Financial reporting quality and its determinants: A machine learning approach

Abstract

The high-quality of financial reporting provides suitable information for economic decision-making of the country whilst, the low quality of financial reporting causes a serious impact on the economy. This research aims to classify financial reporting quality (FRQ) as well as determines the drivers of FRQ. This study uses a panel dataset from 2014 to 2020 that is collected from the Vietnamese listed companies. The study applies machine learning algorithms to classify and assess FRQ of non-financial companies on the Vietnamese stock exchange. New contribution considers the FRQ, on the auditor's opinion and the variance between pre-audit and post-audit profit. This research classifies FRQ into normal and poor categories, and a rate of 9.35% in the sample is considered poor FRQ. This research shows that the return on assets’ ratio and the ownership concentration have the most important influence on FRQ. Furthermore, the results which are predicting FRQ by using the random forest algorithm have an accuracy rate of 94%. This study is valuable for the forecast of FRQ and for the support of stakeholders in decision-making. With the high accuracy of machine learning techniques and its usage, it can help analysts and investors in generating reliable accounting information for decision-making purposes. Corporate sector needs to pay attention towards financial ratios and reinforcement of corporate governance.