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Price, Complexity, and Mathematical Model

Author

Listed:
  • Na Fu

    (Department of Management and Economics, Tianjin University, Tianjin 300072, China
    Tianjin Agricultural University, No. 22 Jinjing Road, Xiqing District, Tianjin 300392, China)

  • Liyan Geng

    (School of Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Junhai Ma

    (Department of Management and Economics, Tianjin University, Tianjin 300072, China)

  • Xue Ding

    (Department of Management and Economics, Tianjin University, Tianjin 300072, China)

Abstract

The whole world has entered the era of the Vuca. Some traditional methods of problem analysis begin to fail. Complexity science is needed to study and solve problems from the perspective of complex systems. As a complex system full of volatility and uncertainty, price fluctuations have attracted wide attention from researchers. Therefore, through a literature review, this paper analyzes the research on complex theories on price prediction. The following conclusions are drawn: (1) The price forecast receives widespread attention year by year, and the number of published articles also shows a rapid rising trend. (2) The hybrid model can achieve higher prediction accuracy than the single model. (3) The complexity of models is increasing. In the future, the more complex methods will be applied to price forecast, including AI technologies such as LLM. (4) Crude-oil prices and stock prices will continue to be the focus of research, with carbon prices, gold prices, Bitcoin, and others becoming new research hotspots. The innovation of this research mainly includes the following three aspects: (1) The whole analysis of all the articles on price prediction using mathematical models in the past 10 years rather than the analysis of a single field such as oil price or stock price. (2) Classify the research methods of price forecasting in different fields, and found the common problems of price forecasting in different fields (including data processing methods and model selection, etc.), which provide references for different researchers to select price forecasting models. (3) Use VOSviewer to analyze the hot words appearing in recent years according to the timeline, find the research trend, and provide references for researchers to choose the future research direction.

Suggested Citation

  • Na Fu & Liyan Geng & Junhai Ma & Xue Ding, 2023. "Price, Complexity, and Mathematical Model," Mathematics, MDPI, vol. 11(13), pages 1-30, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2883-:d:1180658
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    References listed on IDEAS

    as
    1. Chao Zhang & Yihang Zhao & Huiru Zhao, 2022. "A Novel Hybrid Price Prediction Model for Multimodal Carbon Emission Trading Market Based on CEEMDAN Algorithm and Window-Based XGBoost Approach," Mathematics, MDPI, vol. 10(21), pages 1-16, November.
    2. Choudhry, Taufiq & Hassan, Syed S. & Shabi, Sarosh, 2015. "Relationship between gold and stock markets during the global financial crisis: Evidence from nonlinear causality tests," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 247-256.
    3. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
    4. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Sousa, Ricardo M., 2014. "What explain the short-term dynamics of the prices of CO2 emissions?," Energy Economics, Elsevier, vol. 46(C), pages 122-135.
    5. Zhu, Bangzhu & Ye, Shunxin & Han, Dong & Wang, Ping & He, Kaijian & Wei, Yi-Ming & Xie, Rui, 2019. "A multiscale analysis for carbon price drivers," Energy Economics, Elsevier, vol. 78(C), pages 202-216.
    6. Jing Zhang & Qi-zhi He & M. Irfan Uddin, 2021. "Dynamic Cross-Market Volatility Spillover Based on MSV Model: Evidence from Bitcoin, Gold, Crude Oil, and Stock Markets," Complexity, Hindawi, vol. 2021, pages 1-8, April.
    7. Wen, Xiaoqian & Cheng, Hua, 2018. "Which is the safe haven for emerging stock markets, gold or the US dollar?," Emerging Markets Review, Elsevier, vol. 35(C), pages 69-90.
    8. Alvarez-Ramirez, J. & Rodriguez, E. & Ibarra-Valdez, C., 2018. "Long-range correlations and asymmetry in the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 948-955.
    9. Ma, Junhai & Hou, Yaming & Wang, Zongxian & Yang, Wenhui, 2021. "Pricing strategy and coordination of automobile manufacturers based on government intervention and carbon emission reduction," Energy Policy, Elsevier, vol. 148(PA).
    10. Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
    11. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    12. Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    13. Sun, Wei & Zhang, Junjian, 2022. "A novel carbon price prediction model based on optimized least square support vector machine combining characteristic-scale decomposition and phase space reconstruction," Energy, Elsevier, vol. 253(C).
    14. Hussain Shahzad, Syed Jawad & Raza, Naveed & Shahbaz, Muhammad & Ali, Azwadi, 2017. "Dependence of stock markets with gold and bonds under bullish and bearish market states," Resources Policy, Elsevier, vol. 52(C), pages 308-319.
    15. Jong-Min Kim & Seong-Tae Kim & Sangjin Kim, 2020. "On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models," Mathematics, MDPI, vol. 8(11), pages 1-15, October.
    16. Jianguo Zhou & Dongfeng Chen, 2021. "Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
    17. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    18. Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022. "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, vol. 108(C).
    19. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    20. Ma, Junhai & Hou, Yaming & Yang, Wenhui & Tian, Yi, 2020. "A time-based pricing game in a competitive vehicle market regarding the intervention of carbon emission reduction," Energy Policy, Elsevier, vol. 142(C).
    21. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
    22. Fang Wu & Junhai Ma, 2023. "Research Trend, Logical Structure and Outlook on Complex Economic Game," Mathematics, MDPI, vol. 11(5), pages 1-16, February.
    23. Junhai Ma & Tiantong Xu, 2023. "Optimal strategy of investing in solar energy for meeting the renewable portfolio standard requirement in America," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(1), pages 181-194, January.
    24. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    25. Khalfaoui, Rabeh & Sarwar, Suleman & Tiwari, Aviral Kumar, 2019. "Analysing volatility spillover between the oil market and the stock market in oil-importing and oil-exporting countries: Implications on portfolio management," Resources Policy, Elsevier, vol. 62(C), pages 22-32.
    26. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    27. Peng Chen & Andrew Vivian & Cheng Ye, 2022. "Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine," Annals of Operations Research, Springer, vol. 313(1), pages 559-601, June.
    28. Iqbal, Javed, 2017. "Does gold hedge stock market, inflation and exchange rate risks? An econometric investigation," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 1-17.
    29. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    30. Gourav Kumar & Uday Pratap Singh & Sanjeev Jain, 2022. "Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 991-1039, October.
    31. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    32. Demir, Ender & Simonyan, Serdar & García-Gómez, Conrado-Diego & Lau, Chi Keung Marco, 2021. "The asymmetric effect of bitcoin on altcoins: evidence from the nonlinear autoregressive distributed lag (NARDL) model," Finance Research Letters, Elsevier, vol. 40(C).
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