Kevin Corella Nieto, NTT DATA Centers Service Leader for Project Development/AI & Quantum Machine Learning Services, discusses quantum portfolio management from the PMI perspective.
Section 1 – Evolution in Portfolio Management: From Classical Limitations to Quantum Innovation
During the 20th century, portfolio management was characterized by a predominantly subjective approach, relying on unsystematic methods far removed from the rigorous analyses required to construct truly optimal portfolios. This changed radically in 1952 with the publication of “Portfolio Selection” by Harry Markowitz, which gave rise to Modern Portfolio Theory (MPT). For the first time, a mathematical framework was introduced to optimize the relationship between risk and return, transforming this practice into a discipline based on solid quantitative principles.
Despite its revolutionary impact, MPT presents significant limitations. Some of its key assumptions, such as the efficient market hypothesis, the normal distribution of asset returns, and constant correlations between assets, restrict its applicability in real-world scenarios, particularly under conditions of high volatility. These limitations become critical during financial crises, when markets tend to exhibit extreme behaviors that contradict the theory’s premises. In such situations, correlations between assets, which are typically diverse under normal conditions, tend to converge toward 1, nullifying one of the primary benefits of diversification: risk mitigation. This phenomenon, often associated with unpredictable events known as “black swans,” exposes MPT’s inability to effectively manage dynamic, multi-objective, and non-linear environments.
In this context, where demands on portfolio management are increasingly complex and challenging, a new possibility emerges: Quantum Portfolio Management (QPM).
QPM can be understood as a conceptual approach that applies the principles of quantum computing to portfolio optimization (through the areas of knowledge and management processes), offering an innovative perspective to address the inherent complexities of asset management through the efficient processing of large volumes of data within a multidimensional and dynamic framework. This approach not only seeks to overcome the limitations of traditional methods but also to leverage unique properties of quantum mechanics, such as superposition, which allows the simultaneous examination of multiple combinations of assets and constraints. In doing so, QPM promises to transform strategic decision-making, significantly accelerating the search for optimal solutions even in highly complex environments.
Therefore, this publication aims to explore the initial possibilities of Quantum Portfolio Management (QPM) within the context of Portfolio Management, aligned with PMI standards. PMI’s guidelines provide a comprehensive strategic framework that facilitates the connection between portfolios and organizational objectives, ensuring effective value management, prioritization, and asset balancing in dynamic and uncertain contexts. In this sense, QPM positions itself as a disruptive proposal with the potential to transform portfolio management practices, addressing the complexities of strategic decision-making more effectively in rapidly evolving environments.
Section 2 – Possible Contributions Explained Through the Portfolio Management Process Groups (PMI)
The PMI (Project Management Institute) framework for portfolio management is structured into three main process groups:
- Defining
- Aligning
- Authorizing and Controlling
These groups represent a comprehensive approach to managing portfolios, ensuring they align with the organization’s strategic objectives and optimize their value over time.
Key considerations:
- This section does not cover all the processes defined in PMI’s portfolio management standard.
- Instead, it focuses on specific knowledge areas and processes within these groups.
- The aim is to explore how Quantum Portfolio Management (QPM) could contribute to improving portfolio management practices, addressing current challenges, and offering new perspectives.
Process Group: Defining
Knowledge Area: Strategic Management and the Project Charter Process
Key aspects of Strategic Management:
- It ensures that the portfolio aligns with the organization’s strategic objectives.
- It identifies and prioritizes components that maximize value within a long-term strategic vision.
The Scenario Analysis technique:
- Allows exploration of risks, constraints, and opportunities.
- Anticipates how current decisions could influence future portfolio performance.
- Is crucial for building a robust and adaptable Project Charter in uncertain environments.
QPM enhances scenario analysis by introducing innovative capabilities, such as:
- Simultaneous Exploration of Multiple Scenarios: QPM evaluates a wide range of scenario combinations simultaneously, identifying optimal options quickly.
- Comprehensive and Multidimensional Optimization: QPM integrates financial, social, and environmental variables, offering a complete strategic perspective.
- Discovery of Hidden Opportunities and Risks: Quantum algorithms reveal patterns and correlations not visible through traditional techniques.
Process Group: Aligning
Knowledge Area: Performance and the Portfolio Value Management Process
Key aspects of Performance Management:
- Ensures that portfolio components deliver expected value.
- Aligns components with the organization’s strategic objectives.
The Portfolio Value Management process:
- Evaluates the impact of portfolio decisions on overall performance.
- Optimizes the risk-return relationship based on Markowitz’s efficient frontier.
Challenges in traditional methods:
- Difficulty addressing dynamic and multidimensional environments.
QPM offers advanced capabilities, including:
- Quantum-Efficient Frontier Optimization: Integrates multiple dimensions and constraints simultaneously to optimize risk, return, and other factors (e.g., ESG, sustainability).
- Dynamic Real-Time Analysis: Recalibrates portfolios in real time to adapt to market changes or organizational goals.
- Expanded Risk Management: Anticipates nonlinear correlations and complex interdependencies among assets, reducing exposure to unrecognized risks.
Process Group: Authorizing and Controlling
Knowledge Area: Governance and the Portfolio Authorization Process (Resource Allocation)
Key aspects of Governance:
- Ensures portfolio decisions align with strategic objectives and priorities.
- Focuses on efficient resource allocation to maximize portfolio value.
Challenges in resource allocation:
- Limited resources require balancing multiple objectives.
- Traditional methods are often slow and prone to subjective biases.
QPM transforms resource allocation with capabilities such as:
- Simultaneous Allocation and Dynamic Prioritization: Evaluates and prioritizes resource allocation combinations quickly.
- Integration of Multiple Constraints and Objectives: Models scenarios with time, budget, and capacity constraints alongside strategic objectives.
- Real-Time Recalibration: Adjusts resource allocation dynamically in response to changing priorities or unexpected events.
Section 3 – Conclusions and Future Perspectives
Quantum Portfolio Management (QPM) is emerging as a disruptive approach with the potential to profoundly transform the practice of portfolio management. This analysis has made it possible to explore how QPM can be integrated into PMI-defined process groups, such as Define, Align, Authorization, and Control, bringing innovative capabilities such as simultaneous scenario exploration, multidimensional optimization, and real-time recalibration. These features offer a significant advantage in managing the increasing complexity and uncertainties of today’s environment.
However, it is crucial to recognize that QPM is still in an incipient phase both conceptually and technically. Its practical implementation faces several key challenges, including:
- Technology development: The need for more accessible, reliable, and scalable quantum hardware that allows practical use in organizational contexts.
- Algorithmic evolution: The design and refinement of algorithms specifically adapted to solve portfolio optimization problems in a quantum context.
- Operational integration: The harmonization of QPM with already established management frameworks, such as PMI standards, to ensure frictionless adoption.
Despite these limitations, QPM presents a highly promising outlook for addressing some of the most complex challenges in portfolio management, including the need to consider multidimensional criteria such as ESG, sustainability, and strategic alignment in an ever-changing global financial environment.
Future prospects
The path to effective adoption of QPM will require close collaboration between researchers, organizations, and portfolio management practitioners. Key areas for its future development include:
- Applied research: Dive deeper into how quantum algorithms can adapt and optimize practical problems, such as maximizing risk-adjusted return, diversification, and sustainability.
- Education and training: Equip practitioners with the foundations of quantum computing and its applicability in strategic management, ensuring that they understand both its potential and its current limitations.
- Hybrid approaches: Develop gradual integration models, where quantum tools act as complements to traditional methods, allowing organizations to benefit from their capabilities while the technology continues to mature.
In conclusion, QPM not only challenges traditional portfolio management practices, but also introduces a new dimension to address complex problems in a global environment characterized by uncertainty and constant evolution. While the path to full adoption presents significant challenges, the opportunities it offers are invaluable for organizations looking to stay competitive, strategically aligned, and prepared to meet the challenges of the future.