The Effects of Decision Making on Futures Price Volatility

1996 ◽  
Vol 78 (3) ◽  
pp. 591-603 ◽  
Author(s):  
David A. Hennessy ◽  
Thomas I. Wahl
Keyword(s):  
Decision Making ◽  
Price Volatility ◽  
Futures Price

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.

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

scholarly journals Revisiting the relationship between formal planning process and planning effectiveness

2018 ◽  
Vol 30 (2) ◽  
pp. 1016-1034 ◽  
Author(s):  
Said Elbanna ◽  
Tamer H. Elsharnouby
Keyword(s):  
Decision Making ◽  
United Arab Emirates ◽  
Structural Equation ◽  
Planning Process ◽  
Price Volatility ◽  
Organizational Capabilities ◽  
Equation Modeling ◽  
Content Type ◽  
Tourism Sector ◽  
The Impact

Purpose The purpose of this study is to address a timely research question by clarifying whether formal planning is a worthy approach for hotels. In so doing, the authors developed a theoretical model that extends prior research by exploring how the formal planning process influences organizational capabilities and decision-making style. The model also examines the impact of the three identified factors on planning effectiveness. Design/methodology/approach Data were collected from 175 hotels located in United Arab Emirates (UAE) and Qatar and hypotheses were tested using structural equation modeling (SEM). Findings The study concludes that the practice of formal planning in the tourism sector does matter and both organizational capabilities and decision-making style are important factors in predicting planning effectiveness. Research limitations/implications Generalizations to organizations operating in other sectors, such as manufacturing or government sectors, should be drawn cautiously. Practical implications Taking into account oil price volatility and serious political crises in the region, this study provides several insights to hotel managers into how the formal planning process can influence planning effectiveness. Originality/value The findings enrich the debate on the role of formal planning in the tourism sector, which has been relatively devoid of similar studies.

The causal relationship between futures price volatility and the cash price volatility of GNMA securities

1986 ◽  
Vol 6 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Anand K. Bhattacharya ◽  
Anju Ramjee ◽  
Balasubramani Ramjee
Keyword(s):  
Causal Relationship ◽  
Price Volatility ◽  
Futures Price
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