Explainable Artificial Intelligence for Stock Market Trend Prediction and Risk Assessment: A Review

Abstract

Machine learning models are now widely used to forecast stock price movements and to measure financial risk, yet most cannot explain their own outputs. In finance that is a serious weakness, because forecasts influence real investment decisions and regulators expect firms to justify the models they use. This review asks how explainable artificial intelligence (XAI) has been applied to stock trend prediction and risk as-sessment, and how far the resulting explanations can be trusted. We first outline the economic theory of predictability and the standard risk measures, then set out the mathematics of the main explanation methods: Shapley-value attribution, LIME, gradient-based attribution, attention, and counterfactuals. We then compare representative studies, summarise regulatory ex-pectations, and propose a five-layer framework for explainable forecasting. Across the literature we find that explanations are most useful as a validation tool, and that they become un-reliable when features are correlated, when markets change regime, or when a model is overfitted. Evidence that expla-nations improve decisions remains thin. We close with open problems, including causal explanation, shared benchmarks, and monitoring of explanations in production.

Introduction

Machine learning has improved financial forecasting by identifying nonlinear patterns and relationships in stock returns, market trends, and financial risks. However, these models are often difficult to interpret, making it challenging for portfolio managers, risk analysts, and regulators to understand and validate their predictions. Explainable Artificial Intelligence (XAI) addresses this issue by providing insights into the factors influencing model decisions and supporting transparency, accountability, and human oversight.

This review examines the application of XAI to financial forecasting, focusing on stock trend prediction and risk assessment. It discusses major explanation techniques, including SHAP, LIME, Integrated Gradients, attention-based explanations, surrogate models, and counterfactual explanations. These methods help identify influential features, explain individual predictions, assess risk factors, and support model validation. However, their reliability can be limited by correlated financial variables, changing market conditions, weak predictive signals, and overfitting.

The review also examines financial predictability in relation to the Efficient Market Hypothesis, momentum effects, factor premiums, and adaptive market behavior. It emphasizes that financial models generally achieve modest forecasting improvements because market returns contain substantial noise and patterns may change over time. Consequently, explanations must be evaluated for accuracy, stability, and economic relevance rather than accepted at face value.

For risk assessment, XAI supports the interpretation of credit defaults, market volatility, Value-at-Risk (VaR), and Expected Shortfall (ES). These explanations can help identify the factors contributing to financial losses, compare machine learning models with traditional statistical methods, and improve risk monitoring. The review also highlights the importance of regulatory guidance, including SR 11-7, the EU AI Act, and the GDPR, in promoting appropriate transparency and human oversight.

The study identifies three major challenges: model opacity, unreliable explanations on non-stationary financial data, and fragmented research across finance, machine learning, and regulation. Using a narrative literature review, it connects financial theory, explanation methods, empirical applications, and governance practices.

In conclusion, the review proposes a five-layer framework for developing, evaluating, and governing explainable financial forecasting systems. It emphasizes combining reliable predictive models with robust explanation techniques, economic reasoning, out-of-sample testing, stability analysis, and regulatory compliance. The central finding is that XAI can improve the transparency and practical usefulness of financial machine learning, but explanations should not automatically be treated as proof of causality or predictive reliability.

Conclusion

Explainable AI has become a normal part of machine-learning-based financial forecasting. SHAP on tree ensembles is the dominant practice, and gradient- and attention-based meth-ods are common for deep models. In credit risk, explanation is already tied to governance and regulation. The evidence base is still uneven, though. In the studies we read, explana-tions were rarely tested for fidelity, stability, or decision value, and financial data bring hazards of their own: regime change, collinearity, and overfitting. Responsible practice combines an interpretable baseline, validation that respects the order of time, more than one explainer, and a clear statement that attributions describe models, not markets. The five-layer framework pro-posed here is a starting point for putting that practice into a repeatable process.

References

[1] Gu S, Kelly B, Xiu D. Empirical asset pricing via machine learning. Rev Financ Stud. 2020;33(5):2223–73. [2] Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res. 2018;270(2):654–69. [3] Board of Governors of the Federal Reserve System. Supervisory guidance on model risk management (SR 11-7). Washington (DC): Federal Reserve; 2011. [4] European Parliament and Council of the European Union. Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Off J Eur Union. 2024. [5] European Parliament and Council of the European Union. Regulation (EU) 2016/679 (General Data Protection Regulation). Off J Eur Union. 2016;L119:1–88. [6] C? ernevic?iene? J, Kabašinskas A. Explainable artificial intelligence (XAI) in finance: a systematic literature review. Artif Intell Rev. 2024;57:216. doi:10.1007/s10462-024-10854-8. [7] Martins T, de Almeida A, Cardoso E, Nunes L. Explainable ar-tificial intelligence (XAI): a systematic literature review on tax-onomies and applications in finance. IEEE Access. 2024;12:618–29. doi:10.1109/ACCESS.2023.3347028. [8] Fama EF, French KR. Common risk factors in the returns on stocks and bonds. J Financ Econ. 1993;33(1):3–56. [9] Engle RF. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica. 1982;50(4):987–1007. [10] Bollerslev T. Generalized autoregressive conditional heteroskedasticity. J Econom. 1986;31(3):307–27. [11] Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016. p. 785–94. [12] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80. [13] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: Advances in Neural Information Processing Systems 30; 2017. p. 5998–6008. [14] Lipton ZC. The mythos of model interpretability. Commun ACM. 2018;61(10):36–43. [15] Doshi-Velez F, Kim B. Towards a rigorous science of interpretable ma-chine learning. arXiv:1702.08608. 2017. [16] Adadi A, Berrada M. Peeking inside the black-box: a survey on explain-able artificial intelligence (XAI). IEEE Access. 2018;6:52138–60. [17] Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D. A survey of methods for explaining black box models. ACM Comput Surv. 2018;51(5):Article 93. [18] Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Bar-bado A, et al. Explainable artificial intelligence (XAI): concepts, tax-onomies, opportunities and challenges toward responsible AI. Inf Fusion. 2020;58:82–115. [19] Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206–15. [20] Lundberg SM, Lee SI. A unified approach to interpreting model predic-tions. In: Advances in Neural Information Processing Systems 30; 2017. p. 4765–74. [21] Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56–67. [22] Ribeiro MT, Singh S, Guestrin C. “Why should I trust you?”: explain-ing the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016. p. 1135–44. [23] Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep net-works. In: Proceedings of the 34th International Conference on Machine Learning; 2017. p. 3319–28. [24] Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision; 2017. p. 618–26. Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. [25] Friedman JH. Greedy function approximation: a gradient boosting ma-chine. Ann Stat. 2001;29(5):1189–232. [26] Goldstein A, Kapelner A, Bleich J, Pitkin E. Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation. J Comput Graph Stat. 2015;24(1):44–65. [27] Lim B, Ar?k SÖ, Loeff N, Pfister T. Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int J Forecast. 2021;37(4):1748–64. [28] Jain S, Wallace BC. Attention is not explanation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2019. p. 3543–56. [29] Wachter S, Mittelstadt B, Russell C. Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv J Law Technol. 2017;31(2):841–87. [30] Muhammad D, Ahmed I, Naveed K, Bendechache M. An explain-able deep learning approach for stock market trend prediction. Heliyon. 2024;10(21):e40095. doi:10.1016/j.heliyon.2024.e40095. [31] Celik TB, Ican Ö, Bulut E. Extending machine learning prediction capa-bilities by explainable AI in financial time series prediction. Appl Soft Comput. 2023;132:109876. doi:10.1016/j.asoc.2022.109876. [32] Bracke P, Datta A, Jung C, Sen S. Machine learning explainability in finance: an application to default risk analysis. Bank of England Staff Working Paper No. 816. London: Bank of England; 2019. [33] Bussmann N, Giudici P, Marinelli D, Papenbrock J. Explainable machine learning in credit risk management. Comput Econ. 2021;57(1):203–16. [34] Gramegna A, Giudici P. SHAP and LIME: an evaluation of discriminative power in credit risk. Front Artif Intell. 2021;4:752558. [35] Fama EF. Efficient capital markets: a review of theory and empirical work. J Finance. 1970;25(2):383–417. [36] Jegadeesh N, Titman S. Returns to buying winners and selling losers: implications for stock market efficiency. J Finance. 1993;48(1):65–91. [37] Lo AW. The adaptive markets hypothesis: market efficiency from an evolutionary perspective. J Portf Manag. 2004;30(5):15–29. [38] Welch I, Goyal A. A comprehensive look at the empirical performance of equity premium prediction. Rev Financ Stud. 2008;21(4):1455–508. [39] Harvey CR, Liu Y, Zhu H. and the cross-section of expected returns. Rev Financ Stud. 2016;29(1):5–68. [40] Bailey DH, Borwein J, López de Prado M, Zhu QJ. Pseudo-mathematics and financial charlatanism: the effects of backtest overfitting on out-of-sample performance. Not Am Math Soc. 2014;61(5):458–71. [41] Bailey DH, López de Prado M. The deflated Sharpe ratio: correcting for selection bias, backtest overfitting, and non-normality. J Portf Manag. 2014;40(5):94–107. [42] Artzner P, Delbaen F, Eber JM, Heath D. Coherent measures of risk. Math Finance. 1999;9(3):203–28. [43] Basel Committee on Banking Supervision. Minimum capital require-ments for market risk. Basel: Bank for International Settlements; 2019. [44] Markowitz H. Portfolio selection. J Finance. 1952;7(1):77–91. [45] Shapley LS. A value for n-person games. In: Kuhn HW, Tucker AW, editors. Contributions to the theory of games, vol II. Princeton (NJ): Princeton University Press; 1953. p. 307–17. [46] Štrumbelj E, Kononenko I. Explaining prediction models and individual predictions with feature contributions. Knowl Inf Syst. 2014;41(3):647–65. [47] Janzing D, Minorics L, Blöbaum P. Feature relevance quantification in explainable AI: a causal problem. In: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics; 2020. p. 2907–16. [48] Sundararajan M, Najmi A. The many Shapley values for model explana-tion. In: Proceedings of the 37th International Conference on Machine Learning; 2020. p. 9269–78. [49] Kumar IE, Venkatasubramanian S, Scheidegger C, Friedler S. Problems with Shapley-value-based explanations as feature importance measures. In: Proceedings of the 37th International Conference on Machine Learning; 2020. p. 5491–500. [50] Covert I, Lundberg SM, Lee SI. Explaining by removing: a unified framework for model explanation. J Mach Learn Res. 2021;22(209):1–90. [51] Pearl J. Causality: models, reasoning, and inference. 2nd ed. Cambridge: Cambridge University Press; 2009. [52] Peters J, Janzing D, Schölkopf B. Elements of causal inference: founda-tions and learning algorithms. Cambridge (MA): MIT Press; 2017. [53] López de Prado M. Advances in financial machine learning. Hoboken (NJ): Wiley; 2018. [54] Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: repre-senting model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on Machine Learning; 2016. p. 1050–9. [55] Alvarez-Melis D, Jaakkola TS. On the robustness of interpretability methods. arXiv:1806.08049. 2018. [56] Hooker S, Erhan D, Kindermans PJ, Kim B. A benchmark for inter-pretability methods in deep neural networks. In: Advances in Neural Information Processing Systems 32; 2019. [57] Diebold FX, Mariano RS. Comparing predictive accuracy. J Bus Econ Stat. 1995;13(3):253–63. [58] Kupiec PH. Techniques for verifying the accuracy of risk measurement models. J Deriv. 1995;3(2):73–84. [59] Christoffersen PF. Evaluating interval forecasts. Int Econ Rev. 1998;39(4):841–62. [60] Wachter S, Mittelstadt B, Floridi L. Why a right to explanation of au-tomated decision-making does not exist in the General Data Protection Regulation. Int Data Priv Law. 2017;7(2):76–99. [61] Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, et al. Model cards for model reporting. In: Proceedings of the Conference on Fairness, Accountability, and Transparency; 2019. p. 220–9. [62] Ghorbani A, Abid A, Zou J. Interpretation of neural networks is fragile. Proc AAAI Conf Artif Intell. 2019;33(1):3681–8. [63] Slack D, Hilgard S, Jia E, Singh S, Lakkaraju H. Fooling LIME and SHAP: adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society; 2020. p. 180–6.

Copyright

Copyright © 2026 Pallavi Pathare, Bhagyashri Sanap, Pooja Bhagwat, Sanika Wagh, Shantanu Nande . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.