Liquidity Risk Portfolio Optimization Using Swarm Intelligence

2010 ◽  
pp. 551-558 ◽  
Author(s):  
Ben Niu ◽  
Han Xiao ◽  
Lijing Tan ◽  
Yan Fan ◽  
Junjun Rao
Keyword(s):  
Portfolio Optimization ◽  
Swarm Intelligence ◽  
Liquidity Risk

Multivariate portfolio optimization under illiquid market prospects: a review of theoretical algorithms and practical techniques for liquidity risk management

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mazin A. M. Al Janabi
Keyword(s):  
Risk Management ◽  
Portfolio Optimization ◽  
Real World ◽  
Optimization Algorithms ◽  
Liquidity Risk ◽  
Portfolio Risk ◽  
Market Conditions ◽  
Content Type ◽  
Illiquid Market ◽  
Investment Portfolios

Purpose This study aims to examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of liquidity-adjusted value-at-risk (LVaR), to multivariate optimization of investment portfolios. Design/methodology/approach This paper examines the modeling parameters of LVaR technique under event market settings and discusses how to integrate asset liquidity risk into LVaR models. Finally, the authors discuss scenario optimization algorithms for the assessment of structured investment portfolios and present a detailed operational methodology for computer programming purposes and prospective research design with the backing of a graphical flowchart. Findings To that end, the portfolio/risk manager can specify different closeout horizons and dependence measures and calculate the necessary LVaR and resulting investable portfolios. In addition, portfolio managers can compare the return/risk ratio and asset allocation of obtained investable portfolios with different liquidation horizons in relation to the conventional Markowitz´s mean-variance approach. Practical implications The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within machine learning and artificial intelligence, expert systems and smart financial applications, financial technology (FinTech), and within big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk. Originality/value The proposed optimization algorithms can aid in advancing portfolios selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints. Furthermore, the robust risk-algorithms and portfolio optimization techniques can aid in solving some real-world dilemmas under stressed and adverse market conditions, such as the effect of liquidity when it dries up in financial and commodity markets, the impact of correlations factors when there is a switching in their signs and the integration of the influence of the nonlinear and non-normal distribution of assets’ returns in portfolio optimization and management.

Numerical Approximation for a Portfolio Optimization Problem Under Liquidity Risk and Costs

2015 ◽  
Vol 74 (1) ◽  
pp. 163-195 ◽  
Author(s):  
M’hamed Gaigi ◽  
Vathana Ly Vath ◽  
Mohamed Mnif ◽  
Salwa Toumi
Keyword(s):  
Portfolio Optimization ◽  
Optimization Problem ◽  
Numerical Approximation ◽  
Liquidity Risk ◽  
Portfolio Optimization Problem

scholarly journals Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Nebojsa Bacanin ◽  
Milan Tuba
Keyword(s):  
Portfolio Optimization ◽  
Swarm Intelligence ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Constrained Problems ◽  
Portfolio Optimization Problem ◽  
Mean Variance ◽  
Modified Firefly Algorithm ◽  
Nature Inspired Metaheuristics ◽  
Diversity Constraint

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Extended Mean - Variance Portfolio Optimization Model: A Comparative Study Among Swarm Intelligence Algorithms

2019 ◽  
Vol 9 (2) ◽  
pp. 184
Author(s):  
R. K. Jena
Keyword(s):  
Portfolio Optimization ◽  
Swarm Intelligence ◽  
Portfolio Risk ◽  
Expected Return ◽  
Monthly Data ◽  
Bee Colony ◽  
Portfolio Optimization Model ◽  
Mean Variance Portfolio ◽  
Portfolio Selection Model ◽  
Mean Variance

Portfolio optimization is one of the important issues in the effective management of investment. There is plenty of research in the literature addressing these issues. Markowitz’s primary portfolio selection model is a more suitable method to solve the model for obtaining fairly optimum portfolios. But, the problem of portfolio optimization is multi-objective in nature that aims at simultaneously maximizing the expected return of the portfolio and minimizing portfolio risk. The computational complexity increases with an increase in the total number of available assets. Therefore heuristic methods are more suitable for portfolio optimization in compare to deterministic methods. This research compares three well-known swarm intelligence algorithms (e.g. Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC)) for portfolio optimization. The Sharpe ratio was used as one of the important criteria for this comparison. PSO outperformed other algorithms in portfolio optimization experiments. The results were also showed that the portfolios which were made of monthly data had performed better than the yearly data.

Bacterial foraging based approaches to portfolio optimization with liquidity risk

2012 ◽  
Vol 98 ◽  
pp. 90-100 ◽  
Author(s):  
Ben Niu ◽  
Yan Fan ◽  
Han Xiao ◽  
Bing Xue
Keyword(s):  
Portfolio Optimization ◽  
Liquidity Risk ◽  
Bacterial Foraging
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