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CONTENTS

RESEARCH and APPLICATIONS

FINANCIAL PERFORMANCE ANALYSIS OF NETHERLANDS EREDIVISIE WITH THE HELP OF RESAMPLING METHODS 1-20Tolga ZAMAN, Emre YILDIRIM, Hasan CİVANBAY

AGRICULTURAL CREDIT MARKETING IN TURKEY 21-34Erdogan GÜNEŞ, Berkay KESKİN, Mevhibe ALBAYRAK

EXAMINING THE INFLUENCE OF PSYCHOLOGICAL WELL-BEING, RELIGIOUS WORLDVIEWS AND SELF-CONFIDENCE ON FAMILY LEADERSHIP ORIENTATION 35-54Meryem Berrin BULUT

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FINANCIAL PERFORMANCE ANALYSIS OF NETHERLANDS EREDIVISIE WITH THE HELP OF RESAMPLING METHODS 1

Tolga ZAMAN1, Emre YILDIRIM2, Hasan CİVANBAY3

1-2-3 Ondokuz Mayıs University, Department of Statistic Samsun / Turkey

(1) Corresponding Author: Tolga ZAMAN, Ondokuz Mayıs University, Department of Statistic Samsun / Turkey, [email protected] Received: 29.02.2017 Accepted: 23.06.2017 Type ofarticle (Research and Application) Conflict of Interest: None Ethics Committee: None

UHBAB ULUSLARARASI HAKEMLİ BEŞERİ VE AKADEMİK BİLİMLER DERGİSİINTERNATIONAL PEER-REVIEWED JOURNAL OF HUMANITIES AND ACADEMIC SCINENCE

Abstract: Modern computers and programs allow data to be interpreted graphically and numerically in an unimaginable way. Thus, it enables more realistic, accurate and informative analyses. Resampling methods are also part of this innovation. Resampling methods calculate standard errors and confidence intervals and perform tests of significance thus allow the amount of uncertainty to be determined. Bootstrap and Jackknife methods yield effective results about parameter estimation and asymptotic distribution of statistic in interest. From this point within the scope of the study, stability indicators of teams, which were crowned champion after 2000 in Netherlands league, are explored using jackknife and bootstrap methods. To obtain the financial stability measures of the teams, total budgets of the teams in hand and total points, which they acquired that season, are not encountered before 2005. In this regard, financial stability measures belonging to 4 teams that were champion between 2005-2015 are sought and it is explored that which team has homogeneous structure between seasons in question and thus they made their decisions according to criteria which UEFA determined. If the results of both methods are interpreted together, the order of financial stability is found to be the same among the results obtained with jackknife and bootstrap methods between the respective seasons. Both methods show that the PSV team is more stable than the other teams surveyed between 2005-2006 and 2015-2016. In both methods examined, PSV is followed by Ajax, Az Alkmaar and Twente respectively.

Key Words: Jackknife, Bootstrap, Bootstrap Confidence Intervals, Bias, Standart Error, Mean Square Error, Financial Performance

Doi: 10.17368/UHBAB.2017.2.1

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INTRODUCTION

Resampling methods are the methods where observed data is used and which yields analy-sis results based on the computer (Saama, 1996). Resampling methods are suggested to be used when the sample that is going to be used is large enough and parametric assump-tions are not met. If there are doubts about the population then sampling that has empirical distribution need to be used. Whether sam-pling is random or not, resampling method is useful for all kind of data. In some cases, it works by generating more observations even if the data structure is small (Peddada & Chang, 1996:231-241). Also, Resampling methods do not require distributions to be nor-mal and sample sizes to be large. Resampling methods are similar for multivariate statistics and they do not require new formulas for ev-ery statistics. Fewer assumptions are needed to be met compared to classical methods and usually, resampling methods give more accu-rate results (Hesterberg et al, 2005:4).

Jackknife Method is a frequently used meth-od in bias, variance estimation, estimation of confidence intervals of parameters and evalu-ating statistical tests. It is a method that does not require limiting assumptions about dis-tribution in calculations of statistics in inter-est (Davison & Hinkley, 1997). Especially in

the dataset, it is used when there are extreme end values and it lessens the bias of statistic. The logic of the methods comes from calcu-lating statistics from remaining observations through excluding each observation in the dataset. This process is repeated as many as the number of observations. This method is also called “leave one out” method since each time an observation value is excluded while process (Temel et al., 2012:2).

The bootstrap method is a resampling meth-od used to interpret statistically. It is a pro-cess based on generating sample as large as real data with replacement and estimating statistics in interest such as mean, median, and variance by repeating this process many times. The bootstrap method is a simple method. Because heavy assumptions about data distribution are not required, it yields re-liable results when known statistical methods and their assumptions are not enough. Actu-ally, basic problems are presenting estimation values, forming confidence intervals and thus calculating the standard error of estimated value related to the parameter in statistics. However, Bradley Efron (1979) presented bootstrap method so that these problems re-moved. The most important feature of the bootstrap method is that standard error of the statistics in the most interest can be estimated (Yay, 2003: 27).

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In following sections, information about bootstrap and jackknife methods will be giv-en and it will be explored in the application section. Also, confidence intervals belonging to the bootstrap method will be evaluated. In the last part of the study, the stability of the teams that crowned champion after the 2000-2001 season in Netherlands Eredivisie league and how efficient they used their success are explored. The aim of this study is to disquali-fy teams, which cannot use their budgets any-more under the name of UEFA financial fair play and impose sanctions on teams in ques-tion such as transfer prohibition.

There are several studies in the literature about the financial research of budgets of the teams. However, resampling methods are studied rarely. For example, only Zaman et. al. (2015) conducted a study about effec-tive usage of budgets of the teams in Turkey Superleague using bootstrap and jackknife methods. Ecer & Boyukaslan (2014) mea-sured the performances of big four football teams using financial rates with Gray rela-tion analysis approach. Again Sakınç (2014) examined Turkey football clubs to determine their financial performances using Gray rela-tion analysis. Uluyol (2014) also explored fi-nancial performances of super league football clubs. Also, bootstrap method is used to cal-culate the efficiency of football clubs. Halkos

and Tzeremes (2011,2013) analyzed debt levels and market values, which affects per-formances of Europe football clubs. Barros and Barrio (2008) used a random stochastic frontier model to determine the team with the best efficiency in the English league between 1998-2004.

JACKKNIFE METHOD

Jackknife method is suggested by Maurice Quenouille in 1949 to remove statistical bias and then it is improved by John Tukey in 1956 to form hypothesis tests and confidence intervals thus taking its current name. Since it includes resampling in a similar way to the bootstrap method, instead of resampling method, it can be sample by deleting one ob-servation each time without replacement. In some situations, it may be hard to calculate good estimators or standard errors belonging to these estimators. In these situations, Jack-knife method can be used in standard error estimations related to statistics in interest. One observation at a time is excluded with Jackknife method and statistic in interest is calculated with remaining observations. Thus, only different observations from observations can be formed.

Let us have sample and

be our estimator. According to

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jackknife methods, when observation are excluded, new sample is;

Because of this, its estimator is also;

Jackknife estimate of bias is defined as fol-lows,

Here, is the estimate of and cal-culated through the equation,

, Jackknife estimate of stan-dard error is;

And to calculate pseudo values in jackknife methods, following equation (Fenwick ,1979- Abdi and Williams 2010) is used,

Here statistic to use can be mean, median etc..

BOOTSTRAP METHOD

Bootstrap is proposed first time by Efron (1979). Bootstrap method of Efron (1979) is a resampling method that can be used in a lot

of situation when observations are indepen-dent of each other and have the same distri-bution. Bootstrap methods for inference were introduced by Efron (1979), and have been discussed by Bickel & Freedman (1981), Efron (1982), Hall (1992), Efron & Tibshi-rani (1993), and others.

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The bootstrap method is far from heavy mathematical formulas, has limited assump-tions and is very easy to use (Simon & Bruce, 1991: 22-32). It is a resampling method from real data (Chernic, 1999). That is to say, the fundamental logic of the method is randomly replacing observations in the current dataset in any size and forming new datasets through resampling. So, information can be gotten from the current dataset as much as possibles. Firstly, samples are chosen using resam-pling methods from original data cluster and a bootstrap sample is formed. Then, more bootstrap samples can be formed with the same way and with the help of these samples confidence interval belonging to estimator in interest and significance of test statistics can be calculated (Hamajima, 1999, Efron & Tib-shiranni, 1993). Bootstrap samples obtained from the original data depends on the applica-

tion. Actually, from a sample, that has a size of , the maximum number of the bootstrap sample can be formed theoretically (Stine, 1990:325-373).

Repeating the sampling and doing this too many times is not convenient because of rea-sons like time, effort, cost, not availability. Only one data is available. If we wanted to estimate the population mean, we have the sample mean as estimation. If there are no assumptions about the population distribu-tions, in small sized samples, it is not pos-sible to tell confidence intervals for the popu-lation mean and estimation of intervals is not possible. To overcome problems like these, n sized samples are reproduced on the data available, the value of the statistic in interest can be observed many times and it is possible to have an idea about it.

unit samples can be produced from the experimental distribution function thinking observed data represents the distribution in interest and sample distribution can replace distribution function of the equation. The ob-

servation that produced here is called boot-strap samples.

With the help of experimental distribution function, to produce unit sample means to draw times with replacement from

data. With this way,

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bootstrap samples are produced. number of different bootstrap sample cluster can be formed by repeating resampling methods as much as wanted. For number of bootstrap sample, when sample mean is observed as , the histogram belonging to these observations gives an idea about the distribution of . For B number of bootstrap sample, when sample vari-ance is observed as , the histogram belonging to these observa-tions gives an idea about distribution of . Also, similar interpretations can be made for other statistics in interest. (URL-1).

The statistical deduction is based on sam-ple distributions of statistics. The bootstrap method is a way to find sample distribution before anything else. Procedure of Bootstrap method,

1. Resampling: A new dataset is obtained by randomly sampling with replacement us-ing original data cluster. That’s why hun-dreds of sample are formed. Every formed resample is the same size with the original random sample. Here, a number can be drawn once, more than once or never.

2. Calculation of Bootstrap Distribution: For every obtained resample, a statistic in in-terest is obtained. Distribution of these

resample statistics are called bootstrap distribution.

3. Usage of Bootstrap Distribution: Boot-strap distribution gives information about the shape, center, and dispersion of sam-ple distribution of statistic in interest. The original sample represents the population it is drawn from. The bootstrap distribution of a statistic based on resampling many obtained sample shows sample distribu-tion of a statistic based on many samples.

a new dataset, , is obtained through

randomly sampling with replacement us-ing original data cluster consist of *** sample. There is the possibility of once or never to be seen for some data from

data cluster formed with the help of sampling from original dataset (Bark-er, 2005). number of different bootstrap observation data cluster can be formed by repeating resampling process as many times as wanted and bootstrap data cluster for

can be shown as . Statistic in interest is calculated using

these datasets (Okutan, 2009). Bootstrap method is a method used in non parametric estimation problems such as the calculation of standard deviation and confidence interval.

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Forming sample distribution of estimator is extremely, if not impossible, hard and time-consuming. However, bootstrap method, which is also used in order to form experi-mental sampling distribution of estimator, need to remove this disadvantage. In this re-gard, algorithm of bootstrap method can be viewed as below (Fox, 1997);

1. Obtaining n sized sample from population,

2. Since there are not any other information, this sample is accepted as the best estima-tor for the population. Thus the possibility of the observation is taken as to be included to sample with replacement for each time assuming this sample as popula-tion and resampling of a sized sample and repeating this process times.

3. Calculation of estimator in interest for ev-ery bootstrap sample.

4. Starting from *** number of sample, obtaining the sample distributions of these estimators,

5. With the help of this distribution, calcula-tion of important statistics like mean, stan-dard deviation and standard error related to distribution.

6. Finally, making interpretations about the population using these estimations.

Mean and standard error of estimator be-longing to bootstrap method can be calculat-ed as below.

*** expression is the mean belonging to bootstrap samples.

CONFIDENCE INTERVAL of BOOTSTRAP METHOD

Generally, the confidence interval of pa-rameter gives more information than point estimation of parameter (Wehrens et. al. ,2000: 35-52). It is expressed that while forming confidence intervals, need to be taken between 1000 and 2000 (Efron & Tib-shiranni, 1993). In this study, emphasis is put on the most accepted confidence intervals in bootstrap method.

STANDARD BOOTSTRAP NORMAL INTERVALS

When a standard error in interest is not known but sample distribution shows normal distri-bution assumptions it is used.

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It is obtained as above. Here the one obtained using bootstrap method is the standard error of estimator. This method is not preferred due to its assumption and per-centage approach takes its place in applica-tion.

PERCENTAGE BOOTSTRAP CONFI-DENCE INTERVALS

In this system, limits of confidence interval is determined by bootstrap distribution of .

Steps for calculating bootstrap confidence interals for ***

1. Estimations calculated from boot-strap samples are arranged ascend-ingly like

;.

2. For confidence in-terval, if , then or-

der value is lower limit and order value is upper limit.

3. For ***, limits of two way bootstrap confidence interval lim-its are obtained as .

If sample distribution of a bootstrap statistic is about normal, then standard bootstrap and percentage bootstrap confidence intervals will yield similar results.

BOOTSTRAP t-CONFIDENCE INTERVALS

When assumptions are met, to form a confi-dence interval, z or t table is needed in para-metric methods. But in this method, a table, which is valid for the data in hand, is formed and used to find confidence intervals. Steps can be expressed as below.

1. For each bootstrap sample, values can be calculated with following formula

(10)

2. *** values are sorted in ascending or-der.

3. Here, as value is , if order value and value

is taken as order value, it is calculated as below,

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Bootstrap t confidence interval is good in theory but in application, it can yield varying results (Efron & Tibshiranni, 1993).

CONFIDENCE INTERVAL THAT is AC-CELERATED and WİTH ADJUSTED BIAS

This method is called confidence interval considering its name. It is developed to overcome the flaws of percentage bootstrap confidence intervals (Efron & Tibshiranni, 1993). In this method, there are values shown as and , acceleration and bias adjust-ment respectively. When these values are zero, confidence interval turns into percentage confidence interval (Yıldıztepe & Özdemir, 2013).

Confidence interval which is accepted in lit-erature is confidence interval. Since

confidence intervals do not need parameter transformation and are preferred in most nonparametric applications keeps

confidence intervals superior. Even if this interval does not yieeld perfect result, it can be said that it is the interval which gives best results considering all intervals based on bootstrap method (Yay, 2003:103). Also for detailed information about bootstrap confi-

dence intervals (Carpenter and Bithell, 2000: 1141-1164) can be examined.

APPLICATION

In application, financial performances of the teams, which crowned champion after 2000 in Netherlands Eredivisie League, are ex-plored using the bootstrap method. However, the study is made using between 2005-2006 and 2015-2016 seasons because 2005-2006 and the previous season could not be found. Required data is taken from a website called Transfer Market (URL-2). Teams included in the study are PSV, Ajax, Az Alkmaar and Twente athletic clubs. It is explored how suit-ably teams are using their budgets and which team is more consistent between these sea-sons. A variable which is going to be used in this study is determined as total expense made to gain one point through dividing total budgets of mentioned teams to total point for last 11 seasons. Values belonging to the vari-able in question is given in Table 1.

Bootstrap statistics of the teams are calculat-ed based upon these index values. Here, the number of bootstrap renewals are taken as 1000. Thus, it is hard to calculate manually. For this reason, corresponding values are cal-culated using a computer program. Analyses are made in R packet program. Statistics for 4 teams in question are summarized in Table 2.

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Table 1. Cost Performance Index

PSV AJAX AZ ALKMAAR TWENTE

05-06 1.255952 1.429167 0.642568 0.252766

06-07 0,998 1.075067 0.546528 0.292424

07-08 0.974306 1.043478 1.084884 0.344839

08-09 0.957692 0.877206 0.684125 0.372464

09-10 1.000641 1.198824 1.278226 0.534651

10-11 1.306232 1.526027 0.962373 0.821831

11-12 1.12942 1.094079 0.625846 1.326667

12-13 1.276087 1.333553 1.134103 1.191935

13-14 1.605932 1.492254 0.993617 0.797619

14-15 0.879545 1.056338 0.548871 0.976279

15-16 1.052976 0.893902 0.563559 0,777

When Table 2 is examined, it is seen that original values are expressed as the mean of 11 seasons of the teams. Bias is the difference between the mean of 1000 bootstrap iteration and the mean of the original sample. As for standard error and error mean square values, they are the bootstrap estimations of index values in hand. Here, it can be said that being small for error mean square is an indicator of teams’ stability on the basis of years taken.

Error mean square is sorted ascendingly as PSV, Ajax, Az Alkmaar and Twente, when Tablo 2 is investigated. This shows that in the seasons in question PSV is more consistent and has more homogeneous structure than other teams in terms of earned point and ex-penses and PSV used its budget in a stable way in terms of earned point. It can be in-terpreted that tehre ara no surprise situations between seasons in interest.

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Table 2. Bootstrap Statistics of Cost Performance Index

Teams Original Biasness Std. Error EMS

PSV 1.13057 -0.00037 0.06273 0.003935

AJAX 1.18358 -0.00100 0.06666 0.00444

AZ ALKMAR 0.82401 0.00244 0.07652 0.00586

TWENTE 0.69890 -0.00572 0.10538 0.01113

Table 3. Bootstrap Confidence Intervals of Cost Performance Index

Teams Normal Basic Percentages

PSV 1.008-1.254 0.993-1.240 1.021-1.268 1.034-1.309

AJAX 1.054-1.315 1.050-1.319 1.049-1.317 1.058-1.320

AZ ALKMAR 0.671-0.971 0.658-0.967 0.680-0.989 0.686-0.992

TWENTE 0.498-0.911 0.498-0.905 0.492-0.899 0.501-0.917

Now, let us summarize obtained bootstrap confidence intervals in Table 3. When the dis-tribution of bootstrap iterations are near nor-mal, standard normal and percentages give too close results to each other. In this situation, the percentage bootstrap confidence interval is preferred. Also, the BC confidence in-terval is a more advanced version of percent-age confidence interval. There can be inter-pretation for each differenet confidence inter-vals. Even so, for these confidence intervals to be interpreted more satisfyingly, normality

graphs for bootstrap iterations of teams need to be examined. Distribution of bootstrap it-erations which are obtained with relation to 1000 bootstrap iteration belonging to teams are given below. As shown as (a), (b), (c) and (d), graphs shows PSV, Ajax, Az Alkmar and Twente teams respectively. Graphs in left in every figure are distribution of bootstrap it-erations between specified years belonging to teams in question and graphs in right are nor-mal bootstrap Q graphs between same years belonging to same teams.

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UHBAB www.uhbabdergisi.com

Uluslararası Hakemli Beşeri ve Akademik Bilimler Dergisi Nisan / Mayıs / Haziran – İlkbahar Yaz Dönemi Sayı: 20 Yıl:2017

International Peer-Reviewed Journal of Humanities and Academic Science April / May / June - Spring Summer Issue Issue: 20 Year: 2017 JEL CODE: H11-L1-M10-M12-M19-M20-M59 ID:353 K:800

ISSN Print: 2147-4168 Online 2147-5385 (ISO 18001-OH-0090-13001706 / ISO 14001-EM-0090-13001706 / ISO 9001-QM-0090-13001706 / ISO 10002-CM-0090-13001706)

(MARKA PATENT NO: TRADEMARK) (2015/03947-2015-GE-17304)

(a) (b)

(c) (d)

Figure 1. The Bootstrap Plots of the Football Clubs

When the graphs above are examined, the team, which its graphs belonging to bootstrap iterations are closest to normal, is PSV. Besides, error mean squares of PSV team was found as the lowest too. Thus these results support each other. Also, the fact that original index values are inside calculated bootstrap confidence intervals indicates that index values belonging to the team in question for that year are on the

mark. That is to say, if index values obtained between 2005-2016 decrease, it can be said that teams planned their expenses right in accordance with points they earned. If there is a contrary situation, success in that year is incidental or it should be evaluated together with the states of other teams. If results from Jackknife method is evaluated; First, one observation is excluded with jackknife

Histogram of t

t*

Dens

ity

1.0 1.2 1.4

01

23

45

6

-3 -1 1 3

1.01.1

1.21.3

1.4

Quantiles of Standard Normal

t*

Histogram of t

t*D

ensi

ty1.0 1.2 1.4

02

46

-3 -1 0 1 2 3

1.0

1.2

1.4


t*Histogram of t

t*

Den

sity

0.6 0.8 1.0

02

46

-3 -1 1 3

0.6

0.9


t*

Histogram of t

t*

Dens

ity

0.4 0.6 0.8 1.0

01

23

4

-3 -2 -1 0 1 2 3

0.4

0.6

0.8

1.0


t*

Figure 1. The Bootstrap Plots of the Football Clubs

When the graphs above are examined, the team, which its graphs belonging to bootstrap iterations are closest to normal, is PSV. Be-sides, error mean squares of PSV team was found as the lowest too. Thus these results support each other. Also, the fact that origi-nal index values are inside calculated boot-strap confidence intervals indicates that index values belonging to the team in question for that year are on the mark. That is to say, if

index values obtained between 2005-2016 decrease, it can be said that teams planned their expenses right in accordance with points they earned. If there is a contrary situation, success in that year is incidental or it should be evaluated together with the states of other teams. If results from Jackknife method is evaluated; First, one observation is excluded with jackknife method and yielded mean val-ues are summarized in Table 4.

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Table 4. Mean Values Obtained Through Jackknife Method


Not excluded 1.30617 1.183627 0.824064 0.698952

1. excluded 1.118083 1.159073 0.842213 0.743571

2. excluded 1.143878 1.194483 0.851817 0.739605

3. excluded 1.146248 1.197642 0.797982 0.734364

4. excluded 1.147909 1.214269 0.838057 0.731601

5. excluded 1.143614 1.182107 0.778647 0.715382

6. excluded 1.113055 1.149387 0.810233 0.686664

7. excluded 1.130736 1.192582 0.843885 0.636181

8. excluded 1.11607 1.168634 0.79306 0.649654

9. excluded 1.083085 1.152764 0.807108 0.689086

10. excluded 1.155724 1.196356 0.851583 0.67122

11. excluded 1.138381 1.212599 0.850114 0.691148

In the logic of Jackknife method, it lays that calculating the means after excluding one observation each time. When Table 4 is ex-plored, one observation value for each team is separately excluded and it is calculated as the mean values of financial stability index values belonging to the teams. For example, 1.118083 value belonging to PSV in the 1. excluded part is the mean value of remain-ing values after 1. observation is excluded. Of course since the statistics in interest here

is mean value, mean is taken. For example, statistics like median, mode, the coefficient of variation could also have been used. These obtained values are going to be used to find pseudo values belonging to jackknife method.

Calculated pseudo values are given in the table below. Pseudo values (5) are calculated for every team Jackknife means, standard er-rors, bias values, error mean square values and confidence intervals belonging to these values that are calculated later are calculated.

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Table 5. Pseudo Values Obtained Through Jackknife Method


Not excluded 1.30617 1.183627 0.824064 0.698952

1. excluded 1.255952 1.429167 0.642568 0.252766

2. excluded 0,998 1.075067 0.546528 0.292424

3. excluded 0.974306 1.043478 1.084884 0.344839

4. excluded 0.957692 0.877206 0.684125 0.372464

5. excluded 1.000641 1.198824 1.278226 0.534651

6. excluded 1.306232 1.526027 0.962373 0.821831

7. excluded 1.12942 1.094079 0.625846 1.326667

8. excluded 1.276087 1.333553 1.134103 1.191935

9. excluded 1.605932 1.492254 0.993617 0.797619

10. excluded 0.879545 1.056338 0.548871 0.976279

11. excluded 1.052976 0.893902 0.563559 0,777

Mean of pseudo values obtained from Jackknife

Method 1.130617 1.183627 0.824064 0.698952

Standart error of Pseude Mean 0.064068 0.069346 0.081504 0.111528

Bias -2.22045E-15 4.44E-15 2.22045E-15 1.11022E-14

MSE 0.004104721 0.004809 0.006642962 0.012438427

95% Confidence Intervals

0.987873-1.273361.029125-1.338129

0.642472-1.005655

0.450469-0.947436

CONCLUSION

Financial evaluation of teams is quite impor-tant in this day and age. From this point, in

the scope of the study, in Netherlands Eredi-visie league, the financial stabilities of the teams, who have experienced championship

15





after 2000, are evaluated. Evaluation is con-ducted using bootstrap and jackknife meth-ods, two of the sampling methods. Jackknife and bootstrap methods yield efficient results in the estimation of parameters and asymptot-ic distribution. In this regard, a detailed appli-cation belonging to bootstrap and jackknife methods is made and the results are inter-preted. Through the bootstrap method, a large number of bootstrap iterations were created and general results were obtained from the few observations at hand. It is difficult to cal-culate the estimations of 1000 bootstrap iter-ations obtained. However, this difficulty has taken off thanks to the development of com-puter technology. In this regard, all analyses are made using R packet program. Various bootstrap statistics, bootstrap plot graphs and bootstrap confidence intervals for the teams in interest belonging to the league in ques-tion are obtained and various interpretations are made. In the scope of explored teams, it is determined that PSV

is the most consistent team in the league. As for the jackknife method, it is a method that reduces the efficiency of extreme values when there are one and also it decreases bias. Jackknife statistics and jackknife confidence intervals for the teams are calculated. The aim is to find the values of which biasness and standard error, thus error mean square,

is small. When results are analyzed, the amount of bias of teams in question is found very small, as a matter of fact, it is almost zero. When the results obtained with jack-knife method are evaluated, the team with the smallest error mean square is PSV. In this regard, Ajax, Az Alkmaar and Twente follow PSV respectively. If the results belonging to each method are interpreted, its is found that sorting of financial consistency in the results, which jackknife and bootstrap methods yield between seasons in question, are the same. Both methods show that PSV is more con-sistent compared to other teams within the scope of the study between 2005-2006 and 2015-2016. In both methods, Ajax, Az Alk-maar and Twente follow PSV respectively. Based on the Netherlands Eredivisie league, we believe this study will be useful for those in need of scientific research on the evaluation of the financial performance of teams in the football sector. Also in the next study, scoring of the statuses of big clubs of Europe in terms of participation in UEFA and the champions league will be made and evaluated (taking ac-count of various variables).

ACKNOWLEDGE

This paper is an expanded version of the work presented by full text at the congress (URL-3) (2nd Internetional Conference on Applied

16





Economics and Finance) in Cyprus on 5-6 December 2016.

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URL-1.

http://80.251.40.59/science.ankara.edu.tr/ozturk/Dersler/ist312/Ders10/Ders10.pdf . Visit date: May 5, 2016.

URL-2. http://www.transfermarkt.com.tr/ Visit date: March 10, 2016.

URL-3. Website: http//icoaef.gau.edu.tr/, last date accessed 1/12/2016.

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YENİDEN ÖRNEKLEME YÖNTEMLERİ YARDIMIYLA HOLLANDA EREDİVİSİE LİGİNİN FİNANSAL PERFORMANS ANALİZİ

Öz. Modern bilgisayarlar ve yazılımlar verilere daha önce düşünülemez şekilde grafiksel ve sayısal olarak yorumlamayı mümkün kılmaktadır. Bu sayede daha gerçekçi doğru ve bilgile-ndirici analiz yapmamıza olanak tanırlar. Yeniden örnekleme metodları da bu yeniliğin bir parçasıdır. Yeniden örnekleme yöntemleri, standart hataları ve güven aralıklarını hesaplayıp anlamlılık testleri yaparak belirsizlik miktarını belirlememizi sağlar. bootstrap ve jackknife yön-temleri parametre tahmini ve ilgilenilen istatistiğin asimptotik dağılımı hakkında etkili sonuç-lar vermektedir. Buradan hareketle çalışma kapsamında Hollanda Liginde 2000 yılından sonra şampiyonluk yaşamış takımların mali istikrar göstergeleri jackknife ve bootstrap yöntemleri ile incelenmiştir. Takımların mali istikrar ölçülerinin elde edilmesi için, ele alınan takımların to-plam bütçeleri ve o sezon kazanmış oldukları toplam puanlarına ait bilgilere 2005 yılı öncesinde rastlanmamıştır. Bu bağlamda 2005-2015 yılları arasında şampiyon olan 4 takıma ait mali is-tikrar ölçütleri araştırılmış ve hangi takımın söz konusu sezonlar arasında daha homojen bir yapıya sahip olduğu ve dolayısıyla harcamalarını UEFA’nın belirlediği kriterlere uygun olarak yaptığı araştırılmıştır. Amaç. Hollanda Eredivisie Liginde 2000 yılından sonra şampiyonluk yaşamış takımların mali performansları bootstrap ve jackknife yöntemleri ile araştırılmıştır. Ancak 2005-2006 sezonundan önce verilere ulaşılamadığından çalışma 2005-2006 sezonu ile 2015-2016 sezonları arasındaki veriler kullanılarak yapılmıştır. Araştırmaya dahil olan takımlar PSV, Ajax, Az Alkmar ve Twente spor kulüpleridir. İncelenen sezonlar arasında takımların büt-çelerinin ne kadar doğru kullandıkları tespit edilmeye çalışılmış ve bu sezonlar arasında hangi takımın daha istikrarlı olduğu araştırılmıştır. Bu çalışma kapsamında kullanılacak olan değişken, son 11 sezonda söz konusu takımların toplam bütçelerini o sezon içerisinde kazanmış oldukları toplam puana bölerek, bir puan kazanmak için yapılan toplam harcama olarak belirlenmiştir. Sonuç ve Öneriler. Söz konusu lige ait ele alınan takımlar için çeşitli bootstrap istatistikleri, bootstrap plot grafikleri ve bootstrap güven aralıkları elde edilerek çeşitli yorumlar yapılmış olup, incelenen takımlar bazında, PSV takımının ligin en istikrarlı takımı olduğu sonucuna varılmıştır. Jackknife yöntemi ise veride uç değerler olduğunda uç değerlerin etkisini azaltan bir yöntem olmanın yanı sıra yanlılık miktarını da azaltmaktadır. İlgilenilen takımlara ait jackknife

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istatistikleri ve jackknife güven aralıkları hesaplanmıştır. Amaç yanlılığı ve standart hatası, dolayısıyla hata kareler ortalaması küçük değerler bulmaktır. Sonuçlar incelendiğinde söz ko-nusu takımlara ait yanlılık miktarlari çok küçük bulunmuştur, hatta neredeyse sıfırdır. Jackknife yöntemi ile elde edilen sonuçlar kendi içerisinde değerlendirildiğinde, hata kareler ortalaması en küçük olan takım PSV’dir. PSV’yi sırasıyla Ajax, Az Alkmar ve Twente izlemektedir. Her iki yönteme ait sonuçlar birlikte yorumlanacak olursa, söz konusu sezonlar arasında jackknife ve bootstrap yöntemleri ile elde edilen sonuçlarda mali istikrar sıralaması aynı bulunmuştur. Her iki yöntem de PSV takımının 2005-2006 ile 2015-2016 yılları arasındaki çalışma kapsamında ince-lenen diğer takımlara göre daha istikrarlı olduğu görülmektedir. İncelenen her iki yöntemde de PSV’yi sırasıyla Ajax, Az Alkmar ve Twente izlemektedir. Holanda Eredivisie ligi baz alınarak yapılan bu çalışma futbol sektöründe takımların mali performanslarının değerlendirilmesi ile ilgili bilimsel araştırmaya ihtiyaç duyanlar için fayda sağlayacağına inanmaktayız.

Anahtar Kelimeler: Çakı, Bootstrap, Bootstrap Güven Aralıkları, Yan, Standart Hata, Hata Kareler Ortalaması, Finansal Performans

2121

AGRICULTURAL CREDIT MARKETING IN TURKEY 1

Erdogan GÜNEŞ1, Berkay KESKİN2, Mevhibe ALBAYRAK3

1-2-3 Ankara University, Faculty of Agriculture, Depart of Agricultural Economics, 06110 Ankara / Turkey

ORCID ID: 0000-0002-4416-34051, 0000-0003-2686-61712, 0000-0001-9943-165X3

(1) Corresponding Author: Erdogan GÜNEŞ, Ankara University Faculty Of Agriculture, Agricultural Economics Department, [email protected]: 25.02.2017 Accepted: 21.06.2017 Type ofarticle (Research and Application) Conflict of Interest: None Ethics Committee: None

UHBAB ULUSLARARASI HAKEMLİ BEŞERİ VE AKADEMİK BİLİMLER DERGİSİINTERNATIONAL PEER-REVIEWED JOURNAL OF HUMANITIES AND ACADEMIC SCINENCE

Abstract: The funding of agricultural enterprises has many critical aspects for enterprises. Agricultural enter-prises benefit from credits especially on the production and marketing stages. Banking activities in agricultural credit applications in Turkey have increased after 2000’s. While Agricultural Bank of the Republic of Turkey (Ziraat Bankasi) gave almost all of the agricultural credit alone in Turkey until 2000, the share of the bank has decreased by 60-70% with the introduction of private banks in recent years. When agricultural enterprises receive agricultural credits; low interest rate, flexible payment plan, short and easy procedures, promotion are important for bank preference. Banks are also trying to create advantages in these factors for this purpose. For this reason, banks attach importance to factors such as reduction in loan interest rate, ease of repayment, flexibility, maturity shift, promotion of input usage. The banks carry out various marketing activities with the aim of creating a positive image, giving information about services, winning new customers and keeping existing customers. In this study, the strategies applied in agricultural credit market, market actors and credit marketing in Turkey are examined. In addition, the effects of increasing competition in credit marketing on the agricultural sector, agricultural enterprises and credit markets have also been examined.

Key Words: Agriculture, Credit, Finance, Market, Bank

Doi: 10.17368/UHBAB.2017.2.2

22


International Peer-Reviewed Journal of Humanities and Academic ScienceApril / May / June - Spring Summer Issue Issue: 20 Year: 2017

JEL CODE: G21-Q13-Q14 ID:354 K:802ISSN Print: 2147-4168 Online 2147-5385

(ISO 18001-OH-0090-13001706 / ISO 14001-EM-0090-13001706 / ISO 9001-QM-0090-13001706 / ISO 10002-CM-0090-13001706) (MARKA PATENT NO: TRADEMARK)

(2015/03947-2015-GE-17304)

INTRODUCTION

Agricultural credit usage is increasing with massive and qualitative production. Previ-ously, public banks were active in the agri-cultural credit market, but today, proportion of private banks is increasing due to liberal approaches and competition. This situation has led private sector banks to participate in public credit markets as well as public sec-tors. This change in the credit markets has created competition between the banks and has created differentiation of the marketing strategies in credit markets. In this process, an agricultural enterprise has utilized many opportunities such as repayment, interest and so on. This is seen in different forms of the world’s developed or developing countries.

Turkey is a country aiming to pass on the competitive market in the field of agricultural credit in the development process and it aims to move away from public sector banking by going to this field. Nowadays, in the field of agricultural loans, private banks have a signif-icant share in the loan portfolio and this share is on an increasing trend. On the other hand, credit diversification in the field of agricultur-al lending attracts attention, and this diversity has increased with development of agricultur-al culture. In this study, the strategies applied

in agricultural credit market, market actors and credit marketing in Turkey are examined.

METHODOLOGY

This study is based on a literature review re-lated to last development about agricultural credit. Books, articles and internet sources about the research area have been used. Re-cently, many scientists have studies agricul-tural market orientation. At this stage, it is estimated that competition within the banks will increase. Thus, several banks must fol-low many strategies in order to success at this areas. Banks’ agricultural credit market-ing strategies and current developments in research area have been examined in 7Ps of Marketing Mix based on literature.

AGRICULTURAL CREDIT MARKET and DEVELOPMENTS in TURKEY

Agriculture is an important sector for Turkey in terms of social and economic aspects. In order to create a sustainable, competitive and organized agricultural sector Turkey needs to utilize its resources efficiently and effec-tively. For developing such an agricultural sector, farmers of all sizes should have better access to finance (Taşçı, 2015: 175). Of the total GDP, employment and export 8%, 20% and 5%, respectively, comes from agriculture. Turkey had 68 billion $ agricultural output in

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2014. In spite of its importance and contri-bution to the economy, Turkish agricultural sector consisting more than 3 million farm holdings has a very small share in total loans (Yıldız and Koçoğlu, 2014:8-15). The share of agricultural loans among total loans varied from %3 to %4 in the last 5 years (BDDK, 2015a).

Agricultural credit is developing with inter-nal and external factors such as the structural status of agricultural enterprises, production and market conditions, farmer purchasing power and parity in Turkey.

Increasing credit volume for agriculture and sectoral orientation of private sector banking are the main developments in recent years. But the main question that is constantly asked is the ratio between the volume of credit growth and the development of the agricultural sec-tor. This situation is different according to the sub-branches and the structural situations of the enterprises within the agriculture sector. According to the general opinion, enterprises need to have professional, rational manage-ment and organization to make sufficient and effective use of credits. Since agricultural enterprises in Turkey are small, fragmented and not at the expected level in organization, the enterprises can’t benefit from agricultural credits at the desired level (Güneş and Mo-

vassaghi, 2016a: 1719). In this context, or-ganizational studies are being carried out to enable all enterprises to benefit from agricul-tural loans. Farmers get agricultural credits from various sources in Turkey. They can be classified as two groups, formal and informal sources. Agricultural Bank of the Republic of Turkey (Ziraat Bankası), commercial banks (domestic and foreign-owned and operated), and agricultural credit cooperatives (ACC), agricultural sales cooperatives and some cooperatives such as Pankobirlik relayed to sugar processing, are major formal credit suppliers, while other individuals and institu-tions constitute informal resources (Güneş, 2011: 92-93).

Agricultural Bank of the Republic of Tur-key (Ziraat Bankası) has supplied almost all agricultural credits in Turkey to mid-2000s (Güneş and Artukoğlu, 2010: 796). In recent years, other banks have entered the sector, so Agricultural Bank’s share fell to 65-75% (Güneş and Movassaghi, 2016b: 86). The share of Ziraat Bank in agricultural lending market went down to around 66% in spite of the monopoly of the bank on disbursement of subsidized loans and agricultural subsi-dies. It has been aimed to meet the financing needs of the agriculture sector through subsi-dized credit application which is maintained through the bank since 2004. Also, it has

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been encouraged to move towards some ar-eas where development is required with low interest rates applied.

Under the Decree No. 2012/2781, discounted interest rate is applied to agricultural loans and the difference between the bank’s agri-cultural credit interest rate and discounted in-terest rate is covered by the Treasury. In the financing of Turkish agriculture, the agricul-tural credit share of private banks increased from 33.8% to 35.9% from 2007 to 2008. Ap-proximately 45 billion TL agricultural credit was used in the agriculture of Turkey in 2014 and the share of the private sector was deter-mined as 35.8% in the same year. (BDDK, 2015b).

AGRICULTURAL CREDIT MARKET-ING STRATEGIES of BANKS

In today’s banking sector, product-oriented strategies are replaced by customer-focused approaches. In customer-oriented approach-es, it is aimed to establish permanent relation-ships with customers and to provide customer loyalty in the long term (Ocak, 2011: 66). Due to the special characteristics of agriculture, there is an increase in the need for agricul-tural credits. This situation has improved the agricultural banking and increased the credit demands of the enterprises. Agricultural en-terprises have started to benefit from credits

on the production and marketing stages and competition among banks has increased. Ac-cordingly, the agricultural sector has become an important market for banks and a new field of competition has emerged.

When agricultural enterprises receive ag-ricultural credits; low interest rate, flexible payment plan, short and easy procedures, promotion are important for bank preference. For this reason, banks attach importance to factors such as reduction in loan interest rate, ease of repayment, flexibility, maturity shift, promotion of input usage. The banks carry out various marketing activities with the aim of creating a positive image, giving informa-tion about services, winning new customers and keeping existing customers.

Traditional marketing mix elements (4P= product, price, place and promotion) can be used in the banking sector (Balsöz, 2004: 26). But recently it is thought that the traditional marketing mix for service marketing is insuf-ficient, so 3P (people, physical evidence and process) has been added to traditional mar-keting mix elements and the concept which is often seen as 4P has evolved to 7P (Yapraklı and Erdal, 2015: 482; Barouh, 2017: 3). Thus, 7P for service marketing has come to the forefront. The concept of marketing in the banking sector is under the roof of ser-

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vice marketing (Gürsoy, 2006: 65; Karaslar, 2014: 23). In this way, banks’ agricultural credit marketing strategies and current devel-opments in agricultural credit marketing can be examined in 7P (Panigrahi, 2015:6).

Product

The product is the most basic element of the marketing mix. The product is defined as an object that contains important items such as quality, design and brand and is presented to the market by the company (Ocak, 2011: 22). Although money is a tangible that banks offer agricultural credits in a customized format to market to different needs groups and differ-ent masses. In this direction, there are cred-its designed according to different types of needs under groups such as business credits and investment credits. Banks that offer in-

novative products / services in an innovative manner have been found to be more success-ful than satisfying customer needs (Yapraklı and Erdal, 2015: 484).

As a product-related strategy, it is seen that banks use agricultural themes in names and images of agricultural credit cards in Turkey. These credit cards bear the names “Başak (Wheat Head) Kart”, “Üretici (Producer) Card”, “Hasat (Harvest) Card”, “İmece (Col-lective Work) Card”, “Tarım (Agriculture) Card”, “Ekin (Crop) Card”, “ Verimli (Pro-ductive) Card”, “Harman (Blend) Card”, “ Mahsul (Crop) Card” (Figure 1).

These cards can be used as bank cards and shopping can be done with cards and dis-counts provided by cards can be used in con-tracted shops.

Figure 1. Agricultural Credit Cards for Agricultural Banking in Turkey*

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* Credit cards images were taken from the official web pages of various banks

Price

The price is simply defined as the money the buyers need to pay to obtain a good or service. The price must match the value of the product offered, otherwise the buyer may choose the products of others. For this reason, price is an important element of marketing mix. The price mix for banking services includes the determination of price levels, the determina-tion of the payment, terms and costs of credit and the determination of the strategies to be applied in pricing (Ocak, 2011: 77). The two most important issues that banks attach im-portance in price element are the interest rates and the payment plans. For this reason, banks try to keep their payment plans flexible with low interest rates in order to gain a competi-tive edge in agricultural banking. Despite the fact that pricing is very important, it is not enough by itself. Although some banks offer low interest rates, they may be less preferred due to the slow and prolonged lending pro-cess.

Place

Placing seeks solutions to questions about where, how and by whom the bank service will be marketed. Place in the marketing mix

is very important for the bank image. Even if the customer likes the product, may give up from buying the product if there is a problem in the distribution phase (Balsöz, 2004: 37).

Banks usually prefer direct distribution routes and are getting closer to their custom-ers through their branches. But with new in-formation and communication technologies, banks have also developed new services (Yapraklı and Erdal, 2015: 484). Most of the banks today accept agricultural credit appli-cations via SMS or Internet banking without the customer arriving at the branch.

Promotion

Promotion consists of activities such as per-sonal sales, sales promotion, advertising, public relations (Balsöz, 2004: 39). Promo-tion has different purposes in banking sector such as creating a positive bank image, differ-entiating from its competitors and creating customer loyalty (Yapraklı and Erdal, 2015: 485).

Recently, advertisements for agricultural banking and agricultural credits have in-creased. These advertisements are often found on television and in newspapers. There are also various promotional practices besides advertisements. Postponing payments with zero interests in shopping at agreed stations,

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village and farmer visits, farmer information meetings can be classified as other important promotional activities (Sun, 2013).

People

Everyone involved in the delivery of the ser-vice and affecting the perception of the buyer constitutes the “people” element of the mar-keting mix (Al, 2005: 3). Because service marketing cannot be thought independently from the people who make the marketing, people element is very important in service marketing and service presentation (Rakesh et al. 2016: 37).

Since people are an important part of the service provided, the behavior of employees will affect the success of the organization’s actions and functions (Luo et al., 2016: 32). This factor is especially important in banks. Bank employees give tips about the structure of the service. Therefore, clothing style, per-sonal appearance, attitudes and behaviors of employees affect customer perception of ser-vice (Ocak, 2011: 24).

Employees in agricultural credit depart-ments must also have other qualifications along with the obligation to have the skills of bank employees. Employees should have

basic knowledge of agriculture and agricul-tural production, should be able to communi-cate well with farmers and be a good listener (Heney, 2011: 8).

Physical Evidence

Physical evidence refers to the environment in which the service is provided and the envi-ronment in which the customer interacts. (Al, 2005:3). The environment in which banking services are offered is as important as other marketing mix elements. Because, furniture, machinery, equipment, lighting in the bank closely influence the evaluation of the service the customer receives. For this reason, the banks are trying to create bright and modern places (Bacıoğlu, 2009: 80).

Banks can also create a specialized environ-ment in their agricultural branches. There are agricultural bank branches that include pic-tures and symbols of agriculture

This bank branches are designed according to agriculture theme and include agricultural icons such as cows, fruit trees and chickens (Figure 2). In this way, the physical envi-ronment has been made to show that it is a branch where services related to agricultural banking can be obtained (Hürriyet, 2013).

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Figure 2. Example of an Agricultural Bank Branch From Turkey**

**Sun, 2013: 32

Process

Process includes the activities from produc-tion to the customers. It is especially impor-tant for service enterprises (Bacıoğlu, 2009).

It is important for enterprises that credit is easy to get and that the process is not long. Despite the fact that some banks offer low in-terest credits, the fact that the lending process is slower and therefore less preferred, reveals the importance of the process.

For this reason, banks aim to shorten and simplify the process of application and post-marketing as much as possible. In addition to this, it is also possible to apply in different ways. For example, in some banks the appli-cation for agricultural credit can be made via SMS without going to the branch. If the ap-plication reply is positive, the credit limit that

can be used is sent to the mobile phone and credit can be used if there is no obstacle to the credit usage by going to the branch with the identity card.

CONCLUSION

Increased competition in agriculture and service quality has started to contribute to the sector. This is partly due to the growing strength of credit lenders and credit-borrow-ing agricultural enterprises.

The recent situation in Turkey with crediting of agriculture is that private sector banking is involved in the process. Different banks in this area have different marketing strategies in different areas of agriculture for credit ac-tivities. Increasingly, banks have been work-ing to provide suitable term and conditional credits, to determine the right and timely needs, to fast lending and use, to carry out

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demonstration studies and farmer participa-tion, to encourage the use of credit cards suit-able for agriculture financing.

It’s seen that, product-oriented approaches in agricultural credit marketing are replaced by customer-oriented approaches. With the expansion of the 4P marketing mix to 7P, human, physical evidence and process fac-tors have come to the forefront, and these factors have gained importance also in agri-cultural banking. There has also been intense competition in the field of agricultural credit marketing, as many banks have entered the sector. Banks that want to be successful seem to be trying to take advantage by low inter-est rates, ease of repayment and fast lending. In addition to this, farmer visits, meetings, campaign purchases are also frequently ap-plied especially in promotional activities. The mentioned applications are evaluated as im-portant developments in reaching developed country standards in agricultural credit sec-tor. However, it is necessary to emphasize the importance of the holistic organization and to determine the strategies accordingly.

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AL, U., (2005). Hizmet Pazarlaması,

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BDDK,(2015a).Fintürk, http://ebulten.bddk.org.tr/finturk, 10.11.2015

BDDK, (2015b). Financial Indicators, http://www.bddk.org.tr/WebSitesi/default.aspx, 10.11.2015

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