Modeling the Connection between Bank Systemic Risk and Balance-Sheet Liquidity Proxies through Random Forest Regressions

Zeldea, Cristina Georgiana

doi:10.3390/admsci10030052

Cited by 3 publications

References 46 publications

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Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor

Cheng,

Chunhong,

Qianglin

2023

Sci Rep

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Small-scale distributed water treatment equipment such as sequencing batch reactor (SBR) is widely used in the field of rural domestic sewage treatment because of its advantages of rapid installation and construction, low operation cost and strong adaptability. However, due to the characteristics of non-linearity and hysteresis in SBR process, it is difficult to construct the simulation model of wastewater treatment. In this study, a methodology was developed using artificial intelligence and automatic control system that can save energy corresponding to reduce carbon emissions. The methodology leverages random forest model to determine a suitable soft sensor for the prediction of COD trends. This study uses pH and temperature sensors as premises for COD sensors. In the proposed method, data were pre-processed into 12 input variables and top 7 variables were selected as the variables of the optimized model. Cycle ended by the artificial intelligence and automatic control system instead of by fixed time control that was an uncontrolled scenario. In 12 test cases, percentage of COD removal is about 91. 075% while 24. 25% time or energy was saved from an average perspective. This proposed soft sensor selection methodology can be applied in field of rural domestic sewage treatment with advantages of time and energy saving. Time-saving results in increasing treatment capacity and energy-saving represents low carbon technology. The proposed methodology provides a framework for investigating ways to reduce costs associated with data collection by replacing costly and unreliable sensors with affordable and reliable alternatives. By adopting this approach, energy conservation can be maintained while meeting emission standards.

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Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor

Cheng,

Chunhong,

Qianglin

2023

Sci Rep

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Systemic Risk in Banking against the Backdrop of the COVID-19 Pandemic

Huang

2023

Systems

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The aim of the study is to identify the interrelations and interdependencies of systemic risk formation in the banking sector under the influence of the COVID-19 pandemic. The analysis of theoretical sources resulted in the main hypotheses of this study: (H1) The number of COVID-19 cases contributes to the formation of systemic risk in the banking sector through an increase in household debt; (H2) the number of COVID-19 cases contributes to the formation of systemic risk in the banking sector through an increase in overdue loans; (H3) the number of COVID-19 cases contributes to the formation of systemic risk in the banking sector through changes in the liquidity of the capital of banking institutions; (H4) the number of fatal COVID-19 cases contributes to the formation of systemic risk in the banking sector, through an increase in household debt; (H5) the number of fatal COVID-19 cases does not have a significant impact on the formation of systemic risk in the banking sector through an increase in overdue loans; (H6) the number of fatal COVID-19 cases does not have a significant impact on the formation of systemic risk in the banking sector through changes in the liquidity of the capital of banking institutions; (H7) the COVID-19 pandemic has a significant impact on the formation of systemic risk in the banking sector with an increase in the number of cases. The research methodology was based on a quantitative approach. The methodological basis of the study was the time-series model, analyzed using a complex of econometric and economic-statistical methods. The proposed methodological approach was tested on the example of China. As a result of the conducted research, polynomial mathematical models of the selected indicators were developed, and sustainable relations and correlations between individual indicators of the systemic risk formation in the banking sector and indicators of the COVID-19 pandemic were revealed, on the basis of which Hypotheses H1, H4, H6, and H7 were proved and Hypotheses H2 and H5 were refuted. At the same time, the H3 hypothesis was proved with a remark about the need for an individual approach since the negative effect manifests itself primarily in the medium and long term. The results of the study can be used by bank managers to implement measures that prevent the formation of systemic risk. In addition, the results of this study may be of interest to subsequent studies, including in terms of forming promising directions for future research.

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Exploring the Nexus Among the Business Coping Strategy

Basheer

Raoof

Jabeen

et al. 2021

Handbook of Research on Entrepreneurship, Innovation, Sustainability, and ICTs in the Post-Covid-19 Era

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The recession due to COVID- 19 causes uncertainties in the industry's immediate operating landscape as well as other unprecedented impacts. Due to the immense magnitude of impacts posed by recessionary events, this present study proposes the significance of understanding the phenomena while examining the subject of crisis readiness of SMEs during COVID-19 era. The main objective of the study is to examine the impact of business coping strategy and entrepreneurial orientation on the crisis readiness of Pakistani-based SMEs. The study emphasized the necessity of delineating the effects of extremely disastrous and unforeseen events. This is because the understanding of the effects of such cataclysmic event is more important than the prediction of the event's occurrence. The study has employed a Suraya-based method, and data is collected with the aid of a questionnaire. The response rate was 78%, which qualifies the minimum response rate criteria. The SEM-PLS is used to analyse the data. The results of the study have provided support to hypothesized results.

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Modeling the Connection between Bank Systemic Risk and Balance-Sheet Liquidity Proxies through Random Forest Regressions

Cited by 3 publications

References 46 publications

Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor

Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor

Systemic Risk in Banking against the Backdrop of the COVID-19 Pandemic

Exploring the Nexus Among the Business Coping Strategy

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