Deep Learning-Based Model for Financial Distress Prediction.

Predicting bankruptcies and assessing credit risk are two of the most pressing issues in finance. Therefore, financial distress prediction and credit scoring remain hot research topics in the finance sector. Earlier studies have focused on the design of statistical approaches and machine learning models to predict a company's financial distress. In this study, an adaptive whale optimization algorithm with deep learning (AWOA-DL) technique is used to create a new financial distress prediction model. The goal of the AWOA-DL approach is to determine whether a company is experiencing financial distress or not. A deep neural network (DNN) model called multilayer perceptron based predictive and AWOA-based hyperparameter tuning processes are used in the AWOA-DL method. Primarily, the DNN model receives the financial data as input and predicts financial distress. In addition, the AWOA is applied to tune the DNN model's hyperparameters, thereby raising the predictive outcome. The proposed model is applied in three stages: preprocessing, hyperparameter tuning using AWOA, and the prediction phase. A comprehensive simulation took place on four datasets, and the results pointed out the supremacy of the AWOA-DL method over other compared techniques by achieving an average accuracy of 95.8%, where the average accuracy equals 93.8%, 89.6%, 84.5%, and 78.2% for compared models.

A new metaheuristic optimization model for financial crisis prediction: Towards sustainable development.

Global crises such as the COVID-19 pandemic and other recent environmental, financial, and economic disasters have weakened economies around the world and marginalized efforts to build a sustainable economy and society. Financial crisis prediction (FCP) has a significant impact on the economy. The growth and strength of a country's economy can be gauged by accurately predicting how many companies will fail and how many will succeed. Traditionally, there have been a number of approaches to achieving a successful FCP. Despite this, there is a problem with the accuracy of classification and prediction and with the legality of the data that is being used. Earlier studies have focused on statistical, machine learning (ML), and deep learning (DL) models to predict the financial status of a company. One of the biggest limitations of most machine learning models is model training with hyper-parameter fine-tuning. With this motivation, this paper presents an outlier detection model for FCP using a political optimizer-based deep neural network (OD-PODNN). The OD-PODNN aims to determine the financial status of a firm or company by involving several processes, namely preprocessing, outlier detection, classification, and hyperparameter optimization. The OD-PODNN makes use of the isolation forest (iForest) based outlier detection approach. Moreover, the PODNN-based classification model is derived, and the DNN hyperparameters are fine-tuned to boost the overall classification accuracy. To evaluate the OD-PODNN model, three different datasets are used, and the outcomes are inspected under varying performance measures. The results confirmed the superiority of the proposed OD-PODNN methodology over recent approaches.

Internet of Things Enabled Financial Crisis Prediction in Enterprises Using Optimal Feature Subset Selection-Based Classification Model.

At present time, an effective tool becomes essential to forecast business failure as well as financial crisis on small- to medium-sized enterprises. This article presents a new optimal feature selection (FS)-based classification model for financial crisis prediction (FCP). The proposed FCP method involves data acquisition, preprocessing, FS, and classification. Initially, the financial data of the enterprises are collected by the use of the internet of things devices, such as smartphones and laptops. Then, the pigeon-inspired optimization (PIO)-based FS technique is applied to choose an optimal set of features. Afterward, the extreme gradient boosting (XGB)-based classification optimized by the Jaya optimization (JO) algorithm called JO-XGB is employed to classify the financial data. The application of the JO algorithm helps to tune the parameters of the XGB model. A detailed experimental validation process takes place to ensure the performance of the presented PIO-JO-XGBoost model. The obtained simulation results verified the effectiveness of the presented model over the compared methods.

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network.

At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. A diverse set of data classifier approaches were presented for forecasting the financial crisis of an institution using the past data. An essential process toward the design of a precise financial crisis prediction (FCP) approach comprises the choice of proper variables (features) that are related to the issues at hand. This is termed as a feature selection (FS) issue that assists to improvise the classifier results. Besides, computational intelligence techniques can be used as a classification model to determine the financial crisis of an organization. In this view, this article introduces a new FS using elephant herd optimization (EHO) with modified water wave optimization (MWWO) algorithm-based deep belief network (DBN) for FCP. The EHO algorithm is applied as a feature selector, and MWWO-DBN is utilized for the classification process. The application of the MWWO algorithm helps to tune the parameters of the DBN model, and the choice of optimal feature subset from the EHO algorithm leads to enhanced classification performance. The experimental results of the proposed model are tested against three benchmark data sets, namely AnalcatData, German Credit, and Australian Credit. The obtained simulation results indicated the superior performance of the proposed model by attaining maximum classification performance.