Dive into deep learning
This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …
teaching readers the concepts, the context, and the code. The entire book is drafted in …
An overview of machine learning for asset management
The Journal of Portfolio Management | Portfolio Management Research Skip to main content
Portfolio Management Research Logo Main navigation Topics All Topics Portfolio Management …
Portfolio Management Research Logo Main navigation Topics All Topics Portfolio Management …
A portfolio construction model based on sector analysis using Dempster-Shafer evidence theory and Granger causal network: An application to National stock …
K Bisht, A Kumar - Expert Systems with Applications, 2023 - Elsevier
With the emerging areas of economy, the diverse sector-based investment portfolios are
considered more significant. This paper presents an integrated approach of portfolio …
considered more significant. This paper presents an integrated approach of portfolio …
End-to-end learning for stochastic optimization: A bayesian perspective
We develop a principled approach to end-to-end learning in stochastic optimization. First,
we show that the standard end-to-end learning algorithm admits a Bayesian interpretation …
we show that the standard end-to-end learning algorithm admits a Bayesian interpretation …
LinSATNet: the positive linear satisfiability neural networks
Encoding constraints into neural networks is attractive. This paper studies how to introduce
the popular positive linear satisfiability to neural networks. We propose the first differentiable …
the popular positive linear satisfiability to neural networks. We propose the first differentiable …
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 …
Deep reinforcement learning for active high frequency trading
We introduce the first end-to-end Deep Reinforcement Learning (DRL) based framework for
active high frequency trading in the stock market. We train DRL agents to trade one unit of …
active high frequency trading in the stock market. We train DRL agents to trade one unit of …
[HTML][HTML] Portfolio insurance through error-correction neural networks
VN Kovalnogov, RV Fedorov, DA Generalov… - Mathematics, 2022 - mdpi.com
Minimum-cost portfolio insurance (MCPI) is a well-known investment strategy that tries to
limit the losses a portfolio may incur as stocks decrease in price without requiring the …
limit the losses a portfolio may incur as stocks decrease in price without requiring the …
Neural networks for portfolio analysis with cardinality constraints
X Cao, S Li - IEEE Transactions on Neural Networks and …, 2023 - ieeexplore.ieee.org
Portfolio analysis is a crucial subject within modern finance. However, the classical
Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new …
Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new …
Large language models in finance: A survey
Recent advances in large language models (LLMs) have opened new possibilities for
artificial intelligence applications in finance. In this paper, we provide a practical survey …
artificial intelligence applications in finance. In this paper, we provide a practical survey …