS | ITEM 11 | Sales | 230,871 |
12 | Revenue | REV_TOT | 1848 | ||||||||
13 | Net Sales | NET_SALES | 1508 | ||||||||
14 | Consolidated Sales | SALES_C | 19 | ||||||||
15 | Cost of Goods Sold | COGS | 65,109 | 4 | Cost of Goods Sold | COGS | 86,862 | IS | ITEM 12 | Cost of Goods Sold | 96,013 |
16 | Selling, General and Administrative | SGA | 46,095 | IS | ITEM 13 | SG&A Expense | 65,779 | ||||
17 | General and Administrative | G_A_EXP | 32,413 | ||||||||
18 | Sales and Marketing | S_M_EXP | 18,255 | ||||||||
19 | Research and Development | RD_EXP | 27,799 | IS | ITEM 15 | Research and Development | 27,799 | ||||
20 | EBITDA | EBITDA | 75,239 | 5 | EBITDA (Non Per Share) | EBT | 99,986 | IS | ITEM 16 | EBITDA | 108,270 |
6 | EBITDA Per Share | EBS | 27,798 | ||||||||
21 | EBITDA Adjusted | EBITDA_ADJ | 22,386 | ||||||||
22 | EBITDA Reported | EBITDA_REP | 13,282 | ||||||||
23 | Funds From Operations | FFO | 5017 | 7 | Funds From Operations | FFO | 5500 | ||||
24 | Adjusted Funds From Operations | AFFO | 4227 | ||||||||
25 | EBITA | EBITA | 1142 | ||||||||
26 | EBITDAR | EBITDAR | 965 | ||||||||
27 | Depreciation and Amortization | DEPR_AMORT | 37,857 | IS | ITEM 17 | Depreciation and Amortization | 37,857 | ||||
28 | EBIT | EBIT | 120,607 | 8 | EBIT (Non Per Share) | EBI | 114,598 | IS | ITEM 18 | EBIT | 169,554 |
9 | Operating Profit (Non Per Share) | OPR | 95,768 | ||||||||
29 | EBIT Adjusted | EBIT_ADJ | 22,104 | ||||||||
30 | EBIT Reported | EBITR | 17,019 | ||||||||
31 | EBIT Consolidated | EBIT_C | 23 | ||||||||
32 | Interest Expense | INT_EXP | 42,674 | IS | ITEM 19 | Interest Expense | 42,674 | ||||
33 | Pre-Tax Income | PTI | 128,764 | 10 | Pre-tax Profit (Non Per Share) | PRE | 170,455 | IS | ITEM 20 | Pre-Tax Income | 182,838 |
34 | Pre-Tax Profit Reported | PTIAG | 25,943 | ||||||||
35 | Pre-Tax Profit Adjusted | PTPA | 25,577 | ||||||||
36 | Consolidated Pretax Income | PTI_C | 24 | ||||||||
37 | Tax Expense | TAX_EXPENSE | 41,258 | IS | ITEM 21 | Tax Expense | 41,258 | ||||
38 | Earnings Per Share | EPS | 184,956 | 11 | Earnings Per Share | EPS | 371,113 | IS | ITEM 22 | Street Earnings | 379,437 |
39 | Net Profit Adjusted | NETBG | 79,935 | ||||||||
12 | EPS - Before Goodwill | EBG | 3497 | ||||||||
13 | Cash Earnings Per Share | CSH | 90 | ||||||||
40 | EPS Non-GAAP | EPS_NONGAAP | 69,799 | ||||||||
41 | EPS Excluding Exceptionals | EPS_EX_XORD | 16,466 | ||||||||
42 | Reported EPS | EPS_GAAP | 91,581 | 14 | GAAP EPS | GPS | 155,726 | IS | ITEM 23 | GAAP Earnings | 204,090 |
43 | Net Profit | NET | 136,287 | 15 | Net Income (Non Per Share) | NET | 194,189 | ||||
44 | Net Income Reported | BFNG | 45,661 | ||||||||
45 | Consolidated Net Income | NET_C | 26 | ||||||||
46 | Consolidated EPS | EPS_C | 25 | ||||||||
47 | Diluted Reported EPS | EPSRD | 22 | ||||||||
48 | [No Label] | EPSAD | 10 | ||||||||
49 | Cash Flow Per Share | CFPS | 31,213 | 16 | Cash Flow Per Share | CPS | 42,663 | CFS | ITEM 24 | Cash Flow From Operations | 59,927 |
50 | Cash Flow From Operations | CF_OP | 39,620 | ||||||||
51 | Capital Expenditure | CAPEX | 43,345 | 17 | Capital Expenditure (Non Per Share) | CPX | 52,081 | CFS | ITEM 25 | Capital Expenditure | 57,409 |
52 | Maintenance CAPEX | MAINT_CAPEX | 2854 | ||||||||
53 | Free Cash Flow | FCF | 32,541 | CFS | ITEM 26 | Free Cash Flow | 34,088 | ||||
54 | Free Cash Flow Per Share | FCFPS | 20,063 | ||||||||
55 | Cash Flow From Investing | CF_INV | 29,205 | CFS | ITEM 27 | Cash Flow From Investing | 29,205 | ||||
56 | Cash Flow From Financing | CF_FIN | 27,630 | CFS | ITEM 28 | Cash Flow From Financing | 27,630 | ||||
57 | Dividends Per Share | DPS | 55,031 | 18 | Dividends Per Share | DPS | 82,104 | CFS | ITEM 29 | Dividends Per Share | 88,054 |
Appendix 2
1.1 Definitions of abnormal stock returns and non-KPI variables in the IUF dataset
Subscripts.
m | An element of the set of 223 database Measures (194 in FactSet, 29 in I/B/E/S). Excluding KPIs, the set contains 75 database Measures (57 in FactSet, 18 in I/B/E/S) listed in appendix 1. Each Measure is an element of one and only one Item. |
i | An element of the set of 26 researcher-defined financial statement Items listed in appendix 1 for the union of I/B/E/S and FactSet. Each Item is a set of one or more database Measures. |
c | An element of the set of 3 researcher-defined Categories listed: Income Statement, Cash Flow Statement, and Balance Sheet. |
t | Fiscal period end. |
Variable Definitions (listed alphabetically).
ABRET t | Abnormal stock return at earnings announcement for period t. Defined as: \( Raw\ {Return}_{\left[-1,+1\right]}-{\hat{\alpha}}_{EP}-{\hat{\beta}}_{EP}\ast {Market\ Return}_{\left[-1,+1\right]} \) where Raw Return[−1,+1] and Market Return[−1,+1] are the 3-day raw return and value-weighted market returns surrounding the earnings announcement for period t;\( {\hat{\alpha}}_{EP} \), \( {\hat{\beta}}_{EP} \), and \( {\hat{\mu}}_{EP} \) are estimates from a regression model that uses 3-day cumulative, nonoverlapping returns observations during the trading-day period [−130,-10), (+10,+130] relative to the earnings announcement day: Raw Return = αEP + βEP ∗ Market Return + μEP The variable is winsorized at the 2nd and 98th percentiles. |
Analyst Forecast Surprise m,t | Median analyst consensus forecast error in dollars scaled by the Market Value of equity at the end of the day prior to the earnings announcement window. The forecasts are taken from the latest consensus period prior to the earnings announcement for period t and winsorized at ± 10%. Defined as: \( \frac{\left({Actual}_{m,t}-{Median\ Forecast}_{m,t}\right)}{Market\ Value}. \) |
Earnings Announcement t | Defined as the 3-day window < −1, +1 >, where day <0 > is the report date of quarterly earnings (Compustat: rdq) for period t. |
Management Guidance Surprise m,t | New management guidance in US dollars for Measure m in period t + 1 reported at the earnings announcement minus the median analyst consensus forecast in dollars for Measure m in period t + 1 as of just prior to the earnings announcement, scaled by Market Value. Period t + 1 is either the next unreported quarter or next unreported fiscal year. The variable is winsorized at ± 10%. \( \frac{\left( New\ {Guidance}_{m,t+1}- Median\ {Analyst\ Forecast}_{m,t+1}\right)}{Market\ Value} \) |
Rights and permissions
About this article
Cite this article
Hand, J.R.M., Laurion, H., Lawrence, A. et al. Explaining firms’ earnings announcement stock returns using FactSet and I/B/E/S data feeds. Rev Account Stud 27, 1389–1420 (2022). https://doi.org/10.1007/s11142-021-09597-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11142-021-09597-6