Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry

Abstract

Using logistic regression technique and Deep Recurrent Convolutional Neural Network, this study seeks to improve the capacity of existing bankruptcy prediction models for the restaurant industry. In addition, we have verified, in the review of existing literature, the gap in the research of restaurant bankruptcy models with sufficient time in advance and that only companies in the restaurant sector in the same country are considered. Our goal is to build a restaurant bankruptcy prediction model that provides high accuracy, using information distant from the bankruptcy situation. We had a sample of Spanish restaurants corresponding to the 2008–2017 period, composed of 460 solvent and bankrupt companies, for which a total of 28 variables were analyzed, including some of a non-financial nature, such as age of restaurant, quality, and belonging to a chain. The results indicate that the best bankruptcy predictors are financial variables related to profitability and indebtedness and that Deep Recurrent Convolutional Neural Network exceeds logistic regression in predictive capacity.

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

“…They utilized it for deep features extraction. On the other hand, in [49] the authors used Deep Recurrent Convolutional Neural Network (DRCNN) [50] as a predictor model for bankruptcy, while in [51] the authors built their own CNN structure model based on Efficient Net architecture with nine levels (nine layers) to classify X-ray images into three classes (Normal, Pneumonia, and COVID- 19). Table 34 shows the differences and the similarities between the proposed structure and the existing structures in literature.…”

Neural Networks for Estimating Speculative Attacks Models

“…For a simple neural network (NN), the inputs are assumed to be independent of each other. The common structure of RNN is organized by the output of which is depended on its previous computations [ 24 , 25 ]. Given an input sequence vector x , the hidden states of a recurrent layer s , and the output of a single hidden layer y , it can be calculated as appears in expressions (12) and (13): where W xs , W ss , and W so denote the weights from the input layer x to the hidden layer s , the hidden layer to itself, and the hidden layer to its output layer, respectively.…”

“…Recurrent Convolutional Neural Network (RCNN) can be heaped to establish a deep architecture, named “deep recurrent convolutional neural network” [ 25 ]. When DRCNN is used to estimate speculative attacks, the last part of the model is a supervised learning layer, which is determined as appears in Equation (17): where W h is the weight and b h is the bias.…”

“…When DRCNN is used to estimate speculative attacks, the last part of the model is a supervised learning layer, which is determined as appears in Equation (17): where W h is the weight and b h is the bias. The error between predicted observations and actual ones in the training data for speculative attacks estimation can be calculated and back propagated to train the model [ 25 ]. Considering that the actual data at time t is r , the loss function is determined as shown in the next Equation (18): …”

Financial Sustainability Evaluation and Forecasting Using the Markov Chain: The Case of the Wine Business

“…The permanent perturbations in financial markets increase attention to business entities' solvency in the context of mechanisms ensuring their financial security [1][2][3][4]. The external shocks tolerant business with financial security and without signs of liquidity or solvency losses in the medium and long-term perspectives, determines mesoeconomic (sectoral) stability, which, in turn, serves as one of the prerequisites for sustainable development at the macro level.…”