基于信息数据集的知识优化投资决策
Knowledge-Optimising Investment Decisions with Informative Datasets
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中文总结 AI 辅助
该研究针对数据集增长下投资决策的次优问题，提出知识优化金融科技流程，构建三阶段模型并改进事前夏普比率，通过情景分析验证其可提升投资决策中知识的重要性。
中文摘要 AI 辅助
数据集在数量和规模上的巨大增长促使投资者适应新的信息吸收方式。从规范角度看，通常的做法是将这类数据集整合到定价公式中，评估由此创建的投资组合的表现。然而，这类方法仅将数据集的影响局限于定价，低估了其对投资组合投资的作用，虽在理论上成立，但在现实决策约束下可能导致表现次优，还会在绩效归因上出现盲区。我们从知识视角分析投资决策，揭示了一种新结构；随后提出名为知识优化（Knowledge Optimisation）的金融科技流程，旨在整合与数据、模型或提取信息的业务部门相关的知识组件的影响，设计了决策结构、投资组合选择、绩效评估的三阶段流程。我们提出了事前夏普比率（ex-ante Sharpe Ratio）的替代方案，整合了知识单元项；通过涉及投资组合投资场景的情景分析，说明了该流程的实用性，其设计目的是提升知识在投资决策中的重要性。
英文摘要
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.
