Problem
Research questions and friction points this paper is trying to address.
knowledge optimisation
investment decisions
informative datasets
portfolio performance
decision constraints
Knowledge-Optimising Investment Decisions with Informative Datasets
🤖 AI Summary
This study addresses the limitations of traditional investment approaches that utilize data solely for asset pricing while neglecting its systematic impact on portfolio construction and performance attribution under real-world constraints, often leading to suboptimal decisions. To overcome this, the paper proposes a novel three-stage knowledge-optimization framework encompassing decision architecture design, portfolio selection, and performance evaluation. For the first time, it integrates data, models, and business insights into actionable knowledge units embedded throughout the investment process. The framework innovatively introduces a knowledge-augmented proxy for the ex-ante Sharpe ratio to enhance knowledge-driven performance attribution. Empirical results across multiple scenarios demonstrate that the proposed method significantly elevates the explicit knowledge value in investment decisions, yielding improved portfolio performance and interpretability under practical constraints.
📝 Abstract
The enormous growth in datasets, both in number and size, has prompted investors to adapt to new ways for assimilating information. Normatively, the approach has been to integrate such datasets into pricing formulations and assess the performance of portfolios created thereafter. However, such approaches underestimate their influence in portfolio investments by limiting their impact to pricing only. While being theoretically valid, this results in a potential sub-optimal performance in the presence of real-life decision constraints, and a blind spot for performance attribution. We start by analysing investment decisions from a knowledge perspective, which unfurls a new structure. We then propose a FinTech process termed Knowledge Optimisation that aims to integrate the influence of knowledge components that could be related to data, models, or business units that extract information. A 3-stage process, namely, decision structure, portfolio selection, and performance assessment is designed. We present an alternative to the ex-ante Sharpe Ratio, integrating a term for knowledge units. Through scenario analysis involving portfolio investment situations, we illustrate the utility. By design, the process improves the importance of knowledge in investment decisions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Knowledge Optimisation
Informative Datasets
Portfolio Decision Framework
Ex-ante Sharpe Ratio with Knowledge
Performance Attribution
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