A new perspective of performance comparison among machine learning algorithms for financial distress prediction

Huang, Yu-Pei; Yen, Meng Feng

doi:10.1016/j.asoc.2019.105663

Cited by 99 publications

(41 citation statements)

References 48 publications

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“…The composition of indebtedness was also relevant in forecasting FD as deals with the fundraising policy within organizations, and reveals the strategy of the companies in relation to their short-term obligations. Sun et al (2011) and Huang & Yen (2019) also included this variable in their models for forecasting bankruptcy and FD.…”

Section: Resultsmentioning

confidence: 99%

“…The authors concluded that bagging, boosting, and random forests outperformed techniques such as Logit, Discriminant Analysis, and Support Vector Machine. Huang & Yen (2019) made a comparison between six machine learning models in the context of forecasting financial distress, using data from financial statements of Taiwanese companies from 2010 to 2016. The authors demonstrated that among the supervised algorithms, the XGBoost provided the most accurate forecast.…”

Section: Related Literaturementioning

confidence: 99%

“…Therefore, they examined the XGBoost technique, since it is a recent machine learning method used for supervised learning problems (Chen & Guestrin, 2016), considered an improvement and based on the Friedman model 2001. Yet, this method represents an advance in the use of new techniques to predict bankruptcies, as made by Zięba et al (2016) and FD (Huang & Yen, 2019), which are potentially indicative scenarios that your performance is successful in the context of our research, which is to predict financial distress for Latin American companies.…”

Section: Introductionmentioning

confidence: 99%

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Anticipating corporate’s distresses

Barboza

Duarte

Cunha

2022

Exacta

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Master's degree in Business Administration from the Faculty of Business Graduated in Business Administration and Accounting from the University Center of Patos de Minas (UNIPAM), with a specialization in Business Strategy Management and an MBA in People Management. He is currently a Master's student in Administration at the Faculty of Management and Business (FAGEN) of the Federal University of Uberlândia (UFU). Acts as a substitute professor at the Faculty of Management and Business at the Federal University of Uberlândia, teaching classes at the Undergraduate level. His research interests involve: Third Sector, Organizational Behavior, Consumer Behavior, Business Consulting and Risk Management.

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Section: Resultsmentioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Anticipating corporate’s distresses

Barboza

Duarte

Cunha

2022

Exacta

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“…Machine learning algorithms have been widely used in financial applications, such as risk modelling, return forecasting, and portfolio construction (Emerson et al 2019), quantitative finance (Rundo et al 2019), financial distress prediction (Huang and Yen 2019), banking risk management (Leo et al 2019), credit-scoring models and financial crisis prediction (Lin et al 2011), automation through artificial intelligence (Donepudi 2019), market prediction (Henrique et al 2019), and credit risk modeling, detection of credit card fraud and money laundering, and surveillance of conduct breaches at financial institutions (Van Liebergen 2017). Popular algorithms used in these applications are support vector machines (Kim 2003), neural networks (West et al 2005), and random forests (Patel et al 2015).…”

Section: Machine Learning Algorithms In Financementioning

confidence: 99%

Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions

Thompson

Feng

Reesor

et al. 2021

JRFM

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In Canada, financial advisors and dealers are required by provincial securities commissions and self-regulatory organizations—charged with direct regulation over investment dealers and mutual fund dealers—to respectively collect and maintain know your client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor’s guidance, make decisions on their investments that are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients covering the period from January 1st to August 12th 2019. We use a modified behavioral finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and unsupervised machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information—such as gender, residence region, and marital status—does not explain client behaviours, whereas eight variables for trade and transaction frequency and volume are most informative. Hence, our results should encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours.

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“…Machine learning is the use of most data (typically 50% to 80%) for training before making predictions, which enhances prediction accuracy and reduces judgment errors [19][20][21][22]. Machine learning techniques are also very suitable for financial distress prediction [8,11,13,17,[23][24][25]. This study adopts multiple machine learning techniques, first, by selecting key variables with the least absolute shrinkage and selection operator (LASSO) and stepwise regression (SR).…”

Section: Introductionmentioning

confidence: 99%

Financial Distress Prediction Using Hybrid Machine Learning Techniques

Chen

Shen

2020

AJEBA

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The purpose of this study is to establish an effective financial distress prediction model by applying hybrid machine learning techniques. The sample set is 262 financially distressed companies and 786 non-financially distressed companies, listed on the Taiwan Stock Exchange between 2012 and 2018. This study deploys multiple machine learning techniques. The first step is to screen out important variables with stepwise regression (SR) and the least absolute shrinkage and selection operator (LASSO), followed by the construction of prediction models, as based on classification and regression trees (CART) and random forests (RF). Both financial variables and non-financial variables are incorporated. This study finds that the financial distress prediction model built with CART and variables screened by LASSO has the highest accuracy of 89.74%.

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A new perspective of performance comparison among machine learning algorithms for financial distress prediction

Cited by 99 publications

References 48 publications

Anticipating corporate’s distresses

Anticipating corporate’s distresses

Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions

Financial Distress Prediction Using Hybrid Machine Learning Techniques

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