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 …
Neural networks for option pricing and hedging: a literature review
Neural networks have been used as a nonparametric method for option pricing and hedging
since the early 1990s. Far over a hundred papers have been published on this topic. This …
since the early 1990s. Far over a hundred papers have been published on this topic. This …
Enhancing time series momentum strategies using deep neural networks
While time series momentum is a well-studied phenomenon in finance, common strategies
require the explicit definition of both a trend estimator and a position sizing rule. In this …
require the explicit definition of both a trend estimator and a position sizing rule. In this …
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 …
Modern perspectives on reinforcement learning in finance
We give an overview and outlook of the field of reinforcement learning as it applies to
solving financial applications of intertemporal choice. In finance, common problems of this …
solving financial applications of intertemporal choice. In finance, common problems of this …
Deep hedging: learning to simulate equity option markets
M Wiese, L Bai, B Wood, H Buehler - arXiv preprint arXiv:1911.01700, 2019 - arxiv.org
We construct realistic equity option market simulators based on generative adversarial
networks (GANs). We consider recurrent and temporal convolutional architectures, and …
networks (GANs). We consider recurrent and temporal convolutional architectures, and …
Does reinforcement learning outperform deep learning and traditional portfolio optimization models in frontier and developed financial markets?
Advancements in machine learning have opened up a wide range of new possibilities for
using advanced computer algorithms, such as reinforcement learning in portfolio risk …
using advanced computer algorithms, such as reinforcement learning in portfolio risk …
[BOOK][B] Machine learning for factor investing: R version
G Coqueret, T Guida - 2020 - taylorfrancis.com
Machine learning (ML) is progressively reshaping the fields of quantitative finance and
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers …
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers …
[PDF][PDF] Machine learning for active portfolio management
SM Bartram, J Branke, G De Rossi… - The Journal of …, 2021 - wrap.warwick.ac.uk
Abstract Machine learning (ML) methods are attracting considerable attention among
academics in the field of finance. However, it is commonly perceived that ML has not …
academics in the field of finance. However, it is commonly perceived that ML has not …
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 …