Quantile XGBoost and SHAP in Creating and Explaining Forecasting Models for AI Tokens

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Abstract

This research aims to develop a model and methodology for forecasting logarithmic returns (logreturns) for a new asset class of AI (Artificial Intelligence) tokens. To obtain one day ahead forecasts for the 0.9, 0.5, and 0.1 quantiles, the use of XGBoost quantile models is proposed, which represent an ensemble, based on gradient boosted regression trees. Quantile models have an advantage in terms of forecasting over traditionally used regression models because they allow for the estimation of not only point forecasts but also of their confidence intervals, while remaining robust to outliers. This is especially important when forming the forecasts for various cryptocurrencies’ market characteristics, which are known to be highly volatile. In addition to forecasting, the study conducts a post-forecast analysis using the SHAP (Shapley Additive explanations) method, which allows to interpret the XGBoost model, revealing key factors that are important for forecasting AI tokens’ logreturns. Based on the results of feature importance analysis with SHAP, a significant influence of AI stocks’ market characteristics, cryptocurrency market investor sentiment, seasonal fluctuations, as well as features related to the Blockchain ecosystem was identified. The paper also discusses and addresses the shortcomings of modern forecasting and post-forecasting analysis approaches for time series in general. The obtained results, in addition to academic interest, are relevant for private investors, risk managers, firms and regulators.

About the authors

I. I. Kucherov

Centre for Financial Research, Data Analytics, HSE University

Email: unequivocally.ivan@gmail.com
Moscow, Russia

References

  1. Abdullah M., Sarker P. K., Abakah E. J. A., Tiwari A. K., Rehman M. Z. (2024). Tail risk intersection between tech-tokens and tech-stocks. Global Finance Journal, 61, 100989. doi: 10.1016/j.gfj.2024.100989
  2. Adekoya O. B., Oliyide J. A., Saleem O., Adeoye H. A. (2022). Asymmetric connectedness between Google-based investor attention and the fourth industrial revolution assets: The case of FinTech and Robotics, Artificial intelligence stocks. Technology in Society, 68, 101925. doi: 10.1016/j.techsoc.2022.101925
  3. Akpan M. (2024). Attention is all you need: An analysis of the valuation of artificial intelligence tokens. SSRN: 4993784. doi: 10.2139/ssrn.4993784
  4. Ali R., Xu J., Baig M. H., Rehman H. S.U., Waqas Aslam M., Qasim K. U. (2024). From data to decisions: enhancing financial forecasts with LSTM for AI token prices. Journal of Economic Studies, 51 (8), 1677–1693. doi: 10.1108/JES-01-2024-0022
  5. Ali S., Al-Nassar N.S., Khalid A. A., Salloum C. (2024). dynamic tail risk connectedness between artificial intelligence and fintech stocks. Annals of Operations Research, 1–35. doi: 10.1007/s10479-024-06349-y
  6. Ante L., Demir E. (2024). The ChatGPT effect on AI-themed cryptocurrencies. Economics and Business Letters, 13 (1), 29–38. doi: 10.2139/ssrn.4350557
  7. Baklanova V., Kurkin A., Teplova T. (2024). Investor sentiment and the NFT hype index: To buy or not to buy? China Finance Review International, 14 (3), 522–548. doi: 10.1108/CFRI-06-2023-0175
  8. Bao T., Wang X., Mahdavi N., McCarthy C., Rezazadegan D. (2023). Dynamic XGBoost-based quantile predictor for real-time electricity price forecasting. In: 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 1–6). IEEE. doi: 10.1109/ETFG55873.2023.10407379
  9. Blom H. M., Lange P. E. de, Risstad M. (2023). Estimating value-at-risk in the EURUSD currency cross from implied volatilities using machine learning methods and quantile regression. Journal of Risk and Financial Management, 16 (7), 312. doi: 10.3390/jrfm16070312
  10. Bonaparte Y. (2024). Artificial intelligence in finance: Valuations and opportunities. Finance Research Letters, 60, 104851. doi: 10.1016/j.frl.2023.104851
  11. Chen T., Guestrin C. (2016). Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785–794. doi: 10.1145/2939672.2939785
  12. Hafid A., Ebrahim M., Rahouti M., Oliveira D. (2024). Cryptocurrency price forecasting using XGBoost regressor and technical indicators. In: 2024 IEEE international performance, computing, and communications conference (IPCCC), 1–6. IEEE. doi: 10.1109/IPCCC59868.2024.10850357
  13. Hossain S., Kaur G. (2024). Stock market prediction: XGBoost and LSTM comparative analysis. In: 2024 3rd International conference on artificial intelligence for internet of things (AIIoT), 1–6. IEEE. doi: 10.1109/AIIoT58432.2024.10574794
  14. Jabeur S. B., Mefteh-Wali S., Viviani J. L. (2024). Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research, 334 (1), 679–699. doi: 10.1007/s10479-021-04187-w
  15. Jamieson K., Talwalkar A. (2016). Non-stochastic best arm identification and hyperparameter optimization. In: Artificial intelligence and statistics, 240–248. PMLR. doi: 10.48550/arXiv.1502.07943
  16. Jareño F., Yousaf I. (2023). Artificial intelligence-based tokens: Fresh evidence of connectedness with artificial intelligence-based equities. International Review of Financial Analysis, 89, 102826. doi: 10.1016/j.irfa.2023.102826
  17. Kumar K. S., Sree D. I., Devi P. Y., Pujitha M. V. (2024, June). Comparative analysis of LSTM and XGBoost models for short-term bitcoin price prediction. In: 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 932–939). IEEE. doi: 10.1109/ICAAIC60222.2024.10575848
  18. Lu Y. H., Lin Y. C. (2024). The determinants of voluntary disclosure: Integration of eXtreme gradient boost (XGBoost) and explainable artificial intelligence (XAI) techniques. International Review of Financial Analysis, 96, 103577. doi: 10.1016/j.irfa.2024.103577
  19. Lundberg S. M., Lee S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. doi: 10.48550/arXiv.1705.07874
  20. Ma C. Q., Liu X., Klein T., Ren Y. S. (2025). Decoding the nexus: How fintech and ai stocks drive the future of sustainable finance. International Review of Economics & Finance, 103877. doi: 10.1016/j.iref.2025.103877
  21. Malik F., Umar Z. (2024). Quantile connectedness of artificial intelligence tokens with the energy sector. Review of Financial Economics. John Wiley & Sons, 43 (2), 135–146. doi: 10.1002/rfe.1224
  22. Oukhouya H., Kadiri H., El Himdi K., Guerbaz R. (2024). Forecasting international stock market trends: XGBoost, LSTM, LSTM–XGBoost, and Backtesting XGBoost models. Statistics, Optimization & Information Computing, 12 (1), 200–209. doi: 10.19139/soic-2310-5070-1822
  23. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O. et al. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830. doi: 10.48550/arXiv.1201.0490
  24. Shahzad U., Asl M. G., Panait M., Sarker T., Apostu S. A. (2023). Emerging interaction of artificial intelligence with basic materials and oil & gas companies: A comparative look at the Islamic vs. conventional markets. Resources Policy, 80, 103197. doi: 10.1016/j.resourpol.2022.103197
  25. Smelyakov K., Klochko O., Dudar Z. (2023). Building quantile regression models for predicting traffic flow. COLINS, 1, 117–132. DOI: https://ceur-ws.org/Vol-3387/paper10.pdf
  26. Somkunwar R. K., Pimpalkar A., Srivastava V. (2024). A novel approach for accurate stock market forecasting by integrating ARIMA and XGBoost. In: 2024 IEEE International students’ conference on electrical, electronics and computer science (SCEECS), 1–6. IEEE. doi: 10.1109/SCEECS61402.2024.10481891
  27. Trabelsi Karoui A., Sayari S., Dammak W., Jeribi A. (2024). Unveiling outperformance: A portfolio analysis of top AI-related stocks against it indices and robotics ETFs. Risks, 12 (3), 52. doi: 10.3390/risks12030052
  28. Vaka S., Reddy M. S., Prabhu S. N. (2024). Hybrid model for cryptocurrency price prediction using Lstm, bidirectional LSTM, and XGBoost. In: 2024 International conference on IoT based control networks and intelligent systems (ICICNIS), 925–932. IEEE. doi: 10.1109/ICICNIS64247.2024.10823223
  29. Xiaoyang X., Ali S., Naveed M. (2024). Artificial intelligence and big data tokens: Where cognition unites, herding patterns take flight. Research in International Business and Finance, 72, 102506. doi: 10.1016/j.ribaf.2024.102506
  30. Yadav M. P., Abedin M. Z., Sinha N., Arya V. (2024). Uncovering dynamic connectedness of Artificial intelligence stocks with agri-commodity market in wake of COVID-19 and Russia-Ukraine Invasion. Research in International Business and Finance, 67, 102146. doi: 10.1016/j.ribaf.2023.102146
  31. Yousaf I., Ijaz M. S., Umar M., Li Y. (2024). Exploring volatility interconnections between AI tokens, AI stocks, and fossil fuel markets: evidence from time and frequency-based connectedness analysis. Energy Economics, 133, 107490. doi: 10.1016/j.eneco.2024.107490
  32. Yousaf I., Youssef M., Goodell J. W. (2024). Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes. Journal of International Financial Markets, Institutions and Money, 91, 101929. doi: 10.1016/j.intfin.2023.101929
  33. Yu H., Wang L., Jiang S., Zhang C., Li J., Hu T. (2023). Ultra-short-term operating reserve requirement assessment of power system based on improved XGboost quantile regression. In: 2023 2nd Asia power and electrical technology conference (APET), 756–760. IEEE. doi: 10.1109/APET59977.2023.10489014

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