The Price‐Forecasting Performance of Futures Markets for Live Cattle and Hogs: A Disaggregated Analysis

1981 ◽  
Vol 63 (2) ◽  
pp. 209-215 ◽  
Author(s):  
Larry Martin ◽  
Philip Garcia
Keyword(s):  
Futures Markets ◽  
Price Forecasting ◽  
Forecasting Performance

A Preliminary Evaluation of Price Forecasting Performance by Agricultural Economists

1981 ◽  
Vol 63 (4) ◽  
pp. 712-714 ◽  
Author(s):  
James C. Cornelius ◽  
John E. Ikerd ◽  
A. Gene Nelson
Keyword(s):  
Price Forecasting ◽  
Preliminary Evaluation ◽  
Forecasting Performance

scholarly journals ENHANCING THE ACCURACY OF MALAYSIAN HOUSE PRICE FORECASTING: A COMPARATIVE ANALYSIS ON THE FORECASTING PERFORMANCE BETWEEN THE HEDONIC PRICE MODEL AND ARTIFICIAL NEURAL NETWORK MODEL

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Nurul Fazira Sa’at ◽  
Nurul Hana Adi Maimun ◽  
Nurul Hazrina Idris
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural Network Model ◽  
House Price ◽  
Predictive Performance ◽  
Hedonic Price Model ◽  
Price Forecasting ◽  
Hedonic Price ◽  
Price Model ◽  
Forecasting Performance

The Hedonic Price Model (HPM), a prominent model used in real estate appraisal and economics, has been argued to be marred with nonlinearity, multicollinearity and heteroscedasticity problems that affect the accuracy of price predictions. An alternative method called Artificial Neural Network Model (ANN) was identified as capable of addressing the shortcomings of HPM and produces superior predictive performance. Hence, this study aims to evaluate the forecasting performance between HPM and ANN using Malaysian housing transaction data from the period between 2009 to 2018, sourced from the Valuation and Property Service Department, Johor Bahru. The models’ performance was evaluated and compared based on their statistical and predictive performance. Results showed that ANN outperformed HPM in both statistical and predictive performance. This study benefits the expansion of academic and practical knowledge in enhancing the accuracy of house price forecasting.

Grain Price Forecasting Using a Hybrid Stochastic Method

2017 ◽  
Vol 34 (05) ◽  
pp. 1750020 ◽  
Author(s):  
Yu Zhao ◽  
Xi Zhang ◽  
Zhongshun Shi ◽  
Lei He
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Stochastic Method ◽  
Price Forecasting ◽  
Market Participants ◽  
Forecasting Models ◽  
Price Series ◽  
Forecasting Performance ◽  
Stochastic Time Series ◽  
Stochastic Time

Grain price forecasting is significant for all market participants in managing risks and planning operations. However, precise forecasting of price series is difficult because of the inherent stochastic behavior of grain price. In this paper, a novel hybrid stochastic method for grain price forecasting is introduced. The proposed method combines decomposed stochastic time series processes with artificial neural networks. The initial parameters of the hybrid stochastic model are optimized by a random search using a genetic algorithm. The proposed method is finally validated in China’s national grain market and compared with several recent price forecasting models. Results indicate that the proposed hybrid stochastic method provides a satisfactory forecasting performance in grain price series.

scholarly journals A strategy for electricity buyers in futures markets

2020 ◽  
Vol 152 ◽  
pp. 03007
Author(s):  
Claudio Monteiro ◽  
Ignacio J. Ramirez-Rosado ◽  
L. Alfredo Fernandez-Jimenez
Keyword(s):  
Electricity Market ◽  
Risk Premium ◽  
Futures Markets ◽  
Trading Strategy ◽  
Spot Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Wrong Decision ◽  
Futures Contract ◽  
Monthly Average

This paper presents an original trading strategy for electricity buyers in futures markets. The strategy applies a medium-term electricity price forecasting model to predict the monthly average spot price which is used to evaluate the Risk Premium for a physical delivery under a monthly electricity futures contract. The proposed trading strategy aims to provide an advantage relatively to the traditional strategy of electricity buyers (used as benchmark), anticipating the good/wrong decision of buying electricity in the futures market instead in the day-ahead market. The mid-term monthly average spot price forecasting model, which supports the trading strategy, uses only information available from futures and spot markets at the decision moment. Both the new trading strategy and the monthly average spot price forecasting model, proposed in this paper, have been successfully tested with historical data of the Iberian Electricity Market (MIBEL), although they could be applied to other electricity markets.

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