Reinforcement learning for finance: A review

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.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

Deep learning for trading and hedging in financial markets

Deep learning has achieved remarkable results in many areas, from image classification, language translation to question answering. Deep neural network models have proved to be good at processing large amounts of data and capturing complex relationships embedded in the data. In this thesis, we use deep learning methods to solve trading and hedging problems in the financial markets. We show that our solutions, which consist of various deep neural network models, could achieve better accuracies and efficiencies than many conventional mathematical-based methods. We use Technical Analysis Neural Network (TANN) to process high-frequency tick data from the foreign exchange market. Various technical indicators are calculated from the market data and fed into the neural network model. The model generates a classification label, which indicates the future movement direction of the FX rate in the short term. Our solution can surpass many well-known machine learning algorithms on classification accuracies. Deep Hedging models the relationship between the underlying asset and the prices of option contracts. We upgrade the pipeline by removing the restriction on trading frequency. With different levels of risk tolerances, the modified deep hedging model can propose various hedging solutions. These solutions form the Efficient Hedging Frontier (EHF), where their associated risk levels and returns are directly observable. We also show that combining a Deep Hedging model with a prediction algorithm ultimately increases the hedging performances. Implied volatility is the critical parameter for evaluating many financial derivatives. We propose a novel PCA Variational Auto-Enocder model to encode three independent features of implied volatility surfaces from the European stock markets. This novel encoding brings various benefits to generating and extrapolating implied volatility surfaces. It also enables the transformation of implied volatility surfaces from a stock index to a single stock, significantly improving the efficiency of derivatives pricing

Quantum Deep Hedging

Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to $16$ qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging

Pricing options and computing implied volatilities using neural networks

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent's iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly

Machine Learning and Finance: A Review using Latent Dirichlet Allocation Technique (LDA)

The aim of this paper is provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modelling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation Technique (LDA), we can extract the 14 coherent research topics that are the focus of the 6,148 academic articles during the years 1990-2019 analysed. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modelling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors

Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling

Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market conditions, such as market frictions, which are challenging to address within the traditional mathematical finance framework. Since deep hedging relies on market simulation, the underlying asset price process model is crucial. However, existing literature on deep hedging often relies on traditional mathematical finance models, e.g., Brownian motion and stochastic volatility models, and discovering effective underlying asset models for deep hedging learning has been a challenge. In this study, we propose a new framework called adversarial deep hedging, inspired by adversarial learning. In this framework, a hedger and a generator, which respectively model the underlying asset process and the underlying asset process, are trained in an adversarial manner. The proposed method enables to learn a robust hedger without explicitly modeling the underlying asset process. Through numerical experiments, we demonstrate that our proposed method achieves competitive performance to models that assume explicit underlying asset processes across various real market data.Comment: 8 pages, 7 figure