Проблема эндогенности в корпоративных финансах: теория и практика

Эндогенность может вызывать значительное смещение оценок коэффициентов, вплоть до изменения знака влияния. Это приводит к противоречивым результатам в исследованиях, что также мешает адекватно проверять отдельные гипотезы и теории в корпоративных финансах (КФ). А практикам, например, консультантам по оценке стоимости компании, такие проблемы с моделями мешают получать максимально достоверные оценки в интересах заказчика. Цель исследования — систематизировать методы борьбы с эндогенностью в КФ и проиллюстрировать подход борьбы с нею. В работе приведены причины возникновения эндогенности с эконометрической точки зрения, с примерами из КФ, а также эконометрические методы борьбы с ней. В результате системного обзора литературы авторы показали, что в исследованиях, связанных с КФ, для борьбы с эндогенностью чаще всего используют динамические модели оценки панельных данных, в частности методом Бланделла-Бонда. Заключение, сделанное в рамках обзора литературы, авторы проверили эмпирически. Для обнаружения эндогенности использован тест Хаусмана, тест на эндогенность и анализ корреляционной матрицы, включающей сохраненные остатки регрессии. В ходе пошагового нивелирования эндогенности авторы пришли к выводу, что метод Бланделла-Бонда не всегда является оптимальным инструментом для борьбы с эндогенностью в КФ, как и регрессия с фиксированным эффектом. В ходе оценки модели стоимости капитала и устранения эндогенности наиболее подходящим оказался двухшаговый метод наименьших квадратов (IV 2SLS). Кроме этого, были усовершенствованы оценки модели стоимости капитала, анализирующей влияние нефинансовой отчетности.

1. Tucker G. M. On problems of corporate finance. The American Journal of Economics and Sociology. 1948;7(2):235–236. DOI: 10.1111/J.1536–7150.1948.TB 00679.x

2. Wintoki M.B., Linck J.S., Netter J.M. Endogeneity and the dynamics of internal corporate governance. Journal of Financial Economics. 2012;105(3):581–606. DOI: 10.1016/j.jfineco.2012.03.005

3. Gippel J., Smith T., Zhu Y. Endogeneity in accounting and finance research: Natural experiments as a stateof-the-art solution. Abacus. 2015;51(2):143–168. DOI: 10.1111/abac.12048

4. Barros L.A.B.C., Bergmann D. R., Henrique Castro F., da Silveira A. D.M. Endogeneity in panel data regressions: Methodological guidance for corporate finance researchers. Revista Brasileira de Gestao de Negocios. 2020;22:437–461. DOI: 10.7819/rbgn.v22i0.4059

5. Flannery M.J., Hankins K.W. Estimating dynamic panel models in corporate finance. Journal of Corporate Finance. 2013;19:1–19. DOI: 10.1016/j.jcorpfin.2012.09.004

6. Banik A., Chatterjee C. Ownership pattern and governance-performance relation: Evidence from an emerging economy. Global Business Review. 2021;22(2):422–441. DOI: 10.1177/0972150920966699

7. Molina C.A. Are firms underleveraged? An examination of the effect of leverage on default probabilities. The Journal of Finance. 2005;60(3):1427–1459. DOI: 10.1111/j.1540–6261.2005.00766.x

8. Chen C.-W., Lin J.B., Yi B. CEO duality and firm performance: An endogenous issue. Corporate Ownership and Control. 2008;6(1):58–65. DOI: 10.22495/cocv6i1p6

9. Poletti Hughes J. R&D and dividend payments as determinants of corporate value in the UK: Empirical evidence after controlling for endogeneity. International Journal of Managerial Finance. 2008;4(1):76–91. DOI: 10.1108/17439130810837393

10. Zhou T., Li W.-A. Board governance and managerial risk taking: Dynamic analysis. The Chinese Economy. 2016;49(2):60–80. DOI: 10.1080/10971475.2016.1142823

11. Malik Q.A., Hussain S., Ullah N., Waheed A., Naeem M., Mansoor M. Simultaneous equations and endogeneity in corporate finance: The linkage between institutional ownership and corporate financial performance. The Journal of Asian Finance, Economics and Business. 2021;8(3):69–77. DOI: 10.13106/ jafeb.2021.vol8.no3.0069

12. Harada K., Nguyen P. Ownership concentration and dividend policy in Japan. Managerial Finance. 2011;37(4):362–379. DOI: 10.1108/03074351111115313

13. Жукова Н.Ю., Меликова А.Э. Социальная ответственность бизнеса: усиление стоимости бренда и влияние на финансовые показатели компании. Финансы: теория и практика. 2021;25(1):84–102. DOI: https://doi.org/10.26794/2587–5671–2021–25–1–84–102

14. Мартынова М. Раскрытие информации о климате как фактор инвестиционной привлекательности российских компании. Московский Экономический Журнал. 2021;(5):365–379. DOI: 10.24411/2413– 046Х 2021–10277

15. Поляков К.Л., Полякова М.В., Самойленко С.В. Моделирование влияния долговой нагрузки на эффективность деятельности субъектов предпринимательства. Вопросы статистики. 2016;(9):17–29. DOI: 10.34023/2313–6383–2016–0–9–17–29

16. Zahid M., Rahman H.U., Khan M., Ali W., Shad F. Addressing endogeneity by proposing novel instrumental variables in the nexus of sustainability reporting and firm financial performance: A step-by-step procedure for non-experts. Business Strategy and the Environment. 2020;29(8):3086–3103. DOI: 10.1002/bse.2559

17. Cheng Q., Lo K. Insider trading and voluntary disclosures. Journal of Accounting Research. 2006;44(5):815–848. DOI: 10.1111/j.1475–679X.2006.00222.x

18. Gul F.A., Hutchinson M., Lai K.M.Y. Gender-diverse boards and properties of analyst earnings forecasts. Accounting Horizons. 2013;27(3):511–538. DOI: 10.2308/acch 50486

19. Kim C.-J., Nelson C.R. Estimation of a forward-looking monetary policy rule: A time-varying parameter model using ex post data. Journal of Monetary Economics. 2006;53(8):1949–1966. DOI: 10.1016/j. jmoneco.2005.10.017

20. Coles J.L., Lemmon M.L., Meschke J.F. Structural models and endogeneity in corporate finance: The link between managerial ownership and corporate performance. Journal of Financial Economics. 2012;103(1):149– 168. DOI: 10.1016/j.jfineco.2011.04.002

21. Li F. Endogeneity in CEO power: A survey and experiment. Investment Analysts Journal. 2016;45(3):149–162. DOI: 10.1080/10293523.2016.1151985

22. Grieser W.D., Hadlock C.J. Panel-data estimation in finance: Testable assumptions and parameter (in) consistency. Journal of Financial and Quantitative Analysis. 2019;54(1):1–29. DOI: 10.1017/S 0022109018000996

23. Киршин И.А. Эмпирический анализ детерминант структуры капитала фирмы. Финансы: теория и практика. 2017;21(2):106–112. DOI: 10.26794/2587–5671–2017–21–2–106–112

24. Mohammad W.M.W., Wasiuzzaman S. Environmental, social and governance (ESG) disclosure, competitive advantage and performance of firms in Malaysia. Cleaner Environmental Systems. 2021;2:100015. DOI: 10.1016/J.CESYS.2021.100015

25. Alareeni B.A., Hamdan A. ESG impact on performance of US S&P 500-listed firms. Corporate Governance. 2020;20(7):1409–1428. DOI: 10.1108/CG 06–2020–0258

26. Dang V.A., Kim M., Shin Y. In search of robust methods for dynamic panel data models in empirical corporate finance. Journal of Banking & Finance. 2015;53:84–98. DOI: 10.1016/j.jbankfin.2014.12.009

27. Ullah S., Akhtar P., Zaefarian G. Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. Industrial Marketing Management. 2018;71:69–78. DOI: 10.1016/j.indmarman.2017.11.010

28. Tarchouna A., Jarraya B., Bouri A. How to explain non-performing loans by many corporate governance variables simultaneously? A corporate governance index is built to US commercial banks. Research in International Business and Finance. 2017;42:645–657. DOI: 10.1016/j.ribaf.2017.07.008

29. Boneva L., Linton O. A discrete-choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance. Journal of Applied Econometrics. 2017;32(7):1226–1243. DOI: 10.1002/jae.2568

30. Kremer S., Bick A., Nautz D. Inflation and growth: New evidence from a dynamic panel threshold analysis. Empirical Economics. 2013;44(2):861–878. DOI: 10.1007/s00181–012–0553–9

31. Ahn S.C., Lee Y.H., Schmidt P. Panel data models with multiple time-varying individual effects. Journal of Econometrics. 2013;174(1):1–14. DOI: 10.1016/j.jeconom.2012.12.002

32. Chudik A., Pesaran M.H. Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. Journal of Econometrics. 2015;188(2):393–420. DOI: 10.1016/j. jeconom.2015.03.007

33. Pesaran M.H., Zhou Q. Estimation of time-invariant effects in static panel data models. Econometric Reviews. 2018;37(10):1137–1171. DOI: 10.1080/07474938.2016.1222225

34. Su L., Yang Z. QML estimation of dynamic panel data models with spatial errors. Journal of Econometrics. 2015;185(1):230–258. DOI: 10.1016/j.jeconom.2014.11.002

35. Campbell R.C., Nagel G.L. Private information and limitations of Heckman’s estimator in banking and corporate finance research. Journal of Empirical Finance. 2016;37:186–195. DOI: 10.1016/j.jempfin.2016.03.007

36. Hausman J.A. Specification tests in econometrics. Econometrica. 1978;46(6):1251–1271. DOI: 10.2307/1913827

37. Wooldridge J. M. Selection corrections for panel data models under conditional mean independence assumptions. Journal of Econometrics. 1995;68(1):115–132. DOI: 10.1016/0304–4076(94)01645-G

38. Zhang Y., Qin G., Zhu Z., Zhang J. Robust estimation in linear regression models for longitudinal data with covariate measurement errors and outliers. Journal of Multivariate Analysis. 2018;168:261–275. DOI: 10.1016/j. jmva.2018.07.015

39. Qin G., Zhang J., Zhu Z. Simultaneous mean and covariance estimation of partially linear models for longitudinal data with missing responses and covariate measurement error. Computational Statistics & Data Analysis. 2016;96:24–39. DOI: 10.1016/j.csda.2015.11.001

40. Chen H.-Y., Lee A.C., Lee C.-F. Alternative errors-in-variables models and their applications in finance research. Quarterly Review of Economics and Finance. 2015;58:213–227. DOI: 10.1016/j.qref.2014.12.002

41. Wall T.D., Michie J., Patterson M., Wood S.J., Sheehan M., Clegg C.W., West M. On the validity of subjective measures of company performance. Personnel Psychology. 2004;57(1):95–118. DOI: 10.1111/j.1744–6570.2004. tb02485.x

42. Podsakoff P.M., MacKenzie S.B., Lee J.-Y., Podsakoff N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology. 2003;88(5):879– 903. DOI: 10.1037/0021–9010.88.5.879

43. Tehseen S., Ramayah T., Sajilan S. Testing and controlling for common method variance: A review of available methods. Journal of Management Sciences. 2017;4(2):146–175. DOI: 10.20547/jms.2014.1704202

44. Carrasco M., Doukali M. Efficient estimation using regularized jackknife IV estimator. Annals of Economics and Statistics. 2017;(128):109–149. DOI: 10.15609/annaeconstat2009.128.0109

45. Schaffer M.E. XTIVREG2: Stata module to perform extended IV/2SLS, GMM and AC/HAC, LIML and k-class regression for panel data models. 2020.

46. Qian H., Schmidt P. The asymptotic equivalence between the iterated improved 2sls estimator and the 3sls estimator. Econometric Reviews. 1997;16(4):441–457. DOI: 10.1080/07474939708800398

47. Reed W.R. On the practice of lagging variables to avoid simultaneity. Oxford Bulletin of Economics and Statistics. 2015;77(6):897–905. DOI: 10.1111/obes.12088

48. Roodman D.M. How to do Xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal. 2009;9(1):86–136. DOI: 10.1177/1536867X0900900106

49. Hahn J., Hausman J., Kuersteiner G. Long difference instrumental variables estimation for dynamic panel models with fixed effects. Journal of Econometrics. 2007;140(2):574–617. DOI: 10.1016/j.jeconom.2006.07.005

50. Huang R., Ritter J.R. Testing theories of capital structure and estimating the speed of adjustment. Journal of Financial and Quantitative Analysis. 2009;44(2):237–271. DOI: 10.1017/S 0022109009090152

51. Bruno G.S.F. Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models. Economics Letters. 2005;87(3):361–366. DOI: 10.1016/j.econlet.2005.01.005

52. Ezeani E., Salem R., Kwabi F., Boutaine K., Bilal, Komal B. Board monitoring and capital structure dynamics: evidence from bank-based economies. Review of Quantitative Finance and Accounting. 2022;58(2):473–498. DOI: 10.1007/S 11156–021–01000–4

53. Lartey T., Danso A., Boateng A. Co-opted boards and capital structure dynamics. International Review of Financial Analysis. 2021;77:101824. DOI: 10.1016/J.IRFA.2021.101824

54. Badayi S.A., Matemilola B.T., Bany-Ariffin A.N., Theng L.W. Does corporate social responsibility influence firm probability of default? International Journal of Finance & Economics. 2021;26(3):3377–3395. DOI: 10.1002/IJFE.1966 З.В.

55. Schadewitz H., Niskala M. Communication via responsibility reporting and its effect on firm value in Finland. Corporate Social Responsibility and Environmental Management. 2010;17(2):96–106. DOI: 10.1002/csr.234

56. de Klerk M., de Villiers C. The value relevance of corporate responsibility reporting: South African evidence. Meditari Accountancy Research. 2012;20(1):21–38. DOI: 10.1108/10222521211234200

57. Iatridis G.E. Environmental disclosure quality: Evidence on environmental performance, corporate governance and value relevance. Emerging Markets Review. 2013;14:55–75. DOI: 10.1016/j.ememar.2012.11.003

58. Fatemi A., Fooladi I., Tehranian H. Valuation effects of corporate social responsibility. Journal of Banking & Finance. 2015;59:182–192. DOI: 10.1016/j.jbankfin.2015.04.028

59. Oshika T., Saka C. Sustainability KPIs for integrated reporting. Social Responsibility Journal. 2017;13(3):625– 642. DOI: 10.1108/SRJ 07–2016–0122

60. de Villiers C., Venter E.R., Hsiao P.-C.K. Integrated reporting: background, measurement issues, approaches and an agenda for future research. Accounting & Finance. 2017;57(4):937–959. DOI: 10.1111/acfi.12246

61. García-Sánchez I.-M., Noguera-Gámez L. Integrated information and the cost of capital. International Business Review. 2017;26(5):959–975. DOI: 10.1016/j.ibusrev.2017.03.004

62. Albuquerque R., Koskinen Y., Zhang C. Corporate social responsibility and firm risk: Theory and empirical evidence. Management Science. 2019;65(10):4451–4469. DOI: 10.1287/mnsc.2018.3043

63. Bhuiyan M.B.U., Nguyen T.H.N. Impact of CSR on cost of debt and cost of capital: Australian evidence. Social Responsibility Journal. 2020;16(3):419–430. DOI: 10.1108/SRJ 08–2018–0208

64. Yeh C.-C., Lin F., Wang T.-S., Wu C.-M. Does corporate social responsibility affect cost of capital in China? Asia Pacific Management Review. 2020;25(1):1–12. DOI: 10.1016/j.apmrv.2019.04.001

65. Manchiraju H., Rajgopal S. Does corporate social responsibility (CSR) create shareholder value? Evidence from the Indian Companies Act 2013. Journal of Accounting Research. 2017;55(5):1257–1300. DOI: 10.1111/1475–679X.12174

66. Dhaliwal D.S., Li O.Z., Tsang A., Yang Y.G. Voluntary nonfinancial disclosure and the cost of equity capital: The initiation of corporate social responsibility reporting. The Accounting Review. 2011;86(1):59–100. DOI: 10.2308/accr.00000005

67. El Ghoul S., Guedhami O., Kwok C.C.Y., Mishra D.R. Does corporate social responsibility affect the cost of capital? Journal of Banking & Finance. 2011;35(9):2388–2406. DOI: 10.1016/j.jbankfin.2011.02.007

68. Boujelbene M.A., Affes H. The impact of intellectual capital disclosure on cost of equity capital: A case of French firms. Journal of Economics, Finance and Administrative Science. 2013;18(34):45–53. DOI: 10.1016/S 2077–1886(13)70022–2

69. Zhou S., Simnett R., Green W. Does integrated reporting matter to the capital market? Abacus. 2017;53(1):94– 132. DOI: 10.1111/abac.12104

70. Fama E. F., French K.R. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 1993;33(1):3–56. DOI: 10.1016/0304–405X(93)90023–5

71. Fama E.F., French K.R. The cross-section of expected stock returns. The Journal of Finance. 1992;47(2):427– 465. DOI: 10.1111/j.1540–6261.1992.tb04398.x

72. Evdokimova M.S., Kuzubov S.A. Non-financial reporting and the cost of capital in BRICS countries. Higher School of Economics. Basic Research Program Working Paper. 2021;(83/FE). URL: https://wp.hse.ru/data/202 1/06/25/1430000555/83FE 2021.pdf

73. Breush T.S., Pagan A.R. A simple test for heteroscedasticity and random coefficient variation. Econometrica. 1979;47(5):1287–1294. DOI: 10.2307/1911963

74. Park H.M. Practical guides to panel data modeling: A step by step analysis using stata. Niigata: International University of Japan; 2011. 53 p. URL: https://www.iuj.ac.jp/faculty/kucc625/method/panel/panel_iuj.pdf

75. Drukker D.M. Testing for serial correlation in linear panel-data models. The Stata Journal. 2003;3(2):168–177. DOI: 10.1177/1536867X0300300206

76. Baum C.F., Schaffer M.E., Stillman S. Enhanced routines for instrumental variables/generalized method of moments estimation and testing. The Stata Journal. 2007;7(4):465–506. DOI: 10.1177/1536867X0800700402

77. Stock J.H., Yogo M. Testing for weak instruments in linear IV regression. In: Andrews D.W.K., Stock J.H., eds. Identification and inference for econometric models: Essays in honor of Thomas Rothenberg. Cambridge, New York: Cambridge University Press; 2005:80–108. DOI: 10.1017/CBO9780511614491.006