Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

Vochоzka, Marek; Vrbka, Jaromír; Šuleř, Petr

doi:10.3390/su12187529

Cited by 37 publications

(27 citation statements)

References 69 publications

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“…In this case, the psychological effect will play a less important role and the effectiveness of the sanation processes will be much more significant. The use of corporate bankruptcy prediction models (More information on corporate bankruptcy prediction models with a focus on Poland and other Visegrad countries can be found, among others, in: Prusak (2018Prusak ( , 2019; Kliestik et al (2018); Vochozka et al (2020).) to develop investment strategies based on short selling stocks of companies characterised by a high risk of insolvency is also worth consideration.…”

Section: Discussionmentioning

confidence: 99%

Short-Term Price Reaction to Filing for Bankruptcy and Restructuring Proceedings—The Case of Poland

Prusak

Potrykus

2021

Risks

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This study aims to check market reaction to filing for bankruptcy and restructuring proceedings and to verify the short-term effect of a price reversal in the Polish market in the years 2004–2019. The research was conducted by dividing the analysed companies according to the procedure (bankruptcy and restructuring) and market (the main market and the NewConnect market). The research methodology used in the study is the event analysis method (AR, CAR, AAR and CAAR rates were used in the research), with a few statistical tests (T-test, Generalized rank Z Test, Generalized rank T-Test, Patell or Standardized Residual Test, Kolari and Pynnönen adjusted Patell or Standardized Residual Test). It was found that share prices in the Polish share market react quickly to public information about filing an application for bankruptcy or restructuring. For all analysed companies, the mean rate of return on the event day was equal to −14%, and on the next day, it was −3%. Regardless of the type of share market and the form of proceedings, the reversal effect was not confirmed in the short term. It was found that cumulative above-average rates of return fall more strongly for companies listed on the less liquid Newconnect market (−23.6%), and when information on the filing for bankruptcy proceedings is provided (−28.5%), as opposed to the main market (−19.1%) and restructuring proceedings (−17%). The cumulative average rate of return for all analysed companies in the research period (−2, +10 days) was equal to −20.6%.

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Section: Discussionmentioning

confidence: 99%

Short-Term Price Reaction to Filing for Bankruptcy and Restructuring Proceedings—The Case of Poland

Prusak

Potrykus

2021

Risks

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“…LSTMs are considered a typical architecture of neural networks for sequences and lists due to their chain-like nature. LSTMs have been successfully applied in forecasting tasks in a variety of domains, such as financial time series [ 31 ] and sensory data [ 32 , 33 , 34 , 35 , 36 ]. An alternative for auto-regressive methods to include multiple time series in the modeling task is represented by VARIMA models [ 37 , 38 ].…”

Section: Related Workmentioning

confidence: 99%

Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

Arsov

Zdravevski

Lameski

et al. 2021

Sensors

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Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

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“…Horák et al [32], applied an artificial neural network to show how financial and nonfinancial indicators play important roles in the explanation of corporate health and support a company's possibilities of reaching relevant performance levels. Vochozka et al [33], used artificial neural networks to create a methodology for the prediction of a company failure. The study of [34] investigated the influence of the international price of oil on the value of the EUR/USD exchange rate by using the approach of neural networks.…”

Section: Related Workmentioning

confidence: 99%

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

et al. 2021

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Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

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Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

Cited by 37 publications

References 69 publications

Short-Term Price Reaction to Filing for Bankruptcy and Restructuring Proceedings—The Case of Poland

Short-Term Price Reaction to Filing for Bankruptcy and Restructuring Proceedings—The Case of Poland

Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

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