Empowering Real-Time Financial Decision Systems via Reinforcement Learning Driven Large Language Models and Distributed Temporal Pipelines
DOI:
https://doi.org/10.66280/cis.v1i1.240Keywords:
Financial Decision Systems, Reinforcement Learning, Large Language Models, Distributed Temporal Pipelines, Real-Time Systems, Socio-Technical Infrastructure, Algorithmic GovernanceAbstract
The modern financial ecosystem is characterized by an unprecedented volume of high-velocity data and the increasing necessity for context-aware, autonomous decision-making. Traditional quantitative models, while effective at identifying statistical regularities in numerical time series, often lack the semantic depth required to navigate complex market narratives and geopolitical shifts. This paper proposes a novel system architecture that empowers real-time financial decision systems by integrating reinforcement learning-driven large language models with high-throughput distributed temporal pipelines. We explore the structural requirements for a unified infrastructure that can synthesize high-frequency market signals with the qualitative reasoning capabilities of transformer-based architectures. Central to our discussion is the design of a reinforcement learning framework that optimizes large language model outputs for specific financial objectives, such as risk-adjusted returns and market stability, rather than linguistic fluency alone. We provide an extensive analysis of system-level trade-offs, emphasizing the tension between inferential depth and execution latency in sub-millisecond trading environments. Furthermore, the research addresses critical socio-technical dimensions, including the governance of autonomous financial agents, the environmental sustainability of massive-scale distributed inference, and the ethical implications of algorithmic fairness in capital allocation. By aligning the precision of reinforcement learning with the interpretive power of large language models, this framework offers a robust blueprint for the next generation of financial infrastructures, ensuring that autonomous decision-making is both statistically rigorous and contextually grounded in a volatile global economy.
References
1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308-318.
2. Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and creates labor. Journal of Economic Perspectives, 33(2), 3-30.
3. Agrawal, R., & Srikant, R. (2000). Privacy-preserving data mining. ACM Sigmod Record, 28(2), 439-450.
4. Bommasani, R., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
5. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
6. Cartea, A., Jaimungal, S., & Penalva, J. (2015). Algorithmic and High-Frequency Trading. Cambridge University Press.
7. Chen, L., & Zheng, Z. (2023). LLM-augmented financial analysis: Challenges and opportunities. Journal of Financial Data Science, 5(4), 12-28.
8. Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
9. Dwork, C. (2008). Differential privacy: A survey of results. International Conference on Theory and Applications of Models of Computation, 1-19.
10. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
11. Ghoshal, B., & Tucker, A. (2022). Scalable inference for deep learning in finance. Quantitative Finance, 22(10), 1845-1860.
12. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
13. Goyal, N., et al. (2023). High-throughput inference for large language models: A systems perspective. ACM SIGOPS Operating Systems Review, 57(1), 45-56.
14. Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), 1-33.
15. Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., ... & Amodei, D. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
16. Kirilenko, A. S., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The Flash Crash: High-frequency trading in an electronic market. The Journal of Finance, 72(3), 967-998.
17. Lo, A. W. (2017). Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press.
18. Liu, T. (2026). A Comparative Study of Transformer-Based and Classical Models for Financial Time-Series Forecasting. Journal of Risk and Financial Management, 19(3), 203.
19. Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
20. Narayanan, D., Phanishayee, A., Shi, K., Chen, X., & Zaharia, M. (2019). PipeDream: Generalized pipeline parallelism for DNN training. Proceedings of the 27th ACM Symposium on Operating Systems Principles.
21. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
22. Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
23. Rajbhandari, S., Rasley, J., Ruwase, O., & He, Y. (2020). ZeRO: Memory optimizations toward training trillion parameter models. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.
24. Shalf, J. (2020). The future of computing beyond Moore’s Law. Philosophical Transactions of the Royal Society A, 378(2166).
25. Shiller, R. J. (2019). Narrative Economics: How Stories Go Viral and Drive Major Economic Events. Princeton University Press.
26. Stoica, I., et al. (2017). Ray: A distributed framework for emerging AI applications. 13th USENIX Symposium on Operating Systems Design and Implementation.
27. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
28. Varian, H. R. (2007). Position auctions. International Journal of Industrial Organization, 25(6), 1163-1178.
29. Wu, S., et al. (2023). BloombergGPT: A large language model for finance. arXiv preprint arXiv:2303.17564.
30. Zaharia, M., et al. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. 9th USENIX Symposium on Networked Systems Design and Implementation.
31. Zhang, L., et al. (2021). Deep reinforcement learning for automated stock trading: An ensemble strategy. SSRN Electronic Journal.
32. Zhou, Y., et al. (2022). Mixture-of-experts with exponential selection. arXiv preprint arXiv:2202.08906.
33. Mo, F., Haddadi, H., Katiyar, K., Ansari, R., & Chuah, C. N. (2021). PPFL: Privacy-preserving federated learning with trusted execution environments. Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, 94-108.
34. Wang, J., et al. (2021). A field guide to federated optimization. arXiv preprint arXiv:2107.06917.
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