56,595 research outputs found
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning
Reinforcement learning has become one of the best approach to train a
computer game emulator capable of human level performance. In a reinforcement
learning approach, an optimal value function is learned across a set of
actions, or decisions, that leads to a set of states giving different rewards,
with the objective to maximize the overall reward. A policy assigns to each
state-action pairs an expected return. We call an optimal policy a policy for
which the value function is optimal. QLBS, Q-Learner in the
Black-Scholes(-Merton) Worlds, applies the reinforcement learning concepts, and
noticeably, the popular Q-learning algorithm, to the financial stochastic model
of Black, Scholes and Merton. It is, however, specifically optimized for the
geometric Brownian motion and the vanilla options. Its range of application is,
therefore, limited to vanilla option pricing within financial markets. We
propose MQLV, Modified Q-Learner for the Vasicek model, a new reinforcement
learning approach that determines the optimal policy of money management based
on the aggregated financial transactions of the clients. It unlocks new
frontiers to establish personalized credit card limits or to fulfill bank loan
applications, targeting the retail banking industry. MQLV extends the
simulation to mean reverting stochastic diffusion processes and it uses a
digital function, a Heaviside step function expressed in its discrete form, to
estimate the probability of a future event such as a payment default. In our
experiments, we first show the similarities between a set of historical
financial transactions and Vasicek generated transactions and, then, we
underline the potential of MQLV on generated Monte Carlo simulations. Finally,
MQLV is the first Q-learning Vasicek-based methodology addressing transparent
decision making processes in retail banking
Quantitative Trading using Deep Q Learning
Reinforcement learning (RL) is a branch of machine learning that has been
used in a variety of applications such as robotics, game playing, and
autonomous systems. In recent years, there has been growing interest in
applying RL to quantitative trading, where the goal is to make profitable
trades in financial markets. This paper explores the use of RL in quantitative
trading and presents a case study of a RL-based trading algorithm. The results
show that RL can be a powerful tool for quantitative trading, and that it has
the potential to outperform traditional trading algorithms. The use of
reinforcement learning in quantitative trading represents a promising area of
research that can potentially lead to the development of more sophisticated and
effective trading systems. Future work could explore the use of alternative
reinforcement learning algorithms, incorporate additional data sources, and
test the system on different asset classes. Overall, our research demonstrates
the potential of using reinforcement learning in quantitative trading and
highlights the importance of continued research and development in this area.
By developing more sophisticated and effective trading systems, we can
potentially improve the efficiency of financial markets and generate greater
returns for investors
Reinforcement Learning in Stock Trading
Using machine learning techniques in financial markets, particularly in stock trading, attracts a lot of attention from both academia and practitioners in recent years. Researchers have studied different supervised and unsupervised learning techniques to either predict stock price movement or make decisions in the market. In this paper we study the usage of reinforcement learning techniques in stock trading. We evaluate the approach on real-world stock dataset. We compare the deep reinforcement learning approach with state-of-the-art supervised deep learning prediction in real-world data. Given the nature of the market where the true parameters will never be revealed, we believe that the reinforcement learning has a lot of potential in decision-making for stock trading
Application of deep reinforcement learning in stock trading strategies and stock forecasting
The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of deep reinforcement learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are proved by experimental data, and the model is compared with the traditional model to prove its advantages. From the point of view of stock market forecasting and intelligent decision-making mechanism, this paper proves the feasibility of deep reinforcement learning in financial markets and the credibility and advantages of strategic decision-making
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning
Offline reinforcement learning -- learning a policy from a batch of data --
is known to be hard for general MDPs. These results motivate the need to look
at specific classes of MDPs where offline reinforcement learning might be
feasible. In this work, we explore a restricted class of MDPs to obtain
guarantees for offline reinforcement learning. The key property, which we call
Action Impact Regularity (AIR), is that actions primarily impact a part of the
state (an endogenous component) with limited impact on the remaining part of
the state (an exogenous component). AIR is a strong assumption, but it
nonetheless holds in a number of real-world domains including financial
markets. We discuss algorithms that exploits the AIR property, and provide a
theoretical analysis for an algorithm based on Fitted-Q Iteration. Finally, we
demonstrate that the algorithm outperforms existing offline reinforcement
learning algorithms across different data collection policies in simulated and
real world environments where the regularity holds
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search
The construction of approximate replication strategies for derivative
contracts in incomplete markets is a key problem of financial engineering.
Recently Reinforcement Learning algorithms for pricing and hedging under
realistic market conditions have attracted significant interest. While
financial research mostly focused on variations of -learning, in Artificial
Intelligence Monte Carlo Tree Search is the recognized state-of-the-art method
for various planning problems, such as the games of Hex, Chess, Go,... This
article introduces Monte Carlo Tree Search as a method to solve the stochastic
optimal control problem underlying the pricing and hedging of financial
derivatives. As compared to -learning it combines reinforcement learning
with tree search techniques. As a consequence Monte Carlo Tree Search has
higher sample efficiency, is less prone to over-fitting to specific market
models and generally learns stronger policies faster. In our experiments we
find that Monte Carlo Tree Search, being the world-champion in games like Chess
and Go, is easily capable of directly maximizing the utility of investor's
terminal wealth without an intermediate mathematical theory.Comment: Added figures. Added references. Corrected typos. 15 pages, 5 figure
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