Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques

Zhang

et al. 2022

IMDS

PurposeThe purpose of this study is to forecast the development performance of important economies in a smart city using mixed-frequency data.Design/methodology/approachThis study introduces reverse unrestricted mixed-data sampling (RUMIDAS) to support vector regression (SVR) to develop a novel RUMIDAS-SVR model. The RUMIDAS-SVR model was estimated using a quadratic programming problem. The authors then use the novel RUMIDAS-SVR model to forecast the development performance of all high-tech listed companies, an important sector of the economy reflecting the potential and dynamism of urban economic development in Shanghai using the mixed-frequency consumer price index (CPI) producer price index (PPI), and consumer confidence index (CCI) as predictors.FindingsThe empirical results show that the established RUMIDAS-SVR is superior to the competing models with regard to mean absolute error (MAE) and root-mean-squared error (RMSE) and multi-source macroeconomic predictors contribute to the development performance forecast of important economies.Practical implicationsSmart city policy makers should create a favourable macroeconomic environment, such as controlling inflation or stabilising prices for companies within the city, and companies within the important city economic sectors should take initiative to shoulder their responsibility to support the construction of the smart city.Originality/valueThis study contributes to smart city monitoring by proposing and developing a new model, RUMIDAS-SVR, to help the construction of smart cities. It also empirically provides strategic insights for smart city stakeholders.

Section: Mixed-frequency Data-driven Forecasting Methodsmentioning

confidence: 99%

“…Lai et al. (2014) and Wang et al. (2022b) concluded that there is a positive correlation between the stock market and the consumer price index (CPI), but Schwert (1981) observed that the stock market responds to inflation in the CPI negatively.…”

Section: Literature Reviewmentioning

confidence: 99%

Mixed-frequency data-driven forecasting the important economies' performance in a smart city: a novel RUMIDAS-SVR model

Zhang

et al. 2022

IMDS

“…The financial time series in which the true variance deviates from the model fit and the variance of the disturbance term u_t depends on the prior conditional variance 𝑢 , i.e., the ARCH model. The unbiased estimation expression is Equation (5).…”

Section: Var-garch Modelmentioning

confidence: 99%

“…Zhao, Jingfeng, Li, & Bo [4] show the market economy of China consists of enterprises, and the fluctuation of SMEs' finance will lead to supply chain fluctuation, while supply chain budgeting can solve the problems of SMEs' expenditure to a certain extent. Wang et al [5] used an imbalance strategy with machine learning techniques to predict the credit risk of SMEs in China, using financial, operational, innovation, and negative information as predictors to study the credit risk contagion effects among firms in a supply chain system. Thanigai A et al [6] used a gray decision experiment and evaluation laboratory ( DEMATEL) approach, verified that different categories of risks are also contagious to each other, with the financial risk of firms being the most vulnerable to circular supply chains.…”

Section: Introductionmentioning

confidence: 99%

Study on the Risk Contagion Effect of Energy Supply Chain Finance under the "Double Carbon" Target

Hui¹

2022

FBEM

The realization of the "double carbon" target will have a great impact on the contagion effect of supply chain finance risk in the energy market. The article uses social network model and GARCH model to study the contagion effect of energy supply chain finance risk from both enterprise creditworthiness and economic environment. The social network model is used to analyze the degree of differentiation of the supply chain network and the correlation between node enterprises before and after the implementation of the "double carbon" target, using the asset-liability ratio of enterprises as the default risk indicator. The GARCH model was used to empirically analyze the risk contagion effect between CSI energy and Shanghai and Shenzhen stock indices from the perspective of economic environment. The study found that there is a significant correlation and contagion effect between the financial risks of silicon energy enterprises, and the enterprises with higher creditworthiness are less affected by the implementation of the "double carbon" target plan, and the supply chain network is more obviously centralized by the "double carbon" target. The results of GARCH model show that the energy market is influenced by the external economic environment and there is risk contagion effect between markets. Based on the results of the GARCH model, we provide policy recommendations for supply chain risk prevention and control.

“…In this approach, the workplace is divided into a set of geofences. Then, based on the nature and requirement of the work, the workplace environment will be clustered using the idea of a dynamic user-centric clustering [ 8 ]. Different wireless parameters and metrics play important roles as location signatures to apply such geofencing techniques, especially with applying machine learning with it.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Movement Monitoring in Clustered Workplaces to Control COVID-19 Based on Geofencing and Fusion of Wi-Fi and Magnetic Field Metrics

El-Haleem

Mohamed²,

Abdelhakam³

et al. 2022

Sensors

The ubiquitous existence of COVID-19 has required the management of congested areas such as workplaces. As a result, the use of a variety of inspiring tools to deal with the spread of COVID-19 has been required, including internet of things, artificial intelligence (AI), machine learning (ML), and geofencing technologies. In this work, an efficient approach based on the use of ML and geofencing technology is proposed to monitor and control the density of persons in workplaces during working hours. In particular, the workplace environment is divided into a number of geofences in which each person is associated with a set of geofences that make up their own cluster using a dynamic user-centric clustering scheme. Different metrics are used to generate a unique geofence digital signature (GDS) such as Wi-Fi basic service set identifier, Wi-Fi received signal strength indication, and magnetic field data, which can be collected using the person’s smartphone. Then, these metrics are utilized by different ML techniques to generate the GDS for each indoor geofence and each building geofence as well as to detect whether the person is in their cluster. In addition, a Layered-Architecture Geofence Division method is considered to reduce the processing overhead at the person’s smartphone. Our experimental results demonstrate that the proposed approach can perform well in a real workplace environment. The results show that the system accuracy is about 98.25% in indoor geofences and 76% in building geofences.