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

Abstract

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

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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