Indian Journal of Finance

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Evaluation of Pooled Cross-Sectional Earnings Forecasting Models: An Indian Evidence


Affiliations
1 Doctoral Student, Department of Commerce, MIT Campus, Manipal Academy of Higher Education, Manipal - 576 104, Karnataka, India
2 Professor (Corresponding Author), Department of Business Administration, Faculty of Management and Commerce, Manipal University Jaipur, Jaipur - 303 007, Rajasthan, India

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Purpose : Earnings forecasts are essential for valuation, and a bleak coverage of analysts’ forecasts in emerging economies withholds the valuation research and practices. This study compared the pooled cross-sectional earnings forecasting models in the Indian market to choose alternative sources for earnings forecasts to solve the unavailability of analysts’ earnings forecasts. Specifically, evaluating the theoretical earnings forecasting models of three different propositions: the earnings persistence (Li & Mohanram, 2014, EP) model, the Hou, Van Dijk, and Zhang (2012, HVZ) model, and the Pope and Wang (Harris & Wang, 2019, PW) model.

Methodology : This study considered all companies listed on NSE from 1995 – 2022 in an unbalanced panel structure with 36,591 firm years observations. Robust regression was used for the coefficient estimation because of its capability to handle outliers and provide a better model fit.

Findings : The results showed that the pooled cross-sectional models are reasonably accurate with the Indian data, restricting average forecast errors between 3% to 10%. The coefficient of earnings greater than one across models signified a high persistence in earnings. The PW model outperformed the other two models in the short run with share prices as predictor variables; whereas, the EP model performed best in the long run. The PW and EP forecast offered incremental information fully encompassing the HVZ forecast.

Practical Implications : This study elevated the application of valuation in theories in research and managerial practices where firms’earnings forecasts are an essential input.

Originality : This study uniquely compared the earning forecasting models of three proportions in a single setup to validate and suggest sources of earnings forecast for the Indian capital market.


Keywords

Model-Based Earnings Forecast, Mechanical Earnings Forecast, Robust Regression, Cross-Sectional Models, Earnings Persistence.

JEL Classification Codes : G17, G31, G32, M41

Paper Submission Date : January 15, 2023 ; Paper sent back for Revision : June 5, 2023 ; Paper Acceptance Date : June 25, 2023 ; Paper Published Online : August 16, 2023

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  • Aggarwal, R., Mishra, D., & Wilson, C. (2018). Analyst recommendations and the implied cost of equity. Review of Quantitative Finance and Accounting, 50(3), 717–743. https://doi.org/10.1007/s11156-017-0644-y

  • Andersen, R. (2008). Modern methods for robust regression. Sage Publications. https://doi.org/10.4135/9781412985109

  • Ashton, D., & Wang, P. (2013). Terminal valuations, growth rates and the implied cost of capital. Review of Accounting Studies, 18(1), 261–290. https://doi.org/10.1007/s11142-012-9208-5

  • Azevedo, V., Bielstein, P., & Gerhart, M. (2021). Earnings forecasts: The case for combining analysts' estimates with a cross-sectional model. Review of Quantitative Finance and Accounting, 56(2), 545–579. https://doi.org/10.1007/s11156-020-00902-z

  • Azevedo, V. G., & Gerhart, M. (2016). Comparison of cross-sectional earnings forecasting models for the European market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2787277

  • Bradshaw, M. T., Drake, M. S., Myers, J. N., & Myers, L. A. (2012). A re-examination 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

  • Brown, L. D. (1993). Earnings forecasting research: Its implications for capital markets research. International Journal of Forecasting, 9(3), 295–320. https://doi.org/10.1016/0169-2070(93)90023-G

  • Brown, L. D., & Zhou, L. (2015). Interactions between analysts' and managers' earnings forecasts. International Journal of Forecasting, 31(2), 501–514. https://doi.org/10.1016/j.ijforecast.2014.10.002

  • Brownen-Trinh, R. (2019). Effects of winsorization: The cases of forecasting non-GAAP and GAAP earnings. Journal of Business Finance & Accounting, 46(1–2), 105–135. https://doi.org/10.1111/jbfa.12365

  • Chacko, J. P., & Padmakumari, L. (2023). Does ownership structure affect the ex-ante cost of capital? Investment Management and Financial Innovations, 20(1), 112–126. https://doi.org/10.21511/IMFI.20(1).2023.11

  • Chang, W.-J., Monahan, S. J., Ouazad, A., & Vasvari, F. P. (2021). The higher moments of future earnings. The Accounting Review, 96(1), 91–116. https://doi.org/10.2308/TAR-2015-0413

  • Claus, J., & Thomas, J. (2001). Equity premia as low as three percent? Evidence from analysts' earnings forecasts for domestic and international stock markets. The Journal of Finance, 56(5), 1629–1666. https://doi.org/10.1111/0022-1082.00384

  • Easton, P. D. (2004). PE ratios, PEG ratios, and estimating the implied expected rate of return on equity capital. The Accounting Review, 79(1), 73–95. https://doi.org/10.2308/accr.2004.79.1.73

  • Easton, P. D., & Monahan, S. J. (2005). An evaluation of accounting-based measures of expected returns. The Accounting Review, 80(2), 501–538. https://doi.org/10.2308/accr.2005.80.2.501

  • Easton, P. D., & Monahan, S. J. (2016). Review of recent research on improving earnings forecasts and evaluating accounting-based estimates of the expected rate of return on equity capital. Abacus, 52(1), 35–58. https://doi.org/10.1111/abac.12064

  • Easton, P. D., & Sommers, G. A. (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

  • Echterling, F., Eierle, B., & Ketterer, S. (2015). A review of the literature on methods of computing the implied cost of capital. International Review of Financial Analysis, 42, 235–252. https://doi.org/10.1016/j.irfa.2015.08.001

  • Fama, E. F., & French, K. R. (2000). Forecasting profitability and earnings. The Journal of Business, 73(2), 161–175. https://doi.org/10.1086/209638

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

  • Feng, M. (2014). Discussion of “Evaluating cross-sectional forecasting models for implied cost of capital.” Review of Accounting Studies, 19(3), 1186–1190. https://doi.org/10.1007/s11142-014-9288-5

  • Gebhardt, W. R., Lee, C. M., & Swaminathan, B. (2001). Toward an implied cost of capital. Journal of Accounting Research, 39(1), 135–176. https://doi.org/10.1111/1475-679X.00007

  • Gerakos, J. J., & Gramacy, R. B. (2012). Regression-based earnings forecasts. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2112137

  • Griffin, J. M. (2002). Are the Fama and French factors global or country specific? The Review of Financial Studies, 15(3), 783–803. https://doi.org/10.1093/rfs/15.3.783

  • Harris, R. D., & Wang, P. (2019). 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

  • Hess, D., Meuter, M., & Kaul, A. (2019). The performance of mechanical earnings forecasts. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3041364

  • Hou, K., & Van Dijk, M. A. (2010). Profitability shocks and the size effect in the cross-section of expected stock returns. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1536804

  • Hou, K., van Dijk, M. A., & Zhang, Y. (2012). The implied cost of capital: A new approach. Journal of Accounting and Economics, 53(3), 504–526. https://doi.org/10.1016/j.jacceco.2011.12.001

  • Huber, P. J. (1973). Robust regression: Asymptotics, conjectures and Monte Carlo. The Annals of Statistics, 1(5), 799–821. https://doi.org/10.1214/aos/1176342503

  • Iqbal, & Mallikarjunappa, T. (2010). A study of efficiency of the Indian stock market. Indian Journal of Finance, 4(5), 32–38. https://www.indianjournaloffinance.co.in/index.php/IJF/article/view/72610

  • Kamath, A. N., Shenoy, S. S., & Kumar, N. S. (2022). An overview of investor sentiment: Identifying themes, trends, and future direction through bibliometric analysis. Investment Management and Financial Innovations, 19(3), 229–242. https://doi.org/10.21511/imfi.19(3).2022.19

  • Kordzakhia, N., Mishra, G. D., & Reiersølmoen, L. (2001). Robust estimation in the logistic regression model. Journal of Statistical Planning and Inference, 98(1–2), 211–223. https://doi.org/10.1016/S0378-3758(00)00312-8

  • Kumar, V. P., & Kar, S. (2021). Measuring efficiency of selected Indian commercial banks: A DEA-based MPI approach. Indian Journal of Finance, 15(5–7), 44–60. https://doi.org/10.17010/IJF/2021/V15I5-7/164492

  • Kundu, S., & Banerjee, A. (2021). Predictability of earnings and its impact on stock returns: Evidence from India. Cogent Economics & Finance, 9(1), 1898112. https://doi.org/10.1080/23322039.2021.1898112

  • Ledwani, S., Chakraborty, S., & Digal, S. K. (2022). The evolution of Indian Journal of Finance : A retrospective review and future directions. Indian Journal of Finance, 16(4), 8–30. https://doi.org/10.17010/ijf/2022/v16i4/169172

  • Leone, A. J., Minutti-Meza, M., & Wasley, C. E. (2019). Influential observations and inference in accounting research. The Accounting Review, 94(6), 337–364. https://doi.org/10.2308/ACCR-52396

  • Li, K. K. (2011). How well do investors understand loss persistence? Review of Accounting Studies, 16(3), 630–667. https://doi.org/10.1007/S11142-011-9157-4

  • Li, K. 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

  • Lithin, B. M., Chakraborty, S., & Nikhil, M. N. (2023). Are liquidity and credit risk key determinants of corporate credit spreads (CCS) in India? Indian Journal of Finance, 17(6), 8–26. https://doi.org/10.17010/IJF/2023/V17I6/172773

  • Mainrai, G., & Mohania, S. (2021). Rising profits and falling market share - A case of National Bank. Indian Journal of Finance, 15(5–7), 75–85. https://doi.org/10.17010/IJF/2021/V15I5-7/164494

  • Mincer, J. A., & Zarnowitz, V. (1969). The evaluation of economic forecasts. In, Economic forecasts and expectations (NBER). https://www.nber.org/system/files/chapters/c1214/c1214.pdf

  • Mishra, D. R., & O'Brien, T. J. (2019). Fama-French, CAPM, and implied cost of equity. Journal of Economics and Business, 101, 73–85. https://doi.org/10.1016/j.jeconbus.2018.08.002

  • 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

  • Monahan, S. J. (2018). Financial statement analysis and earnings forecasting. Foundations and Trends® in Accounting, 12(2), 105–215. https://doi.org/10.1561/1400000036

  • Nikhil, M. N., Chakraborty, S., Lithin, B. M., & Lobo, L. S. (2023). Does the adoption of Ind AS affect the performance of firms in India? Investment Management and Financial Innovations, 20(2), 171–181. https://doi.org/10.21511/imfi.20(2).2023.15

  • O'brien, P. C. (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

  • Paton, A. P., Cannavan, D., Gray, S., & Hoang, K. (2020). Analyst versus model-based earnings forecasts: Implied cost of capital applications. Accounting & Finance, 60(4), 4061–4092. https://doi.org/10.1111/acfi.12548

  • Penman, S. (2016). Valuation: Accounting for risk and the expected return. Abacus, 52(1), 106–130. https://doi.org/10.1111/abac.12067

  • Pope, P. F., & Wang, P. (2005). Earnings components, accounting bias and equity valuation. Review of Accounting Studies, 10(4), 387–407. https://doi.org/10.1007/s11142-005-4207-4

  • Qu, L. (2021). A new approach to estimating earnings forecasting models: Robust regression MM-estimation. International Journal of Forecasting, 37(2), 1011–1030. https://doi.org/10.1016/j.ijforecast.2020.11.003

  • Richardson, S. A., Sloan, R. G., Soliman, M. T., & 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

  • Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934

  • Sen, R., & Mehrotra, P. (2016). Modeling jumps and volatility of the Indian stock market using high-frequency data. Journal of Quantitative Economics, 14(1), 137–150. https://doi.org/10.1007/S40953-016-0028-5

  • Sinha, R. K. (2021). Macro disagreement and analyst forecast properties. Journal of Contemporary Accounting & Economics, 17(1), 100235. https://doi.org/10.1016/j.jcae.2020.100235

  • Sneed, J. E. (1996). Earnings forecasting models: Adding a theoretical foundation for the selection of explanatory variables. Management Research News, 19(11), 42–57. https://doi.org/10.1108/eb028504

  • Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis. Nederlandse Akademie Wetenchappen, Series A, 53, 386–392.

  • Tripathi, M., Kashiramka, S., & Jain, P. K. (2018). Equity risk premium in India: Comparative estimates from historical returns, dividend and earnings models. Journal of Emerging Market Finance, 17(1_suppl), S136–S156. https://doi.org/10.1177/0972652717751543

  • Venkataramanaiah, M., Latha, C. M., & Rao, K. S. (2018). Determinants of dividend policy in the Indian corporate sector: A study of companies listed on Nifty 50, NSE. Indian Journal of Finance, 12(1), 37–46. https://doi.org/10.17010/ijf/2018/v12i1/120740

  • Wang, P., & Huang, W. (2015). The implied growth rates and country risk premium: Evidence from Chinese stock markets. Review of Quantitative Finance and Accounting, 45(3), 641–663. https://doi.org/10.1007/s11156-014-0450-8

  • Yohai, V. J. (1987). High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics, 15(2), 642–656. https://doi.org/10.1214/aos/1176350366


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  • Evaluation of Pooled Cross-Sectional Earnings Forecasting Models: An Indian Evidence

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Authors

Sanket Ledwani
Doctoral Student, Department of Commerce, MIT Campus, Manipal Academy of Higher Education, Manipal - 576 104, Karnataka, India
Suman Chakraborty
Professor (Corresponding Author), Department of Business Administration, Faculty of Management and Commerce, Manipal University Jaipur, Jaipur - 303 007, Rajasthan, India

Abstract


Purpose : Earnings forecasts are essential for valuation, and a bleak coverage of analysts’ forecasts in emerging economies withholds the valuation research and practices. This study compared the pooled cross-sectional earnings forecasting models in the Indian market to choose alternative sources for earnings forecasts to solve the unavailability of analysts’ earnings forecasts. Specifically, evaluating the theoretical earnings forecasting models of three different propositions: the earnings persistence (Li & Mohanram, 2014, EP) model, the Hou, Van Dijk, and Zhang (2012, HVZ) model, and the Pope and Wang (Harris & Wang, 2019, PW) model.

Methodology : This study considered all companies listed on NSE from 1995 – 2022 in an unbalanced panel structure with 36,591 firm years observations. Robust regression was used for the coefficient estimation because of its capability to handle outliers and provide a better model fit.

Findings : The results showed that the pooled cross-sectional models are reasonably accurate with the Indian data, restricting average forecast errors between 3% to 10%. The coefficient of earnings greater than one across models signified a high persistence in earnings. The PW model outperformed the other two models in the short run with share prices as predictor variables; whereas, the EP model performed best in the long run. The PW and EP forecast offered incremental information fully encompassing the HVZ forecast.

Practical Implications : This study elevated the application of valuation in theories in research and managerial practices where firms’earnings forecasts are an essential input.

Originality : This study uniquely compared the earning forecasting models of three proportions in a single setup to validate and suggest sources of earnings forecast for the Indian capital market.


Keywords


Model-Based Earnings Forecast, Mechanical Earnings Forecast, Robust Regression, Cross-Sectional Models, Earnings Persistence.

JEL Classification Codes : G17, G31, G32, M41

Paper Submission Date : January 15, 2023 ; Paper sent back for Revision : June 5, 2023 ; Paper Acceptance Date : June 25, 2023 ; Paper Published Online : August 16, 2023


References





DOI: https://doi.org/10.17010/ijf%2F2023%2Fv17i8%2F173008