PREREQUISITES FOR THE USE OF MACHINE LEARNING FOR BUSINESS VALUATION

Petr Koklev

Resumen


Goal: The paper examines the fundamental theoretical prerequisites for the use of machine learning in business valuation. Methods: The study demonstrates that the use of statistical methods addresses the shortcomings of traditional approaches to valuation, in particular, the income approach and the discounted cash flow method. Results: Substantiation is given for the rejection of traditional econometric methods (linear regression, estimated by the least squares method) in favor of more complex nonparametric statistical models. Conclusion: Machine learning expands the empirical toolkit of the economist, allows for small datasets, solves the problem of asset valuation complexity, protects against false discoveries, and does not require compliance with Gauss-Markov assumptions. The paper also addresses the black box problem – the difficulty of interpreting models derived from statistical learning.


Palabras clave


Valuation; Statistical learning; DCF; Relative valuation; Econometrics; Feature importance

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Referencias


Athey, S. & Imbens, G.W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3-32. http://dx.doi.org/10.1257/jep.31.2.3

Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199-231. http://dx.doi.org/10.1214/ss/1009213726

Carvalho, D.V., Pereira, E.M., & Cardoso, J.S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832. http://dx.doi.org/10.3390/electronics8080832

Coulombe, P.G., Leroux, M., Stevanovic, D., & Surprenant, S. (2022). How is machine learning useful for macroeconomic forecasting? Journal of Applied Econometrics, 37(5), 920-964. https://doi.org/10.1002/jae.2910

Edwards, E.O., & Bell, P.W. (1965). The theory and measurement of business income. Berkeley, Los Angeles: University of California Press.

Farrell, M.H., Liang, T., & Misra, S. (2021). Deep neural networks for estimation and inference. Econometrica, 89(1), 181-213. http://dx.doi.org/10.3982/ECTA16901

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. http://dx.doi.org/10.1093/rfs/hhaa009

Hindman, M. (2015). Building better models: Prediction, replication, and machine learning in the social sciences. The Annals of the American Academy of Political and Social Science, 659(1), 48-62. http://dx.doi.org/10.1177/0002716215570279

Jacobsen, J.P., Levin, L.M., Tausanovitch, Z. (2016). Comparing standard regression modeling to ensemble modeling: How data mining software can improve economists’ predictions. Eastern Economic Journal, 42(3), 387-398. http://dx.doi.org/10.1057/eej.2015.8

Jarque, C.M., & Bera, A.K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255-259. https://doi.org/10.1016/0165-1765%2880%2990024-5

Jung, J.-K., Patnam, M., & Ter-Martirosyan, A. (2018). An algorithmic crystal ball: Forecasts-based on machine learning. International Monetary Fund Working Papers 18(230). http://dx.doi.org/10.5089/9781484380635.001

Kovalev, V.V., & Koklev, P.S. (2022). Nedostatki dokhodnogo podkhoda dlia otsenki biznesa ["Disadvantages of the income approach to business valuation]. Economic Sciences, 9(214), 49-54. https://doi.org/10.14451/1.214.49

Ksenofontova, T.Y., Bezdudnaya, A.G., & Kadyrova, O.V. (2017). Basic problems of interregional differentiation in Russia and innovative and reproduction prerequisites to overcome them. International Journal of Applied Business and Economic Research, 15(8), 1-10.

Lockhart, R., Taylor, J., Tibshirani, R.J., & Tibshirani, R. (2014). A significance test for the lasso. Annals of Statistics, 42(2), 413-468. http://dx.doi.org/10.1214/13-AOS1175

Lommers, K., El Harzli, O., & Kim, J. (2021). Confronting machine learning with financial research. The Journal of Financial Data Science, 3(3), 67-96. http://dx.doi.org/10.3905/jfds.2021.1.068

Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106. http://dx.doi.org/10.1257/jep.31.2.87

Munkhdalai, L., Munkhdalai, T., Namsrai, O.-E., Lee, J.Y., & Ryu, K.H. (2019). An empirical comparison of machine-learning methods on bank client credit assessments. Sustainability, 11(3), 699. http://dx.doi.org/10.3390/su11030699

Ohlson, J.A. (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

Park, T., & Casella, G. (2008). The Bayesian lasso. Journal of the American Statistical Association, 103(482), 681-686. http://dx.doi.org/10.1198/016214508000000337

de Prado, M.L. (2017). Finance as an industrial science. Journal of Portfolio Management, 43(4), 1-7.

de Prado, M.L. (2018a). The 10 reasons most machine learning funds fail. The Journal of Portfolio Management, 44(6), 120-133. http://dx.doi.org/10.3905/jpm.2018.44.6.120

de Prado, M.L. (2018b). The myth and reality of financial machine learning (presentation slides). SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.3120557

de Prado, M.L. (2018c). Ten financial applications of machine learning (seminar slides). SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.3197726

de Prado, M.L. (2020a). Machine learning for asset managers. Cambridge: Cambridge University Press.

de Prado, M.L. (2020b). Three machine learning solutions to the bias-variance dilemma (seminar slides). SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.3588594

Sala-i-Martin, X.X. (1997). I just ran four million regressions. American Economic Review, 87(2), 178-183.

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288. https://doi.org/10.1111/J.2517-6161.1996.TB02080.X

Tikhonov, A.N. (1963). O reshenii nekorrektno postavlennykh zadach i metode reguliarizatsii ["On solving incorrectly set problems and the regularization method]. Proceedings of the USSR Academy of Sciences, 151(3), 501-504.

Varian, H.R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28. http://dx.doi.org/10.1257/jep.28.2.3

Volkov, D.L. (2004). Upravlenie stoimostiu kompanii: Problema vybora adekvatnoi modeli otsenki ["Managing the value of the company: The problem of choosing an adequate valuation model]. Vestnik of Saint Petersburg University. Management, 4(32), 79-98.

Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228-1242. http://dx.doi.org/10.1080/01621459.2017.1319839




DOI: http://dx.doi.org/10.21902/Revrima.v6i39.6267

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