Yunxian Yan, Lu Tian and Yuejie Zhang
The purpose of this paper is to discover an effective maize price for trading and policy-making reference by assessing the price transmission of the US spot and futures maize…
Abstract
Purpose
The purpose of this paper is to discover an effective maize price for trading and policy-making reference by assessing the price transmission of the US spot and futures maize prices to Chinese counterparts.
Design/methodology/approach
The authors apply a systematic, quantitative method to analyze the integration between US and Chinese maize markets. Based on the residuals of the variables through error correction model, the directed acyclic graph (DAG) among six price variables is conducted. With consideration of the dependence on and direction of six price variables, the variance decomposition of each variable is calculated.
Findings
This paper shows that the vertical price transmission passes from wholesale price to farm-gate price. The horizontal price transmission ranges from spot price to futures price at the domestic market and from the American spot price to domestic spot price, from the American spot price to domestic futures price and from the American futures price to domestic futures price. The American maize spot and futures prices, and in particular the spot price, have greater effects on domestic maize prices contemporaneously. It also indicates that the American spot price is the leader price in the long run at both maize markets.
Originality/value
This paper contributes by using the DAG method in this paper. It also contributes by helping policy makers and market participants find the leading prices and offers insight into ways of gaining power of price setting in the maize market.
Details
Keywords
Jun Deng, Chuyi Zhong, Shaodan Sun and Ruan Wang
This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining…
Abstract
Purpose
This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining technology to promote innovation of humanities research paradigm and method.
Design/methodology/approach
The proposed STEF uses methods of information extraction, sentiment analysis and geographic information system to achieve knowledge extraction and mining. STEF integrates time, space and emotional elements to visualize the spatial and temporal evolution of emotions, which thus enriches the analytical paradigm in digital humanities.
Findings
The case study shows that STEF can effectively extract knowledge from unstructured texts in the field of Chinese Qing Dynasty novels. First, STEF introduces the knowledge extraction tools – MARKUS and DocuSky – to profile character entities and perform plots extraction. Second, STEF extracts the characters' emotional evolutionary trajectory from the temporal and spatial perspective. Finally, the study draws a spatio-temporal emotional path figure of the leading characters and integrates the corresponding plots to analyze the causes of emotion fluctuations.
Originality/value
The STEF is constructed based on the “spatio-temporal narrative theory” and “emotional narrative theory”. It is the first framework to integrate elements of time, space and emotion to analyze the emotional evolution trajectories of characters in novels. The execuability and operability of the framework is also verified with a case novel to suggest a new path for quantitative analysis of other novels.