Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis

2008 ◽  
Vol 42 (3) ◽  
pp. 206-220 ◽  
Author(s):  
Desheng(Dash) Wu ◽  
Liang Liang ◽  
Zijiang Yang
Keyword(s):  
Neural Networks ◽  
Financial Distress ◽  
Probabilistic Neural Networks ◽  
Discriminate Analysis ◽  
Public Companies ◽  
Chinese Public ◽  
Multivariate Discriminate Analysis

Financial Distress, Earnings Benchmark and Earnings Management Practices

2021 ◽  
pp. 097226292110109
Author(s):  
Karan Gandhi
Keyword(s):  
Panel Data ◽  
Earnings Management ◽  
Financial Distress ◽  
Management Practices ◽  
Real Earnings Management ◽  
Public Companies ◽  
Large Panel ◽  
Distressed Firms ◽  
Contradictory Evidence ◽  
Financially Distressed

Prior research exhibits contradictory evidence on earnings management practices, both accrual and real, undertaken by the firms in state of financial distress. This study uniquely examines the issue in the presence of earnings-increasing earnings management motivation- meeting earnings benchmark of avoiding losses. For examining the issue, this study analyzes large panel data of Indian public companies for the period 2000–2016. The findings indicate prevalence of earnings-decreasing real earnings management practices, that is, decrease in overproduction and increase in spending on discretionary expenses, in financially distressed firms despite there being motivation to increase earnings to avoid losses. No evidence of accrual earnings management practices has been observed in such firms.

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

Off-line classification of Polish vowel spectra using artificial neural networks

2004 ◽  
Vol 34 (1) ◽  
pp. 37-52
Author(s):  
Wiktor Jassem ◽  
Waldemar Grygiel
Keyword(s):  
Neural Networks ◽  
Numerical Data ◽  
Word List ◽  
Original Data ◽  
Data Matrix ◽  
Probabilistic Neural Networks ◽  
Independent Variable ◽  
The Individual ◽  
Vowel Phoneme

The mid-frequencies and bandwidths of formants 1–5 were measured at targets, at plus 0.01 s and at minus 0.01 s off the targets of vowels in a 100-word list read by five male and five female speakers, for a total of 3390 10-variable spectrum specifications. Each of the six Polish vowel phonemes was represented approximately the same number of times. The 3390* 10 original-data matrix was processed by probabilistic neural networks to produce a classification of the spectra with respect to (a) vowel phoneme, (b) identity of the speaker, and (c) speaker gender. For (a) and (b), networks with added input information from another independent variable were also used, as well as matrices of the numerical data appropriately normalized. Mean scores for classification with respect to phonemes in a multi-speaker design in the testing sets were around 95%, and mean speaker-dependent scores for the phonemes varied between 86% and 100%, with two speakers scoring 100% correct. The individual voices were identified between 95% and 96% of the time, and classifications of the spectra for speaker gender were practically 100% correct.

Dolomitization geometry and reservoir quality from supervised Bayesian classification and probabilistic neural networks: Midland Basin Leonardian Wichita and Clear Fork Formations

2021 ◽  
Vol 9 (2) ◽  
pp. T585-T598
Author(s):  
Abidin B. Caf ◽  
John D. Pigott
Keyword(s):  
Neural Networks ◽  
Acoustic Impedance ◽  
Probability Distributions ◽  
A Priori ◽  
Bayesian Classification ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Probabilistic Neural Networks ◽  
Neutron Porosity ◽  
Seismic Scale

Extensive dolomitization is prevalent in the platform and periplatform carbonates in the Lower-Middle Permian strata in the Midland and greater Permian Basin. Early workers have found that the platform and shelf-top carbonates were dolomitized, whereas slope and basinal carbonates remained calcitic, proposing a reflux dolomitization model as the possible diagenetic mechanism. More importantly, they underline that this dolomitization pattern controls the porosity and forms an updip seal. These studies are predominately conducted using well logs, cores, and outcrop analogs, and although exhibiting high resolution vertically, such determinations are laterally sparse. We have used supervised Bayesian classification and probabilistic neural networks (PNN) on a 3D seismic volume to create an estimation of the most probable distribution of dolomite and limestone within a subsurface 3D volume petrophysically constrained. Combining this lithologic information with porosity, we then illuminate the diagenetic effects on a seismic scale. We started our workflow by deriving lithology classifications from well-log crossplots of neutron porosity and acoustic impedance to determine the a priori proportions of the lithology and the probability density functions calculation for each lithology type. Then, we applied these probability distributions and a priori proportions to 3D seismic volumes of the acoustic impedance and predicted neutron porosity volume to create a lithology volume and probability volumes for each lithology type. The acoustic impedance volume was obtained by model-based poststack inversion, and the neutron porosity volume was obtained by the PNN. Our results best supported a regional reflux dolomitization model, in which the porosity is increasing from shelf to slope while the dolomitization is decreasing, but with sea-level forcing. With this study, we determined that diagenesis and the corresponding reservoir quality in these platforms and periplatform strata can be directly imaged and mapped on a seismic scale by quantitative seismic interpretation and supervised classification methods.

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