Time-series forecasting with deep learning: a survey
Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …
of time-series datasets across different domains. In this article, we survey common encoder …
Deep reinforcement learning in quantitative algorithmic trading: A review
TV Pricope - arXiv preprint arXiv:2106.00123, 2021 - arxiv.org
Algorithmic stock trading has become a staple in today's financial market, the majority of
trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be …
trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be …
Deep reinforcement learning for trading
We adopt Deep Reinforcement Learning algorithms to design trading strategies for
continuous futures contracts. Both discrete and continuous action spaces are considered …
continuous futures contracts. Both discrete and continuous action spaces are considered …
Deep learning for portfolio optimization
We adopt deep learning models to directly optimise the portfolio Sharpe ratio. The
framework we present circumvents the requirements for forecasting expected returns and …
framework we present circumvents the requirements for forecasting expected returns and …
Machine learning models predicting returns: Why most popular performance metrics are misleading and proposal for an efficient metric
J Dessain - Expert Systems with Applications, 2022 - Elsevier
Numerous machine learning models have been developed to achieve the 'real-life'financial
objective of optimising the risk/return profile of investment strategies. In the current article:(a) …
objective of optimising the risk/return profile of investment strategies. In the current article:(a) …
Reinforcement learning for quantitative trading
Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia and …
techniques in analyzing the financial market, has been a popular topic in both academia and …
[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 …
A universal end-to-end approach to portfolio optimization via deep learning
We propose a universal end-to-end framework for portfolio optimization where asset
distributions are directly obtained. The designed framework circumvents the traditional …
distributions are directly obtained. The designed framework circumvents the traditional …
Slow momentum with fast reversion: A trading strategy using deep learning and changepoint detection
Momentum strategies are an important part of alternative investments and are at the heart of
commodity trading advisors (CTAs). These strategies have, however, been found to have …
commodity trading advisors (CTAs). These strategies have, however, been found to have …
Spatio-temporal momentum: Jointly learning time-series and cross-sectional strategies
We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-
series and cross-sectional momentum strategies by trading assets based on their cross …
series and cross-sectional momentum strategies by trading assets based on their cross …