Sponsor
Portland State University. Department of Engineering and Technology Management
First Advisor
Tugrul U. Daim
Term of Graduation
Spring 2024
Date of Publication
5-6-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.) in Technology Management
Department
Engineering and Technology Management
Language
English
Physical Description
1 online resource (xiii, 250 pages)
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. Researchers actively use these methods to develop more effective systematic investment strategies and produce more reliable outcomes less vulnerable to human decision-making biases. However, the success of such a strategy depends heavily on the scientific rigor applied throughout the process. "Best practices involve understanding how to make better decisions in the research design process. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the decisions made in the research process are crucial to developing successful quantitative strategies." Additionally, as this field is inherently multidisciplinary, it requires a system thinking approach to consider multiple perspectives to provide a clearer understanding of the strategies often referred to as "black boxes."
Therefore, the main objective of this research is 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 is the Hierarchical Decision Model (aka HDM) to provide a 360-degree view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. Finally, this research helps investment researchers and professionals to focus on research process decisions in generating more hypotheses and developing financial theories to be tested empirically rather than cherry-picking investment strategies based on historical simulations.
Rights
© 2024 Mohammadsaleh Saadatmand
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Persistent Identifier
https://archives.pdx.edu/ds/psu/42213
Recommended Citation
Saadatmand, Mohammadsaleh, "A Hierarchical Decision Model for Evaluating the Strategy Readiness of Quantitative Machine Learning/Data Science-Driven Investment Strategies" (2024). Dissertations and Theses. Paper 6612.
Tables of Improvement Simulation Cases
Model Validation Survey.xlsx (3904 kB)
Model Validation Survey
Desirability Curve Quantification Survey.xlsx (3519 kB)
Desirability Curve Quantification Survey