Reinforcement learning for finance: A review
Este artículo ofrece una revisión exhaustiva de la aplicación del aprendizaje por refuerzo (AR) en el dominio de las finanzas, y arroja una luz sobre el innovador progreso alcanzado y los desafíos que se avecinan. Exploramos cómo el AR, un subcampo del aprendizaje automático, ha sido instrumental para resolver problemas financieros complejos al permitir procesos de toma de decisiones que optimizan las recompensas a largo plazo. El AR es una poderosa técnica de aprendizaje automático que se puede utilizar para entrenar a agentes a fin de tomar decisiones en entornos complejos. En finanzas, el AR se ha utilizado para resolver una variedad de problemas, incluyendo la ejecución óptima, la optimización de carteras, la valoración y cobertura de o... Ver más
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Reinforcement learning for finance: A review Kohl, N., & Stone, P. (2004, April). Policy gradient reinforcement learning for fast quadrupedal locomotion. In IEEE International Conference on Robotics and Automation, 2004. https://doi.org/10.1109/ROBOT.2004.1307456 Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489. https://doi.org/10.1038/nature16961 Nath, S., Liu, V., Chan, A., Li, X., White, A., & White, M. (2020). Training recurrent neural networks online by learning explicit state variables. In International conference on learning representations. Minsky, M. L. (1954). Theory of neural-analog reinforcement systems and its application to the brain-model problem. Princeton University. Millea, A., & Edalat, A. (2022). Using deep reinforcement learning with hierarchical risk parity for portfolio optimization. International Journal of Financial Studies, 11(1), 10. https://doi.org/10.3390/ijfs11010010 Michie, D. & Chambers, R. A. (1968). BOXES: An experiment in adaptive control. In E. Dale & D. Michie (eds.), Machine Intelligence. Oliver and Boyd. Li, Y., Szepesvari, C., & Schuurmans, D. (2009). Learning exercise policies for American options. In Artificial intelligence and statistics (pp. 352–359). PMLR. https://proceedings.mlr.press/v5/li09d.html LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436- 444. https://doi.org/10.1038/nature14539 Kapoor, A., Gulli, A., Pal, S., & Chollet, F. (2022). Deep Learning with Tensor Flow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models. Packt Publishing Ltd. Sun, Q., & Si, Y. W. (2022). Supervised actor-critic reinforcement learning with action feedback for algorithmic trading. Applied Intelligence, 53, 16875-16892. https://doi.org/10.1007/s10489-022-04322-5 Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237-285. https://doi.org/10.1613/jair.301 Kaelbling, L. P. (1993). Learning in embedded systems. MIT Press. Hu, Y. J., & Lin, S. J. (2019). Deep reinforcement learning for optimizing finance portfolio management. In 2019 Amity International Conference on Artificial Intelligence (AICAI) (pp. 14-20). IEEE. https://doi.org/10.1109/AICAI.2019.8701368 Halperin, I. (2020). QLBS: Q-learner in the Black-Scholes-Merton world. The Journal of Derivatives, 28(1), 99-122. https://doi.org/10.3905/jod.2020.1.108 Halperin, I. (2019). The QLBS Q-learner goes NuQlear: Fitted Q iteration, inverse RL, and option portfolios. Quantitative Finance, 19(9), 1543–1553. https://doi.org/10.1080/14697688.2019.1622302 Hambly, B., Xu, R., & Yang, H. (2021). Recent advances in reinforcement learning in finance. arXiv preprint arXiv:2112.04553. https://arxiv.org/abs/2112.04553 Gosavi, A. (2009). Reinforcement learning: A tutorial survey and recent advances. INFORMS Journal on Computing, 21(2), 178-192. https://doi.org/10.1287/ijoc.1080.0305 Schlegel, M., Chung, W., Graves, D., Qian, J., & White, M. (2019). Importance resampling for off-policy prediction. Advances in Neural Information Processing Systems, 32. Sutton, R. S. (1990). Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Machine learning proceedings 1990 (pp. 216-224). https://doi.org/10.1016/B978-1-55860-141-3.50030-4 Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., … & Welty, C. (2010). Building Watson: An Overview of the DeepQA Project. AI Magazine, 31(3), 59-79. https://doi.org/10.1609/aimag.v31i3.2303 info:eu-repo/semantics/article Text http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/publishedVersion http://purl.org/redcol/resource_type/ARTREF http://purl.org/coar/resource_type/c_6501 Ziebart, B. D., Maas, A. L., Bagnell, J. A., & Dey, A. K. (2008). Maximum entropy inverse reinforcement learning. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence 2008. Sutton, R. S. (1991). Dyna, an integrated architecture for learning, planning, and reacting. ACM Sigart Bulletin, 2(4), 160-163. https://doi.org/10.1145/122344.122377 Torres Cortés, L. J., Velázquez Vadillo, F., & Turner Barragán, E. H. (2017). El principio de optimalidad de Bellman aplicado a la estructura financiera corporativa. Caso Mexicano. Análisis Económico, 32(81), 151-181. Thorndike, E. L. (1911). Animal intelligence: Experimental studies. Transaction Publishers. Taylor, M. E., & Stone, P. (2009). Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research, 10(7), 1635-1685. https://doi.org/10.5555/1577069.1755839 Thrun, S. B., & Möller, K. (1991). Active exploration in dynamic environments. Advances in neural information processing systems, 4. https://proceedings.neurips.cc/paper/1991/hash/e5f6ad6ce374177eef023bf5d0c018b 6-Abstract.html Théate, T., & Ernst, D. (2021). An application of deep reinforcement learning to algorithmic trading. Expert Systems with Applications, 173, 114632. https://doi.org/10.1016/j.eswa.2021.114632 Tesauro, G. (1995). Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3), 58-68. https://doi.org/10.1145/203330.203343 Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An introduction. MIT Press. Foerster, J., Assael, I. A., De Freitas, N., & Whiteson, S. (2016). Learning to communicate with deep multi-agent reinforcement learning. Advances in Neural Information processing systems, 29, 1-9. Errecalde, M. L., Muchut, A., Aguirre, G., & Montoya, C. I. (2000). Aprendizaje por Refuerzo aplicado a la resolución de problemas no triviales. In II Workshop de Investigadores en Ciencias de la Computación. Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., & Abbeel, P. (2016). RL2: Fast reinforcement learning via slow reinforcement learning. Working paper, arXiv:1611.02779. https://revistas.uexternado.edu.co/index.php/odeon/article/view/9072 Este artículo ofrece una revisión exhaustiva de la aplicación del aprendizaje por refuerzo (AR) en el dominio de las finanzas, y arroja una luz sobre el innovador progreso alcanzado y los desafíos que se avecinan. Exploramos cómo el AR, un subcampo del aprendizaje automático, ha sido instrumental para resolver problemas financieros complejos al permitir procesos de toma de decisiones que optimizan las recompensas a largo plazo. El AR es una poderosa técnica de aprendizaje automático que se puede utilizar para entrenar a agentes a fin de tomar decisiones en entornos complejos. En finanzas, el AR se ha utilizado para resolver una variedad de problemas, incluyendo la ejecución óptima, la optimización de carteras, la valoración y cobertura de opciones, la creación de mercados, el enrutamiento inteligente de órdenes y el robo-asesoramiento. En este artículo revisamos los desarrollos recientes en AR para finanzas. Comenzamos proporcionando una introducción al AR y a los procesos de decisión de Markov (MDP), que es el marco matemático para el AR. Luego discutimos los diversos algoritmos de AR que se han utilizado en finanzas, con un enfoque en métodos basados en valor y políticas. También discutimos el uso de redes neuronales en AR para finanzas. Finalmente, abordamos los resultados de estudios recientes que han utilizado AR para resolver problemas financieros. Concluimos discutiendo los desafíos y las oportunidades para futuras investigaciones en AR para finanzas. León Nieto, Diego Ismael aprendizaje por refuerzo; aprendizaje automático; procesos de decisión de Markov; finanzas 24 Núm. 24 , Año 2023 : Enero-Junio Artículo de revista Universidad Externado de Colombia ODEON application/pdf Publication Cannelli, L., Nuti, G., Sala, M., & Szehr, O. (2020). Hedging using reinforcement learning: Contextual K-armed bandit versus Q-learning. Working paper, arXiv: 2007.01623. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine intelligence, 35(8), 1798-1828. https://doi.org/10.1109/TPAMI.2013.50 Andreae, J. H. (1963). STELLA: A scheme for a learning machine. IFAC Proceedings Volumes, 1(2), 497-502. https://doi.org/10.1016/S1474-6670(17)69682-4 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. Camerer, C. F. (2003). Behavioural studies of strategic thinking in games. Trends in Cognitive Sciences, 7(5), 225-231. https://doi.org/10.1016/S1364-6613(03)00094-9 Buehler, H., Gonon, L., Teichmann, J., & Wood, B. (2019). Deep hedging. Quantitative Finance, 19(8), 1271-1291. https://doi.org/10.1080/14697688.2019.1571683 Diego Ismael León Nieto - 2023 http://creativecommons.org/licenses/by-nc-sa/4.0 Cao, J., Chen, J., Hull, J., & Poulos, Z. (2021). Deep hedging of derivatives using reinforcement learning. The Journal of Financial Data Science, 3(1), 10–27. https://doi.org/10.3905/jfds.2020.1.052 Español Reinforcement learning for finance: A review machine learning; Journal article This paper provides a comprehensive review of the application of Reinforcement Learning (RL) in the domain of finance, shedding light on the groundbreaking progress achieved and the challenges that lie ahead. We explore how RL, a subfield of machine learning, has been instrumental in solving complex financial problems by enabling decision-making processes that optimize long-term rewards. Reinforcement learning (RL) is a powerful machine learning technique that can be used to train agents to make decisions in complex environments. In finance, RL has been used to solve a variety of problems, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising. In this paper, we review the recent developments in RL for finance. We begin by introducing RL and Markov decision processes (MDPs), which is the mathematical framework for RL. We then discuss the various RL algorithms that have been used in finance, with a focus on value-based and policy-based methods. We also discuss the use of neural networks in RL for finance. Finally, we discuss the results of recent studies that have used RL to solve financial problems. We conclude by discussing the challenges and opportunities for future research in RL for finance. Markov decision process; finance Reinforcement learning; https://doi.org/10.18601/17941113.n24.02 https://revistas.uexternado.edu.co/index.php/odeon/article/download/9072/15142 10.18601/17941113.n24.02 7 2023-11-30T09:55:17Z 2023-11-30T09:55:17Z 2023-11-30 1794-1113 2346-2140 24 |
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UNIVERSIDAD EXTERNADO DE COLOMBIA |
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Colombia |
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Revista ODEON |
| title |
Reinforcement learning for finance: A review |
| spellingShingle |
Reinforcement learning for finance: A review León Nieto, Diego Ismael aprendizaje por refuerzo; aprendizaje automático; procesos de decisión de Markov; finanzas machine learning; Markov decision process; finance Reinforcement learning; |
| title_short |
Reinforcement learning for finance: A review |
| title_full |
Reinforcement learning for finance: A review |
| title_fullStr |
Reinforcement learning for finance: A review |
| title_full_unstemmed |
Reinforcement learning for finance: A review |
| title_sort |
reinforcement learning for finance: a review |
| title_eng |
Reinforcement learning for finance: A review |
| description |
Este artículo ofrece una revisión exhaustiva de la aplicación del aprendizaje por refuerzo (AR) en el dominio de las finanzas, y arroja una luz sobre el innovador progreso alcanzado y los desafíos que se avecinan. Exploramos cómo el AR, un subcampo del aprendizaje automático, ha sido instrumental para resolver problemas financieros complejos al permitir procesos de toma de decisiones que optimizan las recompensas a largo plazo. El AR es una poderosa técnica de aprendizaje automático que se puede utilizar para entrenar a agentes a fin de tomar decisiones en entornos complejos. En finanzas, el AR se ha utilizado para resolver una variedad de problemas, incluyendo la ejecución óptima, la optimización de carteras, la valoración y cobertura de opciones, la creación de mercados, el enrutamiento inteligente de órdenes y el robo-asesoramiento. En este artículo revisamos los desarrollos recientes en AR para finanzas. Comenzamos proporcionando una introducción al AR y a los procesos de decisión de Markov (MDP), que es el marco matemático para el AR. Luego discutimos los diversos algoritmos de AR que se han utilizado en finanzas, con un enfoque en métodos basados en valor y políticas. También discutimos el uso de redes neuronales en AR para finanzas. Finalmente, abordamos los resultados de estudios recientes que han utilizado AR para resolver problemas financieros. Concluimos discutiendo los desafíos y las oportunidades para futuras investigaciones en AR para finanzas.
|
| description_eng |
This paper provides a comprehensive review of the application of Reinforcement Learning (RL) in the domain of finance, shedding light on the groundbreaking progress achieved and the challenges that lie ahead. We explore how RL, a subfield of machine learning, has been instrumental in solving complex financial problems by enabling decision-making processes that optimize long-term rewards. Reinforcement learning (RL) is a powerful machine learning technique that can be used to train agents to make decisions in complex environments. In finance, RL has been used to solve a variety of problems, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising. In this paper, we review the recent developments in RL for finance. We begin by introducing RL and Markov decision processes (MDPs), which is the mathematical framework for RL. We then discuss the various RL algorithms that have been used in finance, with a focus on value-based and policy-based methods. We also discuss the use of neural networks in RL for finance. Finally, we discuss the results of recent studies that have used RL to solve financial problems. We conclude by discussing the challenges and opportunities for future research in RL for finance.
|
| author |
León Nieto, Diego Ismael |
| author_facet |
León Nieto, Diego Ismael |
| topicspa_str_mv |
aprendizaje por refuerzo; aprendizaje automático; procesos de decisión de Markov; finanzas |
| topic |
aprendizaje por refuerzo; aprendizaje automático; procesos de decisión de Markov; finanzas machine learning; Markov decision process; finance Reinforcement learning; |
| topic_facet |
aprendizaje por refuerzo; aprendizaje automático; procesos de decisión de Markov; finanzas machine learning; Markov decision process; finance Reinforcement learning; |
| citationissue |
24 |
| citationedition |
Núm. 24 , Año 2023 : Enero-Junio |
| publisher |
Universidad Externado de Colombia |
| ispartofjournal |
ODEON |
| source |
https://revistas.uexternado.edu.co/index.php/odeon/article/view/9072 |
| language |
Español |
| format |
Article |
| rights |
http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. Diego Ismael León Nieto - 2023 http://creativecommons.org/licenses/by-nc-sa/4.0 |
| references |
Kohl, N., & Stone, P. (2004, April). Policy gradient reinforcement learning for fast quadrupedal locomotion. In IEEE International Conference on Robotics and Automation, 2004. https://doi.org/10.1109/ROBOT.2004.1307456 Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489. https://doi.org/10.1038/nature16961 Nath, S., Liu, V., Chan, A., Li, X., White, A., & White, M. (2020). Training recurrent neural networks online by learning explicit state variables. In International conference on learning representations. Minsky, M. L. (1954). Theory of neural-analog reinforcement systems and its application to the brain-model problem. Princeton University. Millea, A., & Edalat, A. (2022). Using deep reinforcement learning with hierarchical risk parity for portfolio optimization. International Journal of Financial Studies, 11(1), 10. https://doi.org/10.3390/ijfs11010010 Michie, D. & Chambers, R. A. (1968). BOXES: An experiment in adaptive control. In E. Dale & D. Michie (eds.), Machine Intelligence. Oliver and Boyd. Li, Y., Szepesvari, C., & Schuurmans, D. (2009). Learning exercise policies for American options. In Artificial intelligence and statistics (pp. 352–359). PMLR. https://proceedings.mlr.press/v5/li09d.html LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436- 444. https://doi.org/10.1038/nature14539 Kapoor, A., Gulli, A., Pal, S., & Chollet, F. (2022). Deep Learning with Tensor Flow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models. Packt Publishing Ltd. Sun, Q., & Si, Y. W. (2022). Supervised actor-critic reinforcement learning with action feedback for algorithmic trading. Applied Intelligence, 53, 16875-16892. https://doi.org/10.1007/s10489-022-04322-5 Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237-285. https://doi.org/10.1613/jair.301 Kaelbling, L. P. (1993). Learning in embedded systems. MIT Press. Hu, Y. J., & Lin, S. J. (2019). Deep reinforcement learning for optimizing finance portfolio management. In 2019 Amity International Conference on Artificial Intelligence (AICAI) (pp. 14-20). IEEE. https://doi.org/10.1109/AICAI.2019.8701368 Halperin, I. (2020). QLBS: Q-learner in the Black-Scholes-Merton world. The Journal of Derivatives, 28(1), 99-122. https://doi.org/10.3905/jod.2020.1.108 Halperin, I. (2019). The QLBS Q-learner goes NuQlear: Fitted Q iteration, inverse RL, and option portfolios. Quantitative Finance, 19(9), 1543–1553. https://doi.org/10.1080/14697688.2019.1622302 Hambly, B., Xu, R., & Yang, H. (2021). Recent advances in reinforcement learning in finance. arXiv preprint arXiv:2112.04553. https://arxiv.org/abs/2112.04553 Gosavi, A. (2009). Reinforcement learning: A tutorial survey and recent advances. INFORMS Journal on Computing, 21(2), 178-192. https://doi.org/10.1287/ijoc.1080.0305 Schlegel, M., Chung, W., Graves, D., Qian, J., & White, M. (2019). Importance resampling for off-policy prediction. Advances in Neural Information Processing Systems, 32. Sutton, R. S. (1990). Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Machine learning proceedings 1990 (pp. 216-224). https://doi.org/10.1016/B978-1-55860-141-3.50030-4 Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., … & Welty, C. (2010). Building Watson: An Overview of the DeepQA Project. AI Magazine, 31(3), 59-79. https://doi.org/10.1609/aimag.v31i3.2303 Ziebart, B. D., Maas, A. L., Bagnell, J. A., & Dey, A. K. (2008). Maximum entropy inverse reinforcement learning. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence 2008. Sutton, R. S. (1991). Dyna, an integrated architecture for learning, planning, and reacting. ACM Sigart Bulletin, 2(4), 160-163. https://doi.org/10.1145/122344.122377 Torres Cortés, L. J., Velázquez Vadillo, F., & Turner Barragán, E. H. (2017). El principio de optimalidad de Bellman aplicado a la estructura financiera corporativa. Caso Mexicano. Análisis Económico, 32(81), 151-181. Thorndike, E. L. (1911). Animal intelligence: Experimental studies. Transaction Publishers. Taylor, M. E., & Stone, P. (2009). Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research, 10(7), 1635-1685. https://doi.org/10.5555/1577069.1755839 Thrun, S. B., & Möller, K. (1991). Active exploration in dynamic environments. Advances in neural information processing systems, 4. https://proceedings.neurips.cc/paper/1991/hash/e5f6ad6ce374177eef023bf5d0c018b 6-Abstract.html Théate, T., & Ernst, D. (2021). An application of deep reinforcement learning to algorithmic trading. Expert Systems with Applications, 173, 114632. https://doi.org/10.1016/j.eswa.2021.114632 Tesauro, G. (1995). Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3), 58-68. https://doi.org/10.1145/203330.203343 Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An introduction. MIT Press. Foerster, J., Assael, I. A., De Freitas, N., & Whiteson, S. (2016). Learning to communicate with deep multi-agent reinforcement learning. Advances in Neural Information processing systems, 29, 1-9. Errecalde, M. L., Muchut, A., Aguirre, G., & Montoya, C. I. (2000). Aprendizaje por Refuerzo aplicado a la resolución de problemas no triviales. In II Workshop de Investigadores en Ciencias de la Computación. Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., & Abbeel, P. (2016). RL2: Fast reinforcement learning via slow reinforcement learning. Working paper, arXiv:1611.02779. Cannelli, L., Nuti, G., Sala, M., & Szehr, O. (2020). Hedging using reinforcement learning: Contextual K-armed bandit versus Q-learning. Working paper, arXiv: 2007.01623. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine intelligence, 35(8), 1798-1828. https://doi.org/10.1109/TPAMI.2013.50 Andreae, J. H. (1963). STELLA: A scheme for a learning machine. IFAC Proceedings Volumes, 1(2), 497-502. https://doi.org/10.1016/S1474-6670(17)69682-4 Camerer, C. F. (2003). Behavioural studies of strategic thinking in games. Trends in Cognitive Sciences, 7(5), 225-231. https://doi.org/10.1016/S1364-6613(03)00094-9 Buehler, H., Gonon, L., Teichmann, J., & Wood, B. (2019). Deep hedging. Quantitative Finance, 19(8), 1271-1291. https://doi.org/10.1080/14697688.2019.1571683 Cao, J., Chen, J., Hull, J., & Poulos, Z. (2021). Deep hedging of derivatives using reinforcement learning. The Journal of Financial Data Science, 3(1), 10–27. https://doi.org/10.3905/jfds.2020.1.052 |
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