Abstract
ABSTRACT The artificial segmentation of the investment management process into silos of human operators can restrict silos from collectively and adaptively pursuing a unified investment goal. In this article, we argue that the investment process can be accelerated and be made more cohesive by replacing batch processing for component tasks of the investment process with online processing. We propose an integrated and online framework for investment workflows, where components produce outputs that are automatically and sequentially updated as new data arrives. The workflow can be further enhanced to refine signal generation and asset class evolution and definitions. Our results demonstrate that we use this framework in conjunction with resampling methods to optimise component decisions with direct reference to investment objectives while making clear the extent of backtest overfitting. We consider such an online update framework to be a crucial step towards developing intelligent portfolio selection algorithms that integrate financial theory, investor views, and data analysis with process-level learning.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
