基于随机搜索方法对影响大宗商品期货螺纹钢期货价格趋势变化的关联特征指标研究

管理评论 ›› 2021, Vol. 33 ›› Issue (9): 25-37.

基于随机搜索方法对影响大宗商品期货螺纹钢期货价格趋势变化的关联特征指标研究

袁先智^1,2,3, 狄岚⁴, 宋冠都⁵, 周云鹏³, 刘海洋³, Guoqi Qian⁶, 严诚幸³, 曾途³

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

收稿日期:2020-01-09 出版日期:2021-09-28 发布日期:2021-10-09
通讯作者: 严诚幸(通讯作者),成都数联铭品科技有限公司高级金融工程师
作者简介:袁先智,成都大学商学院教授,中山大学管理学院特聘教授,成都数联铭品科技有限公司首席风险官,博士;狄岚,江南大学人工智能与计算机学院副教授;宋冠都,新南威尔士大学土木与环境工程学院硕士研究生;周云鹏,成都数联铭品科技有限公司金融分析师;刘海洋,成都数联铭品科技有限公司金融工程师;Guoqi Qian,墨尔本大学数学与统计学院教授,博士;曾途,成都数联铭品科技有限公司董事长。
基金资助:
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）。

The Study for Characteristics of Commodity (Rebar) Futures Price Trend Based on Stochastic Search Approach

George Xianzhi Yuan^1,2,3, Di Lan⁴, Song Guandu⁵, Zhou Yunpeng³, Liu Haiyang³, Guoqi Qian⁶, Yan Chengxing³, Zeng Tu³

1. Business School, Chengdu University, Chengdu 610106;
2. Business School, Sun Yat-Sen University, Guangzhou 510275;
3. BBD Technology Co., Ltd. (BBD), Chengdu 610093;
4. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122;
5. The University of New South Wales:High St, Kensington, New South Wales 2052, Australia;
6. School of Mathematics and Statistics, The University of Melbourne, Melbourne VIC3010, Australia

Received:2020-01-09 Online:2021-09-28 Published:2021-10-09

摘要/Abstract

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。

关键词: 大宗商品期货, 价格趋势分析, 关联特征指标, 马尔科夫链蒙特卡洛(MCMC), AIC或BIC标准

Abstract: 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.

Key words: commodity futures, price trend, related features, Markov Chain Monte Carlo, Akaike Information Criterion & Bayesian Information Criterion

袁先智, 狄岚, 宋冠都, 周云鹏, 刘海洋, Guoqi Qian, 严诚幸, 曾途. 基于随机搜索方法对影响大宗商品期货螺纹钢期货价格趋势变化的关联特征指标研究[J]. 管理评论, 2021, 33(9): 25-37.

George Xianzhi Yuan, Di Lan, Song Guandu, Zhou Yunpeng, Liu Haiyang, Guoqi Qian, Yan Chengxing, Zeng Tu. The Study for Characteristics of Commodity (Rebar) Futures Price Trend Based on Stochastic Search Approach[J]. Management Review, 2021, 33(9): 25-37.

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