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作 者:袁先智 狄岚 宋冠都 周云鹏 刘海洋 Guoqi Qian 严诚幸 曾途 George Xianzhi Yuan;Di Lan;Song Guandu;Zhou Yunpeng;Liu Haiyang;Guoqi Qian;Yan Chengxing;Zeng Tu(Business School,Chengdu University,Chengdu 610106;Business School,Sun Yat-Sen University,Guangzhou 510275;BBD Technology Co.,Ltd.(BBD),Chengdu 610093;School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122;The University of New South Wales:High St,Kensington,New South Wales 2052,Australia;School of Mathematics and Statistics,The University of Melbourne,Melbourne VIC3010,Australia)
机构地区:[1]成都大学商学院,成都610106 [2]中山大学管理学院,广州510275 [3]成都数联铭品科技有限公司,成都610093 [4]江南大学人工智能与计算机学院,无锡214122 [5]新南威尔士大学土木与环境工程学院,新南威尔士2052 [6]墨尔本大学数学与统计学院,墨尔本VIC3010
出 处:《管理评论》2021年第9期25-37,共13页Management Review
基 金:国家自然科学基金面上项目(71971031);国家自然科学基金联合基金项目(U1811462)。
摘 要:本文采用马尔科夫链蒙特卡洛(MCMC)框架下的Gibbs抽样算法,通过OR值(Odds Ratio,又称比值比或优势比)作为验证标准,实现从海量数据中提取与大宗商品期货螺纹钢价格趋势相关的特征因子并进行分类,用于构建支持期货价格趋势变化分析的特征指标。实证分析结果表明,本文讨论的特征提取方法能够有效地刻画螺纹钢期货价格的趋势变化,这为业界进行大宗期货交易和风险对冲的管理提供了一种新的分析维度。另外,本文讨论的从影响价格趋势变化的特征因子中筛选出更加有效的特征指标的方法,这也是与过去对价格趋势分析不同之处和创新点。The goal of this paper is to develop a way to show how we extract related characteristic factors(features)related to the price trend of commodity futures for screw steel materials by applying Gibbs sampling algorithm under the framework of Markov chain Monte Carlo(MCMC),then classify features related to the price trend of commodity futures into different levels by using the concept of the odds ratio associated with logistic regression model.Our empirical analysis results show that the feature extraction method discussed in this paper can effectively describe the trend of rebar futures price,which provides a new analysis method for bulk futures trading business,risk hedging and related risk management in the practice of financial industry.In addition,the method discussed to extract highly related effective characteristic factors that affect the change of price trend is also different from those in existing literature for the analysis of trend of price change.
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