Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing

Lang, Jonas B.; Zielinski, Sebastian; Feld, Sebastian

doi:10.3390/app122312288

Cited by 12 publications

(8 citation statements)

References 34 publications

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“…See refs. (Cohen et al 2020a(Cohen et al , 2020bLang et al 2022;Mugel et al 2021;Sakuler et al 2023;Venturelli and Kondratyev 2019) for more examples of hybrid approaches to portfolio optimization.…”

Section: Discussionmentioning

confidence: 99%

Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios

Catalano,

Nasello,

Guterding

2024

Risks

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Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets, they deteriorate as budgets decrease. To alleviate this issue, a discrete Markowitz portfolio theory (DMPT) with finite budgets and integer stock weights can be formulated, but results in a non-polynomial (NP)-hard problem. Recent progress in quantum processing units (QPUs), including quantum annealers, makes solving DMPT problems feasible. Our study explores portfolio optimization on quantum annealers, establishing a mapping between continuous and discrete Markowitz portfolio theories. We find that correctly normalized discrete portfolios converge to continuous solutions as budgets increase. Our DMPT implementation provides efficient frontier solutions, outperforming traditional rounding methods, even for moderate budgets. Responding to the demand for environmentally and socially responsible investments, we enhance our discrete portfolio optimization with ESG (environmental, social, governance) ratings for EURO STOXX 50 index stocks. We introduce a utility function incorporating ESG ratings to balance risk, return and ESG friendliness, and discuss implications for ESG-aware investors.

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Section: Discussionmentioning

confidence: 99%

Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios

Catalano,

Nasello,

Guterding

2024

Risks

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“…This feature, combined with the L-Lipschitz continuity of ESG risk-performance functions, allows Bayesian optimization to optimize various ESG criteria with minimal prior knowledge and minimal assumptions about the function's structure, making it a highly adaptable and flexible approach for ESG portfolio management. Prior in the literature, other black-box models -including metaheuristics such as Genetic algorithm (GA) [17] or Simulated Annealing (SA) [18]have also been proposed as alternative portfolio optimization techniques [44][45][46][47][48][49][50][51][52][53]. Bayesian optimization (BO) is a state-of-the-art class of methods that optimize black-boxes.…”

Section: State Of the Art In Portfolio Optimization With Esg And Baye...mentioning

confidence: 99%

Bayesian optimization of ESG (Environmental Social Governance) financial investments

Garrido-Merchán¹,

Piris²,

Vaca³

2023

Environ. Res. Commun.

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Financial experts seek to predict the variability of financial markets to ensure investors’ successful investments. However, there hasbeen a big trend in finance in the last few years, which are the ESG (Economic, Social and Governance) criteria, due to the growing importance of investments being socially responsible, and because of the financial impact companies suffer when not complying with them. Consequently, creating a stock portfolio should consider not only its financial performance but compliance with ESG criteria. Portfolio optimization (PO) techniques previously applied to ESG portfolios, are all closed-form analytical ones. But the real world is rather a black box with unknown analytical expressions. Thus, in this paper we use Bayesian optimization (BO), a sequential state-of-the-art design strategy to optimize black-boxes with unknown analytical and costly-to-compute expressions, to maximize the performance of a stock portfolio under the presence of ESG criteria soft constraints incorporated into the objective function.And we compare it to two other black-box techniques widely applied for the optimization of “conventional portfolios” (non-ESG ones): the metaheuristics Genetic Algorithm (GA) and Simulated Annealing (SA). Although BO has many theoretical advantages over GA and SA, it has never been applied to PO. Thus, this paper investigates whether BO can be used in the ESG PO framework as an alternative and compares it with GA and SA. This is the research gap to which this paper responds. To show the empirical performance of BO, we carry out four illustrative experiments and find evidence of BO outperforming the baselines. Thus we add another different optimization approach to the world of ESG investing: a black-box non-heuristic optimization approach through BO. Our study is the first paper that leverages BO and ESG scores into a PO technique. This paper opens the door to many new research lines in (ESG) portfolio optimization

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“…This change imposes certain limitations on the allocation of assets, which are discussed in detail below. We make use of an n-bit binary variable, similar to the methods used by Lang [103] and Ottaviani [104]. To enable binary encoding of the decision variable Q, we can represent it as a matrix q containing binary elements.…”

Section: B Colopt Qubo Formulationmentioning

confidence: 99%

Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing (May 2023)

Giron,

Korpas,

Parvaiz

et al. 2023

IEEE Trans. Quantum Eng.

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Collateral optimization refers to the systematic allocation of financial assets to satisfy obligations or secure transactions, while simultaneously minimizing costs and optimizing the usage of available resources. This involves assessing number of characteristics, such as cost of funding and quality of the underlying assets to ascertain the optimal collateral quantity to be posted to cover exposure arising from a given transaction or a set of transactions. One of the common objectives is to minimise the cost of collateral required to mitigate the risk associated with a particular transaction or a portfolio of transactions while ensuring sufficient protection for the involved parties. Often, this results in a large-scale combinatorial optimization problem. In this study, we initially present a Mixed Integer Linear Programming (MILP) formulation for the collateral optimization problem, followed by a Quadratic Unconstrained Binary optimization (QUBO) formulation in order to pave the way towards approaching the problem in a hybridquantum and NISQ-ready way. We conduct local computational small-scale tests using various Software Development Kits (SDKs) and discuss the behavior of our formulations as well as the potential for performance enhancements. We find that while the QUBO based approaches fail to find the global optima in the small scale experiments, they are reasonably close suggesting their potential for large instances. We further survey the recent literature that proposes alternative ways to attack combinatorial optimization problems suitable for collateral optimization.

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Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing

Cited by 12 publications

References 34 publications

Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios

Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios

Bayesian optimization of ESG (Environmental Social Governance) financial investments

Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing (May 2023)

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