首页|Investigators at Business School Detail Findings in Machine Learning (Do Industries Predict Stock Market Volatility? Evidence From Machine Learning Models)
Investigators at Business School Detail Findings in Machine Learning (Do Industries Predict Stock Market Volatility? Evidence From Machine Learning Models)
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NETL
NSTL
Elsevier
Investigators publish new report on Machine Learning. According to news originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “In a novel take on the gradual information diffusion hypothesis of Hong et al. (2007), we examine the predictive role of industries over aggregate stock market volatility.” Financial support for this research came from National Office of Philosophy and Social Sciences. Our news journalists obtained a quote from the research from Business School, “Using high frequency data for U.S. industry indexes and various heterogeneous autoregressive (HAR) type and machine learning models, we show that most industries are informative for future aggregate market volatility in out-of-sample tests. While the oil and gas industry plays a more dominant role for the component of aggregate market volatility that is associated with discount rate fluctuations, consumer services are most informative over market volatility that is attributable to cash flow fluctuations.”
ChangshaPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesFinance and InvestmentInvestment and FinanceMachine LearningBusiness School
NETL
NSTL
Elsevier
