IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i11p513-d437638.html
   My bibliography  Save this article

Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea

Author

Listed:
  • Tserenpurev Chuluunsaikhan

    (Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea)

  • Ga-Ae Ryu

    (Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea)

  • Kwan-Hee Yoo

    (Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea)

  • HyungChul Rah

    (Department of Management Information System, Chungbuk National University, Cheongju 28644, Korea)

  • Aziz Nasridinov

    (Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea)

Abstract

Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.

Suggested Citation

  • Tserenpurev Chuluunsaikhan & Ga-Ae Ryu & Kwan-Hee Yoo & HyungChul Rah & Aziz Nasridinov, 2020. "Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea," Agriculture, MDPI, vol. 10(11), pages 1-22, October.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:11:p:513-:d:437638
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/11/513/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/10/11/513/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yoo, Do-il, "undated". "Vegetable Price Prediction Using Atypical Web-Search Data," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236211, Agricultural and Applied Economics Association.
    2. Duc Hong Vo & Tan Ngoc Vu & Anh The Vo & Michael McAleer, 2019. "Modeling the Relationship between Crude Oil and Agricultural Commodity Prices," Energies, MDPI, vol. 12(7), pages 1-41, April.
    3. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    4. Hyun No Kim & Ik-Chang Choi, 2018. "The Economic Impact of Government Policy on Market Prices of Low-Fat Pork in South Korea: A Quasi-Experimental Hedonic Price Approach," Sustainability, MDPI, vol. 10(3), pages 1-16, March.
    5. Ga-Ae Ryu & Aziz Nasridinov & HyungChul Rah & Kwan-Hee Yoo, 2020. "Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data," Agriculture, MDPI, vol. 10(1), pages 1-14, January.
    6. Yongli Zhang & Sanggyun Na, 2018. "A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, November.
    7. Eleni Zafeiriou & Garyfallos Arabatzis & Paraskevi Karanikola & Stilianos Tampakis & Stavros Tsiantikoudis, 2018. "Agricultural Commodities and Crude Oil Prices: An Empirical Investigation of Their Relationship," Sustainability, MDPI, vol. 10(4), pages 1-11, April.
    8. Jingdong Li & Weidong Liu & Zhouying Song, 2020. "Sustainability of the Adjustment Schemes in China’s Grain Price Support Policy—An Empirical Analysis Based on the Partial Equilibrium Model of Wheat," Sustainability, MDPI, vol. 12(16), pages 1-22, August.
    9. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, vol. 12(22), pages 1-18, November.
    10. Mateusz Tomal & Agata Gumieniak, 2020. "Agricultural Land Price Convergence: Evidence from Polish Provinces," Agriculture, MDPI, vol. 10(5), pages 1-20, May.
    11. Tan Ngoc Vu & Chi Minh Ho & Thang Cong Nguyen & Duc Hong Vo, 2020. "The Determinants of Risk Transmission between Oil and Agricultural Prices: An IPVAR Approach," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    12. Antonia Weishaupt & Felix Ekardt & Beatrice Garske & Jessica Stubenrauch & Jutta Wieding, 2020. "Land Use, Livestock, Quantity Governance, and Economic Instruments—Sustainability Beyond Big Livestock Herds and Fossil Fuels," Sustainability, MDPI, vol. 12(5), pages 1-27, March.
    13. Vasilii Erokhin, 2017. "Factors Influencing Food Markets in Developing Countries: An Approach to Assess Sustainability of the Food Supply in Russia," Sustainability, MDPI, vol. 9(8), pages 1-13, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wuyue An & Lin Wang & Yu‐Rong Zeng, 2023. "Text‐based soybean futures price forecasting: A two‐stage deep learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 312-330, March.
    2. Ga-Ae Ryu & Tserenpurev Chuluunsaikhan & Aziz Nasridinov & HyungChul Rah & Kwan-Hee Yoo, 2023. "SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization for Agricultural Time-Series Prediction," Agriculture, MDPI, vol. 13(11), pages 1-21, October.
    3. Xiaohong Yu & Bin Liu & Yongzeng Lai, 2024. "Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications," Sustainability, MDPI, vol. 16(4), pages 1-26, February.
    4. Khishigsuren Davagdorj & Ling Wang & Meijing Li & Van-Huy Pham & Keun Ho Ryu & Nipon Theera-Umpon, 2022. "Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering," IJERPH, MDPI, vol. 19(10), pages 1-21, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kai Ye & Yangheran Piao & Kun Zhao & Xiaohui Cui, 2021. "A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion," Agriculture, MDPI, vol. 11(4), pages 1-14, April.
    2. Roberto Louis Forestal & Shih-Ming Pi, 2021. "Using Artificial Neural networks and Optimal Scaling Model to Forecast Agriculture Commodity Price: An Ecological-economic Approach," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(3), pages 1-3.
    3. Waseem Khan & Vishal Sharma & Saghir Ahmad Ansari, 2022. "Modeling the dynamics of oil and agricultural commodity price nexus in linear and nonlinear frameworks: A case of emerging economy," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1733-1784, August.
    4. Miroslava Ivanova & Lilko Dospatliev, 2023. "Effects of Diesel Price on Changes in Agricultural Commodity Prices in Bulgaria," Mathematics, MDPI, vol. 11(3), pages 1-22, January.
    5. Clement Oteng & Omowumi Iledare & Joshua Sebu, 2024. "Vulnerability of the Agricultural Commodity Markets in Ghana to Global Oil Price Fluctuations: An Empirical Analysis," SAGE Open, , vol. 14(1), pages 21582440231, March.
    6. Hanif, Waqas & Areola Hernandez, Jose & Shahzad, Syed Jawad Hussain & Yoon, Seong-Min, 2021. "Tail dependence risk and spillovers between oil and food prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 195-209.
    7. El Montasser, Ghassen & Malek Belhoula, Mohamed & Charfeddine, Lanouar, 2023. "Co-explosivity versus leading effects: Evidence from crude oil and agricultural commodities," Resources Policy, Elsevier, vol. 81(C).
    8. Yan Guo & Xiaonan Hu & Zepeng Wang & Wei Tang & Deyu Liu & Yunzhong Luo & Hongxiang Xu, 2021. "The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(11), pages 457-467.
    9. Tan Ngoc Vu & Chi Minh Ho & Thang Cong Nguyen & Duc Hong Vo, 2020. "The Determinants of Risk Transmission between Oil and Agricultural Prices: An IPVAR Approach," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    10. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.
    11. Jesus Crespo Cuaresma & Jaroslava Hlouskova & Michael Obersteiner, 2021. "Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1245-1273, November.
    12. Ahmed, Abdullahi D. & Huo, Rui, 2021. "Volatility transmissions across international oil market, commodity futures and stock markets: Empirical evidence from China," Energy Economics, Elsevier, vol. 93(C).
    13. Li, Sisi & Khan, Sufyan Ullah & Yao, Yao & Chen, George S. & Zhang, Lin & Salim, Ruhul & Huo, Jiaying, 2022. "Estimating the long-run crude oil demand function of China: Some new evidence and policy options," Energy Policy, Elsevier, vol. 170(C).
    14. Qi Zhang & Yi Hu & Jianbin Jiao & Shouyang Wang, 2022. "Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    15. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    16. Pietro De Marinis & Omar Ferrari & Erica Allisiardi & Chiara De Mattia & Giuliana Caliandro & Elio Dinuccio & Maurizio Borin & Paolo Ceccon & Guido Sali & Giorgio Provolo, 2021. "Insights about the Choice of Pig Manure Processing System in Three Italian Regions: Piemonte, Friuli Venezia Giulia, and Veneto," Sustainability, MDPI, vol. 13(2), pages 1-24, January.
    17. Sun, Yunpeng & Gao, Pengpeng & Raza, Syed Ali & Shah, Nida & Sharif, Arshian, 2023. "The asymmetric effects of oil price shocks on the world food prices: Fresh evidence from quantile-on-quantile regression approach," Energy, Elsevier, vol. 270(C).
    18. Bartosz Łamasz & Natalia Iwaszczuk, 2020. "The Impact of Implied Volatility Fluctuations on Vertical Spread Option Strategies: The Case of WTI Crude Oil Market," Energies, MDPI, vol. 13(20), pages 1-23, October.
    19. Vasilii Erokhin, 2017. "Self-Sufficiency versus Security: How Trade Protectionism Challenges the Sustainability of the Food Supply in Russia," Sustainability, MDPI, vol. 9(11), pages 1-17, October.
    20. Stubenrauch, Jessica & Garske, Beatrice, 2023. "Forest protection in the EU's renewable energy directive and nature conservation legislation in light of the climate and biodiversity crisis – Identifying legal shortcomings and solutions," Forest Policy and Economics, Elsevier, vol. 153(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:10:y:2020:i:11:p:513-:d:437638. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.