Crisis Prediction Research Articles

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

AbstractFinancial Technology (FinTech) is treated as a distinctive taxonomy which majorly examines the financial technology sectors in a broader set of operations for enterprises by the use of Information Technology (IT) applications. Since the Internet of Things (IoT) is increasing tremendously, artificial intelligence (AI) assisted agile IoT is the way forward for sustainable finance. The deepness of the agile IoT has probably transformed the financial market today, and it may rapidly develop as a dominant tool in the future. The integration of AI and IoT techniques will considerably extract valued financial data and avail better services to the customers. One of the important concepts involved in FinTech is financial crisis prediction (FCP), which is a process of determining the financial status of a company. With this motivation, this paper designs a novel artificial intelligence assisted IoT based FCP (AIAIoT-FCP) model in the FinTech environment. The proposed AIAIoT-FCP model encompasses different stages such as data collection, data preprocessing, feature selection, and classification. At the primary stage, the financial data of the enterprises are collected by the use of the IoT devices such as smartphones and laptops. Besides, a chaotic Henry gas solubility optimization based feature selection (CHGSO-FS) technique is applied to select optimum features. In addition, a deep extreme learning machine (DELM) based classifier is used to determine the class labels of the financial data. Finally, the Nesterov-accelerated Adaptive Moment Estimation (NADAM) based hyperparameter optimizer of the DELM model is involved to boost the classification performance of the DELM model. An extensive simulation analysis is carried out on the benchmark financial dataset to highlight the betterment of the AIAIoT-FCP model. The resultant values portrayed the superior performance of the AIAIoT-FCP model over the state of art techniques in a considerable way.

Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

Traditional financial crisis prediction approaches have a tough time extracting the properties of financial data, resulting in financial crisis prediction with insufficient accuracy. As a result, based on the random forest algorithm, an intelligent financial crisis prediction approach for listed enterprises is proposed. The random forest method is used to mine the characteristics of financial data based on financial index data from publicly traded companies. This research develops a financial crisis prediction index system based on the findings of data feature mining. The CCR model is used to assess the efficiency of listed firms’ decision-making units with more input and output, and the efficiency index of each decision-making unit is calculated. The efficiency evaluation index of publicly traded companies is used to divide the severity of the financial crisis. The experimental results reveal that, when compared to standard prediction methods, this method’s forecast accuracy is commensurate with the actual state of businesses, and it can reduce the time it takes to predict financial crises.

Prediction of Enterprise Financial Crisis based on Improved Neural Network

Objectives: Neural network is a very important research model in human brain research, and it has been cross researched and applied in many disciplines and fields. Methods: However, there are some shortcomings in the neural network, such as long learning cycle and slow convergence speed. Results: Therefore, in this paper, the enterprise financial crisis prediction based on improved neural network was proposed. Then in the light of the shortcomings of the neural network, the optimization and improvement were carried out. After that, the genetic algorithm was introduced to update the neural network structure and improve the prediction accuracy of the neural network. Finally, the improved neural network was applied to the financial crisis prediction, and good results were achieved. Conclusion: It is proved that the research has good application value and promotion prospect.

Predicting stock market crisis via market indicators and mixed frequency investor sentiments

At present, integrating investor sentiment into the prediction of stock market crisis has attracted more and more attention. However, the existing researches only considered the impact of the market indicators and the micro investor sentiment on stock market, while ignored the impact of the macro one. Therefore, in this paper, we develop an early warning system for predicting stock market crisis via market indicators and mixed frequency investor sentiments. The proposed early warning system consists of five components, which includes the construction of mixed frequency investor sentiments that consider both macro and micro investor sentiments, the identification of stock market crisis, the determination of the forecast horizon using Ensemble Empirical Mode Decomposition (EEMD), the definition of the early warning signal, and the building of prediction model using artificial neural network (ANN). Lastly, we apply the developed warning system to China’s stock markets. Experimental results show that mixed frequency investor sentiments can improve the early warning ability in predicting the stock market crisis, and the ANN model has better performance than other methods including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Logistic Regression (LR).

Prediction of Crisis in Tehran Stock Exchange with Entropy and Analyzing the Identified Crises such as Covid-19

Prediction of Crisis in Tehran Stock Exchange with Entropy and Analyzing the Identified Crises such as Covid-19

Research on the Sustainable Operation of Low-Carbon Tourism Supply Chain under Sudden Crisis Prediction

This paper integrates a low-carbon tourism supply chain consisting of a low-carbon tourist attraction (LTA) providing a low-carbon service and an online travel agency (OTA) responsible for big data marketing. Consumers may also encounter sudden crisis events that occur in the tourist attraction during their visit, and the occurrence of crisis events can damage the low-carbon goodwill of the tourist attraction to the detriment of the sustainable development of the supply chain. Therefore, this paper aims to investigate how tourism firms can develop dynamic strategies in the pre-crisis environment if they envision the occurrence of a crisis event and how crisis events affect interfirm cooperation. This paper uses stochastic jump processes to portray the dynamic evolution of low-carbon goodwill in the context of crisis events and introduces the methods of the differential game and Bellman’s continuous dynamic programming theory to study the sustainable operations of low-carbon tourism supply chains. Our findings provide important managerial insights for enterprises in the tourism supply chain and suggest that they need to not only become aware of the tourist attraction crisis events, but also, more importantly, they need to adjust their appropriate input strategies based on the degree of anticipation of the crisis.

Predicting of Banking Stability Using Machine Learning Technique of Random Forests

The purpose of this research is to create a predicting model of banking stability in Indonesia. Authors use a small set of explanatory indicators primarily related to the banking industry and some relevant economic variables. Among the indicators candidate to be used in this study are the indicator of banking industry, the money markets, capital markets and creditors, and the macro-economic indicator. The source of data in this research is obtained from CEIC and SEKI (Indonesian Economic and Financial Statistics) that published by Central Bank of Indonesia from 2004 and 2011. Principal Component Analysis is used to avoid the multi-collinearity problem when construct the model. Authors train the model using Random Forest Regression with data over the 2004-2007 period, and made predictions of global financial crisis that happened in 2008/9. Python 2.7.10 and scikit-learn version 0.20.0 module has been exploited for simulations and evaluation of the model. Numerical illustration is provided to demonstrate the efficiency of proposed model. As the result nine most components analysis obtained as input for the machine learning model with explained variance ratio around 97%, accuracy around 89%, and precision 91% and mean absolute error around 11%.

Research on financial early warning of mining listed companies based on BP neural network model

Mining industry is the basic industry of the national economy. However, in recent years, listed mining companies have suffered serious financial risks due to special reasons such as poor spot market liquidity of their products, strong policy dependence, and long investment payback periods. In the previous studies, most of the financial crisis prediction focused on the whole industry and manufacturing industry. The research on the financial risk of mining enterprises focuses more on how to adjust R&D activities, environmental performance to improve the financial performance of enterprises. There is still a lot of room for in-depth research on the systematic prevention and early warning of financial risks of listed mining companies. At the same time, in terms of research methods, many scholars used multivariate discriminant model, logistic regression model and support vector machine model. Compared with the Back-Propagation (BP) neural network model, these model methods have more or less defects. Therefore, we take mining listed companies as the research object, select the financial data of China's A-share mining listed companies in 2018, and construct the BP neural network financial early warning model, trying to provide more practical means for the financial risk early warning of mining companies. The research conclusions of this paper are as follows: (1) The BP neural network financial early warning model constructed in this paper has high prediction accuracy, which can be well used in the practice of financial early warning of mining listed companies; (2) The financial situation of China's A-share mining listed companies in 2018 is generally in a good state. The companies with good financial status can effectively control the cost and have good debt paying ability while earning income; (3) For companies with financial status that require early warning, the root cause is mainly that they do not pay attention to the risk of bad debt losses, which makes current credit sales income and accounts receivable are at high levels, and they also do not have good profitability.

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.

Year-Independent Prediction of Food Insecurity Using Classical and Neural Network Machine Learning Methods

Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p < 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.

Early warning systems using dynamic factor models: An application to Asian economies

This study develops an early warning system for financial crises with a focus on small open economies. We contribute to the literature by developing macro-financial dynamic factor models that extract useful information from a rich but unbalanced mixed frequency data set that includes a range of global and domestic economic and financial indicators. The framework is applied to several Asian countries—Thailand, South Korea, Singapore, Malaysia, the Philippines and Indonesia. Logit regression models that use the extracted factors and other leading indicators have significant power in predicting systemic events. In-sample and out-of-sample test results indicate that the extracted factors help to improve the predictive power over a model that uses only sufficiently long history indicators. Importantly, models that include the dynamic factors yield consistently better out-of-sample crisis prediction results for key performance measures such as a usefulness index, the noise to signal ratio, and AUROC.

Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques

Sovereign debt and currencies play an increasingly influential role in the development of any country, given the need to obtain financing and establish international relations. A recurring theme in the literature on financial crises has been the prediction of sovereign debt and currency crises due to their extreme importance in international economic activity. Nevertheless, the limitations of the existing models are related to accuracy and the literature calls for more investigation on the subject and lacks geographic diversity in the samples used. This article presents new models for the prediction of sovereign debt and currency crises, using various computational techniques, which increase their precision. Also, these models present experiences with a wide global sample of the main geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe, and globally. Our models demonstrate the superiority of computational techniques concerning statistics in terms of the level of precision, which are the best methods for the sovereign debt crisis: fuzzy decision trees, AdaBoost, extreme gradient boosting, and deep learning neural decision trees, and for forecasting the currency crisis: deep learning neural decision trees, extreme gradient boosting, random forests, and deep belief network. Our research has a large and potentially significant impact on the macroeconomic policy adequacy of the countries against the risks arising from financial crises and provides instruments that make it possible to improve the balance in the finance of the countries.

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.

Product-harm crisis intelligent warning system design based on fine-grained sentiment analysis of automobile complaints

Due to the huge losses caused by product-harm crises and subsequent recalls in the automobile industry, companies must urgently design a product-harm crisis warning system. However, the designs of existing warning systems use the recurrent neural network algorithm, which suffers from gradient disappearance and gradient explosion issues. To compensate for these defects, this study uses a long and short-term memory algorithm to achieve a final prediction accuracy of 90%. This study contributes to the research and design of automatic crisis warning systems by considering sentiment and improving the accuracy of automobile product-harm crisis prediction.

Volatility spillover between economic sectors in financial crisis prediction: Evidence spanning the great financial crisis and Covid-19 pandemic

This paper measures volatility spillovers between sectors of economic activity using network connectivity measures. Volatility spillovers are an accurate proxy for the transmission of risk across sectors and are particularly informative during crisis periods. To do this, we apply the novel methodology proposed in Diebold and Yilmaz (2012) to seven economic sectors of U.S. economic activity and find that Banking&Insurance, Energy, Technology and Biotechnology are the main channels through which shocks propagate to the rest of the economy. Banking&Insurance is especially relevant during the 2007–2009 global financial crisis while the Energy sector and Technology are especially relevant during the COVID-19 crisis. We also show that volatility spillovers exhibit ability to predict high episodes of volatility for the S&P 500 index being useful as early financial crisis indicators.

A Machine Learning-based DSS for mid and long-term company crisis prediction

In the field of detection and prediction of company defaults and bankruptcy, significant effort has been devoted to evaluating financial ratios as predictors using statistical models and machine learning techniques. This problem becomes crucially important when financial decision-makers are provided with predictions on which to act, based on the output of prediction models. However, research has shown that such predictors are sufficiently accurate in the short-term, with the focus mainly directed towards large and medium-large companies. In contrast, in this paper, we focus on mid- and long-term bankruptcy prediction (up to 60 months) targeting small and/or medium enterprises. The key contribution of this study is a substantial improvement of the prediction accuracy in the short-term (12 months) using machine learning techniques, compared to the state-of-the-art, while also making accurate mid- and long-term predictions (measure of the area under the ROC curve of 0.88 with a 60 month prediction horizon). Extensive computational tests on the entire set of companies in Italy highlight the efficiency and accuracy of the developed method, as well as demonstrating the possibility of using it as a tool for the development of strategies and policies for entire economic systems. Considering the recent COVID-19 pandemic, we show how our method can be used as a viable tool for large-scale policy-making.

A SYSTEMIC APPROACH TO ENTERPRISE CRISIS MANAGEMENT IN THE CURRENT ECONOMIC CONDITIONS

Crises are characteristic of any system, including economic entities in the process of its evolution. These usually signal the accumulation of a critical mass - of the restriction factors, the elimination or activation of which is necessary, either to prolong the reproduction process or to move to a new quality. The recent global economic crisis, related to the global pandemic situation, has made crisis management at both macro-economic and micro-economic levels one of the most important management tools to revive the critical situation globally. The crisis at all levels is always accompanied by a chronic organizational and financial instability, determining the need to develop measures that would be coherent in the new conditions of the economic environment accompanied by the political and social. The critical situation in the country's economy is confirmed by the worsening financial situation of domestic enterprises, the increase in losses, and, respectively, the number of enterprises on the verge of bankruptcy. Such a critical situation makes the issue of efficient organization of the management of the activity of enterprises in economic difficulty/crisis extremely topical. From this point of view, choosing the methods of crisis management and transforming them into an effective tool in entrepreneurship is a priority. The decisive role, in this sense, must belong to the diagnosis and financial forecasting of the dangers of crises at the enterprise by considering the factors that caused the crisis or even bankruptcy. The management of financial crises was constrained by the insufficient development of the informational-methodical base in this direction. In this context, not only the local experience but also the global one showed that the use of crisis management is quite difficult from a practical point of view, respectively, it requires an improvement and development in methodological-methodological terms, which requires evaluation of meticulous crises at the enterprise, which allows the diagnosis and prediction of crises with higher accuracy at each stage of its development.

Novel Stock Crisis Prediction Technique—A Study on Indian Stock Market

A stock market crash is a drop in stock prices more than 10% across the major indices. Stock crisis prediction is a difficult task due to more volatility in the stock market. Stock price sell-offs are due to various reasons such as company earnings, geopolitical tension, financial crisis, and pandemic situations. Crisis prediction is a challenging task for researchers and investors. We proposed a stock crisis prediction model based on the Hybrid Feature Selection (HFS) technique. First, we proposed the HFS algorithm to removes the irrelevant financial parameters features of stock. The second is the Naive Bayes method is considered to classify the strong fundamental stock. The third is we have used the Relative Strength Index (RSI) method to find a bubble in stock price. The fourth is we have used moving average statistics to identify the crisis point in stock prices. The fifth is stock crisis prediction based on Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) regression method. The performance of the model is evaluated based on Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error(RMSE). HFS based XGBoost method was performed better than HFS based DNN method for predicting the stock crisis. The experiments considered the Indian datasets to carry out the task. In the future, the researchers can explore other technical indicators to predict the crisis point. There is more scope to improve and fine-tune the XGBoost method with a different optimizer.

USE OF FUZZY LOGIC TO DEMONSTRATE POSSIBILITY OF HEALTH SYSTEM COLLAPSE DUE TO CORONAVIRUS PANDEMIC / USO DA LÓGICA FUZZY PARA DEMONSTRAR A POSSIBILIDADE DE COLAPSO DO SISTEMA DE SAÚDE DEVIDO À PANDEMIA DE CORONAVÍRUS

Introduction: At the end of 2019, contamination caused by an unknown virus affected the population of Wuhan, China. The virus, known as SARS-CoV-2, was found to cause flu-like symptoms, such as fever, cough, among others. Objective: using the fuzzy logic method graphically demonstrated the Risk of Collapse of the Health System, with the availability of beds /equipment to increase the number of infected, from the Sars-CoV-2 pandemic. Methods: Modeled a fuzzy system with a Mamdani model, later defined the physical variables of entry (Number of infected people and number of beds/equipment available) and output (Risk of collapse of the health system), then inserted the nine rules and the pertinence functions in the trapezoidal and triangular type graph, generating from this information a three-dimensional graph with the results. Results: From the three-dimensional graph, it can be seen that the results were consistent and demonstrate that countries must prevent the number of infected people from increasing, because when this situation occurs, the risk of collapse of the health system is high due to the unavailability of beds /equipment, in addition, this modeling of fuzzy logic along with the generated three-dimensional graph can be used to demonstrate a health crisis in countries. Conclusion: Concluded that the fuzzy logic technique allows many realistic predictions of possible crises and collapses of the health system, but to obtain good results in it, having the knowledge of what is expected will generate the necessary notion to make the insertion of information accurately in the software.

Credit Growth, the Yield Curve and Financial Crisis Prediction: Evidence from a Machine Learning Approach

We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of our machine learning models using a novel framework based on Shapley values, uncovering non-linear relationships between the predictors and crisis risk. Throughout, the most important predictors are credit growth and the slope of the yield curve, both domestically and globally. A flat or inverted yield curve is of most concern when nominal interest rates are low and credit growth is high.