Modern Portfolio Theory

A Simplified Perspective of the Markowitz Portfolio Theory ArXiv ID: ssrn-2147880 “View on arXiv” Authors: Unknown Abstract Noted economist, Harry Markowitz (“Markowitz) received a Nobel Prize for his pioneering theoretical contributions to financial economics and corporate finance. Keywords: Harry Markowitz, Modern Portfolio Theory, Asset Allocation, Risk-Return Trade-off, Equities Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper presents a simplified perspective of Markowitz’s theory and focuses on using Excel as a computational shortcut, indicating low mathematical density and minimal empirical backtesting or data-heavy implementation. flowchart TD A["Research Goal<br>Test Simplified MPT Approach"] --> B["Input Data<br>Historical Equity Returns"] B --> C["Computational Process<br>Mean-Variance Optimization"] C --> D["Core Calculation<br>Efficient Frontier Construction"] D --> E["Output<br>Risk-Return Efficient Portfolios"] E --> F["Key Finding<br>Validation of Risk-Return Trade-off"] F --> G["Outcome<br>Practical Asset Allocation Tool"]

Impact IRR: Leveraging Modern Portfolio Theory to Define Impact Investments ArXiv ID: 2509.22600 “View on arXiv” Authors: Daniel Soliman Abstract The impact investment market has an estimated value of almost $1.6 trillion. Significant progress has been made in determining the financial returns of impact investing. Investors are still, however, in the early stages of determining impact return. In this study, the author proposes the use of impact internal rate of return (impact IRR) to evaluate and monitor impact investments. This approach, which utilizes components of modern portfolio theory, adapted financial tools, and existing datasets, is demonstrated herein through initial use cases and examples showing how it can be employed to optimize impact. ...

Building crypto portfolios with agentic AI ArXiv ID: 2507.20468 “View on arXiv” Authors: Antonino Castelli, Paolo Giudici, Alessandro Piergallini Abstract The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility. To address the challenge of managing dynamic portfolios in such an environment, this paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations. Using data on daily frequencies of the ten most capitalized cryptocurrencies from 2020 to 2025, we compare two automated investment strategies. These are a static equal weighting strategy and a rolling-window optimization strategy, both implemented to maximize the evaluation metrics of the Modern Portfolio Theory (MPT), such as Expected Return, Sharpe and Sortino ratios, while minimizing volatility. Each step of the process is handled by dedicated agents, integrated through a collaborative architecture in Crew AI. The results show that the dynamic optimization strategy achieves significantly better performance in terms of risk-adjusted returns, both in-sample and out-of-sample. This highlights the benefits of adaptive techniques in portfolio management, particularly in volatile markets such as cryptocurrency markets. The following methodology proposed also demonstrates how multi-agent systems can provide scalable, auditable, and flexible solutions in financial automation. ...

Theoretical Frameworks for Integrating Sustainability Factors into Institutional Investment Decision-Making ArXiv ID: 2502.13148 “View on arXiv” Authors: Unknown Abstract This paper explores key theoretical frameworks instrumental in understanding the relationship between sustainability and institutional investment decisions. The study identifies and analyzes various theories, including Behavioral Finance Theory, Modern Portfolio Theory, Risk Management Theory, and others, to explain how sustainability considerations increasingly influence investment choices. By examining these frameworks, the paper highlights how investors integrate Environmental, Social, and Governance (ESG) factors to optimize financial outcomes and align with broader societal goals. ...

Advanced Course in Asset Management (Presentation Slides) ArXiv ID: ssrn-3773484 “View on arXiv” Authors: Unknown Abstract These presentation slides have been written for the Advanced Course in Asset Management (theory and applications) given at the University of Paris-Saclay. They Keywords: Asset Management, Modern Portfolio Theory, Risk Management, Factor Investing, Multi-Asset Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The slides present advanced mathematical theory including Markowitz optimization, CAPM, and Black-Litterman models with quadratic programming formulations and covariance matrix algebra. While it includes tutorial exercises and practice sections, it lacks empirical backtesting data, code implementations, or statistical performance metrics, remaining primarily theoretical and educational. flowchart TD A["Research Goal<br>Modern Asset Management"] --> B["Key Methodology<br>Portfolio Optimization"] B --> C["Data Inputs<br>Market Factors & Risk"] C --> D["Computational Process<br>Factor Analysis & MPT"] D --> E["Key Outcomes<br>Strategic Asset Allocation"] E --> F["Applications<br>Risk-Adjusted Returns"]

Beyond Markowitz: A Comprehensive Wealth Allocation Framework for Individual Investors ArXiv ID: ssrn-925138 “View on arXiv” Authors: Unknown Abstract In sharp contrast to the recommendations of Modern Portfolio Theory (MPT), a vast majority of investors are not well diversified. This neglect of diversificatio Keywords: portfolio diversification, modern portfolio theory, asset allocation, investor behavior, risk management, Multi-Asset / Equities Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper proposes a conceptual framework extending Markowitz by adding personal and aspirational risk dimensions, relying on qualitative discussion and examples rather than dense mathematical derivations or rigorous backtesting. flowchart TD R["Research Goal: Why do investors fail to diversify despite MPT?"] --> M["Methodology: Qualitative Analysis of Investor Behavior"] M --> D["Data Inputs: Empirical Data & Behavioral Observations"] D --> C["Computational Process: Multi-Asset Portfolio Simulation"] C --> F["Key Findings: Investors prioritize simplicity and familiarity over theoretical optimal allocation"] F --> O["Outcome: Proposed Comprehensive Wealth Allocation Framework"]