Mean-Variance

End-to-End Portfolio Optimization with Quantum Annealing ArXiv ID: 2504.08843 “View on arXiv” Authors: Unknown Abstract Hybrid-quantum classical optimization has emerged as a promising direction for addressing financial decision problems under current quantum hardware constraints. In this work we present a practical end-to-end portfolio optimization pipeline that combines (i) a continuous mean-variance and Sharpe-ratio formulation, (ii) a QUBO/CQM-based discrete asset selection stage solved using D-Wave’s hybrid quantum annealing solver, (iii) classical convex optimization for computing optimal asset weights, and (iv) a quarterly rebalancing mechanism. Rather than claiming quantum advantage, our goal is to evaluate the feasibility and integration of these components within a deployable financial workflow. We empirically compare our hybrid pipeline against a fund manager in real time and indexes used in Indian stock market. The results indicate that the proposed framework can construct diversified portfolios and achieve competitive returns. We also report computational considerations and scalability observations drawn from the hybrid solver behaviour. While the experiments are limited to moderate sized portfolios dictated by current annealing hardware and QUBO embedding constraints, the study illustrates how quantum assisted selection and classical allocation can be combined coherently in a real-world setting. This work emphasizes methodological reproducibility and practical applicability, and aims to serve as a step toward larger-scale financial optimization workflows as quantum annealers continue to mature. ...

A Framework for Treating Model Uncertainty in the Asset Liability Management Problem ArXiv ID: 2310.11987 “View on arXiv” Authors: Unknown Abstract The problem of asset liability management (ALM) is a classic problem of the financial mathematics and of great interest for the banking institutions and insurance companies. Several formulations of this problem under various model settings have been studied under the Mean-Variance (MV) principle perspective. In this paper, the ALM problem is revisited under the context of model uncertainty in the one-stage framework. In practice, uncertainty issues appear to several aspects of the problem, e.g. liability process characteristics, market conditions, inflation rates, inside information effects, etc. A framework relying on the notion of the Wasserstein barycenter is presented which is able to treat robustly this type of ambiguities by appropriate handling the various information sources (models) and appropriately reformulating the relevant decision making problem. The proposed framework can be applied to a number of different model settings leading to the selection of investment portfolios that remain robust to the various uncertainties appearing in the market. The paper is concluded with a numerical experiment for a static version of the ALM problem, employing standard modelling approaches, illustrating the capabilities of the proposed method with very satisfactory results in retrieving the true optimal strategy even in high noise cases. ...

Fundamental Indexation ArXiv ID: ssrn-713865 “View on arXiv” Authors: Unknown Abstract A trillion-dollar industry is based on investing in or benchmarking to capitalization-weighted indexes, even though the finance literature rejects the mean-vari Keywords: capitalization-weighted indexes, mean-variance, passive investing, benchmarking, portfolio optimization, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper presents a straightforward, intuitive strategy (fundamental indexing) with minimal mathematical derivations, but heavily relies on empirical backtests, real-world benchmark comparisons, and data analysis to challenge capitalization-weighted norms. flowchart TD A["Research Goal:<br/>Test if capitalization-weighted indexes<br/>are truly optimal"] --> B["Methodology:<br/>Compare Cap-Weighted vs.<br/>Fundamental Indexation"] B --> C["Data: Equities &<br/>Fundamental Metrics"] C --> D["Computation:<br/>Mean-Variance Optimization<br/>& Portfolio Simulation"] D --> E["Key Finding:<br/>Fundamental Indexation<br/>Outperforms Cap-Weighting"] E --> F["Outcome:<br/>Rejection of passive indexing<br/>as mean-variance efficient"]

Fundamental Indexation ArXiv ID: ssrn-604842 “View on arXiv” Authors: Unknown Abstract A trillion-dollar industry is based on investing in or benchmarking to capitalization-weighted indexes, even though the finance literature rejects the mean-vari Keywords: capitalization-weighted indexes, mean-variance, passive investing, benchmarking, portfolio optimization, Equities Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper involves moderate mathematical finance concepts like portfolio optimization and benchmark analysis, but it is heavily data-driven, featuring extensive backtesting, real-world index performance comparisons, and discussion of implementation for a trillion-dollar industry. flowchart TD A["Research Goal<br>Test: Does capitalization weighting<br>violate mean-variance efficiency?"] --> B["Methodology<br>Constrained Optimization<br>vs. Capitalization Weighting"] B --> C["Input: Historical Returns<br>U.S. Large Cap Equities"] C --> D["Computational Process<br>Maximize Sharpe Ratio<br>Under Optimization Constraints"] D --> E{"Key Finding 1: Efficiency<br>Optimal Portfolio Sharpe Ratio<br>> Cap-Weighted Portfolio?"} E -- Yes --> F["Outcome: Cap-weighting is<br>Mean-Variance Inefficient"] E -- No --> G["Outcome: Cap-weighting is<br>Mean-Variance Efficient"] F --> H["Key Finding 2: Performance<br>Fundamental Indexation<br>Outperforms Cap-Weighting"] G --> H H --> I["Key Takeaway<br>Trillion-dollar cap-weighted industry<br>is suboptimal vs. optimized portfolios"]