Multi-market portfolio optimization with conditional value at risk

Nasini, Stefano; Brotcorne, Luce

doi:10.1016/j.ejor.2021.10.010

Cited by 14 publications

(6 citation statements)

References 58 publications

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“…Johansson (2017) developed a model to determine which segmentation variables are most effective if customers are segmented based on their potential to purchase and attractiveness to give short-term sales success. Nasini et al (2022) proposed a framework for multi-market investment portfolio management that involves a central headquarters delegating the selection of a market portfolio to affiliates. Perrot et al (2018) used multi-market preference mapping to create a product portfolio for 10 markets for three products in the food industry.…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: Selecting and balancing market portfolio using artificial intelligence and fuzzy multiobjective decision-making model

Shih

Wei

Lin

et al. 2022

Concurrent Engineering

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Most enterprises focus on product portfolio management (PPM) and exclude market portfolio management, and individual markets are selected solely based on financial performance which may not be appropriate because other factors may dictate the outcome of the market selection. This study proposes a market portfolio model that considers market share, market growth, market competition, market risk, and market cost to maximize overall profit. A three-stage approach is proposed to identify potential product markets using an AI algorithm. A set of the most promising market is then determined based on the results of Stage 1 using a fuzzy multi-objective mathematical programming model by concurrently maximizing the total market share and market growth, and minimizing the market competition and market risk. A single-objective mathematical programming model is then used to determine a market portfolio to maximize the total profit using the results for Stage 2. The single-objective model is tested using two sets of threshold constraints. The balance of the market portfolio is discussed and the results are compared. The novelty of the article: (1) the first study involves a product market portfolio, (2) the decision of market selection is scientific (using AI instead of personal judgment), holistic (factors include market share, growth, competition, risk, cost, and profit) and optimized (using mathematical optimization), and (3) the market portfolio maximizes total profit. (4) the proposed method produces a portfolio with a higher profit than the TOPSIS method.

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Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: Selecting and balancing market portfolio using artificial intelligence and fuzzy multiobjective decision-making model

Shih

Wei

Lin

et al. 2022

Concurrent Engineering

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“…and conditional value-at-risk (CVaR), financial practitioners can better evaluate the risk exposure of the portfolio and adopt corresponding hedging and insurance strategies to reduce the overall risk of the portfolio (Han & Vinel, 2022;Nasini, Labbé, & Brotcorne, 2022).…”

Section: The Definition Of Fetmentioning

confidence: 99%

Application and Effect Analysis of Financial Engineering Tools in Portfolio Optimization

Cai

2024

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The purpose of this study is to deeply discuss the application of financial engineering tools (FET) in portfolio optimization, and analyze its effect in detail. Through the comprehensive application of modern investment theory and advanced mathematical modeling technology, this study discusses the potential advantages of FET in improving portfolio efficiency, reducing risks and adapting to market fluctuations. This study focuses on the application of Kalman filtering (KF) algorithm in portfolio optimization. The algorithm provides a powerful and effective tool for investors by estimating and adjusting the market state in real time. The advantage of KF algorithm lies in dealing with noise, missing data and dynamic weight adjustment, thus improving the efficiency of portfolio, especially in the rapidly changing market environment. By adopting advanced risk measurement and model, investors can identify and measure the risk of portfolio more comprehensively and formulate more effective hedging and insurance strategies. This helps to reduce the overall risk level of portfolio and improve the robustness of asset allocation when market volatility increases. The application of FET in portfolio optimization provides investors with more comprehensive and accurate decision support.

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“…The objectives of a portfolio maker is to maximize the “return” objectives and minimize the “risk” measures to reach a trade‐off (Gabrielli et al, 2022; Geissel et al, 2022; Nasini et al, 2022), which forms a multiobjective “return‐risk” optimization problem. Here are some classic single return objectives (

R O

) and risk measures (

R M

).…”

Section: The Multiobjective For Portfolio Makersmentioning

confidence: 99%

Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity

Dai

Zhang

Lau

et al. 2023

Journal of Forecasting

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This study designs a methodological framework to forecasting allocation weights and evaluating multiobjective portfolios considering the investment horizon heterogeneity. The investment horizon heterogeneity may induce the terrible data loss problem under traditional asset return definition, while in the real world, there are many portfolio makers whose investment horizons are not consistent. The methodological framework has three parts. First, this paper gives a novel multitimescale analysis (MTA) tool as a computation procedure to decompose the raw return series, and the decomposed subreturn series could represent the information for a portfolio maker with specific investment horizon and has the same data length as the raw return series. Second, proposed methodological framework uses a time‐varying parameter GAS‐D‐Vine‐Copula model to construct the joint distribution of subreturn series of multiassets in a portfolio. Third, due to the stochastic dominance consistency issue, this paper applies three different utility functions as the outputs of a portfolio strategy and two cost functions as the inputs of a portfolio strategy in an efficiency evaluation model. The empirical example of US aviation stock market data from 2013 to 2021 reveals that the Mean‐Skewness‐Volatility‐HMCR‐LPM multiobjective has the greatest numbers of optimal strategy timings for portfolio makers with 2‐, 3–5‐, and 10–50‐day‐length investment horizons. The investment horizon of 1‐day length is the least efficient, and the investment horizon between 10‐ and 50‐day length is the most efficient. The proposed methodology indicates that more multiobjectives in a portfolio strategy are not necessarily better, and there is an optimal range of investment horizons for portfolio makers.

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Multi-market portfolio optimization with conditional value at risk

Cited by 14 publications

References 58 publications

RETRACTED: Selecting and balancing market portfolio using artificial intelligence and fuzzy multiobjective decision-making model

RETRACTED: Selecting and balancing market portfolio using artificial intelligence and fuzzy multiobjective decision-making model

Application and Effect Analysis of Financial Engineering Tools in Portfolio Optimization

Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity

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