An Evaluation Framework for Machine Learning and Data Science-Based Financial Strategies: A Case Study-Driven Decision Model

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Abstract
Big data and computational technologies are increasingly important worldwide in asset and investment management. Many investment management firms are adopting these data science methods and technologies to improve performance across all investment processes. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the main objective of this research was to develop a multi-criteria assessment framework and scoring decision support system to evaluate quantitative investment strategies that apply machine learning and data science techniques in their research and development. Subject matter experts will assess all framework perspectives from a systematic literature review to approve their reliability. The perspectives consist of economic and financial foundations, data perspective, features perspective, modeling perspective, and performance perspective. The research methodology applied was the Hierarchical Decision Model (HDM) to provide a 360-degree view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. This study accomplished a rigorous integration of an extensive literature review connecting data science (DS), machine learning (ML), and investment decision-making in developing quantitative investment strategies. As a result, the major contribution of this study is the comprehensive examination which included identifying and quantifying perspectives and criteria. The results, while limited indicated significant gaps in strategies examined therefore generated critical knowledge to improve ML/DS-driven investment strategies which are valuable for financial companies, and policymakers.

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