Optimization of risk control in financial markets based on particle swarm optimization algorithm

Zhang, Huaping

doi:10.1016/j.cam.2019.112530

Cited by 18 publications

(7 citation statements)

References 13 publications

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“…Kaucic [39] expanded a superseded portfolio plan that merged the risk equality approach with cardinality-constrained portfolio optimization, then for the complex integer programming problem, an ameliorated multi-objective PSO algorithm was used.Konstantinou et al [40] optimized the cardinality of the S&P 500 index portfolio using GA and Sonar algorithms. Zhang [41] developed an improved typical transaction cost function based on CVaR, and managed the market risk by solving it with PSO. He demonstrated that the PSO algorithm effectively ameliorated the precocious phenomenon and showed higher convergence, velocity, and precision.…”

Section: Literature Reviewmentioning

confidence: 99%

Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms

et al. 2022

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Investors always pay attention to the two factors of return and risk in portfolio optimization. There are different metrics for the calculation of the risk factor, among which the most important one is the Conditional Value at Risk (CVaR). On the other hand, Data Envelopment Analysis (DEA) can be used to form the optimal portfolio and evaluate its efficiency. In these models, the optimal portfolio is created by stocks or companies with high efficiency. Since the search space is vast in actual markets and there are limitations such as the number of assets and their weight, the optimization problem becomes difficult. Evolutionary algorithms are a powerful tool to deal with these difficulties. The automotive industry in Iran involves international automotive manufacturers. Hence, it is essential to investigate the market related to this industry and invest in it. Therefore, in this study we examined this market based on the price index of the automotive group, then optimized a portfolio of automotive companies using two methods. In the first method, the CVaR measurement was modeled by means of DEA, then Particle Swarm Optimization (PSO) and the Imperial Competitive Algorithm (ICA) were used to solve the proposed model. In the second method, PSO and ICA were applied to solve the CVaR model, and the efficiency of the portfolios of the automotive companies was analyzed. Then, these methods were compared with the classic Mean-CVaR model. The results showed that the automotive price index was skewed to the right, and there was a possibility of an increase in return. Most companies showed favorable efficiency. This was displayed the return of the portfolio produced using the DEA-Mean-CVaR model increased because the investment proposal was basedon the stock with the highest expected return and was effective at three risk levels. It was found that when solving the Mean-CVaR model with evolutionary algorithms, the risk decreased. The efficient boundary of the PSO algorithm was higher than that of the ICA algorithm, and it displayed more efficient portfolios.Therefore, this algorithm was more successful in optimizing the portfolio.

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Section: Literature Reviewmentioning

confidence: 99%

Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms

et al. 2022

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“…Some representative studies on supply chain finance emerged in recent years are as follows: Zhu et al [1] classifie the business model of supply chain finance in China from different angles, analyzed the shortcomings of this business model, and finally proposed to establish a visual platform to innovate the business operation model and risk control model of domestic supply chain finance. Zhang [2] studied the coal power supply chain financing strategy of coal companies with capital constraints and income uncertainty, proposed the prepayment mechanism, obtained the optimal operation strategy of coal companies and power companies under the prepayment mechanism, and compared it with the bank loan financing case.…”

Section: Related Workmentioning

confidence: 99%

Supply Chain Financial Default Risk Early Warning System Based on Particle Swarm Optimization Algorithm

Yin

2022

Mathematical Problems in Engineering

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With the advancement of the linkage between financial markets, the probability of credit risk infection is also increasing. Traditional financing methods, which mostly relied on corporate credit to give credit to the whole supply chain, have been replaced by supply chain finance. This paper studies the supply chain financial credit risk through the logistic model and chooses the financial data and supply chain financial operation indicators of relevant listed companies from 2014 to 2016 for analysis. Because not all of companies can find the bad debt rate of accounts receivable from 2014 to 2016, and some agricultural listed companies only have one or two years of relevant data, this paper creates an unbalanced panel data with 91 sample sizes, which is larger than previous studies. Binary logistic regression and principal component analysis are mainly used to accurately calculate the compliance probability of cooperative customers in agricultural supply chain financial products. Unlike the existing literature, which mainly uses s.t to define whether an enterprise defaults, this paper uses Z value to define the default risk of listed companies in agricultural supply chain finance. In terms of the default risk value of the company, Z value not only has high accurate value but also has advantages in accurate prediction, which effectively complements and improves the existing research on supply chain finance.

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“…Kaucic [12] integrated risk parity with cardinality constrained portfolio selection model, and a multiobjective PSO algorithm solves this issue. Zhang [13] optimized the risk of the financial market with a PSO algorithm. Cura [14] presented a heuristic model for portfolio selection employing an artificial bee colony approach considering the weekly prices of indexes of different countries and found that the presented approach is superior to its peers.…”

Section: Introductionmentioning

confidence: 99%

Portfolio selection model using teaching learning-based optimization approach

Kumar

Shahid

2023

IJ-AI

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<p>Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem. However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization problems. This article presents a portfolio selection model using the TLBO approach to maximize the portfolio's Sharpe Ratio. The Sharpe ratio combines both expected return and risk. This algorithm model the natural teaching process of the classroom with two main phases, viz., teaching and learning. Performance analysis has been undertaken to investigate the suitability of TLBO based solution approach by comparing it with genetic algorithm (GA) and particle swarm optimization (PSO) on the real datasets, Deutscher Aktienindex (DAX) 100, Hang Seng 31, Standard and Poor’s (S&P) 100, financial times stock exchange (FTSE) 100, and Nikkei 225. The empirical results verify the superiority of the TLBO over GA and PSO.</p>

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Optimization of risk control in financial markets based on particle swarm optimization algorithm

Cited by 18 publications

References 13 publications

Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms

Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms

Supply Chain Financial Default Risk Early Warning System Based on Particle Swarm Optimization Algorithm

Portfolio selection model using teaching learning-based optimization approach

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