Recovering Missing Firm Characteristics with Attention-Based Machine Learning

PurposeThis paper aims to identify the disproportionate impacts of the COVID-19 pandemic on labor markets.Design/methodology/approachThe authors conduct a large-scale survey on 16,000 firms from 82 industries in Ho Chi Minh City, Vietnam, and analyze the data set by using different machine-learning methods.FindingsFirst, job loss and reduction in state-owned enterprises have been significantly larger than in other types of organizations. Second, employees of foreign direct investment enterprises suffer a significantly lower labor income than those of other groups. Third, the adverse effects of the COVID-19 pandemic on the labor market are heterogeneous across industries and geographies. Finally, firms with high revenue in 2019 are more likely to adopt preventive measures, including the reduction of labor forces. The authors also find a significant correlation between firms' revenue and labor reduction as traditional econometrics and machine-learning techniques suggest.Originality/valueThis study has two main policy implications. First, although government support through taxes has been provided, the authors highlight evidence that there may be some additional benefit from targeting firms that have characteristics associated with layoffs or other negative labor responses. Second, the authors provide information that shows which firm characteristics are associated with particular labor market responses such as layoffs, which may help target stimulus packages. Although the COVID-19 pandemic affects most industries and occupations, heterogeneous firm responses suggest that there could be several varieties of targeted policies-targeting firms that are likely to reduce labor forces or firms likely to face reduced revenue. In this paper, the authors outline several industries and firm characteristics which appear to more directly be reducing employee counts or having negative labor responses which may lead to more cost–effect stimulus.

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Auditor Choice Prediction Model Using Corporate Governance and Ownership Attributes: Machine Learning Approach

International Journal of Emerging Technology and Advanced Engineering ◽

10.46338/ijetae0721_11 ◽

2021 ◽

Vol 11 (7) ◽

pp. 87-94

Author(s):

Rahayu Abdul Rahman ◽

◽

Suraya Masrom ◽

Nor Balkish Zakaria ◽

Sunarti Halid

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Financial Reporting ◽

Learning Algorithm ◽

Stock Exchange ◽

Features Selection ◽

Firm Characteristics ◽

Auditor Choice ◽

Machine Learning Model ◽

Bayes Algorithm

-External auditor is one of the governance mechanisms in mitigating corporate managerial misconduct and thereby enhance the credibility of accounting information. Thus, the main objective of this study is to develop machine learning prediction model on auditor choice of the firm which signal the quality of auditing and financial reporting processes.This paper presents the fundamental knowledge on the design and implementation of machine learning model based on four selected algorithms tested on the real dataset of 2,262 firm-year observations of companies listed on Malaysian stock exchange from 2000 to 2007. The performance of each machine learning algorithm on the auditor choice dataset has been observed based on three groups of features selection namely firm characteristics, governance and ownership. The findings indicated that the machine learning models present better accuracy performance with ownership features selection mainly with the Naïve Bayes algorithm. Keywords-Auditor Choice, Machine Learning, Prediction

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An application of machine learning on corporate tax avoidance detection model

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i4.pp721-725 ◽

2020 ◽

Vol 9 (4) ◽

pp. 721

Author(s):

Rahayu Abdul Rahman ◽

Suraya Masrom ◽

Normah Omar ◽

Maheran Zakaria

Keyword(s):

Machine Learning ◽

Cross Validation ◽

Tax Avoidance ◽

Machine Learning Algorithms ◽

Accuracy Score ◽

Features Selection ◽

Firm Characteristics ◽

Corporate Tax ◽

Training Approach ◽

The Cross

Corporate tax avoidance reduces government revenues which could limit country development plans. Thus, the main objectives of this study is to establish a rigorous and effective model to detect corporate tax avoidance to assist government to prevent such practice. This paper presents the fundamental knowledge on the design and implementation of machine learning model based on five selected algorithms tested on the real dataset of 3,365 Malaysian companies listed on bursa Malaysia from 2005 to 2015. The performance of each machine learning algorithms on the tested dataset has been observed based on two approaches of training. The accuracy score for each algorithm is better with the cross-validation training approach. Additionationally, with the cross-validation training approach, the performances of each machine learning algorithm were tested on different group of features selection namely industry, governance, year and firm characteristics. The findings indicated that the machine learning models present better reliability with industry, governance and firm characteristics features rather than single year determinant mainly with the Random Forest and Logistic Regression algorithms.

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Auditor Choice Prediction Model Using Corporate Governance and Ownership Attributes: Machine Learning Approach

International Journal of Emerging Technology and Advanced Engineering ◽

10.46338/ijetae0721_01 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1-7

Author(s):

Rahayu Abdul Rahman ◽

◽

Suraya Masrom ◽

Nor Balkish Zakaria ◽

Sunarti Halid

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Financial Reporting ◽

Learning Algorithm ◽

Stock Exchange ◽

Features Selection ◽

Firm Characteristics ◽

Auditor Choice ◽

Machine Learning Model ◽

Bayes Algorithm

t-External auditor is one of the governance mechanisms in mitigating corporate managerial misconduct and thereby enhance the credibility of accounting information. Thus, the main objective of this study is to develop machine learning prediction model on auditor choice of the firm which signal the quality of auditing and financial reporting processes.This paper presents the fundamental knowledge on the design and implementation of machine learning model based on four selected algorithms tested on the real dataset of 2,262 firm-year observations of companies listed on Malaysian stock exchange from 2000 to 2007. The performance of each machine learning algorithm on the auditor choice dataset has been observed based on three groups of features selection namely firm characteristics, governance and ownership. The findings indicated that the machine learning models present better accuracy performance with ownership features selection mainly with the Naïve Bayes algorithm. Keywords-Auditor Choice, Machine Learning, Prediction, Malaysia

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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