Reinforcement Learning Approaches to Optimal Market Making

Gasperov, Bruno; Begušić, Stjepan; Šimović, Petra Posedel; Kostanjčar, Zvonko

doi:10.3390/math9212689

Cited by 9 publications

(4 citation statements)

References 60 publications

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“…These methods have dramatically improved the state-of-the-art in various domains such as financial analysis, fraud detection, trading, and risk management (Sharma, 2023). For instance, machine learning algorithms have been used for sentiment analysis to develop market-neutral trading strategies, while deep neural networks have been employed for optimal market making in response to market conditions (Wang et al, 2021;Gasperov et al, 2021). Additionally, natural language processing has been utilized for customer service and portfolio optimization (Sharma, 2023).…”

Section: Ai In Financial Marketsmentioning

confidence: 99%

The Intersection of Ai and Quantum Computing in Financial Markets: A Critical Review

Akoh Atadoga,

Chinedu Ugochukwu Ike,

Onyeka Franca Asuzu

et al. 2024

Comput. sci. IT res. j.

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This review explores the intricate and evolving relationship between Artificial Intelligence (AI) and Quantum Computing within the realm of financial markets. As technology continues to advance, the integration of AI and quantum computing has emerged as a paradigm-shifting force, promising unprecedented capabilities to analyze and navigate the complexities of financial systems. This critical review delves into the synergies, challenges, and potential disruptions arising from the intersection of these two transformative technologies. The utilization of AI in financial markets has witnessed remarkable progress in recent years, with machine learning algorithms, deep neural networks, and natural language processing contributing to enhanced data analysis, predictive modeling, and decision-making. However, the computational demands of these sophisticated algorithms often surpass the capabilities of classical computing architectures, paving the way for the exploration of quantum computing as a potential solution. Quantum computing, with its ability to process vast datasets and perform complex calculations at speeds inconceivable by classical computers, presents a revolutionary approach to addressing the computational challenges faced by AI in financial applications. The review critically examines the potential advantages of quantum computing, such as its capacity to solve optimization problems, simulate financial scenarios, and secure data through quantum cryptography. Despite the promises, the integration of AI and quantum computing in financial markets is not without hurdles. The review investigates the current limitations, including hardware constraints, error correction challenges, and the high costs associated with quantum computing infrastructure. Ethical considerations and regulatory frameworks surrounding the implementation of such powerful technologies in financial decision-making also warrant careful examination. This critical review provides a comprehensive analysis of the intersection of AI and quantum computing in financial markets, shedding light on the transformative potential, challenges, and ethical implications that accompany this cutting-edge convergence of technologies. Understanding this intersection is crucial for stakeholders seeking to navigate the evolving landscape of finance and technology. Keywords: AI, Quantum, Computing, Financial Market, Review.

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Section: Ai In Financial Marketsmentioning

confidence: 99%

The Intersection of Ai and Quantum Computing in Financial Markets: A Critical Review

Akoh Atadoga,

Chinedu Ugochukwu Ike,

Onyeka Franca Asuzu

et al. 2024

Comput. sci. IT res. j.

View full text Add to dashboard Cite

show abstract

“…Other works that take a comprehensive look at RL in finance include [35,36]. In addition to these broader surveys, narrower reviews have examined the use of RL applications in specific financial subdomains, such as algorithmic trading [37,38], market making [39], economics [40,41] and portfolio optimization [42].…”

Section: Similar Workmentioning

confidence: 99%

Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review

Pickard,

Lawryshyn

2023

Mathematics

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This paper reviews 17 studies addressing dynamic option hedging in frictional markets through Deep Reinforcement Learning (DRL). Specifically, this work analyzes the DRL models, state and action spaces, reward formulations, data generation processes and results for each study. It is found that policy methods such as DDPG are more commonly employed due to their suitability for continuous action spaces. Despite diverse state space definitions, a lack of consensus exists on variable inclusion, prompting a call for thorough sensitivity analyses. Mean-variance metrics prevail in reward formulations, with episodic return, VaR and CvaR also yielding comparable results. Geometric Brownian motion is the primary data generation process, supplemented by stochastic volatility models like SABR (stochastic alpha, beta, rho) and the Heston model. RL agents, particularly those monitoring transaction costs, consistently outperform the Black–Scholes Delta method in frictional environments. Although consistent results emerge under constant and stochastic volatility scenarios, variations arise when employing real data. The lack of a standardized testing dataset or universal benchmark in the RL hedging space makes it difficult to compare results across different studies. A recommended future direction for this work is an implementation of DRL for hedging American options and an investigation of how DRL performs compared to other numerical American option hedging methods.

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“…Corresponding to the DQN method based on Q-function estimation is the policy gradient method [44]. e policy gradient method uses the deep learning model as the policy function π θ (s, a) and directly optimizes the policy function by calculating the policy gradient.…”

Section: Intelligent Routing Algorithm Based Onmentioning

confidence: 99%

Comparative Analysis of Routing Schemes Based on Machine Learning

Shaoyu

Tan

Madsen

et al. 2022

Mobile Information Systems

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Machine learning-based distributed routing algorithms, in contrast to traditional mathematical model-driven distributed routing algorithms, are typically data-driven, allowing them to adapt to dynamically changing network environments and various performance evaluation index optimization requirements. It is quite likely that it will become a key part of the next-generation Internet in the future. However, current intelligent routing research is still in its early stages. This article provides a comprehensive review of the state-of-the-art routing algorithms based on machine learning. First, important research on existing data-driven intelligent routing algorithms is presented with the key concepts and applications of these systems demonstrated. To enable intelligent routing algorithms to be deployed in real scenarios with cheap cost and high reliability, two appropriate training deployment frameworks and intelligent routing algorithm training and deployment strategies are given. Finally, the future development of machine learning-based intelligent routing systems is examined. The opportunities and problems that have been encountered, as well as prospective research directions, are discussed.

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Reinforcement Learning Approaches to Optimal Market Making

Cited by 9 publications

References 60 publications

The Intersection of Ai and Quantum Computing in Financial Markets: A Critical Review

The Intersection of Ai and Quantum Computing in Financial Markets: A Critical Review

Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review

Comparative Analysis of Routing Schemes Based on Machine Learning

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