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A Dual-Output Deep Neural Network Model for Stock Risk and Potential-Loss Prediction in Organisational Finance

Collins O. Odondiri, V. I. E. Anireh, O. E. Taylor

Abstract

Organisational stock risk management continues to rely on parametric models whose core assumptions break down during periods of market stress, leaving a gap between forecast accuracy and interpretable risk transparency. This study addresses that gap with a dual-output Deep Neural Network that fuses a correlation feature module with an LSTM-based sequence feature module to jointly produce a continuous Value-at-Risk /Conditional Value-at-Risk - style risk score and a categorical (Low/Medium/High) risk label. The model was trained via backpropagation with the Adam optimiser on financial, sentiment, macroeconomic, and microstructure indicators drawn from Nigerian Exchange tickers, and implemented as a full-stack platform with an integrated model-comparison and external-benchmarking module. Internally, the DNN outperformed LSTM, Random Forest, and Support Vector Machine baselines, achieving the highest classification accuracy of 86 percent and an R-squared value of 0.8426. Benchmarked externally against two published systems, the DNN achieved the lowest mean absolute error and the highest accuracy, outperforming a reinforcement-learning-based Deep Portfolio Optimisation framework (79 percent) and a federated-learning framework (72 percent); however, both existing systems achieved a materially higher Sharpe ratio, reflecting their design around portfolio-level optimisation rather than pointwise risk prediction. These results position the proposed model as a complementary, transparency-focused alternative to reward-shaped reinforcement-learning approaches rather than a wholesale replacement for them.

Keywords

Deep neural networks; stock risk prediction; long short-term memory; Value-at-Risk; organisational finance; explainable AI

References

(In IEEE Format) [1] J. W. Goodell, “COVID-19 and finance: Agendas for future research,” Finance Research Letters, Vol.35, pp.101512, 2020. [2] A. Zaremba, R. Kizys, D. Y. Aharon, E. Demir, “Infected markets: Novel coronavirus, government interventions, and stock return volatility around the globe,” Finance Research Letters, Vol.35, pp.101597, 2021. [3] M. A. Naeem, E. Bouri, Z. Peng, S. J. H. Shahzad, X. V. Vo, “Asymmetric efficiency of cryptocurrencies during COVID-19,” Physica A: Statistical Mechanics and Its Applications, Vol.565, pp.125562, 2021. [4] J. Choi, “Value at risk and expected shortfall during the COVID-19 market turmoil: A comparison of parametric and non-parametric approaches,” Journal of Risk and Financial Management, Vol.14, Issue.7, pp.326, 2021. [5] Y. Miao, M. Wu, X. Zhao, “Deep learning for risk management in banking: A comprehensive review,” International Review of Financial Analysis, Vol.87, pp.102618, 2023. [6] L. Chen, M. Pelger, J. Zhu, “Deep learning in asset pricing,” Management Science, Vol.68, Issue.3, pp.2029-2054, 2021. [7] W. Jiang, “Applications of deep learning in stock market prediction: Recent progress,” Expert Systems with Applications, Vol.184, pp.115537, 2021. [8] A. M. Ozbayoglu, M. U. Gudelek, O. B. Sezer, “Deep learning for financial applications: A survey,” Applied Soft Computing, Vol.93, pp.106384, 2020. [9] O. B. Sezer, M. U. Gudelek, A. M. Ozbayoglu, “Financial time series forecasting with deep learning: A systematic literature review: 2005–2019,” Applied Soft Computing, Vol.90, pp.106181, 2020. [10] N. Bussmann, P. Giudici, D. Marinelli, J. Papenbrock, “Explainable AI in fintech risk management,” Frontiers in Artificial Intelligence, Vol.3, pp.26, 2021. [11] L. Zhang, C. Aggarwal, G. J. Qi, “Stock price prediction via discovering multi-frequency trading patterns,” In the Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.2141-2149, 2020. [12] Y. Qin, J. Wu, X. Xiao, B. Chen, L. Zhang, “Machine learning and the Chinese stock market: Empirical evidence from the COVID-19 pandemic,” Finance Research Letters, Vol.43, pp.102008, 2021. [13] Y. Huang, L. F. Capretz, D. Ho, “Machine learning for stock prediction based on fundamental analysis,” IEEE Access, Vol.11, pp.23277-23293, 2023. [14] S. P. Chatzis, V. Siakoulis, A. Petropoulos, E. Stavroulakis, N. Vlachogiannakis, “Forecasting stock market crisis events using deep and statistical machine learning techniques,” Expert Systems with Applications, Vol.112, pp.353-371, 2022. [15] J. Sirignano, R. Cont, “Universal features of price formation in financial markets: Perspectives from deep learning,” Quantitative Finance, Vol.19, Issue.9, pp.1449-1459, 2019. [16] Z. T. Ke, B. T. Kelly, D. Xiu, “Predicting returns with text data,” NBER Working Paper No. 26186, National Bureau of Economic Research, 2019. [17] F. Feng, H. Chen, X. He, J. Ding, M. Sun, T. S. Chua, “Enhancing stock movement prediction with adversarial training,” In the Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp.5843-5849, 2019. [18] A. Bucci, “Realized volatility forecasting with neural networks,” Journal of Financial Econometrics, Vol.18, Issue.3, pp.502-531, 2020. IJCSMT [19] H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” Proceedings of the AAAI Conference on Artificial Intelligence, Vol.35, Issue.12, pp.11106-11115, 2021. [20] Y. Li, S. Jiang, L. Zhang, “Stock market forecasting using deep learning and technical analysis: Systematic review,” IEEE Access, Vol.11, pp.14206-14229, 2023. [21] M. F. Dixon, I. Halperin, P. Bilokon, “Machine learning in finance: From theory to practice,” Springer, 2020. [22] P. Bracke, A. Datta, C. Jung, S. Sen, “Machine learning explainability in finance: An application to default risk analysis,” Bank of England Staff Working Paper No. 816, Bank of England, 2019. [23] B. H. Misheva, J. Osterrieder, A. Hirsa, O. Kulkarni, S. F. Lin, “Explainable AI in credit risk management,” Journal of Risk and Financial Management, Vol.14, Issue.6, pp.242, 2021. [24] F. Prenzel, A. Fejza, “Explainable AI for credit risk management,” Journal of Risk and Financial Management, Vol.16, Issue.2, pp.78, 2023. [25] Y. Liu, Y. Kang, C. Xing, T. Chen, Q. Yang, “A secure federated transfer learning framework,” IEEE Intelligent Systems, Vol.35, Issue.4, pp.70-82, 2020. [26] R. Yan, J. Jin, K. Han, “Reinforcement learning for deep portfolio optimization,” Electronic Research Archive, Vol.32, Issue.9, pp.5176-5200, 2024. [27] H. Markowitz, “Portfolio selection,” The Journal of Finance, Vol.7, Issue.1, pp.77-91, 1952.