scholarly journals On the price volatility of steel futures and its influencing factors in China

Accounting ◽  
2021 ◽  
pp. 771-780 ◽  
Author(s):  
Tzuchia Chen ◽  
Wenjing Li ◽  
Shuyan Yu
Keyword(s):  
Influencing Factors ◽  
Price Volatility ◽  
Hedging Strategy ◽  
Open Interest ◽  
Proposed Model ◽  
Steel Bars ◽  
Managerial Implications ◽  
Futures Price ◽  
Hot Rolled ◽  
Sustainable Capacity

Steel futures have the function of price discovery and hedging. Steel related enterprises can judge the hedging strategy through the direction of steel futures price volatility, and reasonably avoid the risk brought by price volatility. Therefore, it is particularly important to study steel futures price volatility and its influencing factors. Because steel futures in China have characteristics of peak and rear tail aggregation, the paper constructs Model of GARCH (1,1) to make positive analysis of futures price volatility and its influencing factors of deformed steel bars and hot rolled coils, and the following conclusions have been drawn: (1) The volume and open interest of deformed steel bars have very significant explanatory ability to futures price volatility of deformed steel bars; (2) The volume and open interest of hot rolled coils also have very significant explanatory ability to futures price volatility of hot rolled coils; (3) The sustainable capacity of the price volatility of deformed steel bars and hot rolled coils is relatively small; (4) Iron ore price have no obvious explanatory ability to futures price volatility. Finally, some managerial implications and suggestions are derived from the analysis of the proposed model.

A CONCEPTUAL MODEL TO UNDERSTAND THE EFFECTS OF PERCEPTION ON THE CONTINUANCE INTENTION IN FACEBOOK

2012 ◽  
Vol 01 (08) ◽  
pp. 29-34
Author(s):  
Shih-Chih Chen ◽  
Huei-Huang Chen ◽  
Mei-Tzu Lin ◽  
Yu-Bei Chen
Keyword(s):  
Information System ◽  
Social Networking ◽  
Conceptual Model ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
System Quality ◽  
Perceived Ease Of Use ◽  
Continuance Intention ◽  
Proposed Model ◽  
Managerial Implications

Recently, the social networking applications expand rapidly and attract a lot of users in a short time period. This study attempts to develop a conceptual model to understand the continuance intention in the context of social networking. The conceptual model integrates the post-acceptance model of information system continuance with perceived ease-of-use and perceived usefulness proposed by Bhattacherjee (2001a) and Davis (1989), respectively. In the proposed model, continuance intention is influenced by the relationship quality and information system quality. Additionally, nine propositions are developed based the proposed model and literature review. Finally, conclusions, managerial implications, and future direction of research are also provided.

Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

2021 ◽  
pp. 1-18
Author(s):  
Zhang Zixian ◽  
Liu Xuning ◽  
Li Zhixiang ◽  
Hu Hongqiang
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Influencing Factors ◽  
Association Rules ◽  
Coal And Gas Outburst ◽  
Feature Subset ◽  
Gas Outburst ◽  
Proposed Model ◽  
Optimal Feature Subset ◽  
Feature Dimension

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.

The Reaction of Coffee Futures Price Volatility to Crop Reports

2016 ◽  
Vol 53 (10) ◽  
pp. 2361-2376 ◽  
Author(s):  
Rodrigo Lanna F. da Silveira ◽  
Fabio L. Mattos ◽  
Maria Sylvia M. Saes
Keyword(s):  
Price Volatility ◽  
Futures Price

scholarly journals Nowcasting of Lumber Futures Price with Google Trends Index Using Machine Learning and Deep Learning Models

2021 ◽  
Vol 72 (1) ◽  
pp. 11-20
Author(s):  
Mingtao He ◽  
Wenying Li ◽  
Brian K. Via ◽  
Yaoqi Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Price Volatility ◽  
Google Trends ◽  
Learning Models ◽  
Fresh Perspective ◽  
Futures Price

Abstract Firms engaged in producing, processing, marketing, or using lumber and lumber products always invest in futures markets to reduce the risk of lumber price volatility. The accurate prediction of real-time prices can help companies and investors hedge risks and make correct market decisions. This paper explores whether Internet browsing habits can accurately nowcast the lumber futures price. The predictors are Google Trends index data related to lumber prices. This study offers a fresh perspective on nowcasting the lumber price accurately. The novel outlook of employing both machine learning and deep learning methods shows that despite the high predictive power of both the methods, on average, deep learning models can better capture trends and provide more accurate predictions than machine learning models. The artificial neural network model is the most competitive, followed by the recurrent neural network model.

scholarly journals Modeling and Forecasting Gold Prices

2020 ◽  
Author(s):  
Richmond Sam-Quarm ◽  
Mohamed Osman Elamin Busharads
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Price Volatility ◽  
Gold Price ◽  
Oil Price ◽  
Future Market ◽  
Open Interest ◽  
Long Run ◽  
Short Run ◽  
Japanese Yen

The aim of this paper is to explore the reasons of gold price volatility. It analyses the information function of the gold future market by open interest contracts as speculation effect, and further fundamental factors including inflation, Chinese yuan per dollar, Japanese yen per dollar, dollar per euro, interest rate, oil price, and stock price, in the short-run. The study proceeds to build a Dynamic OLS model for long-run equilibrium to produce reliable gold price forecasts using the following variables: gold demand, gold supply, inflation, USD/SDR exchange rate, speculation, interest rate, oil price, and stock prices. Findings prove that in the short-run, changes in gold price does granger cause changes in open interest, and changes in Japanese yen per dollar does granger cause changes in gold price. However, in the long-run, the results prove that gold demand, gold supply, USD/SDR exchange rate, inflation, speculation, interest rate, and oil price are associated in a long-run relationship.References

Calculation of microstructure and hardness of hot rolled steel bars

1991 ◽  
Vol 7 (2) ◽  
pp. 118-128 ◽  
Author(s):  
B. Jansson ◽  
M. Rolfson ◽  
A. Thuvander ◽  
A. Melander ◽  
C. Wullimann
Keyword(s):  
Rolled Steel ◽  
Steel Bars ◽  
Hot Rolled ◽  
Hot Rolled Steel

Forecasting the oil futures price volatility: Large jumps and small jumps

2018 ◽  
Vol 72 ◽  
pp. 321-330 ◽  
Author(s):  
Jing Liu ◽  
Feng Ma ◽  
Ke Yang ◽  
Yaojie Zhang
Keyword(s):  
Price Volatility ◽  
Oil Futures ◽  
Futures Price
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