Determining the relationship between stock return and financial performance: an analysis on Turkish deposit banks

Abstract

Banks play a very important role in financial markets due to their intermediary function. The availability of financing to businesses and individuals, the prevalence of branches throughout the country as well as the preference status at the collection point as a result of the habits of savings holders, have made deposit banks more active among other financial institutions. Since the banking system affects the whole economy, their performance and their performance evaluation become important. Performance measurement can be defined as one of the most important issues in the financial field. In this study, the relationship between stock return and financial performance of Turkish deposit banks was examined via CRITIC method, TOPSIS method and Spearman’s rank correlation analysis for 2014–2018 periods. According to the results of the analysis, there is no statistically significant correlation between the stock return ranking and financial performance rankings of deposit banks in Turkey.

1. Introduction

Banks play a very important role in financial markets due to their intermediary function. Since the banking system affects the whole economy, their performance becomes important, also performance evaluation. In addition to determining the factors affecting the performance of the units to be evaluated, performance measurement can be defined as one of the most important issues in the financial field.

The interest of researchers and practitioners in the development of effective decision-making modeling methodologies has increased over the past decade [8]. Multi-criteria decision-making techniques are used to perform the performance ranking of the units to be evaluated in performance.

There have been many studies search performance issue for many organizations like banks and determine the factors affecting the performance of the banks. Studies can also be conducted in which factors related to bank performance are handled with multi-criteria decision-making or other techniques. However, a few of the bank-focused studies can be shown as follows.

Beccalli et al. [3] examine the relationship between stock prices and efficiency of European banks. Kirkwood and Nahm [18] assess the relationship between changes in profit efficiency of Australian banks and stock returns. Majid and Sufian [20] examine the relationship between China banks’ efficiency and its share price performance. Ioannidis et al. [15] analyzed the relationship between bank efficiency change and stock price returns on Asian and Latin American banks. Wu et al. [23] conducted performance measurements with the help of SAW, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIKOR, both financial and non-financial data, which they address in four different dimensions of the three banks they weigh with fuzzy AHP.

There are studies on the Turkish banking sector, which predominantly determine the performance of banks, investigating the determinants of stock price in the banking sector and investigating the relationship between bank performance and stock performance. Rjoub et al. [22] examined the micro and macroeconomic determinants of stock prices of the Turkish banking sector. As a result of the study, in which panel data analysis and causality test was applied, the findings were obtained in order for investors to pay attention to bank-specific information in their decisions. Erdem and Erdem [9] before measured the technical, allocative and economic efficiency of Turkish banks whose stocks traded in Istanbul Stock Exchange, with data envelopment analysis. Later, it was found to be related to the economic efficiency scores of banks and stock prices. Kasman and Kasman [17]’s paper investigated the link between stock performance of the listed commercial banks in the Turkish stock exchange and bank performance. Bank performance was determined by technical efficiency, scale efficiency and productivity measurement. The results of the paper indicated that the changes in the measures of performance have a positive and significant effect on stock returns.

For investors, as a result of the partnership right they have, the returns of the banks are important. The challenge on there is whether there is a relationship between the banks’ performances and returns to awaken the investor’s appeal. This issue poses a problem for the investor to reveal the return to be provided by the bank they are investing in and its relationship with the bank’s performance. This is also a challenge for investors to ensure effectiveness in the decision-making process.

The aim of this study is to determine the relationship between stock return and financial performance of Turkish Deposit Banks and in this way, to provide solutions to the challenges of the relevant interest groups in this regard. The relationship between stock return and financial performance of deposit banks traded on Borsa Istanbul (BIST) have been analyzed in realizing this aim. In other words, relevant data are the ratios of deposit banks traded on BIST.

This study differs in determining the relationship between performance and stock returns in the Turkish banking sector, in terms of measuring performance with multi-criteria decision-making techniques and determining the relationship with correlation, and will contribute to the literature with this feature.

This paper is organized in the following order: Section 2 presents Criteria Importance Through Intercriteria Correlation (CRITIC), TOPSIS and Spearman Correlation analysis models. Section 3 contains applications of performance analysis of deposit banks indexed on BIST for the period 2014–2018. Section 3 also includes the analysis of relationship between stock return and financial performance of these banks. In Section 4, a general evaluation of the analysis is made.

2. Methodology

In this study, CRITIC and TOPSIS methods were used. While the criteria weights are obtained with the CRITIC method, the TOPSIS method has been used in ordering the alternatives. Spearman rank correlation coefficient was used to determine the relationship between financial performance and stock returns.

2.1. CRITIC method

The relative importance of each ratio preferred in the application of multi-criteria decision-making techniques depends upon the scope of analysis performed and the analyst's subjective judgement. Therefore, despite the importance of decision makers’ expertise and experiences, the subjective judgments should be combined with and supported by objective methods whenever possible. In order to overcome such problems, objective weighting methods should be used despite the experience of decision makers [12]. For this reason, various weighting methods have been developed. One of these methods is the method proposed by [7], the CRITIC method that incorporates the standard deviation of the criterion and the correlation between other criteria into the weighting process. CRITIC method uses correlation analysis to identify conflicts between decision criteria [19]. In this way, the standard deviation of the criterion and correlation between other criteria is included in the weighting process.

The process steps to be carried out in the CRITIC method and the application steps of these steps can be listed and explained as follows [7].

1. Creating of the decision matrix;

2. Creating a normalized decision matrix;

3. Establishing the matrix for correlation coefficients;

4. Calculation of c_j value;

5. Determining of the weight values (w_j) for the criteria.

In the first stage of the CRITIC method, a decision matrix consisting of n evaluation criteria represented by j and m alternatives represented by i is created. The decision matrix X is created as follows:

$$\begin{array}{r}X=(X_{ij})=\left[\begin{array}{ccc}\begin{array}{c}{x}_{11}\\ x_{21}\\ \begin{array}{c}{\cdots }_{.}\\ x_{m1}\end{array}\end{array}& \begin{array}{c}{x}_{12}\\ x_{22}\\ \begin{array}{c}{\cdots }_{.}\\ x_{m2}\end{array}\end{array}& \begin{array}{cc}\begin{array}{c}{\cdots }_{.}\\ {\cdots }_{.}\\ \begin{array}{c}{\cdots }_{.}\\ {\cdots }_{.}\end{array}\end{array}& \begin{array}{c}{x}_{1n}\\ x_{2n}\\ \begin{array}{c}{\cdots }_{.}\\ x_{mn}\end{array}\end{array}\end{array}\end{array}\right]i=1,\dots ,m;j=1,\dots ,n.\end{array}$$

In the second stage of the method, the criteria for each alternative in the decision matrix are normalized by using Equation (1). Normalization is carried out according to the benefit and cost feature. If the criteria have the benefit feature it is expected to be maximum and if it has the cost feature it is expected to be the minimum. Define:

$$x_{ij}^{\ast }=\frac{x_{ij}-x_j^{min}}{x_j^{max}-x_j^{min}}i=1,\dots ,m;j=1,\dots ,n,$$ (1)

where $x_{ij}^{\ast }$ is the normalized value of jth criteria of ith alternative; $x_{ij}$ is the ith alternative’s jth criteria value; $x_j^{max}$ is the ideal (best) performance criteria; $x_j^{min}$ is the anti-ideal (worst) performance criteria.

In the third stage, the correlation coefficients between the criteria are calculated and the degree of the relationship between the evaluation criteria is determined.

In the fourth stage of the method, the value of c_j, which represents the amount of information for each evaluation criterion, is calculated as shown in Equation (2). The value σ_j represents the standard deviation of each evaluation criterion.

$$c_j={\sigma }_j\sum _{k=1}^n(1-r_{jk})j=1,\dots ,n.$$ (2)

In the last stage of the method, w_j values representing the weight coefficient for each criterion are calculated. The value which shows the importance of the criteria is calculated as shown in Equation (3).

$$w_j=\frac{c_j}{{\sum }_{k=1}^n{c}_k}j=1,\dots ,n.$$ (3)

When the values obtained as a result of the CRITIC examination technique are taken into consideration, the evaluation criterion having the largest w_j value is accepted as the most important evaluation criterion.

2.2. TOPSIS method

The TOPSIS method based upon the concept that the chosen alternative should have the shorter distance from the positive ideal solution and the farthest from the negative ideal solution was developed by [14,16]. The processing stages of the TOPSIS method and the application steps of these stages can also be listed and explained as follows [1,4,5,16].

1. Creating of the decision matrix;

2. Creating a normalized decision matrix;

3. Calculating the weighted normalized decision matrix;

4. Determining the positive ideal and negative ideal solution;

5. Identification of the alternatives’ separation from the positive and negative ideal solutions.

6. Calculating of the relative closeness $(c_i^{\ast })$ of each decision point to the ideal solution.

In the first stage of the TOPSIS method, a decision matrix consisting of n evaluation criteria and m alternatives is created. In the second step of method, decision matrix is normalized by the Equation (4).

$$x_{ij}^{\ast }=\frac{x_{ij}}{\sqrt{{\sum }_{i=1}^m{x}_{ij}^2}}i=1,\dots ,m;j=1,\dots ,n$$ (4)

In Step (3), the weighted normalized decision matrix is calculated. The weighted normalized value v_ij is calculated by Equation (5).

$$v_{ij}=x_{ij}^{\ast }.w_{ij}i=1,\dots ,m;j=1,\dots ,n$$ (5)

The positive ideal and negative ideal solution are determined in Step (4) as Equations (6) and (7):

$$\begin{array}{rl}{A}^{+}& =\{(\text{max }{V}_{ij}|j{ϵ}_{}J),(\text{min }{V}_{ij}|j{ϵ}_{}{J}^{\mathrm{\prime }}),i=1,\dots ,m\},\end{array}$$ (6)

$$\begin{array}{rl}{A}^{-}& =\{(\text{min }{V}_{ij}|j{ϵ}_{}J),(\text{max }{V}_{ij}|j{ϵ}_{}{J}^{\mathrm{\prime }}),i=1,\dots ,m\},\end{array}$$ (7)

where J is the index set of benefit criteria and J′ is the index set of cost criteria.

In Step (5), separation of alternatives from the positive ideal ( $S_i^{+}$) and negative ideal ( $S_i^{-}$) solutions is measured by Equations (8) and (9), for the definition of the separation of alternatives from the positive and negative ideal solutions, where

$$\begin{array}{rl}{S}_i^{+}& =\sqrt{\sum _{j=1}^n{(v_{ij}-A_j^{+})}^2}i=1,\dots ,m\end{array}$$ (8)

$$\begin{array}{rl}{S}_i^{-}& =\sqrt{\sum _{j=1}^n{(v_{ij}-A_j^{-})}^2}i=1,\dots ,m\end{array}$$ (9)

In the last step, the relative closeness of each decision point to the ideal solution is calculated by Equation (10).

$$C_i^{\ast }=\frac{S_i^{-}}{S_i^{-}+S_i^{+}},i=1,\dots ,m,$$ (10)

where $0<c_i^{\ast }<1$. The alternative with the highest (lowest) closeness value $(C_i^{\ast })$ is considered the best (worst) performing alternative.

2.3. Spearman rank correlation coefficient

Calculating the Pearson’s correlation coefficient needs the assumption that the two samples are normally distributed. If the assumption of normality is violated, a very best alternative for Pearson’s correlation coefficient may be the use of Spearman’s rank correlation [13]. Dependency of the ordinal variables is denoted as a rank correlation; Spearman’s correlation coefficient is one of the most used ordinal coefficients [21]. Equation (11) can be used for obtaining Spearman’s rank correlation r_s [13].

$$r_s=1-\frac{6.{\sum }_{i=1}^n{d_i}^2}{n(n^2-1)},$$ (11)

where d_i represents the difference in the ranks assigned to the values of the paired variables and n is the sample size.

2.4. Data set

Deposit banks in BIST bank index are included in the analysis. However, banks have been analyzed considering the years, they involved in the index. In determining the banks during their years in the index, the stock index report obtained from Borsa Istanbul was taken as a basis, and the BIST bank share index report, which includes only the deposit banks included in the data set, is presented in Table 1. As can be seen from Table 1, 12 deposit banks in 2014, 9 in 2015 and 2016, and 10 deposit banks in 2017 and 2018 are included in the index and are included in the analysis in this way.

Table 1.

Banks included in the analysis.

BIST Bank Stock Index Report
Code	Stock name (i)	Starting date	End date	Years of banks in the scope of analysis
AKBNK	Akbank	02.01.1997		2014, 2015, 2016, 2017, 2018
ALNTF	Alternatifbank	02.01.1997	31.12.2014	2014
DENIZ	Denizbank	15.10.2004	31.12.2014	2014
DENIZ	Denizbank	02.01.2017		2017, 2018
ICBCT	ICBC Turkey Bank	02.01.1997		2014, 2015, 2016, 2017, 2018
QNBFB	Finansbank	02.01.1997		2014, 2015, 2016, 2017, 2018
SKBNK	Şekerbank	01.07.1997		2014, 2015, 2016, 2017, 2018
TEBNK	Türk Ekonomi Bankası	20.03.2000	17.04.2015	2014
GARAN	Türkiye Garanti Bankası	02.01.1997		2014, 2015, 2016, 2017, 2018
HALKB	Türkiye Halk Bankası	24.05.2007		2014,2015,2016,2017,2018
ISCTR	Türkiye İş Bankası	02.01.1997		2014,2015,2016,2017,2018
VAKBN	Türkiye Vakıflar Bankası	02.12.2005		2014,2015,2016,2017,2018
YKBNK	Yapı ve Kredi Bankası	02.01.1997		2014,2015,2016,2017,2018

Note: Bank Stock Index Report was prepared using [2].

The criteria taken into consideration in determining the financial performance of the alternative banks can be grouped as profitability, liquidity, capital adequacy, balance sheet structure, asset quality, income-expense and activity rates. These criteria (ratios) are shown in Table 2.

Table 2.

Criteria considered in financial performance analysis.

Number	Code	Criteria name (j)	Benefit-cost expectation
1	ROA	Average Asset Profitability	Max
2	ROE	Average Return on Equity	Max
3	LqA	Liquid Assets / Total Assets	Max
4	CAR	Capital Adequacy Ratio	Max
5	DpA	Total Deposits / Total Assets	Max
6	NpL	Non-performing Loans / Total Loans	Min
7	IeA	Interest Expenses / Total Assets	Min
8	OeA	Other Operating Expenses / Total Assets	Min

These ratios were obtained from the Selected Ratios data published on the Banks Association of Turkey’ website [6]. Apart from this, the stock return data which its relationship to be determined between financial performance were obtained from Finnet (Financial Information News Network).

3. Application

In determining the relationship between the share returns and financial performances of the banks included in the BIST Bank Index, the financial performances of the relevant banks included in the data set were determined in 2014–2018, and then the relationship between performance and share returns was analyzed.

CRITIC method uses for the determination of the importance levels of the criteria, TOPSIS method uses the determination of the performances of alternatives and Spearman Correlation coefficient uses for the determination of the existence of a relationship between variables.

In order to determine the financial performance, firstly, the determination of the importance levels of the financial ratios, which are considered as the evaluation criteria, on the performance was carried out with the CRITIC method. Then, the performances of banks were determined with the TOPSIS method. And finally, the existence of a relationship between bank financial performance and return on stock was analyzed by the Spearman Correlation coefficient. On the other words, here simulation studies and results related to methods CRITIC and TOPSIS are given.

3.1. CRITIC method application

In the first stage of the CRITIC method, a decision matrix consisting of 8 evaluation criteria represented by ratios and 12–10 alternatives represented by banks is created for 2014–2018 period and shown in Table 3 for 2018. And decision matrix for 2014–2017 period, calculation of the standard deviation and calculation of the value of c_j (2018) are shown in Tables A1–A3 given in the Appendix.

Table 3.

Decision matrix (2018).

	Max	Max	Max	Max	Max	Min	Min	Min
Banks	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
AKBNK	1.77	13.51	14.85	18.16	57.50	4.23	5.81	1.69
DENIZ	1.69	15.45	13.14	19.49	61.11	6.78	7.02	2.31
ICBCT	0.44	5.60	24.68	30.81	52.89	1.22	4.94	1.84
QNBFB	1.70	18.03	12.61	15.42	55.33	6.55	5.53	2.13
SKBNK	0.28	3.39	11.88	15.14	73.72	5.74	8.71	3.55
GARAN	1.94	15.08	17.47	18.31	60.66	5.11	5.45	2.08
HALKB	0.74	9.27	10.60	13.80	65.76	3.40	7.56	1.44
ISCTR	1.74	14.59	11.65	16.49	58.90	4.30	5.23	1.93
VAKBN	1.38	16.10	11.25	16.99	54.14	4.87	6.98	1.77
YKBNK	1.45	13.51	16.47	16.07	58.20	5.86	5.54	1.82

In the second stage of the CRITIC method, the criteria for each bank in the decision matrix are normalized. And normalized decision matrix for 2018 is shown in Table 4.

Table 4.

Normalized decision matrix according to the CRITIC method (2018).

	Max	Max	Max	Max	Max	Min	Min	Min
Banks	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
AKBNK	0.8971	0.6910	0.3021	0.2563	0.2213	0.4580	0.7703	0.8839
DENIZ	0.8487	0.8234	0.1805	0.3344	0.3949	0.0000	0.4493	0.5905
ICBCT	0.0970	0.1508	1.0000	1.0000	0.0000	1.0000	1.0000	0.8129
QNBFB	0.8572	1.0000	0.1428	0.0955	0.1169	0.0417	0.8439	0.6753
SKBNK	0.0000	0.0000	0.0912	0.0790	1.0000	0.1871	0.0000	0.0000
GARAN	1.0000	0.7986	0.4882	0.2651	0.3731	0.3006	0.8638	0.6982
HALKB	0.2778	0.4015	0.0000	0.0000	0.6180	0.6077	0.3066	1.0000
ISCTR	0.8794	0.7646	0.0750	0.1582	0.2888	0.4459	0.9223	0.7687
VAKBN	0.6641	0.8679	0.0464	0.1878	0.0602	0.3427	0.4603	0.8437
YKBNK	0.7032	0.6910	0.4169	0.1335	0.2548	0.1657	0.8418	0.8222

In the third stage of CRITIC method, the correlation coefficients between the banking ratios are calculated and the degree of the relationship between the ratios is determined. Correlation coefficients between the ratios for 2018 are shown in Table 5.

Table 5.

Cross criteria correlation matrix (2018).

	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
ROA	1	0.9222	−0.1886	−0.2692	−0.4362	−0.4763	0.5015	0.3639
ROE	0.9222	1	−0.3051	−0.3221	−0.5404	−0.5170	0.4181	0.4342
LqA	−0.1886	−0.3051	1	0.8849	−0.4416	0.5627	0.5966	0.1570
CAR	−0.2692	−0.3221	0.8849	1	−0.4907	0.6430	0.4642	0.1376
DpA	−0.4362	−0.5404	−0.4416	−0.4907	1	−0.2649	−0.7917	−0.6756
NpL	−0.4763	−0.5170	0.5627	0.6430	−0.2649	1	0.2890	0.4277
IeA	0.5015	0.4181	0.5966	0.4642	−0.7917	0.2890	1	0.5558
OeA	0.3639	0.4342	0.1570	0.1376	−0.6756	0.4277	0.5558	1

In the fourth stage of the method, the standard deviation of each evaluation criteria (ratio) is calculated for calculation of c_j and the value of c_j, which represents the amount of information for each ratio, is calculated and shown in Table 6.

Table 6.

Standard deviation values of criteria by years and c_j values.

	Years	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
σj	2014	0.2729	0.2436	0.2964	0.2841	0.3265	0.3512	0.3196	0.3391
	2015	0.3194	0.3217	0.3484	0.3227	0.2835	0.3430	0.3013	0.3445
	2016	0.3290	0.3518	0.2959	0.3059	0.2856	0.4027	0.2834	0.3087
	2017	0.3434	0.3639	0.3022	0.3467	0.2894	0.3483	0.3151	0.3090
	2018	0.3622	0.3270	0.3019	0.2811	0.2952	0.2963	0.3245	0.2746
cj	2014	1.4333	1.4983	1.5136	2.5631	3.1691	1.5931	1.5719	1.6263
	2015	1.3485	1.4190	2.4958	1.7757	1.8104	1.3716	2.0059	1.5411
	2016	1.6957	1.8761	1.5293	2.0305	2.7198	1.7789	1.2423	1.2694
	2017	1.7105	1.9951	2.1377	2.0919	2.3779	1.9639	2.0072	1.6377
	2018	2.3845	2.2596	1.7309	1.6735	3.1409	1.8773	1.6117	1.5376

In the last stage of the method, w_j values representing the weight coefficient for each banking ratio are calculated and w_j values of criteria analyzed is shown in Table 7.

Table 7.

Weight values of the criteria by years (w_j).

Years	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
2014	0.0958	0.1001	0.1011	0.1712	0.2117	0.1064	0.1050	0.1086
2015	0.0979	0.1031	0.1813	0.1290	0.1315	0.0996	0.1457	0.1119
2016	0.1199	0.1327	0.1081	0.1436	0.1923	0.1258	0.0878	0.0898
2017	0.1074	0.1253	0.1343	0.1314	0.1493	0.1233	0.1261	0.1029
2018	0.1470	0.1393	0.1067	0.1032	0.1937	0.1158	0.0994	0.0948

The ratio having the largest w_j value is the most important ratio. According to the analysis results ‘total deposits / total assets’ ratio showing the balance sheet structure is the most important indicator for banks performance, except 2015.

3.2. TOPSIS method application

In the first stage of the TOPSIS method, a decision matrix is created for analysis period. In the second step of the method, decision matrix is normalized and this normalized matrix is shown in Table 8 for 2018.

Table 8.

Normalized decision matrix according to the TOPSIS method (2018).

	Max	Max	Max	Max	Max	Min	Min	Min
Banks	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
AKBNK	0.391	0.322	0.313	0.308	0.303	0.265	0.288	0.251
DENIZ	0.373	0.369	0.277	0.331	0.322	0.424	0.348	0.343
ICBCT	0.097	0.134	0.520	0.523	0.278	0.077	0.245	0.273
QNBFB	0.376	0.430	0.266	0.262	0.291	0.410	0.274	0.316
SKBNK	0.061	0.081	0.250	0.257	0.388	0.359	0.432	0.528
GARAN	0.429	0.360	0.368	0.311	0.319	0.320	0.270	0.309
HALKB	0.163	0.221	0.223	0.234	0.346	0.213	0.374	0.214
ISCTR	0.384	0.348	0.245	0.280	0.310	0.269	0.259	0.287
VAKBN	0.305	0.384	0.237	0.288	0.285	0.305	0.346	0.263
YKBNK	0.320	0.322	0.347	0.273	0.306	0.367	0.274	0.270

In the Step 3 of the method, the weighted with CRITIC method results normalized decision matrix is calculated. And this weighted normalized decision matrix is shown in Table 9 for 2018.

Table 9.

Weighted normalized decision matrix (2018).

	Max	Max	Max	Max	Max	Min	Min	Min
Banks	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
AKBNK	0.057	0.045	0.033	0.032	0.059	0.031	0.029	0.024
DENIZ	0.055	0.051	0.030	0.034	0.062	0.049	0.035	0.033
ICBCT	0.014	0.019	0.055	0.054	0.054	0.009	0.024	0.026
QNBFB	0.055	0.060	0.028	0.027	0.056	0.047	0.027	0.030
SKBNK	0.009	0.011	0.027	0.027	0.075	0.042	0.043	0.050
GARAN	0.063	0.050	0.039	0.032	0.062	0.037	0.027	0.029
HALKB	0.024	0.031	0.024	0.024	0.067	0.025	0.037	0.020
ISCTR	0.057	0.048	0.026	0.029	0.060	0.031	0.026	0.027
VAKBN	0.045	0.054	0.025	0.030	0.055	0.035	0.034	0.025
YKBNK	0.047	0.045	0.037	0.028	0.059	0.042	0.027	0.026

The positive ideal and negative ideal solution is determined for ratios in Step 4 and the separation of banks from the positive ideal $(S_i^{+})$ and negative ideal $(S_i^{-})$ solutions are measured in Step 5.

Positive ideal (A⁺) and negative ideal (A⁻) values is shown in Table 10 and the separation of banks from the positive ideal ( $S_i^{+}$) and negative ideal ( $S_i^{-}$) solutions are shown in Table 11. The calculation of banks’ separation values (2018) is shown in Table A4 given in the Appendix.

Table 10.

Positive ideal (A⁺) ve negative ideal (A⁻) values (2018).

	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
A+	0.063	0.060	0.055	0.054	0.075	0.009	0.024	0.020
A−	0.009	0.011	0.024	0.024	0.054	0.049	0.043	0.050

Table 11.

Banks’ separation values.

	2014		2015		2016		2017		2018
Banks	( $S_i^{+}$)	( $S_i^{-}$)	( $S_i^{+}$)	( $S_i^{-}$)	( $S_i^{+}$)	( $S_i^{-}$)	( $S_i^{+}$)	( $S_i^{-}$)	( $S_i^{+}$)	( $S_i^{-}$)
AKBNK	0.020	0.059	0.015	0.094	0.022	0.089	0.019	0.073	0.045	0.070
ALNTF	0.040	0.044	–	–	–	–	–	–	–	–
DENIZ	0.037	0.034	–	–	–	–	0.047	0.057	0.057	0.066
ICBCT	0.061	0.025	0.084	0.056	0.076	0.061	0.062	0.057	0.068	0.067
QNBFB	0.043	0.033	0.064	0.057	0.055	0.056	0.055	0.046	0.059	0.072
SKBNK	0.051	0.028	0.077	0.040	0.085	0.032	0.076	0.032	0.096	0.023
TEBNK	0.032	0.041	–	–	–	–	–	–	–	–
GARAN	0.023	0.052	0.033	0.088	0.028	0.084	0.029	0.068	0.044	0.075
HALKB	0.029	0.051	0.045	0.088	0.040	0.069	0.039	0.062	0.069	0.048
ISCTR	0.020	0.055	0.029	0.083	0.026	0.079	0.030	0.059	0.049	0.070
VAKBN	0.031	0.044	0.041	0.080	0.039	0.070	0.045	0.059	0.056	0.063
YKBNK	0.031	0.041	0.043	0.068	0.046	0.060	0.048	0.047	0.054	0.061

In the fifth step and last step, the relative closeness of each decision point to the ideal solution is calculated and is shown Table 12. Alternative which has the higher $C_i^{\ast }$, is accepted the alternative has the better the rank.

Table 12.

Relative closeness of each decision point to the ideal solution.

	2014		2015		2016		2017		2018
Banks	R	$(C_i^{\ast })$	R	$(C_i^{\ast })$	R	$(C_i^{\ast })$	R	$(C_i^{\ast })$	R	$(C_i^{\ast })$
AKBNK	1	0.749	1	0.865	1	0.803	1	0.794	2	0.609
ALNTF	8	0.528	–	–	–	–	–	–	–	–
DENIZ	9	0.480	–	–	–	–	6	0.547	5	0.535
ICBCT	12	0.292	8	0.399	8	0.447	8	0.477	8	0.499
QNBFB	10	0.439	7	0.472	7	0.507	9	0.455	4	0.551
SKBNK	11	0.358	9	0.343	9	0.275	10	0.293	10	0.192
TEBNK	7	0.559	–	–	–	–	–	–	–	–
GARAN	3	0.693	3	0.725	2	0.751	2	0.704	1	0.633
HALKB	4	0.638	4	0.664	5	0.632	4	0.611	9	0.410
ISCTR	2	0.730	2	0.737	3	0.751	3	0.659	3	0.585
VAKBN	5	0.587	5	0.663	4	0.639	5	0.569	6	0.532
YKBNK	6	0.570	6	0.612	6	0.564	7	0.495	7	0.530

Note: R: Ranking.

3.3. Spearman correlation application

In order to determine the method of correlation analysis, it is necessary to perform normality test first. Within the scope of the study, the normality test related to stock return and performance data was performed and the results are presented in Table 13. According to the results, performance data provides normality assumption, but data stock return does not provide normality assumption.

Table 13.

Tests of normality outputs.

	Kolmogorov–Smirnov			Shapiro–Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
Stock return	0.213	50	.000	0.619	50	.000
Performance	0.065	50	.200*	0.986	50	.796

*This is a lower bound of the true significance.

Since the stock return data of the bank do not provide normality assumption, the test of the relationship between them should be continued with non-parametric tests. With the creation of two different rankings, Spearman rank correlation analysis investigated whether there was a significant relationship between the series. Analysis results are given in Table 14.

Table 14.

Spearman’s correlations outputs.

		Stock return	Performance
Stock return	Correlation coefficient	1.000	.213
	Sig. (2-tailed)	.	.137
	N	50	50
Performance	Correlation coefficient	.213	1.000
	Sig. (2-tailed)	.137	.
	N	50	50

According to the correlation matrix, the Spearman rank correlation coefficient is 0.213 which shows the magnitude of the relationship between banks’ share return and performance. Since the p value that measures the significance of the coefficients is 0.137 the calculated coefficient was not found significant at any significance level. According to the results of the analysis, there is a positive correlation between the stock return ranking and financial performance rankings as measured by TOPSIS of deposit banks in Turkey but this relationship was not statistically significant.

For the studies noted that the ideal market is a market in which prices provide accurate signals for resource allocation, investors can choose among the securities that represent ownership of firms’ activities under the assumption that security prices at any time ‘fully reflect’ all available information. This market in which prices always ‘fully reflect’ available information is called efficient [11].

In this regard, there are investigations regarding the banking sector, where investors are made to evaluate their bank performance in order to make the right decision about their investments. Investigation of banks’ asset quality, management quality, profitability and determination of macroeconomic factors on returns are discussed. Despite studies in which bank-specific and macroeconomic variables have been determined to have a positive impact on stock returns, it may be appropriate to approach the issue with the fact that other variables are involved in investment decisions.

The issue is that the general expectation situation in question is not always the case for investors and that there are other determinants other than risk and return in investors’ investment decisions and investments do not always have rational decisions, and these are also caused different points of view. In this direction, despite the expectation that there is a positive relationship between performance and return, an approach should be made in line with behavioral finance, which states that psychological and other factors are effective in financial markets. The result of this study can be perceived to support this.

As a result, behavioral finance, which finds a study area, is first observed the behaviors in the market, and then, a model is explained that explains the behaviors according to the results of these observations [10]. In these reviews, it is important to determine how investors act rather than how they should act in financial markets.

4. Conclusion

Stock market indexes reflect the future expectations of the economic units and the performance of the companies in these indexes is of great importance for both the company and the economy of the country. Existence of capital markets gains importance in the efficient transfer of funds and savings. In connection with this importance, the relationship between the performance of organizations and stock returns should be examined.

In this study, the existence of the relationship between the performances and stock returns of the deposit banks included in the Borsa Istanbul banking index was examined. The analysis was made for the 2014–2018 period.

In the study, firstly, the performance analysis of the banks was carried out with the TOPSIS method, which was weighted with the CRITIC method. The criteria taken into consideration in determining the financial performance of the banks can be grouped as profitability, liquidity, capital adequacy, balance sheet structure, asset quality, income-expense and activity rates.

Then, the relationship between the determined performance and the stock returns of the banks was examined with the non-parametric Spearman rank correlation coefficient.

According to the results of the analysis, there is no statistically significant correlation between the stock return ranking and financial performance rankings of deposit banks in Turkey. There is no significant finding in terms of investors’ taking into account the financial performance of banks while directing their investments.

Financial studies address the efficient market hypothesis that reflects the general situation and requires rational maximization of investors’ investment decisions, diversifying their portfolios and avoiding risk, and the behavioral finance that takes into account the impact of social and emotional situations.

Despite the expectation that there is a positive relationship between performance and stock return, there may be an expectation in line with behavioral finance, which states that psychological and other factors are effective in the financial markets. The result of this study can be perceived to support this. Because, contrary to the expectation of a positive relationship, the results of the study reveal the approach that factors other than investors’ risk and return preferences are impressive in their stock returns.

Appendix.

Table A1.

Decision Matrix (2014–2017).

		Max	Max	Max	Max	Max	Min	Min	Min
Year	Banks	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
2014	AKBNK	1.62	13.60	31.82	15.16	55.18	1.85	3.49	1.81
	ALNTF	1.24	16.74	22.45	15.11	53.24	4.96	5.54	2.48
	DENIZ	0.88	10.69	28.60	14.09	62.04	3.92	4.09	3.17
	ICBCT	0.33	2.06	19.77	18.90	68.63	5.65	4.39	3.47
	QNBFB	1.24	10.82	21.14	16.98	55.95	5.44	4.53	3.10
	SKBNK	1.12	10.07	17.63	14.60	63.90	5.73	5.29	3.78
	TEBNK	1.07	11.13	23.51	13.96	62.61	2.47	4.19	3.11
	GARAN	1.54	13.17	25.37	15.23	54.96	2.46	3.49	2.15
	HALKB	1.49	14.38	19.71	13.62	66.73	3.64	4.08	1.92
	ISCTR	1.51	12.79	27.98	16.02	56.17	1.55	3.63	2.40
	VAKBN	1.19	12.80	26.01	13.96	57.99	3.80	4.25	1.97
	YKBNK	1.12	10.13	25.78	15.03	58.01	3.55	3.40	2.17
2015	AKBNK	1.36	11.56	33.01	14.58	59.17	2.38	3.37	1.79
	ICBCT	−0.33	−2.82	36.17	12.78	33.91	4.38	2.58	2.29
	QNBFB	0.88	8.02	20.90	15.40	56.65	6.64	4.26	3.19
	SKBNK	0.45	4.17	19.46	13.66	60.89	6.04	5.03	3.49
	GARAN	1.44	11.96	23.37	15.03	55.40	2.77	3.22	2.31
	HALKB	1.35	12.88	19.88	13.83	65.06	3.14	4.26	1.86
	ISCTR	1.20	10.05	27.43	15.65	55.78	2.03	3.70	2.29
	VAKBN	1.13	12.24	24.26	14.52	60.08	3.92	4.45	1.97
	YKBNK	0.93	8.82	24.11	13.81	57.59	4.12	3.83	2.18
2016	AKBNK	1.79	15.79	31.45	14.30	58.62	2.64	3.72	1.58
	ICBCT	0.18	2.31	34.68	19.80	41.17	2.27	3.00	2.18
	QNBFB	1.29	12.57	24.41	14.53	53.14	6.11	4.15	2.76
	SKBNK	0.52	4.95	13.36	13.10	67.75	6.13	5.96	3.72
	GARAN	1.88	15.25	21.07	16.21	56.74	2.83	3.46	2.15
	HALKB	1.22	12.56	20.34	13.08	64.92	3.25	4.32	1.67
	ISCTR	1.60	13.83	26.50	15.17	56.91	2.42	3.69	2.09
	VAKBN	1.37	15.01	22.44	14.16	58.27	4.34	4.51	1.80
	YKBNK	1.24	11.92	21.81	14.21	61.02	5.04	4.04	2.01
2017	AKBNK	2.06	17.0	29.8	17.0	58.5	2.4	4.0	1.48
	DENIZ	1.68	16.1	24.1	19.5	62.2	4.8	4.7	2.35
	ICBCT	0.40	5.0	35.3	14.4	26.1	1.3	3.0	1.61
	QNBFB	1.41	14.4	22.4	15.0	53.7	5.2	4.2	2.36
	SKBNK	0.42	4.4	23.6	15.4	62.9	4.9	5.00	3.16
	GARAN	2.08	16.5	22.5	18.7	55.7	2.6	3.73	2.00
	HALKB	1.39	16.0	23.3	14.2	63.3	3.0	5.01	1.49
	ISCTR	1.58	13.4	24.9	16.7	56.2	2.2	4.0	2.04
	VAKBN	1.54	17.5	21.3	15.5	57.4	4.2	4.7	1.63
	YKBNK	1.31	12.9	24.8	14.5	56.9	4.6	4.1	1.85

Table A2.

Calculation of standard deviation values of criteria in CRITIC method (2018).

	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
AKBNK	0.0754	0.0052	0.0008	0.00003	0.0124	0.0106	0.0155	0.0304
DENIZ	0.0512	0.0418	0.0088	0.0070	0.0039	0.1260	0.0386	0.0142
ICBCT	0.2761	0.2191	0.5266	0.5610	0.1108	0.4161	0.1254	0.0107
QNBFB	0.0551	0.1452	0.0173	0.0242	0.0466	0.0981	0.0392	0.0012
SKBNK	0.3875	0.3830	0.0336	0.0296	0.4452	0.0282	0.4171	0.5035
GARAN	0.1425	0.0323	0.0458	0.0002	0.0016	0.0030	0.0475	0.0001
HALKB	0.1188	0.0472	0.0753	0.0630	0.0813	0.0639	0.1151	0.0844
ISCTR	0.0660	0.0212	0.0397	0.0086	0.0019	0.0083	0.0764	0.0035
VAKBN	0.0017	0.0620	0.0520	0.0040	0.0743	0.0002	0.0344	0.0180
YKBNK	0.0065	0.0052	0.0203	0.0138	0.0061	0.0358	0.0384	0.0127
$\sum _{j=1}^n(x_{ij}-{\overline{x}}_j{)}^2$	1.1809	0.9623	0.8201	0.7114	0.7841	0.7901	0.9478	0.6786
1/(n − 1)	0.1111	0.1111	0.1111	0.1111	0.1111	0.1111	0.1111	0.1111
σj	0.3622	0.3270	0.3019	0.2811	0.2952	0.2963	0.3245	0.2746

Table A3.

Calculation of c_j values (2018).

	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
1 − rjk	0.00000	0.07781	1.18860	1.26920	1.43625	1.47631	0.49849	0.63609
1 − rjk	0.07781	0.00000	1.30512	1.32215	1.54044	1.51697	0.58188	0.56577
1 − rjk	1.18860	1.30512	0.00000	0.11514	1.44157	0.43735	0.40336	0.84300
1 − rjk	1.26920	1.32215	0.11514	0.00000	1.49067	0.35703	0.53581	0.86242
1 − rjk	1.43625	1.54044	1.44157	1.49067	0.00000	1.26493	1.79169	1.67564
1 − rjk	1.47631	1.51697	0.43735	0.35703	1.26493	0.00000	0.71098	0.57235
1 − rjk	0.49849	0.58188	0.40336	0.53581	1.79169	0.71098	0.00000	0.44423
1 − rjk	0.63609	0.56577	0.84300	0.86242	1.67564	0.57235	0.44423	0.00000
$\sum _{k=1}^n(1-r_{jk})$	6.58275	6.91014	5.73415	5.95243	10.64118	6.33591	4.96644	5.59949
σj	0.3622	0.3270	0.3019	0.2811	0.2952	0.2963	0.3245	0.2746
cj	2.3845	2.2596	1.7309	1.6735	3.1409	1.8773	1.6117	1.5376

Table A4.

Calculation of banks’ separation values in TOPSIS method (2018).

	ROA	ROE	LqA	CAR	DpA	NpL	IeA	OeA
A+	0.063	0.060	0.055	0.054	0.075	0.009	0.024	0.020	$(S_i^{+}{)}^2$	( $S_i^{+}$)
AKBNK	0.00003	0.00023	0.00049	0.00049	0.00027	0.00048	0.00002	0.00001	0.002	0.045
DENIZ	0.00007	0.00007	0.00067	0.00039	0.00016	0.00162	0.00010	0.00015	0.003	0.057
ICBCT	0.00238	0.00171	0.00000	0.00000	0.00045	0.00000	0.00000	0.00003	0.005	0.068
QNBFB	0.00006	0.00000	0.00074	0.00073	0.00035	0.00149	0.00001	0.00009	0.003	0.059
SKBNK	0.00292	0.00237	0.00083	0.00075	0.00000	0.00107	0.00035	0.00088	0.009	0.096
GARAN	0.00000	0.00010	0.00026	0.00048	0.00018	0.00079	0.00001	0.00008	0.002	0.044
HALKB	0.00153	0.00085	0.00100	0.00089	0.00007	0.00025	0.00017	0.00000	0.005	0.069
ISCTR	0.00004	0.00013	0.00086	0.00063	0.00023	0.00050	0.00000	0.00005	0.002	0.049
VAKBN	0.00033	0.00004	0.00091	0.00059	0.00040	0.00070	0.00010	0.00002	0.003	0.056
YKBNK	0.00026	0.00023	0.00034	0.00067	0.00025	0.00113	0.00001	0.00003	0.003	0.054
A−	0.009	0.011	0.024	0.024	0.054	0.049	0.043	0.050	$(S_i^{-}{)}^2$	( $S_i^{-}$)
AKBNK	0.00235	0.00113	0.00009	0.00006	0.00002	0.00034	0.00020	0.00069	0.005	0.070
DENIZ	0.00211	0.00161	0.00003	0.00010	0.00007	0.00000	0.00007	0.00031	0.004	0.066
ICBCT	0.00003	0.00005	0.00100	0.00089	0.00000	0.00162	0.00035	0.00058	0.005	0.067
QNBFB	0.00215	0.00237	0.00002	0.00001	0.00001	0.00000	0.00025	0.00040	0.005	0.072
SKBNK	0.00000	0.00000	0.00001	0.00001	0.00045	0.00006	0.00000	0.00000	0.001	0.023
GARAN	0.00292	0.00151	0.00024	0.00006	0.00006	0.00015	0.00026	0.00043	0.006	0.075
HALKB	0.00023	0.00038	0.00000	0.00000	0.00017	0.00060	0.00003	0.00088	0.002	0.048
ISCTR	0.00226	0.00138	0.00001	0.00002	0.00004	0.00032	0.00029	0.00052	0.005	0.070
VAKBN	0.00129	0.00178	0.00000	0.00003	0.00000	0.00019	0.00007	0.00063	0.004	0.063
YKBNK	0.00145	0.00113	0.00017	0.00002	0.00003	0.00004	0.00024	0.00060	0.004	0.061

Disclosure statement

No potential conflict of interest was reported by the author(s).