EVALUACIÓN EMPÍRICA DE LOS MÉTODOS DE PREDICCIÓN DE LA RENTABILIDAD: ¿RANDOM WALK O MODELOS DE REGRESIÓN?
Palabras clave:
Predicción. Rentabilidad. Random walk.
Resumen
En el estudio se planteó evaluar la capacidad predictiva de la rentabilidad de los modelos de reversión a la media, ajustes por devengo y señales financieras de desempeño con relación al proceso de random walk de las empresas del Mercado de Valores de Buenos Aires. El estudio de la rentabilidad es efectuado por la rentabilidad de activo. Primero, el método de datos de panel con efectos fijos exhibe una mayor precisión de los pronósticos de la rentabilidad en relación con MCO. Segundo, el análisis de los modelos revela que la rentabilidad del activo actual es la principal variable que explica el cambio de la rentabilidad de los activos del año siguiente, seguido por los ajustes por devengo y la rotación de los activos no corrientes. Tercero, la validación demuestra una mayor precisión de los pronósticos de los 3 modelos con relación a random walk y el modelo más preciso es el de señales financieras de desempeño. No obstante, random walk no debe descartárselo.
Citas
Albretch, W.S., Lookabill, L. y McKeon, J. (1977). The time series properties of annual earnings. Journal of Accounting Research, 15, pp. 226-244.
Anand, V., Brunner, R., Ikegwu, K. y Sougiannis, T. (2019). Predicting Profitability Using Machine Learning. Disponible en SSRN: https://ssrn.com/abstract=3466478.
Anuarios del Instituto Argentino del Mercado de Capitales-IAMC (2006-2015). Disponible en http: //www.iamc.sba.com.ar/informes/informe_anuario/
Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 23, 589–609.
Arbanell, J.S. y Bushee, B. J. (1997). Fundamental analysis, future earnings, and stock prices. Journal of Accounting Research, pp. 35, 1-24.
Baginski, S. P.; Lorek, K. S.; Willinger, G. L. y Branson, B. C. (1999). The relationship between economic characteristics and alternative annual earnings persistence measures. The Accounting Review, 74(1), 105-120.
Baltagi, B H. (1995). Econometric analysis of panel data (Vol. 2). New York: Wiley.
Bauman, M. P. (2014). Forecasting operating profitability with DuPont analysis: Further evidence. Review of Accounting and Finance, 13(2), pp. 191-205.
Castiñeira, R. (2016). El retorno de Argentina a una economía de mercado. Escenario 2017-2021. Econométrica: Informe Macro - diciembre 2016. Obtenido de https://www.econometrica.com.ar/ attachments/article/312/Econométrca%20-%20Informe% 20Macro%20-%20Dic-16.pdf
Chiou, J. R., Cheng, L., & Wu, H. W. (2006). The determinants of working capital management. Journal of American Academy of Business, 10(1), pp. 149-155.
De Albuquerque, A. A. y Do Valle, M. R. (2014). Capacity of Future Earnings´ Prediction of EVA® in the Brazilian Public Companies. International Business Research 8, 38-49.
De Andrés, J., Landajo, M. y Lorca, P. (2005). Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case. European Journal of Operational Research, 167(2), pp. 518-542.
Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of economics and statistics, 80(4), pp. 549-560.
Esplin, A., Hewitt, M., Plumlee, M. y Yohn, T. L. (2014). Disaggregating operating and financial activities: Implications for forecasts of profitability. Review of Accounting Studies 19, pp. 328-362.
Fairfield, P. M., Sweeney, R. J. y Yohn, T. L. (1996). Accounting classification and the predictive content of earnings. Accounting Review 71, 337-355
Fabris, T. R. y da Costa Jr, N. C. A. (2010). Propriedades das series temporais dos lucros trimestrais das empresas brasileiras negociadas em bolsa. Revista Brasileira de Finanças, 8(3), pp. 351-376.
Fama, E. F. y French, K.R. (2000). Forecasting Profitability and Earnings. The Journal of Business 73, pp. 161-175.
Farfield, P.M. y Yohn T.L. (2001). Using asset turnover and profit margin to forecast changes in profitability. Review of Accounting Studies, 6, pp. 371-385.
Fairfield, P. M., Ramnath, S. y Yohn. T. L. (2009). Do Industry‐Level Analyses Improve Forecasts of Financial Performance? Journal of Accounting Research 47,147-178.
Freeman, R. N., Ohlson, J. A. y Penman, S. H. (1982). Book rate-of-return and prediction of earnings changes: An empirical investigation. Journal of accounting research, pp. 639-653.
Gerakos, J. J. y Gramacy, R. (2013). Regression-based earnings forecasts. Chicago Booth Research Paper, (12-26). Disponible en https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 2112137
Hoechle, D. (2007). Robust standard errors for panel regressions with cross-sectional dependence. The stata journal, 7(3), 281-312.
Lipe, R. y Kormendi, R. (1994). Mean Reversion in Annual Earnings and Its Implications for Security Valuation. Review of Quantitative Finance and Accounting 4, 24-46.
Little, I.M.D. (1962). Higgledy piggledy growth. Bulletin of the Oxford Institute of Economics and Statistics, 24, pp. 389-342.
Monahan, S. J. (2018). Financial Statement Analysis and Earnings Forecasting. Foundations and Trends® in Accounting, 12(2), pp. 105-215.
Monterrey Mayoral, J y Sánchez Segura, A (2017). Una evaluación empírica de los métodos de predicción de la rentabilidad y su relación con las características corporativas. Revista de Contabilidad 20, 95-106.
Mullainathan, S. y Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), pp. 87-106.
Mueller, D. C. (1977). The Persistence of Profits above the Norm. Economica 44, 369-380.
Myers, S. C. (1984). The Capital Structure Puzzle. The Journal of Finance 39, pp. 575-592.
Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18, pp. 109-131.
Ohlson, J. (1995). Earnings, book values, and dividends in equity valuation. Contemporary Accounting Research 22, pp. 661-687.
Ou, J.A. (1990). The information content of nonearnings accounting numbers as earnings predictors. Journal of Accounting Research 28, pp. 144-163.
Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. CESifo Working Paper Series No. 1229; IZA Discussion Paper No. 1240. Disponible en https: www.// ssrn.com //ssrn.com/abstract=572504
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, pp. 1-52.
Penman, S.H. y Zhang X. (2002). Modeling sustainable earnings and P/E ratios with financial statement analysis. Working paper de la Columbia University y University of California, Berkeley. Disponible en http://papers.ssrn.com/sol3 /papers.cfm? Abstra ctid =318967.
Richardson, S., Sloan, R, Soliman, M. y Tuna, I (2005). Accrual reliability, earnings persistence, and stock prices. Journal of Accounting and Economics 39, 437–485
Schipper, K. y Vincent. L. (2003). Earnings quality. Accounting Horizons 17 (supplement), pp. 97–110.
Sloan, R. (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? Accounting Review 71, 289–315.
Stigler, G. (1963). Capital and Rates of Return in Manufacturing Industries. Princeton, NJ: Princeton University Press.
Swanson, E. P., Rees, L., & Juarez-Valdes, L. F. (2003). The contribution of fundamental analysis after a currency devaluation. The Accounting Review, 78(3), pp. 875-902.
Terreno, D. D., Sattler, S. A., & Castro González, E. L. (2018). Capacidad predictiva de la rentabilidad en empresas del mercado de capitales de Argentina. Contaduría y administración, 63(4), pp. 1-20.
Vorst, P. y Yohn, T. L. (2018). Life cycle models and forecasting growth and profitability. The Accounting Review, 93(6), pp. 357-381.
Wahlen, J.M., Baginski, S.P. y Bradsaw, M.T. (2011). Financial Reporting, Financial Statement Analysis, and Valuation: A Strategic Perspective (7th Ed). Mason, USA: South-Western Cengage Learning.
Watts, R. L. y Leftwich, R. W. (1977). The time series of annual accounting earnings. Journal of Accounting Research, pp. 253-271.
Verbeek, M. (2000). A guide to modern econometrics. Chichester: John Wiley
Anand, V., Brunner, R., Ikegwu, K. y Sougiannis, T. (2019). Predicting Profitability Using Machine Learning. Disponible en SSRN: https://ssrn.com/abstract=3466478.
Anuarios del Instituto Argentino del Mercado de Capitales-IAMC (2006-2015). Disponible en http: //www.iamc.sba.com.ar/informes/informe_anuario/
Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 23, 589–609.
Arbanell, J.S. y Bushee, B. J. (1997). Fundamental analysis, future earnings, and stock prices. Journal of Accounting Research, pp. 35, 1-24.
Baginski, S. P.; Lorek, K. S.; Willinger, G. L. y Branson, B. C. (1999). The relationship between economic characteristics and alternative annual earnings persistence measures. The Accounting Review, 74(1), 105-120.
Baltagi, B H. (1995). Econometric analysis of panel data (Vol. 2). New York: Wiley.
Bauman, M. P. (2014). Forecasting operating profitability with DuPont analysis: Further evidence. Review of Accounting and Finance, 13(2), pp. 191-205.
Castiñeira, R. (2016). El retorno de Argentina a una economía de mercado. Escenario 2017-2021. Econométrica: Informe Macro - diciembre 2016. Obtenido de https://www.econometrica.com.ar/ attachments/article/312/Econométrca%20-%20Informe% 20Macro%20-%20Dic-16.pdf
Chiou, J. R., Cheng, L., & Wu, H. W. (2006). The determinants of working capital management. Journal of American Academy of Business, 10(1), pp. 149-155.
De Albuquerque, A. A. y Do Valle, M. R. (2014). Capacity of Future Earnings´ Prediction of EVA® in the Brazilian Public Companies. International Business Research 8, 38-49.
De Andrés, J., Landajo, M. y Lorca, P. (2005). Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case. European Journal of Operational Research, 167(2), pp. 518-542.
Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of economics and statistics, 80(4), pp. 549-560.
Esplin, A., Hewitt, M., Plumlee, M. y Yohn, T. L. (2014). Disaggregating operating and financial activities: Implications for forecasts of profitability. Review of Accounting Studies 19, pp. 328-362.
Fairfield, P. M., Sweeney, R. J. y Yohn, T. L. (1996). Accounting classification and the predictive content of earnings. Accounting Review 71, 337-355
Fabris, T. R. y da Costa Jr, N. C. A. (2010). Propriedades das series temporais dos lucros trimestrais das empresas brasileiras negociadas em bolsa. Revista Brasileira de Finanças, 8(3), pp. 351-376.
Fama, E. F. y French, K.R. (2000). Forecasting Profitability and Earnings. The Journal of Business 73, pp. 161-175.
Farfield, P.M. y Yohn T.L. (2001). Using asset turnover and profit margin to forecast changes in profitability. Review of Accounting Studies, 6, pp. 371-385.
Fairfield, P. M., Ramnath, S. y Yohn. T. L. (2009). Do Industry‐Level Analyses Improve Forecasts of Financial Performance? Journal of Accounting Research 47,147-178.
Freeman, R. N., Ohlson, J. A. y Penman, S. H. (1982). Book rate-of-return and prediction of earnings changes: An empirical investigation. Journal of accounting research, pp. 639-653.
Gerakos, J. J. y Gramacy, R. (2013). Regression-based earnings forecasts. Chicago Booth Research Paper, (12-26). Disponible en https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 2112137
Hoechle, D. (2007). Robust standard errors for panel regressions with cross-sectional dependence. The stata journal, 7(3), 281-312.
Lipe, R. y Kormendi, R. (1994). Mean Reversion in Annual Earnings and Its Implications for Security Valuation. Review of Quantitative Finance and Accounting 4, 24-46.
Little, I.M.D. (1962). Higgledy piggledy growth. Bulletin of the Oxford Institute of Economics and Statistics, 24, pp. 389-342.
Monahan, S. J. (2018). Financial Statement Analysis and Earnings Forecasting. Foundations and Trends® in Accounting, 12(2), pp. 105-215.
Monterrey Mayoral, J y Sánchez Segura, A (2017). Una evaluación empírica de los métodos de predicción de la rentabilidad y su relación con las características corporativas. Revista de Contabilidad 20, 95-106.
Mullainathan, S. y Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), pp. 87-106.
Mueller, D. C. (1977). The Persistence of Profits above the Norm. Economica 44, 369-380.
Myers, S. C. (1984). The Capital Structure Puzzle. The Journal of Finance 39, pp. 575-592.
Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18, pp. 109-131.
Ohlson, J. (1995). Earnings, book values, and dividends in equity valuation. Contemporary Accounting Research 22, pp. 661-687.
Ou, J.A. (1990). The information content of nonearnings accounting numbers as earnings predictors. Journal of Accounting Research 28, pp. 144-163.
Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. CESifo Working Paper Series No. 1229; IZA Discussion Paper No. 1240. Disponible en https: www.// ssrn.com //ssrn.com/abstract=572504
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, pp. 1-52.
Penman, S.H. y Zhang X. (2002). Modeling sustainable earnings and P/E ratios with financial statement analysis. Working paper de la Columbia University y University of California, Berkeley. Disponible en http://papers.ssrn.com/sol3 /papers.cfm? Abstra ctid =318967.
Richardson, S., Sloan, R, Soliman, M. y Tuna, I (2005). Accrual reliability, earnings persistence, and stock prices. Journal of Accounting and Economics 39, 437–485
Schipper, K. y Vincent. L. (2003). Earnings quality. Accounting Horizons 17 (supplement), pp. 97–110.
Sloan, R. (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? Accounting Review 71, 289–315.
Stigler, G. (1963). Capital and Rates of Return in Manufacturing Industries. Princeton, NJ: Princeton University Press.
Swanson, E. P., Rees, L., & Juarez-Valdes, L. F. (2003). The contribution of fundamental analysis after a currency devaluation. The Accounting Review, 78(3), pp. 875-902.
Terreno, D. D., Sattler, S. A., & Castro González, E. L. (2018). Capacidad predictiva de la rentabilidad en empresas del mercado de capitales de Argentina. Contaduría y administración, 63(4), pp. 1-20.
Vorst, P. y Yohn, T. L. (2018). Life cycle models and forecasting growth and profitability. The Accounting Review, 93(6), pp. 357-381.
Wahlen, J.M., Baginski, S.P. y Bradsaw, M.T. (2011). Financial Reporting, Financial Statement Analysis, and Valuation: A Strategic Perspective (7th Ed). Mason, USA: South-Western Cengage Learning.
Watts, R. L. y Leftwich, R. W. (1977). The time series of annual accounting earnings. Journal of Accounting Research, pp. 253-271.
Verbeek, M. (2000). A guide to modern econometrics. Chichester: John Wiley
Publicado
2020-12-10
Cómo citar
Terreno, D., Sattler, S., & Castro González, E. (2020). EVALUACIÓN EMPÍRICA DE LOS MÉTODOS DE PREDICCIÓN DE LA RENTABILIDAD: ¿RANDOM WALK O MODELOS DE REGRESIÓN?. Revista De Investigación En Modelos Financieros, 2, 106-126. Recuperado a partir de https://ojstest.economicas.uba.ar/index.php/RIMF/article/view/1971
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