首页|Investigators at Hunan University Discuss Findings in Machine Learning (Identify ing Systemic Risk Drivers of Fintech and Traditional Financial Institutions: Mac hine Learning-based Prediction and Interpretation)
Investigators at Hunan University Discuss Findings in Machine Learning (Identify ing Systemic Risk Drivers of Fintech and Traditional Financial Institutions: Mac hine Learning-based Prediction and Interpretation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting out of Changsha, People's R epublic of China, by NewsRx editors, research stated, "We study systemic risk dr ivers of FinTech and traditional financial institutions under normal and extreme market conditions." Financial supporters for this research include Huxiang Youth Talent Support Prog ram, National Natural Science Foundation of China (NSFC), National Social Scienc e Fund of China, Natural Science Foundation of Hunan Province. Our news journalists obtained a quote from the research from Hunan University, " We use machine learning (ML) techniques (i.e. random forest and gradient boosted regression trees) to evaluate the role of macroeconomic variables, firm charact eristics, and network topologies as systemic risk drivers and perform the ML-bas ed interpretation by Shapley individual and interaction values. We find that (i) the feature importance in driving systemic risk depends on market conditions; n amely, market volatility (MVOL), individual stock volatility (IVOL), and market capitalization (MC) are positive drivers of systemic risk under extreme (downsid e and upside) market conditions, while under normal market conditions, instituti ons with high price-earnings ratio, large MC, and low IVOL play an essential rol e in stabilizing markets; (ii) macroeconomic variables are the most important ex treme systemic risk drivers, while firm characteristics are more important under normal market conditions; and (iii) the interaction between IVOL and MC or MVOL is the significant source of extreme systemic risk, and MC is the most crucial interaction attribute under normal market conditions."
ChangshaPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesFinanceInvestment and FinanceMachine Lea rningHunan University
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