바로가기메뉴

본문 바로가기 주메뉴 바로가기

ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

logo

  • P-ISSN1738-3110
  • E-ISSN2093-7717
  • SCOPUS, ESCI

Earnings Forecasts and Firm Characteristics in the Wholesale and Retail Industries

Earnings Forecasts and Firm Characteristics in the Wholesale and Retail Industries

The Journal of Distribution Science(JDS) / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2022, v.20 no.12, pp.117-123
https://doi.org/https://doi.org/10.15722/jds.20.12.202212.117
LIM, Seung-Yeon (College of Business Administration, Kookmin University)

Abstract

Purpose: This study investigates the relationship between earnings forecasts estimated from a cross-sectional earnings forecast model and firm characteristics such as firm size, sales volatility, and earnings volatility. Research design, data and methodology: The association between earnings forecasts and the aforementioned firm characteristics is examined using 214 firm-year observations with analyst following and 848 firm-year observations without analyst following for the period of 2011-2019. I estimate future earnings using a cross-sectional earnings forecast model, and then compare these model-based earnings forecasts with analysts' earnings forecasts in terms of forecast bias and forecast accuracy. The earnings forecast bias and accuracy are regressed on firm size, sales volatility, and earnings volatility. Results: For a sample with analyst following, I find that the model-based earnings forecasts are more accurate as the firm size is larger, whereas the analysts' earnings forecasts are less biased and more accurate as the firm size is larger. However, for a sample without analyst following, I find that the model-based earnings forecasts are more pessimistic and less accurate as firms' past earnings are more volatile. Conclusions: Although model-based earnings forecasts are useful for evaluating firms without analyst following, their accuracy depends on the firms' earnings volatility.

keywords
Analyst Forecasts, Cross-Sectional Earnings Model, Model-Based Earnings Forecasts, Forecast Bias, Forecast Accuracy

참고문헌

1.

Bradshaw, M., Drake, M., Myers, J., & Myers, L. (2012). A Reexamination of analysts' superiority over time-series forecasts of annual earnings. Review of Accounting Studies, 17(4), 944–968. https://doi.org/10.1007/s11142-012-9185-8.

2.

Brown, L., Richardson, G., & Schwager, S. (1987). An Information Interpretation of Financial Analyst Superiority in Forecasting Earnings. Journal of Accounting Research, 25(1), 49-67. https://doi.org/10.2307/2491258.

3.

Dugar, A., & Nathan, S. (1995). The effect of investment banking relationships on financial analysts' earnings forecasts and investment recommendations. Contemporary Accounting Research, 12(1), 131–160. https://doi.org/10.1111/j.1911-3846.1995.tb00484.x.

4.

Easton, P. & Sommers, G. (2007). Effect of analysts’ optimism on estimates of the expected rate of return implied by earnings forecasts. Journal of Accounting Research, 45(5), 983–1015. https://doi.org/10.1111/j.1475-679X.2007.00257.x.

5.

Evans, M., Njoroge, K., & Yong, K. O. (2017). An examination of the statistical significance and economic relevance of profitability and earnings forecasts from models and analysts. Contemporary Accounting Research, 34(3), 1453–1488. https://doi.org/10.1111/1911-3846.12307.

6.

Fama, E., & French, K. (2006). Profitability, investment and average returns. Journal of Financial Economics, 82(3), 491–518. https://doi.org/10.1016/j.jfineco.2005.09.009.

7.

Feltham, G., & Ohlson, J. (1996). Uncertainty resolution and the theory of depreciation measurement. Journal of Accounting Research, 34(2), 209–234. https://doi.org/10.2307/2491500.

8.

Francis, J., LaFond, R., Olsson, P. M., & Schipper, K. (2004). Cost of equity and earnings attributes. The Accounting Review, 79(4),967-1010. https://doi.org/10.2308/accr.2004.79.4.967.

9.

Francis, J., LaFond, R., Olsson, P. M., & Schipper, K. (2005). The market pricing of accruals quality. Journal of Accounting and Economics, 39(2), 295-327. https://doi.org/10.1016/j.jacceco.2004.06.003.

10.

Francis, J., & Philbrick, D. (1993). Analysts' decisions as products of a multi-task environment. Journal of Accounting Research, 31(2), 216–230. https://doi.org/10.2307/2491271.

11.

Fried, D., & Givoly, D. (1982). Financial analysts’ forecasts of earnings: A better surrogate for market expectations. Journal of Accounting and Economics, 4(2), 85–107. https://doi.org/10.1016/0165-4101(82)90015-5.

12.

Harris, R. D. F., & Wang, P. (2021). Model-based earnings forecasts vs. financial analysts’ earnings forecasts. The British Accounting Review, 51(4), 424–437. https://doi.org/10.1016/j.bar.2018.10.002.

13.

Hou, K., van Dijk, M., & Zhang, Y. (2012). The implied cost of capital: A new approach. Journal of Accounting and Economics, 3(3), 504–526. https://doi.org/10.1016/j.jacceco.2011.12.001.

14.

Keung, E. C. (2010). Do supplementary sales forecasts increase the credibility of financial analysts’ earnings forecasts? The Accounting Review, 85(6), 2047-2074. https://doi.org/10.2308/accr.2010.85.6.2047.

15.

Larocque, S. (2013). Analysts’ earnings forecast errors and cost of equity capital estimates. Review of Accounting Studies, 18(1), 135–166. https://doi.org/10.1007/s11142-012-9207-6.

16.

Lee, C. M., & So, E. C. (2017). Uncovering expected returns:Information in analyst coverage proxies. Journal of Financial Economics, 124(2), 331–348. https://doi.org/10.1016/j.jfineco.2017.01.007.

17.

Li, K., & Mohanram, P. (2014). Evaluating cross-sectional forecasting models for implied cost of capital. Review of Accounting Studies, 19(3), 1152–1185. https://doi.org/10.1007/s11142-014-9282-y.

18.

Lin, H., & McNichols, M. (1998). Underwriting relationships, analysts' earnings forecasts and investment recommendations. Journal of Accounting and Economics, 25(1), 101–127. https://doi.org/10.1016/S0165-4101(98)00016-0.

19.

McInnis, J. (2010). Earnings smoothness, average returns, and implied cost of equity capital. The Accounting Review, 85(1), 315-341. https://doi.org/10.2308/accr.2010.85.1.315.

20.

Mohanram, P., & Gode, D. (2013). Removing predictable analyst forecast errors to improve implied cost of equity estimates. Review of Accounting Studies, 18(2), 443–478. https://doi.org/10.1007/s11142-012-9219-2.

21.

Myers, J., Myers, L., & Skinner, D. (2007). Earnings momentum and earnings management. Journal of Accounting, Auditing and Finance, 22(2), 249–284. https://doi.org/10.1177/0148558X0702200.

22.

O’Brien, P. (1988). Analysts’ forecasts as earnings expectations. Journal of Accounting and Economics, 10(1), 53–83. https://doi.org/10.1016/0165-4101(88)90023-7.

23.

Ohlson, J. (1995). Earnings, book values, and dividends in equity valuation. Contemporary Accounting Research, 11(2), 661–687. https://doi.org/10.1111/j.1911-3846.1995.tb00461.x.

24.

Richardson, S., Sloan, R., Soliman, M., & Tuna. I. (2005). Accrual reliability, earnings persistence and stock prices. Journal of Accounting and Economics, 39(3), 437–485. https://doi.org/10.1016/j.jacceco.2005.04.005.

The Journal of Distribution Science(JDS)