Financial Risk Prediction Based on Stochastic Block and Cox Proportional Hazards Models
10th International Conference on Communications, Signal Processing, and Systems, CSPS 2021
; 878 LNEE:548-556, 2022.
Article
in English
| Scopus | ID: covidwho-1826328
ABSTRACT
Since 2019, the sudden outbreak of COVID-19 has made huge impacts on various aspects of society, especially the financial industries that are closely related to the national economy and people’s livelihood. Finance is a data-intensive field and its traditional research models include supervised and unsupervised models, state-based models, econometric models, and stochastic models. However, the above models are prone to lose their effectiveness in the situation of an extremely complex financial ecosystem with a large number of nonlinear unpredictable effects, such as those caused by COVID-19. To address this issue, we comprehensively explore and fuse Stochastic Block Model (SBM) and Cox Proportional Hazards Model (COX) for a reliable and accurate financial risk prediction. Specifically, SBM, which is popular in social network analysis, is employed to capture the impact factors on the financial industry in public emergencies, and COX is then leveraged to determine the duration of the impact factors. An extensive experimental evaluation validates the effectiveness of our framework in predicting financial risk. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Cox proportional hazards model; Financial risk prediction; Public emergency; Stochastic block model; Forecasting; Hazards; Stochastic models; Stochastic systems; Cox proportional hazard model; Financial industry; Financial risks; Impact factor; Prediction-based; Public emergencies; Risk predictions; Stochastic block models; Stochastics; Finance
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
10th International Conference on Communications, Signal Processing, and Systems, CSPS 2021
Year:
2022
Document Type:
Article
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