Deep learning and asset management - it is here but requires significant work

The use of deep learning techniques has exploded in finance, but few papers summarize what has been achieved. That has changed with a new paper,  see Deep Learning in Asset Management: Architectures, Applications, and Challenges. This work provides a good survey of how to apply deep learning within asset management. While the authors suggest that the use of deep learning is promising, they also note significant challenges, including low signal-to-noise ratios, non-stationarity in financial time series, and the market's adaptive nature, which can face regime changes and adapt to patterns. Along with outlining the problems with CNNs, RNNs, and transformers, there are many practical challenges in using deep learning models, from data usage to the usual overfitting problems. While deep learning has focused on quality predictions, there may be useful ways to integrate deep learning into holistic approaches to asset management