Predicting the Relation Between Audit Quality and Share Return Using Artificial Neural Networks

Navid Reza Namazi, Mohsen Rashidi, Hayder Fadhil Kareem Almasoodi

Abstract

Agency problems arise due to conflicts of interest between managers and shareholders. Auditing is considered an effective solution to limit managers’ power in contractual relationships. This study aims to predict the effect of audit quality on stock return changes. For this purpose, data on companies listed on the Tehran Stock Exchange for the period 2006-2023 were extracted, and a nonlinear prediction approach using artificial neural networks was used to predict stock returns. The outputs obtained from the estimation of the artificial neural networks and the results obtained from the estimation using this method with evaluation scales are (MAE = 0.017, MSE = 0.075, and R=0.986). Concerning the random amount and comparing it with R = 0.986, we find a meaningful relationship between audit quality and share returns.
However, such a network had the least error compared to the other networks (MAE=0.027, MSE = 0.004). In this study, using a nonlinear approach, an attempt was made to consider issues related to collinearity between variables and latent factors.

 

Keywords: audit quality, prediction, artificial neural networks, stock return.

 

https://doi.org/10.55463/issn.1674-2974.52.1.7


Full Text:

PDF


References


KWON S. Y., LIM Y., & SIMNETT R. The effect of mandatory audit firm rotation on audit quality and audit fees: Empirical evidence from the Korean audit market. Auditing: A Journal of Practice & Theory, 2014, 33(4): 167–196. https://doi.org/10.2308/ajpt-50814

HAY D., and KNECHEL W. R. The effects of advertising and solicitation on audit fees. Journal of Accounting and Public Policy, 2010, 29(1): 60-81. https://doi.org/10.1016/j.jaccpubpol.2009.10.001

EUROPEAN COMMISSION. Green paper: Audit policy: Lessons from the crisis. Brussels: European Commission, 2010.

VAEZ S. A., RAMZAN AHMADI M., & RASHIDI BAGHI M. Impact of audit quality on audit fees of listed companies. Financial Accounting Science, 2013, 3(1), 92-114. (In Persian).

NIKBAKHT M. & TANANI M. Examination of factors affecting audit fees of financial statements. Financial Accounting Research, 2010, 2(4): 111-132. (in Persian)

BALKIN S. D., & ORD J. K. Automatic neural network modelling for univariate time series. International Journal of Forecasting, 2000, 16(4): 509-515. https://doi.org/10.1016/S0169-2070(00)00072-8

DARBELLAY G. A., & SLAMA M. Forecasting the short-term demand for electricity: Do neural networks stand a better chance? International Journal of Forecasting, 2000, 16(1): 71–83. https://doi.org/10.1016/S0169-2070(99)00045-X

TACZ G. Neural network forecasting of Canadian GDP growth. International Journal of Forecasting, 2001, 17(1): 57–69. https://doi.org/10.1016/S0169-2070(00)00063-7

BROOKS C. Linear and non-linear (non-) forecastability of high frequency exchange rates. Journal of Forecasting, 1997, 16(2): 125–145. https://doi.org/10.1002/(SICI)1099-131X(199703)16:2%3C125::AID-FOR648%3E3.0.CO;2-T

QI, M. Predicting US recessions with leading indicators via neural network models. International Journal of Forecasting, 2001, 17(3): 383–401. https://doi.org/10.1016/S0169-2070(01)00092-9

DEANGELO L. Auditor size and auditor quality. Journal of Accounting and Economics, 1981, 3(3): 183-199. https://doi.org/10.1016/0165-4101(81)90002-1

CATANACH A. H. Jr., and WALKER P. L. The international debate over mandatory auditor rotation: a conceptual research framework. Journal of International Accounting, Auditing & Taxation, 1999, 8(1): 43-66. https://doi.org/10.1016/S1061-9518(99)00004-X


Refbacks

  • There are currently no refbacks.