Stock portfolio optimization using priority index and genetic algorithm

Vasiani, Very Dwi; Handari, Bevina D.; Hertono, Gatot Fatwanto

doi:10.1088/1742-6596/1442/1/012031

Cited by 6 publications

(4 citation statements)

References 1 publication

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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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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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“…To determine the exact amount of investment, two objective functions are considered in accordance with equations ( 1) and (2). In relation (1), the goal of maximizing the stock portfolio based on the projected profit (R t(predict) i , i ∈ m) and in relation (2) the goal of minimizing investment risk or in other words minimizing the covariance between the two shares of the company (cov ij , i, j ∈ m). e investment must be made in such a way that in accordance with equation (3) the total volume of transactions does not exceed 1.…”

Section: Random Forestmentioning

confidence: 99%

“…In addition to providing benefits (returns), the investment also has a risk borne by the investor. The higher the rate of return expected by an investor, the higher the risk to be covered by the investor [ 1 ]. The level of risk can be minimized at a certain rate of stock portfolio expectations by forming an appropriate portfolio.…”

Section: Introductionmentioning

confidence: 99%

Stock Portfolio Optimization Using a Combined Approach of Multi Objective Grey Wolf Optimizer and Machine Learning Preselection Methods

Mazraeh

Daneshvar

Zaj

et al. 2022

Computational Intelligence and Neuroscience

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The present paper deals with optimizing the stock portfolio of active companies listed on the Tehran Stock Exchange based on the forecast price. This paper is based on a combination of different filtering methods such as optimization of trading rules based on technical analysis (ROC, SMA, EMA, WMA, and MACD at six levels—Very Very Weak (VVW), Very Weak (VW), Weak (W), Strong (S), Very Strong (VS), and Very Very Strong (VVS)), Markov Chains, and Machine Learning (Random Forest and Support Vector Machine) Filter stock exchanges and provide buy signals between 2011 and 2020. In proportion to each combination of filtering methods, a buy signal is issued and based on the mean-variance (M-V) model, the stock portfolio is optimized based on increasing the portfolio return and minimizing the stock portfolio risk. Based on this, out of 480 companies listed on the Tehran Stock Exchange, 85 active companies have been selected and stock portfolio optimization is based on two algorithms, MOGWO and NSGA II. The analysis results show that the use of SVM learning machine leads to minor correlation error than the random forest method. Therefore, this method was used to predict stock prices. Based on the results, it was observed that if the shares of companies are filtered, the risk of transactions decreases, and the return on the stock portfolio increases. Also, if two filtering methods are applied simultaneously, the stock portfolio returns slightly and the risk increases. In the analysis, MOGWO algorithm has obtained 133.13% stock return rate with a risk of 3.346%, while the stock portfolio returns in NSGA II algorithm 107.73, with a risk of 1.459%. Comparison of solution methods shows that the MOGWO algorithm has high efficiency in stock portfolio optimization.

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“…As a result, they found that each algorithm showed different advantages over different data sets, and they showed that all these algorithms can be applied for efficient portfolio selection. These techniques have also been used by Moral-Escudero, Ruiz-Torrubiano and Suarez (2006), Chang, Yang and Chang (2009), Özdemir (2011), Eshlaghy, Abdolahi, Moghadasi and Maatofi (2011), Gorgulho, Neves and Horta (2011), Chen, Mabu, and Hirasawa (2011), Woodside-Oriakhi, Lucas, and Beasley (2011, Bermúdez, Segura and Vercher (2012), Pandari, Azar and Shavazi (2012), Ackora-Prah, Gyamerah, Andam and Gyamfi (2014), Hsu (2014), Yakut andÇankal (2016), Zeren andBayğın (2015), Chen, Lin, Zeng, Xu, and Zhang (2017), Jalota and Thakur (2018), Sasaki, Laamrani, Yamashiro, Alehegn and Kamoyedji (2018), Garcia, Guijarro and Oliver (2018), Vasiani, Handari and Hertono (2020.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

GJEB

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One of the key concepts in finance is Markowitz's constrained meanvariance model, the number of assets to be included in the portfolio is restricted. The solution of this generalized problem, which belongs to the quadratic and integer programming problem class, as the number of dimensions increases, is difficult to obtain with standard methods. In this study, the simulated annealing (SA) algorithm, which is one of the local search-based meta-heuristic methods, was preferred. The developed SA algorithm was applied to the Hang-Seng benchmark data set, and the results were compared with pioneering studies. According to the experimental results, upon the performance of the algorithm was found to be sufficient, the SA algorithm was applied for the Borsa Istanbul 30 index. The results of the experiments based on the Markowitz mean-variance model demonstrate that, while more assets must be maintained at lower risk levels to converge an unconstrained efficient frontier and the number of assets needed to do so decreases as risk rises

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Stock portfolio optimization using priority index and genetic algorithm

Cited by 6 publications

References 1 publication

Bayesian optimization of ESG (Environmental Social Governance) financial investments

Bayesian optimization of ESG (Environmental Social Governance) financial investments

Stock Portfolio Optimization Using a Combined Approach of Multi Objective Grey Wolf Optimizer and Machine Learning Preselection Methods

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