Bankruptcy prediction using imaged financial ratios and convolutional neural networks

Hosaka, Tadaaki

doi:10.1016/j.eswa.2018.09.039

Cited by 217 publications

(134 citation statements)

References 17 publications

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“…The convolutional neural networks approach uses two methods of the balance sheet and the profit and loss account to test for bankruptcy. Hosaka (2019) established that predicting bankruptcy through trained networks is shown to have higher performance as compared with decision trees, intelligent machines, and linear discriminant analysis, which was according to a study they conducted in the Japanese Stock Markets using 102 delisted companies and 2062 financial statements of listed companies.…”

Section: Earnings Management and Bankruptcy Predictionmentioning

confidence: 99%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

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Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy.

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Section: Earnings Management and Bankruptcy Predictionmentioning

confidence: 99%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

View full text Add to dashboard Cite

show abstract

“…Among deep learning methods, RNN is a powerful method for processing time series data. It is widely used in many fields such as finance [31,32], industry and engineering [33], machine translation [34], speech recognition [35], economic prediction [36], and so on. As shown in Figure 2, RNN has certain information persistence capabilities, which enable information to be passed from one time-step to the next.…”

Section: Rnn and Grumentioning

confidence: 99%

Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks

Lin

Sun

2020

Energies

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Accurate prediction of crude oil prices is meaningful for reducing firm risks, stabilizing commodity prices and maintaining national financial security. Wrong crude oil price forecasts can bring huge losses to governments, enterprises, investors and even cause economic and social instability. Many classic econometrics and computational approaches show good performance for the ordinary time series prediction tasks, but not satisfactory in crude oil price predictions. They ignore the characteristics of non-linearity and non-stationarity of crude oil prices data, which hinder an accurate prediction and eventually lead to poor accuracy or the wrong result. Empirical mode decomposition (EMD) and ensemble EMD (EEMD) solve the problems of non-stationary time series forecasting, but they also generate new problems of mode mixing and reconstruction errors. We propose a hybrid method that is combination of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer gated recurrent unit (ML-GRU) neural network to solve the abovementioned issues. This not only deals with the issue of mode mixing effectively, but also makes the reconstruction error of data close to zero. Multi-layer GRU has an excellent ability of nonlinear data-fitting. The experimental results of real WTI crude oil dataset show that the proposed approach perform better in crude oil prices forecasts than some state-of-the-art models.

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“…Convolutional neural networks were used to business failure prediction of Japan listed companies in Hosaka (2019). This trained model achieves much higher prediction ability compared to models created by other methods (decision trees, support vector machines, etc.).…”

Section: Literature Reviewmentioning

confidence: 99%

Business failure prediction using cart-based model: A case of Slovak companies

Ďurica¹,

Podhorská²,

Ďurana³

2019

EMS

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Over the last decades, many financial distress prediction models have been created all over the world. Usually, these models are not very useful for Slovak companies because of their lower accuracy. The main aim of the paper is to create a business failure prediction model for the companies in Slovakia. To achieve it, we use the financial data of more than 100,000 real companies, covering the year 2016 and 2017, operating in the Slovak national economic conditions. Realizing the CART algorithm, the most significant predictors of the company prosperity are identified and the final model is formed. This model achieves 92% overall classification ability. Obtained results are important for companies themselves, but also for their business partners, to eliminate financial and other corporate risks related to the business failure situation of the companies.

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Bankruptcy prediction using imaged financial ratios and convolutional neural networks

Cited by 217 publications

References 17 publications

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks

Business failure prediction using cart-based model: A case of Slovak companies

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