Recent advances in reinforcement learning in finance
The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …
revolutionized the techniques on data processing and data analysis and brought new …
An overview of machine learning for asset management
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Portfolio Management Research Logo Main navigation Topics All Topics Portfolio Management …
Portfolio Management Research Logo Main navigation Topics All Topics Portfolio Management …
Deep hedging of derivatives using reinforcement learning
J Cao, J Chen, J Hull, Z Poulos - arXiv preprint arXiv:2103.16409, 2021 - arxiv.org
This paper shows how reinforcement learning can be used to derive optimal hedging
strategies for derivatives when there are transaction costs. The paper illustrates the …
strategies for derivatives when there are transaction costs. The paper illustrates the …
Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory
With artificial intelligence and data quality development, portfolio optimization has improved
rapidly. Traditionally, researchers in the financial market have utilized the modern portfolio …
rapidly. Traditionally, researchers in the financial market have utilized the modern portfolio …
AC2CD: An actor–critic architecture for community detection in dynamic social networks
A vital problem tackled in the network analysis literature is community structure identification
in social networks. There are many solutions to the community detection problem …
in social networks. There are many solutions to the community detection problem …
Deep hedging: Continuous reinforcement learning for hedging of general portfolios across multiple risk aversions
We present a method for finding optimal hedging policies for arbitrary initial portfolios and
market states. We develop a novel actor-critic algorithm for solving general risk-averse …
market states. We develop a novel actor-critic algorithm for solving general risk-averse …
Cva hedging with reinforcement learning
R Daluiso, M Pinciroli, M Trapletti, E Vittori - Proceedings of the Fourth …, 2023 - dl.acm.org
This work considers the problem of a trader who must manage the Credit Valuation
Adjustment (CVA) of a derivative, defined as the risk-neutral expectation of losses incurred if …
Adjustment (CVA) of a derivative, defined as the risk-neutral expectation of losses incurred if …
Reinforcement Learning for Credit Index Option Hedging
F Mandelli, M Pinciroli, M Trapletti, E Vittori - arXiv preprint arXiv …, 2023 - arxiv.org
In this paper, we focus on finding the optimal hedging strategy of a credit index option using
reinforcement learning. We take a practical approach, where the focus is on realism ie …
reinforcement learning. We take a practical approach, where the focus is on realism ie …
Empirical deep hedging
O Mikkilä, J Kanniainen - Quantitative Finance, 2023 - Taylor & Francis
Existing hedging strategies are typically based on specific financial models: either the
strategies are directly based on a given option pricing model or stock price and volatility …
strategies are directly based on a given option pricing model or stock price and volatility …
Deep bellman hedging
H Buehler, P Murray, B Wood - arXiv preprint arXiv:2207.00932, 2022 - arxiv.org
We present an actor-critic-type reinforcement learning algorithm for solving the problem of
hedging a portfolio of financial instruments such as securities and over-the-counter …
hedging a portfolio of financial instruments such as securities and over-the-counter …