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A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence

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  • Ghosh, Indranil
  • Chaudhuri, Tamal Datta
  • Alfaro-Cortés, Esteban
  • Gámez, Matías
  • García, Noelia

Abstract

The paper presents a framework to forecast futures prices of stocks listed on the National Stock Exchange (NSE) in India during normal (unaffected by the COVID-19 pandemic) and new normal times (affected by COVID-19 and a macroeconomic slowdown). The model leverages a structural model that determines the relevance of the explanatory features used in the study; namely, spot prices, market sentiment, sectoral outlook, historic and implied volatility, crude price volatility, and exchange rate volatility. The proposed Ensemble Feature Selection (EFS) methodology comprising Boruta and Regularized Random Forest (RRF) algorithms is used to screen the explanatory features. Two advanced Artificial Intelligence techniques—Regularized Greedy Forest (RGF) and Deep Neural Network (DNN)—are used in conjunction with Kernel Principal Component Analysis (KPCA) and Autoencoder (AE) for forecasting. To understand the extent and nature of the influence of the explanatory variables, the Explainable Artificial Intelligence (AI) approach has been used. Statistical checks confirm that our hybrid framework is effective. The results indicate that the relative importance of the explanatory variables in forecasting futures prices differs depending on the company concerned and the period under consideration.

Suggested Citation

  • Ghosh, Indranil & Chaudhuri, Tamal Datta & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2022. "A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:tefoso:v:181:y:2022:i:c:s0040162522002827
    DOI: 10.1016/j.techfore.2022.121757
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    as
    1. Qingfu Liu & Qian Luo & Yiuman Tse & Yuchi Xie, 2020. "The market quality of commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(11), pages 1751-1766, November.
    2. Ma, Richie Ruchuan & Xiong, Tao, 2021. "Price explosiveness in nonferrous metal futures markets," Economic Modelling, Elsevier, vol. 94(C), pages 75-90.
    3. Zhang, Shuchang & Guo, Yaoqi & Cheng, Hui & Zhang, Hongwei, 2021. "Cross-correlations between price and volume in China's crude oil futures market: A study based on multifractal approaches," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    4. Botterud, Audun & Kristiansen, Tarjei & Ilic, Marija D., 2010. "The relationship between spot and futures prices in the Nord Pool electricity market," Energy Economics, Elsevier, vol. 32(5), pages 967-978, September.
    5. Piñeiro-Chousa, Juan & López-Cabarcos, M.Ángeles & Caby, Jérôme & Šević, Aleksandar, 2021. "The influence of investor sentiment on the green bond market," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    6. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Precious metals, oil, and ASEAN stock markets: From global financial crisis to global health crisis," Resources Policy, Elsevier, vol. 73(C).
    7. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Pérez-Pico, Ada María & Ribeiro-Navarrete, Belén, 2018. "Does social network sentiment influence the relationship between the S&P 500 and gold returns?," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 57-64.
    8. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    9. Chang, Kuang-Liang & Lee, Chingnun, 2020. "The asymmetric spillover effect of the Markov switching mechanism from the futures market to the spot market," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 374-388.
    10. Marwa Talbi & Christian de Peretti & Lotfi Belkacem, 2020. "Dynamics and causality in distribution between spot and future precious metals: A copula approach," Post-Print hal-03671392, HAL.
    11. Tamal Datta Chaudhuri & Indranil Ghosh, 2016. "Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework," Papers 1607.02093, arXiv.org.
    12. Talbi, Marwa & de Peretti, Christian & Belkacem, Lotfi, 2020. "Dynamics and causality in distribution between spot and future precious metals: A copula approach," Resources Policy, Elsevier, vol. 66(C).
    13. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    14. Wen, Danyan & Wang, Yudong & Zhang, Yaojie, 2021. "Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism," Economic Modelling, Elsevier, vol. 96(C), pages 209-219.
    15. Guntur Anjana Raju & Sanjeeta Shirodkar, 2020. "The Lead Lag Relationship between Spot and Futures Markets in the Energy Sector: Empirical Evidence from Indian Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 409-414.
    16. Ballestra, Luca Vincenzo & Guizzardi, Andrea & Palladini, Fabio, 2019. "Forecasting and trading on the VIX futures market: A neural network approach based on open to close returns and coincident indicators," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1250-1262.
    17. Wang, Jian & Shao, Wei & Kim, Junseok, 2020. "Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    18. Datta Chaudhuri, T. & Chaudhuri, Sarmila, 1989. "The extent of wage indexation in Indian industries," Journal of Macroeconomics, Elsevier, vol. 11(3), pages 455-461.
    19. Laws, Jason & Thompson, John, 2004. "The efficiency of financial futures markets: Tests of prediction accuracy," European Journal of Operational Research, Elsevier, vol. 155(2), pages 284-298, June.
    20. Piñeiro-Chousa, Juan & López-Cabarcos, M.Ángeles & Ribeiro-Soriano, Domingo, 2020. "Does investor attention influence water companies’ stock returns?," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    21. Cui, Jinxin & Goh, Mark & Zou, Huiwen, 2021. "Coherence, extreme risk spillovers, and dynamic linkages between oil and China’s commodity futures markets," Energy, Elsevier, vol. 225(C).
    22. Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).
    23. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    24. Claudiu Tiberiu Albulescu & Aviral Kumar Tiwari & Phouphet Kyophilavong, 2021. "Nonlinearities and Chaos: A New Analysis of CEE Stock Markets," Mathematics, MDPI, vol. 9(7), pages 1-13, March.
    25. Olaf Korn & Paolo Krischak & Erik Theissen, 2019. "Illiquidity transmission from spot to futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(10), pages 1228-1249, October.
    26. Sifat, Imtiaz & Ghafoor, Abdul & Ah Mand, Abdollah, 2021. "The COVID-19 pandemic and speculation in energy, precious metals, and agricultural futures," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    27. Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).
    28. Liu, Weiping & Wang, Chengzhu & Li, Yonggang & Liu, Yishun & Huang, Keke, 2021. "Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    29. R. K. Jana & Indranil Ghosh & Debojyoti Das, 2021. "A differential evolution-based regression framework for forecasting Bitcoin price," Annals of Operations Research, Springer, vol. 306(1), pages 295-320, November.
    30. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    31. Juan Piñeiro-Chousa & M.Ángeles López-Cabarcos & Jérôme Caby & Aleksandar Šević, 2021. "The influence of investor sentiment on the green bond market," Post-Print hal-02960892, HAL.
    32. Liang, Qi & Sun, Wenjia & Li, Wenyu & Yu, Fengyan, 2021. "Media effects matter: Macroeconomic announcements in the gold futures market," Economic Modelling, Elsevier, vol. 96(C), pages 1-12.
    33. López-Cabarcos, M. Ángeles & Pérez-Pico, Ada M. & Piñeiro-Chousa, Juan & Šević, Aleksandar, 2021. "Bitcoin volatility, stock market and investor sentiment. Are they connected?," Finance Research Letters, Elsevier, vol. 38(C).
    34. Ghosh, Indranil & Datta Chaudhuri, Tamal & Alfaro-Cortés, Esteban & Gámez Martínez, Matías & García Rubio, Noelia, 2021. "Estimating the relative effects of raw material prices, sectoral outlook and market sentiment on stock prices," Resources Policy, Elsevier, vol. 73(C).
    35. Zhou, Xinmiao & Zhang, Junru & Zhang, Zhaoyong, 2021. "How does news flow affect cross-market volatility spillovers? Evidence from China’s stock index futures and spot markets," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 196-213.
    36. Zhang, Lihong & Wang, Jun & Wang, Bin, 2020. "Energy market prediction with novel long short-term memory network: Case study of energy futures index volatility," Energy, Elsevier, vol. 211(C).
    37. Oliveira, Fernando S. & Ruiz, Carlos, 2021. "Analysis of futures and spot electricity markets under risk aversion," European Journal of Operational Research, Elsevier, vol. 291(3), pages 1132-1148.
    38. Wang, Bin & Wang, Jun, 2021. "Energy futures price prediction and evaluation model with deep bidirectional gated recurrent unit neural network and RIF-based algorithm," Energy, Elsevier, vol. 216(C).
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    Cited by:

    1. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    2. Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2023. "Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI," International Review of Financial Analysis, Elsevier, vol. 87(C).

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