Crisis Prediction Research Articles

Analysis of the Effectiveness of Policy Guarantee Support under the COVID-19 Pandemic Economic Crisis

The purpose of this study is to suggest some policy implications by analyzing the support effectiveness of policy guarantees supported through the supplementary budget. During the COVID-19 period, the sales growth rate of the policy guarantee beneficiary companies was about 4.98% and the employment growth rate was about 0.46%, indicating a significant difference in growth potential compared to the non-beneficiary companies. First, among the economic crises caused by COVID-19, the crisis response through policy guarantees was relatively appropriate. Second, it is necessary to maintain an ‘appropriate’ level of support for ‘necessary’ economic sectors according to the government’s policy direction. Third, policy guarantee support can be effectively used as one of crisis response policies based on the effectiveness of the policy. Fourth, it is necessary to prepare for preemptive policy guarantee support by operating the crisis prediction system. Fifth, it is necessary to identify and prepare for possible crises after COVID-19 at the present time.

Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation

ABSTRACT Hyper automation is the group of approaches and software companies utilised to automate manual procedures. Financial Technology (FinTech) was processed as a distinctive classification that highly inspects the financial technology sector from a broader group of functions for enterprises with utilise of Information Technology (IT) application. Financial crisis prediction (FCP) is the most essential FinTech technique, defining institutions’ financial status. This study proposes an Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest (ESAOA-ODCF) based FinTech Application for Hyperautomation. The ESAOA-ODCF technique has achieved exceptional performance with maximum accu y of 98.61%, and F score of 98.59%. Extensive experimental research revealed that the ESAOA-ODCF model beat more modern, cutting-edge approaches in terms of overall performance.

Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?*

This study proposes using the Least Absolute Shrinkage and Selection Operator (LASSO) method with cross‐validation to automate the variable selection process of the conventional multivariate logit early warning system (EWS), the purpose being to improve the prediction of systemic banking crises. Using a dataset covering 23 OECD countries with quarterly data from 1970Q1 to 2018Q3, model performance is evaluated in a recursive out‐of‐sample forecasting exercise, taking policy‐makers' preference of missed crises and false alarms into account. The results suggest that the automatic variable selection process can enhance the predictive performance of the EWS. It also highlights the importance of extracting information from variable interactions and lags that may not be easily identified and accessed by typical subjective variable pre‐selection. This simple approach is easy to interpret and is transparent, which are important aspects for effective policy communication. Five variables, namely credit growth, domestic and global credit gaps, real house price growth and the real effective exchange rate, are identified as the most important key indicators of systemic banking crises.

An Intelligent Internet-of-Things-Aided Financial Crisis Prediction Model in FinTech

Financial technology (FinTech) has become a hot research topic recently, moreover, an area of major investment for most financial institutions. As the FinTech unleashed many new strategic solutions for major financial problems. One of those pivotal strategies of FinTech is financial crisis prediction (FCP) that dictates the financial status of an institution. Also, the rise of the Internet-of-Things (IoT) technology has paved a new way for interaction between humans and the physical world. Therefore, IoT can feasibly be incorporated into the FCP model to obtain a real-time analysis of the financial data from the clients. With this perspective, we propose an intelligent IoT-aided FCP model using metaheuristic algorithms. The proposed FCP method comprises data acquisition, preprocessing, feature selection (FS), and classification. First, the financial data of the enterprises are collected using IoT devices, such as smartphones, laptops, etc. Next, the quantum artificial butterfly optimization (QABO) approach for FS is applied to choose an optimal set of features. Afterward, long short-time memory (LSTM) with recurrent neural network (RNN) model is employed to classify the collected financial data. An exhaustive experimental validation process is carried out to ensure the performance of the proposed QABO-LSTM-RNN model. The simulation results accredited the efficacy of the proposed model contrasted with other baseline methods in terms of best cost, sensitivity, specificity, accuracy, F-score, kappa, and Mathew correlation coefficient (MCC).

Basel Agreements in the Efficiency of Algerian Banks Financial Evolution and Interdisciplinary Research

The recent crisis has attracted much interest on the part of economists and prudential authorities. It resembles in some aspects: over-indebtedness widespread panic caused by the sharp devaluation of financial assets, poor management and prediction of crises by the prudential rules of Basel II. The Algerian banking authorities are engaged in a series of reforms of independence till the nowadays, to modernize their banking sectors. They include reforms to the restructuring of the liberalization and privatization of public banks, the establishment of prudential laws and systems of risk management and the strengthening of the powers of supervision. The aim of this article is to learn about the adequacy of the Basel agreements to achieve the basic ideas of reducing risks, stabilizing the financial sector, and maintaining its level of efficiency. Add to, the central idea is to show that our commercial banks are under the obstacle of the presence of the State in a very strong way in the shareholders of the banks, which weakens the regulatory governance. This obstacle more other political constraints preclude the application of the second Basel accord. This was done by first evaluating the different Basel agreements imposed internationally to banks by the Prudential Basel Committee authorities to avoid crises or at least prevent. Secondly the situation of Algerian bank compared to us neighbors and they are forced or the reasons for the slowness with the monetary authorities for the application of the agreement Basel 2, and finally analysis of some banks soundness indicators to measure the efficiency of the Algerian banking sector for the period from 2000 to 2021. The finding revealed that us to point out that they are improving for some and a slight decline for other. An analysis of the development of deposits by institutional sector appears at the end of December 2020 a decrease of -13.2% for deposits collected with public institutions and after government agencies, after the recorded height increased by 7.6% in 2018 and -2% in 2019 and 2020. These deposits moved from 3689.1billion dinars at the end of 2019 to 3202.5 billion dinars in 2020 and 3885.2 billion dinars in 2021.It also represents 33.9% of the total bank deposits collected compared to 39.3% at the end of 2019.This decline is due to the significant decrease in demand deposit for public institutions by (-18%) at the end of 2020, compared to a decrease of (-19.7%) at the end 2019. The Algerian economy’s indicators, it can be said temporarily because Algeria’s economy is rentier. In 2022, it has been expected that, the current account balance posts its first surplus since 2013 and international reserves have risen, stopping the constant downward trend of recent years. Similarly, A fiscal surplus is projected in 2022 reflecting windfall gains from hydrocarbon revenues and a significant under-implementation of budget spending, resulting from the global recovery and the war in Ukraine. The economic recovery strengthened, with non-hydrocarbon GDP growth projected to accelerate to 3.2% in from 2.1%.

Deep Learning Enabled Financial Crisis Prediction Model for Small-Medium Sized Industries

Recently, data science techniques utilize artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to take an influence and grow their businesses. For SMEs, owing to the inexistence of consistent data and other features, evaluating credit risks is difficult and costly. On the other hand, it becomes necessary to design efficient models for predicting business failures or financial crises of SMEs. Various data classification approaches for financial crisis prediction (FCP) have been presented for predicting the financial status of the organization by the use of past data. A major process involved in the design of FCP is the choice of required features for enhanced classifier outcomes. With this motivation, this paper focuses on the design of an optimal deep learning-based financial crisis prediction (ODL-FCP) model for SMEs. The proposed ODL-FCP technique incorporates two phases: Archimedes optimization algorithm based feature selection (AOA-FS) algorithm and optimal deep convolution neural network with long short term memory (CNN-LSTM) based data classification. The ODL-FCP technique involves a sailfish optimization (SFO) algorithm for the hyperparameter optimization of the CNN-LSTM method. The performance validation of the ODL-FCP technique takes place using a benchmark financial dataset and the outcomes are inspected in terms of various metrics. The experimental results highlighted that the proposed ODL-FCP technique has outperformed the other techniques.

Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction

Financial crisis prediction (FCP) received significant attention in the financial sector for decision-making. Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy. Conventionally, numerous approaches have been developed in the design of accurate FCP processes. At the same time, classifier efficacy and predictive accuracy are inadequate for real-time applications. In addition, several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets. Thus, there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets. This paper presents a novel multi-vs. optimization (MVO) based feature selection (FS) with an optimal variational auto encoder (OVAE) model for FCP. The proposed multi-vs. optimization based feature selection with optimal variational auto encoder (MVOFS-OVAE) model mainly aims to accomplish forecasting the financial crisis. For achieving this, the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization. In addition, the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach. Followed by, the variational auto encoder (VAE) model is applied for the categorization of financial data into financial crisis or non-financial crisis. Finally, the differential evolution (DE) algorithm is utilized for the parameter tuning of the VAE model. A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.

A study on corporate financial crisis prediction strategy based on particle swarm improved fuzzy clustering method from accounting perspective

A study on corporate financial crisis prediction strategy based on particle swarm improved fuzzy clustering method from accounting perspective

A study on corporate financial crisis prediction strategy based on particle swarm improved fuzzy clustering method from accounting perspective

A study on corporate financial crisis prediction strategy based on particle swarm improved fuzzy clustering method from accounting perspective

An Intelligent Hybrid Model Using Artificial Neural Networks and Particle Swarm Optimization Technique For Financial Crisis Prediction

Financial crisis prediction is a critical issue in the economic phenomenon. Correct predictions can provide the knowledge for stakeholders to make policies to preserve and increase economic stability. Several approaches for predicting the financial crisis have been developed. However, the classification model's performance and prediction accuracy, as well as legal data, are insufficient for usage in real applications. So that, an efficient prediction model is required for higher performance results. This paper adopts a novel two-hybrid intelligent prediction model using an Artificial Neural Network (ANN) for prediction and Particle Swarm Optimization (PSO) for optimization. At first, a PSO technique produces the hyperparameter value for ANN to fit the best architecture. They are weights and thresholds. Then, they are used to predict the performance of the given dataset. In the end, ANN-PSO generates predictions value of crisis conditions. The proposed ANN-PSO model is implemented on time series data of economic conditions in Indonesia. Dataset was obtained from International Monetary Fund and the Indonesian Economic and Financial Statistics. Independent variable data using 13 potential indicators, namely imports, exports, trade exchange rates, foreign exchange reserves, the composite stock price index, real exchange rates, real deposit rates, bank deposits, loan and deposit interest rates, the difference between the real BI rate and the real FED rate, the M1, M2 multiplier, and the ratio of M2 to foreign exchange reserves. Meanwhile, the dependent variable uses the perfect signal value based on the Financial Pressure Index. A detailed statistical analysis of the dataset is also given by threshold value to convey crisis conditions. Experimental analysis shows that the proposed model is reliable based on the different evaluation criteria. The case studies show that the result for predictive data is basically consistent with the actual situation, which has greatly helped the prediction of a financial crisis.

Retraction Note: Intelligent hybrid model for financial crisis prediction using machine learning techniques

Retraction Note: Intelligent hybrid model for financial crisis prediction using machine learning techniques

Financial crisis prediction based on multilayer supervised network analysis

Financial crisis prediction is essential in preventing financial problems as its monitoring indicators help regulators judge the probability of future crises. In this context, the activities of the scientific community have been focused on the dynamics of single/multiple sequences and utilized unsupervised/supervised methods for financial crisis prediction. It is noteworthy that the cross-correlation between the risks of multiple economic entities makes financial network analysis paramount in crisis prediction. Focusing on this point, we propose a multilayer supervised network analysis (MSNA) method to train the multilayer network, and select the most suitable layer for financial crisis prediction. Specifically, we use 37 crucial stock market indices from 4 continents to create successive multilayer financial networks with 120-day windows and 1-day step by Pearson cross-correlation (PCC), variance decompositions (VD), transfer entropy (TE), minimum spanning tree (MST), directed MST (DMST), planar maximally filtered graph (PMFG) and directed PMFG (DPMFG) methods. Based on the multilayer network, we embed the graph neural network classification (GNNC) model and train the dynamic multilayer networks at each window scale (240,120, and 60 days). Finally, we conclude that the accuracy of the short window (60 days) is significantly higher than that of the long window. The network constructed by PCC with MST is the most suitable for short sequence (60 days) crisis prediction (AUC = 0.959), and the network constructed by TE with DMST is the most suitable for long sequence (240 days) crisis prediction (AUC = 0.772).

Study on the Pakistan stock market using a new stock crisis prediction method.

A Stock market collapse occurs when stock prices drop by more than 10% across all main indexes. Predicting a stock market crisis is difficult because of the increased volatility in the stock market. Stock price drops can be triggered by a variety of factors, including corporate results, geopolitical tensions, financial crises, and pandemic events. For scholars and investors, predicting a crisis is a difficult endeavor. We developed a model for the prediction of stock crisis using Hybridized Feature Selection (HFS) approach. Firstly, we went for the suggestion of the HFS method for the removal of stock’s unnecessary financial attributes. The Naïve Bayes approach, on the other hand, is used for the classification of strong fundamental stocks. In the third step, Stochastic Relative Strength Index (StochRSI) is employed to identify a stock price bubble. In the fourth step, we identified the stock market crisis point in stock prices through moving average statistics. The fifth is the prediction of stock crises by using deep learning algorithms such as Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Root Mean Square Error (RMSE), Mean Squared Error (MSE) and Mean Absolute Error (MAE) are implemented for assessing the performance of the models. The HFS-based GRU technique outperformed the HFS-based LSTM method to anticipate the stock crisis. To complete the task, the experiments used Pakistan datasets. The researchers can look at additional technical factors to forecast when a crisis would occur in the future. With a new optimizer, the GRU approach may be improved and fine-tuned even more.

Prediction and diagnostics of crises and critical states in an unusual vibro-impact system with soft impact

In nonlinear dynamical systems, whenchanging control parameters, critical states may occur. To predict these undesirable events, it is necessary to study the system dynamic behavior with changing in different control parameters. Two-body 2-DOF vibro-impact system is under consideration. The huge mass of the upper body, which can break away from the lower one and be in “free flight” for a short time, may be attributed to its specific and is a cause to consider it unusual. Some changes in the parameters of both external loading and the system itself can increase the efficiency of the machine, the mathematical model of which is the considered vibro-impact system. But the same changes lead to appearance of unwanted nonlinear phenomena, such as permanent sustained chaos, transient chaos, crisis-induced intermittency, and coexisting regimes with larger oscillatory amplitudes. The article shows their occurrence, traces the precursors, carries out their diagnostics by both traditional and less common methods. The description of many numerical experiments is accompanied by numerous figures and tables that clearly and convincingly demonstrate the results obtained.

Construction and Application of the Financial Early-Warning Model Based on the BP Neural Network.

In order to further improve the early-warning effect of enterprise financial crisis management and reduce the occurrence of enterprise financial crisis, by taking listed companies as examples and combining the operating conditions of listed companies, a financial crisis early-warning indicator system was built from five aspects of profitability, debt-paying ability, development ability, operation ability, and cash flow ability. In addition, a financial management early-warning model based on the BP neural network algorithm was built. Through the experimental prediction, it is showed that the financial crisis early-warning model of listed companies based on the BP neural network algorithm for crisis prediction accuracy was more than 75%. The accuracy of the first three years of model prediction was 93.33% and 72.34%, respectively. The accuracy of model prediction in the first two years was 94.67% and 82.98%, respectively. In the first year, the accuracy rate increased to 100% and 89.36%. Compared with the prediction accuracy of the logistic model (50%), it is fully reflected that the financial early-warning model proposed in the research had a good crisis prediction ability.

On fiscal rules, gross domestic product forecasts and prediction of economic crises: a reply to ‘Macroeconomic equilibriums, crises and fiscal policy’ by Fredrik N.G. Andersson

On fiscal rules, gross domestic product forecasts and prediction of economic crises: a reply to ‘Macroeconomic equilibriums, crises and fiscal policy’ by Fredrik N.G. Andersson

Research on Financial Risk Crisis Prediction of Listed Companies Based on IWOA-BP Neural Network

<p>To avoid the risk brought by the financial crisis, the Improved Whale Optimization Algorithm-Back Propagation neural network (IWOA-BP) financial crisis early warning model is proposed. This paper selects the data from financial statements of some of the listed Chinese manufacturing companies from 2015-2019 as the research sample. First, the financial data of enterprises are screened by principal component analysis, and the early warning model is constructed from the financial and nonfinancial factors of six indicators: solvency, operating capacity, profitability, development capacity, cash flow and risk level factors. Second, the Whale Optimization Algorithm is optimized by the chaos strategy, as well as by the dynamic weight and sine cosine algorithm. Finally, the improved Whale Algorithm is optimized for BP neural network parameters. In the simulation experiments, the performance of the improved whale optimization algorithm is substantially improved. In addition, in the empirical analysis, compared to the prediction model with other algorithms, the prediction model of this paper has better results in terms of prediction accuracy.</p> <p> </p>

Prediction of debt crisis in Southern African Development Community (SADC)

Debt levels in the Southern African Development Community (SADC) have been rising over the years as countries undertake infrastructure projects and the increased use of bilateral and private credit. Although on aggregate the debt levels are within SADC recommendations on macroeconomic convergence, there are growing fears that a number of countries in the region might default and indeed some are already in default. The frequent occurrence of debt crisis is a cause for concern. This research is an attempt to determine the significant predictors from a small set of variables commonly touted as important in debt crisis prediction. The research considered the output gap, real exchange rate, external debt ratios, commodity shocks and quality of governance as potential predictors. The factor variables being of particular interest Empirical findings on these potential predictors is somehow mixed, which partly is accounted for by the differences in model specifications from author to author. The research employed the event study and fixed effects logistic regression for modelling the probability of default of public debt. Results of the model illustrate that governance indicators and commodity price shocks (global level) were not statistically significant predictors of debt crisis as commonly suggested by theory. However the external debt, output and the real foreign exchange rate were all significant. Real output was shown to be one of the most important predictors of debt crisis. The estimated probability model fared relatively better than a random model.

Quantification of fabric morphology and prediction of drag crisis based on the fractal dimension

This paper focused on investigating the relationship between fabric morphology and aerodynamic drag. The fractal theory is used to quantify the fabric surface morphology, and its influence on the minimum drag coefficient and critical Reynolds number is evaluated. In addition, the effectiveness of the fractal dimension in predicting the drag crisis is verified. It is observed that with the increase of the fractal dimension, the minimum drag coefficient increases, and the correlation coefficient can reach 0.9728, while the corresponding critical Reynolds number decreases, and the correlation coefficient can reach 0.9334. In addition, the accuracy of predicting drag crisis by the fractal dimension is verified and the relative error of predicting the drag crisis by the fractal dimension is less than 10%, which is significantly better than that by roughness.

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment.

As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country's economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches.