Research Article

A framework for online investment decisions

Published in: Investment Analysts Journal
Volume 49, issue 3: SAFA Special Issue, 2020 , pages: 215–231
DOI: 10.1080/10293523.2020.1806460
Author(s): Andrew B. Paskaramoorthy, South Africa, Tim J. Gebbie, South Africa, Terence L. van Zyl, South Africa


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.

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