Document Type : Original Article
Authors
^{1} Associate Prof., Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran. Pardis St. Molasadra Ave., Vanak Sq, Tehran 193951999, Iran
^{2} Ph.D. Candidate, Department of Financial Engineering, Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran. Pardis St. Molasadra Ave., Vanak Sq, Tehran 193951999, Iran
^{3} Assistant Prof., Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Abstract
Keywords
The current literature highlights the importance of choosing the optimal set of investments in the capital market to maximize the expected wealth of investors. In doing so, an investor needs proper methods or criteria to identify and measure the potential value of each investment opportunity. These criteria should be sufficiently reliable and accurate so that investors can decide with high confidence and low risk. Risk and returns are two main critical factors in capital market decisions. The selection of a set of stocks, called portfolio, is usually driven by the interaction between risk and return. The higher the risk of an investment, demands a higher return (Jones, 2010).
Since the early 1950s of the inauguration of modem portfolio theory, the rate of return and the risk of a portfolio has been recognized as the most important factors for investors in every capital market. Markowitz theory (1952) proved that the risk and returns could be at an optimal point by investing in a diversified portfolio of financial assets. The degree of risktaking among individuals is a tradeoff between risk and expected return, and asset returns are unpredictable or risky. Diversification means choosing different financial assets to reduce the risk of one specific asset. A diversity index is a mathematical measure that can show how much the initial wealth is distributed between different assets. In other words, the diversity index contributes investors to choose the appropriate number of assets for investing based on their initial wealth. Diversity has many advantages including aggregate competition in the capital market, maximizing investor wealth and mitigates portfolio risk (Chan, Peter, and William, 1989). According to the stock portfolio theory, portfolio risk is not only affected by the average standard deviation, but also by the diversity of investment. In other words, the larger the variety in an investment portfolio, the lower would be the risk (Reilly and Keith, 2002). This goal requires that the variability of the return on a particular asset be adjusted to the variability of the return on other assets in the portfolio, which would reduce the unsystematic risk (Platanakis, Athanasios, and Charles, 2018; Jackwerth, and Anna, 2016)
This research seeks to provide an alternative model to select the optimal stock portfolio and a useful tool to estimate the degree of diversification by adjusting risk win returns. VaR, which is one of the significant indicators of undesirable risk, is integrated with systematic and unsystematic risks when forming a portfolio. We also present the Euclidean distance measure for stock portfolio diversification and formulate a multiobjective model to choose optimal stock portfolios. The Euclidean distance is a novel measure to index the level of diversity in the portfolio, this index calculates the distance between different assets and optimizes the diversification. The results of the model are compared with Sterling diversity index which is a wellknown index that integrated variety and balance into a dual concept that can explicit the condition of different parts of society. The model attempts to maximize diversification by minimizing the VaR and stock risk.
Moreover, maximizing returns are considered to be a constraint of this model. A genetic algorithm is used to optimize the nonlinear multiobjective model and compare the results of different dimensions to validate the framework. The results display that the average yield of selected portfolios by the model is higher than the desirable condition and confirm the positive performance of the multiobjective model.
According to investment theory, financial asset's risks can be classified into systematic risk and unsystematic risk. An unsystematic risk, referred to a controllable risk, is exclusive to an asset because the risk is related to a portion of the return on an asset. This amount of risk is specific to a company or an industry, and it is due to several factors such as worker strikes, management practices, advertising competition, and changes in consumer tastes. Systematic risk, an uncontrollable risk, is related to the general market conditions such as interest rates, the national currency rate fluctuations, inflation rates, monetary and financial policies, and political conditions (Gagliardini, and Christian, 2013) Systematic risks cannot be eliminated at all ( Kim et al. 2018).
Markowitz (1952) suggests that the risk and returns could be at an optimal point by investing in a diversified portfolio of financial assets. He created twodirectional reforms in the management of investment, with the idea that a financial decision to be taken from the swap between risk and the return of the stock market. First, he assumed that the investor performs a quantitative evaluation of the risk and return of the stock portfolio at the same time pays attention to integrate the portfolio return and the motion of the portfolio returns, which it is the main idea of diversification. Second, the financial decisionmaking process assumes to be an optimization problem; the investor chooses a portfolio among the various types of available combinations which has the least variance (Georgalos, Ivan, and David, 2018). Markowitz approach is a diversification method that is used in the analysis of the portfolio of investments. This kind of diversification involves the inclusion of covariance between the securities and the combination of less correlated capital assets to reduce the risk in the portfolio without jeopardizing returns. In other words, the less correlation in an investment portfolio will reduce the risk of the portfolio (Francis, and Dongcheol, 2013).
Other methods introduced include the use of the HirschmanHerfindahl Index (HHI) and the Shannon entropy index (Chen, Yong, Xianhua, and Lingling, 2014). Oh, et al. (2005) used a genetic algorithm to optimize the indexbased portfolio. Their goal was to build a portfolio that had the same performance as the stock index. The proposed algorithm was applied to one of the Korean stock market indices from 1999 to 2001 and was compared with traditional methods of constructing the indexbased basket. The results show that the genetic algorithm has many advantages over traditional methods when the fluctuations of the market are increasing. It is fully effective and shows the average performance when the market trend is constant.
This study contributes to the literature as follows: First, we develop an innovative multiobjective mathematical model by combining VaR and portfolio diversification as a task to optimize portfolio, stock returns, and risks. Second, we consider both systematic and unsystematic risks in the model formulation using the Euclidean distance criterion as a tool for the quantitative assessment of the diversity index. Third, we apply a realworld case study to verify the proposed model and solve the proposed model using two robust metaheuristic algorithms. Fourth, the model can be tested in any other capital market in the world.
Applying the diversification strategy to portfolio optimization is considered by many scholars and organizations (Steinberg, 2018; Pola, 2016; Briere, Kim, and Ariane, 2015; Dang, 2019; Kara, Ayşe and GerhardWilhelm, 2019; Paut, Rodolphe, and Marc, 2019; Aluko, Oladapo, and Bolanle, 2018; Beaudreau, Maggie, and Philip, 2018). For instance, Liu (2018) used experimental data of several key cryptocurrencies to study the role of diversity and investment in the digital asset market. Similarly, Kajtazi et al. (2018) searched the effects of considering bitcoin to the ideal portfolio using the meanCVaR method. They reported that this consideration could play a significant role in portfolio diversification.
Diyarbakırlıoğlu and Satman (2013) offered a new approach to evaluate the diversification risk of an investment portfolio by the covariance matrix of returns. They solved their problem using Maximum Diversification Index (MDI) through the genetic algorithm. Their problem is verified by existed stock returns data, and results show the MDI can be powerfully applied to define a large set of investable assets. Oyenubi (2016) reported an acceptable description for the elusiveness of the optimum amount of stocks in a portfolio. He used the Portfolio Diversification Index (PDI) to quantify diversification. Having used a novel quantification approach, Oyenubi attempted to quantify the level of the interdependency of stocks in a set, and the problem is solved by using Pareto based algorithm to find optimal portfolios. Jadhao and Chandra (2017) utilized sample entropy and approximate entropy indicators for diversifying portfolio in the rotation strategy based on size and style. Oloko (2018) investigated the benefits of diversification in Nigeria’s stock market. Kalashnikov et al. (2017) suggested a new integrated approach to address the Lean Six Sigma project portfolio and solved the problem using a binary mixedinteger biobjective quadratic model. Their model is solved via branchandbound solver of CPLEX software, and it is verified using numerical examples.
Chang et al. (2009) examined the optimization of the portfolio considering different scales for risk measurement using a genetic algorithm. In their research, the genetic algorithm was used because of its ability to solve complex problems in various risk assessments. The results showed that most of the optimization problems, including cardinal limitations, can be solved through a genetic algorithm within a reasonable time; if the meanvariance, semivariance, and variance associated with skewness are used as a risk measurement criterion. He also found that the smaller portfolio has better performance than a larger one. The stock returns derived from the genetic algorithm are less than other models, but risk reduction and adjusted riskbased criteria offset the reduction in returns implying the superiority of the response from the genetic algorithm.
Investors should consider the following rules when launching their diversification strategy. First, the whole investment portfolio in one stock does not have a significant impact on the overall strategy. Second, targeting a maximum of 20 stocks in the portfolio would result in better control of the managerial costs. Third, investing more than 10% in one stock is not recommended because it can be against diversification. Forth, to achieve optimal diversity, the focus on investing in companies that significantly are affected by others should be avoided; because they are affiliated companies or large suppliers or customers. Investing in a large company that is affected by others, may prevent diversification.
Assumptions
First, investors are generally riskaverse and more interested in higher expected returns per less risk, yet they seek to balance between risk and returns. As a result, the stock portfolio is selected based on minimizing the amount of fitness function. Second, investors do not tend to invest in a small stock that has low liquidity, and there are no restrictions on transaction costs and taxes. Third, there are no restrictions on the market and the short sale. A mathematical model to consider portfolio diversity, stock returns, and risks simultaneously are developed from a portfolio analysis viewpoint. To this end, at first, we provide the assumptions, indices, parameters, and decision variables of the proposed model and then using these relevant components, we formulate the mathematical model.
Indices
j=1,2,…,n 
Index of stocks of active investor companies 
t=1,2,…,T 
Time period index 
Parameters
r_{jt} 
The returns of stock j in the period t 
ȓ_{j} 
The average return on stock j 
σ_{j}^{2} 
The variance of stock j 
σ_{ij} 
Covariance between stock i and j 
d_{ij} 
Euclidean distance between two selected stocks i and j 
n 
Total number of stocks in the portfolio 
T 
Number of time periods past 
k 
Number of related characteristics of each stock 
β_{j} 
Systematic risk of stock j 
S_{jk} 
The value of characteristic k from the stock j 
M 
A big positive number 
E_{Ω}^{max} 
Maximum possible expected returns of the stock portfolio 
α 
Desired confidence level 
U 
The maximum investment in stocks 
λ 
Percentage of the minimum expected return of the portfolio 
MaxNp 
The maximum number of stocks to be selected for the investment portfolio 
Decision variables
x_{j} 
Percentage of stock j in the investment portfolio 
q_{j} 
Equal to 1, if the stock j is in the investment portfolio; otherwise 0 
y_{t} 
Equal to 1, if the portfolio returns in time t are negative, otherwise 0 
VaR 
VaR 
SR 
The systematic risk value of the investment portfolio 
USR 
The unsystematic risk value of the investment portfolio 
Multiobjective model
After describing the relevant components, the proposed multiobjective model can be formulated as follow. This model has three main objective functions that attempt to maximize stock diversification and minimize VaR and stock risks. Three mentioned objective functions along with its relevant constraints shows as follow:
Objective functions

(1) 

(2) 

(3) 
Subject to

(4) 

(5) 

(6) 

(7) 

(8) 

(9) 

(10) 

(11) 

(12) 

(13) 

(14) 

(15) 
where,

(16) 

(17) 

(18) 

(19) 
The objective function (1) is to maximize the diversity of stock portfolio. Since the distance is an essential component to measure the difference between elements, then this criterion should play a fundamental role in the classification of investments. Therefore, variety can be calculated using the difference between the elements. Accordingly, a portfolio of stocks as N={S1, S2, …, Sn} is considered, and the Euclidean distance between the two selected stocks i and j is defined using equations (16) to (18). Finally, the integrated multicriteria diversity index based on the Euclidean distance between each pair of elements can be introduced as D=∑ni=1 ∑nj=1 dij xixj. In this regard, increasing the value of 𝐷 means increasing the variation between items. Then, the variety of elective elements is estimated as total Euclidean distances between each pair of them.
On the other hands, the objective function (2) is to minimize the systematic risk and unsystematic risk. Different indices have been proposed for risk measurement of stock portfolios, including the portfolio standard deviation introduced by Markowitz (1952). In order to calculate the variance of the stock portfolio, the weight of each share must be determined (x_{j}) in the stock portfolio. The stock variance is calculated as equation (4) which represents the unsystematic risk. Also, equation (5) measure the systematic risk of the stock portfolio to calculate parameter β_{j} the equation (19) is used. The objective function (3) is to minimize VaR which it represents the risk of loss for investments and estimates how much of the investment may be lost within a given time period when the market condition is stable.
Equation (6) implies that the purchased stocks must be precisely the same as all available resources. Constraints (7) shows that the selected stocks in an investment portfolio should be less than or equal to the appropriate maximum amount. Constraints (8) specified an upper bound for the weight of each share (x_{j}) in the stock portfolio if this stock is selected, it can increase the diversification of the stock portfolio. In determining the upper bound for the decision variables, the investor's opinion is decisive, and it is determined by the minimum number of shares which the investor is willing to invest in them. Constraints (9) shows that the portfolio returns plus the VaR is related to the y_{t}. Equation (10) shows that in α percentage of time the variable y_{t} is equal to one. When this variable is equal to one, the corresponding constraint in the first constraints category is redundant, in other words, the portfolio returns in α percent of the time or in [αT] of the time period can be negative. For the rest of the time, y_{t} is equal to zero; it means the portfolio returns plus the VaR in periods that portfolio return is negative should be greater than zero. In constraints (11) ȓ_{j} represents the average return of each stock and E_{Ω}^{max} is the maximum expected return of the stock’s portfolio and the expression (1λ) E_{Ω}^{max} indicates the minimum expected return of the portfolio. Given that the portfolio return is assumed to be the weighted sum of the return, the value of E_{Ω}^{max} can be calculated using the equation (12). Finally, constraints (13)(15) show the binary or nonnegative decision variables.
Solution approach
To solve the proposed multiobjective model, two wellknown metaheuristics algorithms including a genetic algorithm (GA), and particle swarm optimization (PSO) are used. Moreover, Lingo software is used to obtain the ideal values of each objective functions.
Genetic Algorithm (GA)
GA is a highly effective and efficient random and metaheuristic optimization method that has been used to solve many complex problems developed by Holland (1974).In this algorithm, first, the problem variables are chosen randomly; then they are combined to draw other points.GA as one of these algorithms is basically a computer search method composed of the gene and chromosome structures.
This algorithm initially begins with a set of random solutions (chromosomes) which is known as the population base, and then the value of each chromosome is determined according to the fitness function. Therefore, higher qualities of chromosomes have a greater chance of producing offspring; on this basis, the choice of parents is taken, and then the offspring are created by crossover operator on the parents. Finally, some of the genes of the offspring change with the mutation process, and then the new offspring are replaced with the weakest chromosomes in the initial population. The main steps to solve an optimization problem via GA can be illustrated as Figure. 1 Also, the mutation and crossover operators are presented in (Holland, 1974), and the reader advised to see the research.
Step 1. Create the initial population; a) Generate random chromosomes. Step 2. Perform the main loop; b) Calculate the fitness functions of each chromosome. c) Select two chromosomes from the initial population using the roulettewheel operator. d) Do a crossover operator. e) Do mutation operator. f) Repeat steps b to e until enough members to form the next generation be created. Step 3. Repeat step 2 while stopping criteria is not satisfied. Step 4. Display the obtained results. 
Figure 1. The pseudocode of the genetic algorithm
In the face of multiobjective optimization problems, one of the approaches to solving is to use an LPmetric approach which was introduced by Zelany (1974). This method is one of the compromise programming methods, and it works without achieving knowledge from the decisionmaker, and it attempts to minimize the distance between some of the reference points and the probable solution (deviation). In this method, the choice of the reference point and the criterion for measuring the distance is an important topic.
Based on this model, each objective of the problem (k objectives) is solved separately, and after normalizing, the answers are combined to find the optimal solution (the optimal answer that is the closest answer to the ideal answer). The mathematical form of this method can be displayed as follows.

(20) 
p=1 refers to the same weight of all deviations, and an increase of 𝑝 means more weight of larger deviations. Now, to form a fitness function (goal) for using in the genetic algorithm, the LPmetric approach is used according to the following form:

(21) 
Where 𝑍_{1} is the objective function of the greatest diversity (diversification), 𝑍_{2} is the objective function of total risks includes systematic and unsystematic risks, and 𝑍_{3} is the objective of the VaR. Also, 𝑍_{1}^{*}, 𝑍_{2}^{*}, and 𝑍_{3}^{*} are the optimal amount of each of the objectives in terms of the problem constraints.
Particle Swarm Optimization
Eberhart and Kennedy (1995) proposed a particle swarm optimization algorithm which is inspired by the social behavior of animals, such as the collective migration of birds and fishes. Initially, this algorithm was used to explore the effective patterns on the simultaneous flying of birds, the sudden change of direction, and the optimal deformation of their groups. The change in the location of birds in the search space is influenced by the experience and knowledge of themselves and their neighbors. Therefore, the position of other birds affects the search of a bird. The result of the modeling of this social behavior is the process of searching for birds in the direction of successful areas. Birds learn from each other and move on to their best neighbors based on their knowledge. The basis of this algorithm is on the principle that at any given moment, each bird adjusts its location in the search space, according to the best place ever located and the best place in its entire neighborhood. The following relationships are also used to update the velocity and location of each of the particles.

(22) 

(23) 
Where w is the inertial weighting factor or moving in its path, which indicates the effect of the velocity vector of the previous iteration (V_{i }(t1)) on the velocity vector in the current iteration (V_{i }(t)). Also, c_{1} and c_{2} represent the constant coefficient of training or motion in the direction of the best value of the examined particle and the best value among all population, respectively. Moreover, rand_{1} and rand_{2} are two random numbers with uniform distribution in (0, 1). The x_{i }(t) and x_{i }(t1) represent the position vector of particles in the current iteration and previous iteration, respectively.
The best position found for particle i is defined by P_{i .best} while P_{g .best} represents the best position found by the best particle among all particles.
To prevent the excessive movement speed of a particle when they move from one location to another or velocity vector divergence, the velocity variations are limited to (V_{min} ≤ V_{i }(t) ≤ V_{max}). The pseudocode of the particle swarm optimization algorithm is presented in Figure. 2.
Initialize particle For each particle Calculate fitness value of the particle f_{i} /*updating particle’s best fitness value so far*/ If f_{i} is better than P_{i .best} Set current value as the new P_{i .best} End For /*updating population’s best fitness value so far*/ Set P_{g .best} to the best fitness value of all particles For each particle Calculate particle velocity according to equation (22) Update particle position according to equation (23) Calculate the fitness value of the particle f_{i} /*Updating population’s best fitness value so far*/ Set P_{g .best} to the best fitness value of all particles End For End 
Figure 2. The pseudo code of particle swarm optimization algorithm
Also, the chromosome used for these two algorithms is shown in Figure. 3 By way of example, we suppose that eight stocks (j=5) exist and MaxNS=5. So, activate genes of the chromosome is less than or equal to MaxNS (see constraint (7). For instance, it supposed with 5 includes gens 1, 3, 4, 6, and 7. The initial chromosome with random data between (0, 1) are generated which sum of them be equal to 1 (based on the constraint (6) of the model). So, the proposed chromosome represents the percentage of stock j in the investment portfolio (x_{j}).
0.23 
0 
0.17 
0.11 
0 
0.14 
0.35 
0 
Figure 3. The example of proposed chromosome used by algorithms
Parameter setting
Here, the parameters of algorithms are tuned to achieve the best performance, to this end Taguchi method are used (Taguchi, 1986). This method as one of the designs of experiments approaches seeks to tune parameters using a set of the orthogonal array instead of full factorial experiments. To perform this method, Minitab software is utilized, and since the LPmetric approach is used to combine objective functions, so following response as “smaller is the better” for SignaltoNoise ratio is used.
S/N = −10×log(Σ(Y^{2})/n)) 
(24) 
To do this at first, the levels of the parameters of each algorithm are provided in Table 1. Then, by performing the Taguchi method using Minitab software, the orthogonal arrays L^{9} and L^{27} are chosen for GA and PSO respectively. Finally, these two algorithms are ran using these sets of experiments and the results are presented in Tables 23. Furthermore, to decide on these results the SignaltoNoise plots are illustrated in Figure. 56.
Table 1. The parameters levels of each algorithm
Algorithms 
Parameters 
Parameters level 

Level 1 
Level 2 
Level 3 

PSO 
C_{1} 
0.5 
1 
2 
C_{2} 
0.5 
1 
2 

W 
0.5 
0.75 
1 

Npop 
100 
200 
300 

Max iteration 
200 
400 
600 

GA 
Pc 
0.7 
0.8 
0.9 
Pm 
0.05 
0.10 
0.15 

Npop 
100 
200 
300 

Max iteration 
200 
400 
600 
Table 2. The orthogonal array L^{9} and results for GA
Pc 
Pm 
Npop 
Max iteration 
Response 
0.7 0.7 
0.05 0.1 
100 200 
200 400 
25.23688 27.47174 
0.7 
0.15 
300 
600 
26.57779 
0.8 
0.05 
200 
600 
26.85483 
0.8 
0.1 
300 
200 
25.36971 
0.8 
0.15 
100 
400 
27.27282 
0.9 
0.05 
300 
400 
27.93733 
0.9 
0.1 
100 
600 
26.68067 
0.9 
0.15 
200 
200 
26.77781 
Figure 4. The SignaltoNoise plots for GA
Table 3. The orthogonal array L^{27} and results for PSO
C_{1} 
C_{2} 
W 
Npop 
Max iteration 
Response 
0.5 
0.5 
0.5 
100 
200 
26.51833 
0.5 
0.5 
0.5 
100 
400 
25.59528 
0.5 
0.5 
0.5 
100 
600 
28.93421 
0.5 
1 
0.75 
200 
200 
27.42218 
0.5 
1 
0.75 
200 
400 
28.92652 
0.5 
1 
0.75 
200 
600 
25.59709 
0.5 
2 
1 
300 
200 
26.7662 
0.5 
2 
1 
300 
400 
26.97564 
0.5 
2 
1 
300 
600 
25.57436 
1 
0.5 
0.75 
300 
200 
26.71327 
1 
0.5 
0.75 
300 
400 
26.30941 
1 
0.5 
0.75 
300 
600 
25.34503 
1 
1 
1 
100 
200 
27.99651 
1 
1 
1 
100 
400 
26.18041 
1 
1 
1 
100 
600 
25.10245 
1 
2 
0.5 
200 
200 
26.97777 
1 
2 
0.5 
200 
400 
26.78379 
1 
2 
0.5 
200 
600 
26.02241 
2 
0.5 
1 
200 
200 
26.20638 
2 
0.5 
1 
200 
400 
25.45439 
2 
0.5 
1 
200 
600 
27.79816 
2 
1 
0.5 
300 
200 
26.10074 
2 
1 
0.5 
300 
400 
27.69621 
2 
1 
0.5 
300 
600 
27.77765 
2 
2 
0.75 
100 
200 
25.41882 
2 
2 
0.75 
100 
400 
28.62868 
2 
2 
0.75 
100 
600 
28.57616 
Figure 5. The SignaltoNoise plots for PSO
Finally, based on Figure. 4 it can be found that the values of 0.7, 0.1, 100, and 200 are selected for Pc, Pm, Npop, and Max iteration, respectively. Likewise, Figure. 5 shows that the values of 1, 0.5, 1, 300, and 200 are selected for C_{1}, C_{2}, W, Npop, and Max iteration, respectively. Therefore, these values set for final running via two algorithms.
To implement the model and measure its efficiency, we performed a test of data from the top 30 companies in Tehran Stock Exchange (TSE) by considering several features such as volume, value, no. of trades, closing price, and market capitalization are reported in Table 4. These mentioned values are used as S_{jk} (characteristic k from the stock j) to calculate d_{ij} (Euclidean distance between two selected stocks i and j).
Table 4. The data of several characteristics of TSE
No. of stock 
Stock 
Volume 
Value 
No. of trades 
Closing price 
Market Cap. 
1 
PSER1 
85129 
1346570522 
30 
15818 
949080000000 
2 
SROD1 
3419185 
6250270180 
137 
1828 
1480680000000 
3 
GHND1 
213094 
2458456478 
105 
11537 
2322400000000 
4 
LAMI1 
85005 
822253365 
4 
9673 
580380000000 
5 
STEH1 
703972 
1865525800 
64 
2650 
4637500000000 
6 
SKHS1 
53487 
576054990 
10 
10770 
1346250000000 
7 
LTOS1 
6935364 
16499230956 
3926 
2379 
11895000000000 
8 
KFAN1 
107918 
597541966 
12 
5537 
1107400000000 
9 
KPRS1 
134324 
741468480 
25 
5520 
687820000000 
10 
TAMI1 
13607192 
75180532959 
949 
5525 
895050000000 
11 
KSKA1 
255239 
866791644 
69 
3396 
849000000000 
12 
SDAB1 
16242679 
30337442919 
1240 
1868 
1681200000000 
13 
SGRB1 
8707993 
35504401399 
662 
4077 
2038500000000 
14 
SHGN1 
1098484 
3337877588 
173 
3039 
1528310000000 
15 
SSEP1 
13316758 
16381365367 
680 
1230 
3013500000000 
16 
BSTE1 
5100963 
5629783797 
345 
1104 
6624000000000 
17 
SRMA1 
1221949 
4292722164 
143 
3513 
519924000000 
18 
DMVN1 
207080 
4753109980 
144 
22953 
688590000000 
19 
CIDC1 
2972619 
4214050974 
249 
1418 
6806400000000 
20 
SPKH1 
3589819 
25670837173 
457 
7151 
1430200000000 
21 
SHSI1 
183910 
3213792791 
124 
17475 
536701000000 
22 
SMAZ1 
2698947 
5347058509 
210 
1981 
2442570000000 
23 
SURO1 
5125563 
18111440764 
598 
3534 
2473800000000 
24 
SKOR1 
3397425 
4126797445 
217 
1215 
2673000000000 
25 
KHOC1 
5552238 
24675514218 
698 
4444 
2888600000000 
26 
SIMS1 
14952728 
24632102295 
892 
1647 
1770640000000 
27 
ABAD1 
7113800 
15744651573 
855 
2213 
531120000000 
28 
SFKZ1 
41145366 
74593472749 
1523 
1813 
10198100000000 
29 
SSOF1 
983403 
2694829420 
153 
2740 
1507000000000 
30 
NSTH1 
11588115 
31572081897 
847 
2725 
2043750000000 
Also, the returns values of stocks in each time period are provided in Table 5 and the values of average returns, β coefficients, and variance, per 12 months are given in Table 6. Furthermore, the values of d_{ij}_{ }and σ_{ij} can be obtained using their related formulas and the values of desired confidence level (α), percentage of the minimum expected return of portfolio (λ), maximum investment in stocks (U), and the maximum number of stocks in the investment portfolio (MaxNp) are assumed as 0.2, 0.6, 0.1, and 20, respectively.
Moreover, in order to find the ideal values of each objective function, the model is solved using Lingo software and applying these values of parameters. Therefore, values of 1.637845E+15, 0.04760250, and 0.1019152 are obtained as goal values for Z_{1}^{*}, Z_{2}^{*}, and Z_{3}^{*}, respectively.
Table 5. The values of return per time period
Stock 
Time period 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 

1 
0.074 
0.009 
0.021 
0.007 
0.087 
0.063 
0.008 
0.047 
0.05 
0.043 
0.04 
0.094 
2 
0.018 
0.01 
0.002 
0.031 
0.025 
0.084 
0.076 
0.023 
0.002 
0.067 
0.075 
0.067 
3 
0.083 
0.099 
0.022 
0.056 
0.019 
0.042 
0.015 
0.011 
0.06 
0.021 
0.085 
0.054 
4 
0.095 
0.041 
0.074 
0.076 
0.054 
0.083 
0.095 
0.031 
0.056 
0.059 
0.016 
0.07 
5 
0.049 
0.066 
0.008 
0.076 
0.039 
0.024 
0.095 
0.043 
0.01 
0.068 
0.029 
0.045 
6 
0.08 
0.006 
0.027 
0.03 
0.042 
0.005 
0.052 
0.031 
0.021 
0.097 
0.092 
0.037 
7 
0.077 
0.017 
0.003 
0.093 
0.001 
0.059 
0.045 
0.094 
0.088 
0.044 
0.052 
0.037 
8 
0.02 
0.008 
0.098 
0.037 
0.021 
0.049 
0.03 
0.003 
0.048 
0.018 
0.078 
0.092 
9 
0.035 
0.038 
0.019 
0.003 
0.064 
0.056 
0.022 
0.087 
0.055 
0.006 
0.08 
0.046 
10 
0.033 
0.078 
0.053 
0.016 
0.052 
0.089 
0.05 
0.083 
0.043 
0.025 
0.056 
0.036 
11 
0.007 
0.043 
0.027 
0.067 
0.01 
0.032 
0.014 
0.09 
0.066 
0.049 
0.044 
0.084 
12 
0.047 
0.023 
0.069 
0.083 
0.026 
0.076 
0.026 
0.067 
0.049 
0.026 
0.084 
0.056 
13 
0.084 
0.022 
0.008 
0.045 
0.082 
0.064 
0.002 
0.037 
0.053 
0.084 
0.035 
0.009 
14 
0.052 
0.013 
0.005 
0.053 
0.082 
0.043 
0.036 
0.032 
0.05 
0.023 
0.097 
0.001 
15 
0.037 
0.02 
0.016 
0.018 
0.001 
0.003 
0.068 
0.088 
0.091 
0.033 
0.076 
0.052 
16 
0.037 
0.051 
0.051 
0.087 
0.062 
0.096 
0.075 
0.062 
0.009 
0.01 
0.043 
0.029 
17 
0.05 
0.043 
0.054 
0.083 
0.02 
0.072 
0.059 
0.084 
0.042 
0.043 
0.044 
0.031 
18 
0.026 
0.097 
0.083 
0.092 
0.08 
0.034 
0.024 
0.079 
0.049 
0.041 
0.065 
0.037 
19 
0.014 
0.063 
0.058 
0.032 
0.032 
0.082 
0.016 
0.056 
0.021 
0.075 
0.081 
0.013 
20 
0.083 
0.004 
0.088 
0.011 
0.077 
0.057 
0.01 
0.021 
0.073 
0.088 
0.094 
0.067 
21 
0.032 
0.032 
0.002 
0.058 
0.081 
0.062 
0.043 
0.07 
0.022 
0.004 
0.029 
0.034 
22 
0.008 
0.064 
0.068 
0.099 
0.034 
0.036 
0.029 
0.004 
0.075 
0.012 
0.017 
0.037 
23 
0.048 
0.042 
0.015 
0.028 
0.061 
0.026 
0.061 
0.035 
0.062 
0.075 
0.026 
0.086 
24 
0.096 
0.041 
0.036 
0.06 
0.02 
0.089 
0.068 
0.05 
0.02 
0.045 
0.092 
0.099 
25 
0.027 
0.075 
0.035 
0.031 
0.093 
0.046 
0.003 
0.012 
0.017 
0.06 
0.042 
0.033 
26 
0.028 
0.065 
0.018 
0.011 
0.075 
0.006 
0.044 
0.054 
0.025 
0.073 
0.008 
0.09 
27 
0.055 
0.031 
0.052 
0.038 
0.03 
0.004 
0.015 
0.044 
0.016 
0.039 
0.048 
0.049 
28 
0.099 
0.042 
0.009 
0.002 
0.098 
0.067 
0.011 
0.022 
0.059 
0.078 
0.005 
0.094 
29 
0.021 
0.062 
0.01 
0.014 
0.056 
0.091 
0.076 
0.04 
0.002 
0.075 
0.013 
0.069 
30 
0.097 
0.027 
0.065 
0.01 
0.049 
0.078 
0.023 
0.016 
0.074 
0.024 
0.09 
0.024 
Table 6. The values average return, risk, and variance
No. of stock 
Stock 
ȓ_{j} 
β_{j} 
σ_{j}^{2} 
1 
PSER1 
0.0323 
0.87093 
0.0020148 
2 
SROD1 
0.0380 
0.06868 
0.0011504 
3 
GHND1 
0.0429 
2.01574 
0.001351 
4 
LAMI1 
0.0440 
0.22713 
0.0027536 
5 
STEH1 
0.0317 
0.47747 
0.0019204 
6 
SKHS1 
0.0433 
0.60596 
0.0009699 
7 
LTOS1 
0.0508 
1.85937 
0.0010876 
8 
KFAN1 
0.0388 
0.04512 
0.0012971 
9 
KPRS1 
0.0421 
0.27834 
0.0007863 
10 
TAMI1 
0.0512 
0.83632 
0.000522 
11 
KSKA1 
0.0444 
0.77006 
0.0007828 
12 
SDAB1 
0.0483 
0.06571 
0.0010219 
13 
SGRB1 
0.0434 
0.75252 
0.000943 
14 
SHGN1 
0.0321 
2.26474 
0.001517 
15 
SSEP1 
0.0356 
1.22938 
0.0015803 
16 
BSTE1 
0.0493 
2.46817 
0.0009395 
17 
SRMA1 
0.0488 
0.86654 
0.0007515 
18 
DMVN1 
0.0589 
0.70185 
0.0007121 
19 
CIDC1 
0.0431 
0.95924 
0.0009286 
20 
SPKH1 
0.0526 
2.53406 
0.0016097 
21 
SHSI1 
0.0388 
0.60239 
0.0006408 
22 
SMAZ1 
0.0306 
0.62865 
0.0016452 
23 
SURO1 
0.0471 
0.26890 
0.0004872 
24 
SKOR1 
0.0310 
0.89911 
0.0036724 
25 
KHOC1 
0.0375 
0.41613 
0.0008477 
26 
SIMS1 
0.0404 
0.85472 
0.000953 
27 
ABAD1 
0.0246 
0.67493 
0.0009492 
28 
SFKZ1 
0.0470 
0.49921 
0.0016605 
29 
SSOF1 
0.0403 
0.52709 
0.0013175 
30 
NSTH1 
0.0464 
0.21421 
0.0011461 
The results of testing the models using various approaches are reported. They are representing the proportions of the budgets to be invested in each company's stock. Several criteria are used to compare three approaches such as the return rate, the real diversity index, systematic risk, unsystematic risk, amount of stock in the portfolio, VaR, CVaR, Treynor ratio, and Sharpe ratio. The comparison of these approaches is illustrated in Table 7.
Also, it should be noted that all calculations are based on using the branch and bound solver of the Lingo 9 software and MATLAB software. After solving the problem with the data of the previous section and the mentioned goals, Tables 79 have been obtained.
Table 7. Comparison of related criteria in three methods
Criteria 
LPmetric based methods 

Lingo (local optimum) 
GA 
PSO 

No. of stock in the basket 
15 
20 
18 
Portfolio return 
7.2601982 
7.3625147 
6.8695723 
Sterling diversity index 
5.75207E+12 
5.849442E+12 
5.7125365E+12 
Unsystematic risk 
0.0001763 
0.00014698 
0.00018759 
Systematic risk 
1.003981 
1.009777 
1.022365 
VaR 
0.1365164 
0.1275286 
0.1478549 
Treynor ratio 
2.848356 
2.736524 
2.7475632 
Sharpe ratio 
0.268086 
0.245869 
0.2763576 
CPU time 
2700.53 
58.36 
112.42 
According to Table 7, the GA has the least CPU time and VaR, and the highest portfolio return and sterling diversity index. So, it can be selected as the best approach and the related decision variables of it are provided in Table 8.
Table 8. Obtained values of decision variables using GA
Variable 
Value 
Variable 
Value 
Variable 
Value 
Variable 
Value 
E_{Ω}^{max} 
0.00508 
x(19) 
0.10000 
q(7) 
1.00 
q(25) 
0.00 
x(1) 
0.00000 
x(20) 
0.00000 
q(8) 
1.00 
q(26) 
1.00 
x(2) 
0.01848 
x(21) 
0.03430 
q(9) 
1.00 
q(27) 
0.00 
x(3) 
0.02140 
x(22) 
0.02262 
q(10) 
1.00 
q(28) 
1.00 
x(4) 
0.03222 
x(23) 
0.00000 
q(11) 
0.00 
q(29) 
0.00 
x(5) 
0.10000 
x(24) 
0.03918 
q(12) 
1.00 
q(30) 
0.00 
x(6) 
0.02104 
x(25) 
0.00000 
q(13) 
1.00 
y(1) 
1.00 
x(7) 
0.10000 
x(26) 
0.01795 
q(14) 
0.00 
y(2) 
0.00 
x(8) 
0.02733 
x(27) 
0.00000 
q(15) 
1.00 
y(3) 
0.00 
x(9) 
0.03438 
x(28) 
0.10000 
q(16) 
1.00 
y(4) 
0.00 
x(10) 
0.03156 
x(29) 
0.00 
q(17) 
1.00 
y(5) 
0.00 
x(11) 
0.00000 
x(30) 
0.00 
q(18) 
0.00 
y(6) 
0.00 
x(12) 
0.01618 
q(1) 
0.00 
q(19) 
1.00 
y(7) 
0.00 
x(13) 
0.02490 
q(2) 
1.00 
q(20) 
0.00 
y(8) 
1.00 
x(14) 
0.00000 
q(3) 
1.00 
q(21) 
1.00 
y(9) 
0.00 
x(15) 
0.09204 
q(4) 
1.00 
q(22) 
1.00 
y(10) 
0.00 
x(16) 
0.10000 
q(5) 
1.00 
q(23) 
0.00 
y(11) 
0.00 
x(17) 
0.06643 
q(6) 
1.00 
q(24) 
1.00 
y(12) 
0.00 
x(18) 
0.00000 






According to Table 8, the following 20 companies can be selected as an optimal portfolio: SROD1, GHND1, LAMI1, STEH1, SKHS1, KFAN1, KPRS1, TAMI1, KSKA1, SGRB1, SHGN1, BSTE1, SRMA1, DMVN1, SPKH1, SMAZ1, SURO1, KHOC1, ABAD1, and SSOF1. And the values of Z_{1}, Z_{2}, and Z_{3} result in 5.849442E+12, 1.004157 and 0.1275286, respectively.
Furthermore, to show the efficiency of the proposed multiobjective model, it is compared with Markowitz and diversificationrisks models. These values are presented in Table 9.
Table 9. Compression of the proposed model with Markowitz and diversificationrisks models using LPmetric based GA
Criteria 
Models 

Markowitz 
Diversificationrisks 
DiversificationrisksVaR 
DiversificationrisksCVaR 

Portfolio return 
7.2324586 
7.1854796 
7.3625147 
7.4101649 
Sterling diversity index 
5.72326 
5.94315 
5.849442 
5.713564 
Unsystematic risk 
0.0001632 
0.000149687 
0.00014698 
0.00014911 
Systematic risk 
1.0037235 
1.0056985 
1.009777 
1.008813 
Treynor ratio 
2.475325 
2.811276 
2.736524 
2.684121 
Sharpe ratio 
0.2138545 
0.2643252 
0.245869 
0.240647 
CPU time 
51.12 
54.75 
58.36 
61.05 
To validate the framework, their model includes Markowitz, diversificationrisks, diversificationrisksVaR, and diversificationrisksCVaR models are solved 15 times with different population sizes to display the average returns of stock portfolios, as shown in Figure 6. In this investigation, we have used the conventional method of calculation VaR (VaR= σ^{2} Z_{α} µ) with the assumption that the distribution of returns is normal with a mean of µ and σ^{2} variance at the α confidence level, based on these assumptions the conditional value at risk (CVaR) model is calculated which is one of the developed versions of VaR model. This risk measure quantifies the amount of tail risk of the portfolio that proposed in 2000 by Rockafellar and Uryasev.
Since the optimal return rate for investment based on a market index is defined as an interval between 6.5 and 8.5, so based on Fig. 6, only in the diversificationrisks model the average return is lower than the desired value. In other models, the average portfolio returns are higher than the mentioned values. Therefore, it can be concluded that the proposed model can be chosen as a suitable model.
Conclusion
This study introduces the Euclidean distance criterion as a measure of stock portfolio diversification and uses a multiobjective model attempts to select optimal stock portfolios. The model aims to maximize diversification and minimize VaR, and stock risks including systematic and unsystematic risks. Also, maximizing returns are considered as a constraint of this model. Since the proposed model is nonlinear (and regarding computational complexity, it is NPhard) the study utilizes two metaheuristic algorithms to solve the model. It further validates portfolio selection by using the data of the top 30 active companies in the TSE for the 12 months. The findings show that the GA has the least CPU time and VaR, and the highest portfolio return and sterling diversity index. So, it can be selected as the best approach, and the related decision variables can be reported based on the results of 20 active companies in the TSE that were selected as the optimal portfolio. Moreover, to show the effectiveness of the proposed multiobjective model, we compared our proposed model with Markowitz and diversificationrisks models. After testing our model for 15 times with different population sizes, we found that the average portfolio returns are higher than the desirable values resulted from the market index.
Various studies have been conducted to investigate the relationship between returns and risk in the selection of stock portfolios. However, in the field of quantification of diversity index, no particular mathematical formulation has not been introduced so far. In this research, the Euclidean distance criterion has been tested as a tool for the quantitative assessment of the diversity index. Also, the research model is designed based on deterministic parameters, the unfeasibility of short selling, and without considering the transaction cost. For the future study, it is suggested that in addition to the uncertainties of parameters of a model, the effect of short selling and transaction costs on the index of diversity be studied. In addition, the different methods in VaR computations can be utilized in the model and the results should be compared.
Funding: This research received no external funding