Forecasting Performance of Lumber Futures Prices

Mehrotra, Shiv N.; Carter, Douglas R.

doi:10.1155/2017/1650363

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Exchange since 1969 (Mehrotra and Carter 2017). Since the COVID-19 pandemic, the lumber futures price has experienced huge volatility.…”

Section: Lumber Futures Have Been Traded At Chicago Mercantilementioning

confidence: 99%

“…In recent decades, several lumber price prediction methods have been proposed, such as ordinary least-squares regression (Mehrotra and Carter 2017), vector autoregressive model (VAR) (Song 2006), autoregressive integrated moving average model (ARIMA) Balsiger 1977, Oliveira et al 1977, Banas ´and Utnik-Banas ´2021), seasonal autoregressive moving average model (SARIMA) (Banas ´and Utnik-Banas ´2021), seasonal autoregressive moving average model with exogenous variables (SARIMAX) (Banas ´and Utnik-Banas ´2021), forest simulation model (FORSIM) (Buongiorno et al 1984), and sales & operations planning network model (Marier et al 2014). Most of the literature on lumber price prediction is based on traditional statistical models (Marier et al 2014), econometric models (Banas ´and Utnik-Banas ´2021, Buongiorno and Balsiger 1977, Mehrotra and Carter 2017, Oliveira et al 1977, Song 2006, or mathematical models (Buongiorno et al 1984). So far, only one paper has used a recurrent neural networks model, which is a deep learning method to predict the closing price of lumber futures in the next few days using the price obtained from the previous few days (Verly Lopes et al 2021).…”

Section: Lumber Futures Have Been Traded At Chicago Mercantilementioning

confidence: 99%

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

He¹,

Li²,

Via³

et al. 2022

Forest Products Journal

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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.

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“…Exchange since 1969 (Mehrotra and Carter 2017). Since the COVID-19 pandemic, the lumber futures price has experienced huge volatility.…”

Section: Lumber Futures Have Been Traded At Chicago Mercantilementioning

confidence: 99%

Section: Lumber Futures Have Been Traded At Chicago Mercantilementioning

confidence: 99%

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

He¹,

Li²,

Via³

et al. 2022

Forest Products Journal

View full text Add to dashboard Cite

show abstract

“…Log is the foundation of the supply chain. Logs are the raw materials for most forest products [45]. Therefore, the prices of log and finished products are not exactly the same, but the trend of log prices may affect the price change of wood products [46].…”

Section: Datamentioning

confidence: 99%

The Dynamic Impacts of the COVID-19 Pandemic on Log Prices in China: An Analysis Based on the TVP-VAR Model

Tao

Diao

Cheng

2021

Forests

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China’s wood industry is vulnerable to the COVID-19 pandemic since wood raw materials and sales of products are dependent on the international market. This study seeks to explore the speed of log price recovery under different control measures, and to perhaps find a better way to respond to the pandemic. With the daily data, we utilized the time-varying parameter autoregressive (TVP-VAR) model, which can incorporate structural changes in emergencies into the model through time-varying parameters, to estimate the dynamic impact of the pandemic on log prices at different time points. We found that the impact of the pandemic on oil prices and Renminbi exchange rate is synchronized with the severity of the pandemic, and the ascending in the exchange rate would lead to an increase in log prices, while oil prices would not. Moreover, the impulse response in June converged faster than in February 2020. Thus, partial quarantine is effective. However, the pandemic’s impact on log prices is not consistent with changes of the pandemic. After the pandemic eased in June 2020, the impact of the pandemic on log prices remained increasing. This means that the COVID-19 pandemic has long-term influences on the wood industry, and the work resumption was not smooth, thus the imbalance between supply and demand should be resolved as soon as possible. Therefore, it is necessary to promote the development of the domestic wood market and realize a “dual circulation” strategy as the pandemic becomes a “new normal”.

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A review of the financial performance of lumber futures and some prospects

Zhang,

Mei,

2023

Forest Policy and Economics

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Forecasting Performance of Lumber Futures Prices

Cited by 3 publications

References 20 publications

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

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

The Dynamic Impacts of the COVID-19 Pandemic on Log Prices in China: An Analysis Based on the TVP-VAR Model

A review of the financial performance of lumber futures and some prospects

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