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\title{Are Brand Value Reports Compatible with Financial Reports?}
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             \author[1]{Murat  Akyildiz}

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\date{\small \em Received: 6 December 2019 Accepted: 31 December 2019 Published: 15 January 2020}

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\begin{abstract}
        


The purpose of this paper is to uncover whether brand value reports published by brand valuation organizations are compatible with the financial reports and whether brand value contributes to profitability and financial performance. For this purpose, four panel data model were built up to investigate the impact of brand value on profitability and performance, The data belongs to food companies which are among Turkey's top 100 listed brands. The data are collected from the brand valuation report and financial statements published between the years 2008-2018. Results of analysis indicate that there is no relationship between brand value and financial performance. However, the relations between brand value and profitability ratios are significant. This paper conclude that financial statements and brand valuation reports are not sufficiently compatible with each other. Additionally, this paper suggests that Turkish firms should try to increase their brand strengths.

\end{abstract}


\keywords{brand value, financial performance, brand equity, panel data. TOPSIS.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
lthough the origin of the brand dates back 1500s BC, it has reached its current meaning after industrial revolution \hyperref[b27]{(Perry \& Wisnom, 2003)}. The reason why the brand has gained importance day by day is the assumption that strong brands create value added for companies  {\ref (Kriegbaum, 1998;}\hyperref[b16]{Kalicanin et al., 2015)}. That assumption has promoted many companies to increase their financial performances via brand. This trend has also led to a rise in the importance of brand value concept, which enables managers to compare their competitors.\par
Despite different findings, it is prevalent accepted that the brand value contributes positively to all activities of the company by providing status (O'Cast and Frost, 2002) and reducing the importance of price \hyperref[b32]{(Stanton and Furrel, 1987)} as well as creating customer loyalty  {\ref (Pride and Ferrel,1991)}. In a sense, brand value itself is a kind of performance measure.\par
Therefore, brand value attracts attention of not only company managers but also of many stakeholders such as investors and credit corporations. This interest has caused the establishment of various brand valuation companies that aim to guide users' decision-making. Including "Interbrand" valuation firm which was founded in 1974 as the first one, Millward Brown and Brand Finance companies are considered among the most important ones \hyperref[b13]{(Haig and ?lgüner, 2015)}. Today, majority of investors have been taking the reports published by brand valuation companies into account to invest.\par
However, each of these companies adopts different valuation methods and accordingly they may calculate brand value differently. Hence, one of the most important supporting resources for investors' decisionmaking is the financial statements of companies.\par
Although, brand valuation reports include the information in financial statements, they contain data based on subjective criteria and estimations. Yet financial statements indicate only the realized financial structure of the firms preceding year. However, in the long-run, the increase or decrease in the brand value is expected to reflect on financial performance and profitability. In other words, contribution of brand value to financial performance and profitability requires both to be compatible with each other. Otherwise, inconsistency between them needs questioning.\par
To date, considerable amount of research has dealt with testing the assumption that brand value contributes to financial performance. Most of these studies tend to measure the relationship between brand equity and financial performance \hyperref[b4]{(Barth et al., 1998}; Abratt, R, \& Bick, G., 2003; \hyperref[b18]{Kim, et al., 2005;} Verbeeten \& Vijn, 2010; \hyperref[b22]{Liu et al., 2017)}. On the other hand, there are also studies using the term brand value although they employ the brand equity measurement as in the study by  {\ref Yeung and Ramasamy (2007)}.\par
However, particularly in Turkey, the amount of research using brand value published by consulting firms is still very limited. From this point of view, the purpose of this paper is to determine whether brand values published by brand valuation organizations are associated with the financial reporting system and whether brand value contribute to profitability and financial performance. Moreover, this paper, which provides an idea about the reliability level of the reports in relation to financial statements and brand value may provide invaluable insight to investors and brand valuation organizations. It also contributes to the relevant literature. 
\section[{II. The Measurement Methods of Brand Equity and Brand Value}]{II. The Measurement Methods of Brand Equity and Brand Value}\par
Brand equity (BE) briefly can define as the set of values created in consumers' minds because of comparing the brand name, symbols and connotations of the products offered by the company with competitor brands  {\ref (Tiwari, 2010)}. Research measuring brand equity uses non-monetary methods. Therefore, studies measuring brand equity aim to measure what consumers' attitudes towards brand dimensions and how they perceive them. For example, \hyperref[b0]{Aaker (1991}\hyperref[b1]{Aaker ( , 1996) )} measures brand equity with dimensions such as brand awareness, brand connotation, perceived quality, and other brand assets (patents, trademarks, etc.).\par
Brand value (BV), on the other hand, is the embodied form of brand equity and expresses the monetary value of the brand. Tiwari (2010) defines brand value as the sale or replacement value of the brand. Research that measures brand value uses monetary methods. However, there are many monetary measurement methods such as cost based, -market value, licensing, price-premium  {\ref (Kriegbaum, 1998)}. Brand valuation companies use a mixed method that includes monetary and non-monetary approaches to calculate brand value. This paper, only explains Brand Finance's brand valuation method because it is data source. 
\section[{III. Brand Valuation Method of Brand Finance}]{III. Brand Valuation Method of Brand Finance}\par
Brand Finance is an England based consulting firm and has been publishing the most valuable 100 brands in Turkey since 2008. We may summaries the method it used as follows:\par
Brand Finance defines the brand value as the part of the brand contribution that is able to transfer by means of sale or license. Using a mixed method, Brand Finance bases on the brand strength index for brand valuation. Brand strength consists of brand investments, brand capital and brand performance dimensions. These dimensions, which consist of tangible and intangible qualities, are evaluated over 100 points. Brand Finance uses it as brand strength score.\par
Later, Brand Finance applies the calculated brand strength score to the copyright payment range. The Royalty payment method bases on the assumption that a company does not own brand or licenses its brand from another company. Royalty payment interval differs from sector to sector within the frame of existing license agreements. For instance, in the case brand strength score is 75, in a sector where royalty payment interval is 1-5 percent, royalty payment ratio is 4 percent. Next, company applies revenues estimated the calculated royalty payment ratio to be obtained in the following years. In the last stage, it obtains net brand value by discounting proprietary revenue after tax \hyperref[b13]{(Haig \& ?lgüner, 2015)}.\par
IV. 
\section[{Measurement of Financial Performance}]{Measurement of Financial Performance}\par
The researchers examining the relationship between the monetary value of the brand and financial performance adopt different financial measurement method. For example, \hyperref[b28]{Rasti and Gharibvand (2013)} prefer book value and shareholder value as financial performance criteria. \hyperref[b39]{Yeung \& Ramasamy (2008)} as well as Arora \& Chaudhary (2016) adopt performance criteria such as return on investment-ROI, return on asset -ROA, gross profit margin -GPM, net margin -NM and pretax margin -PM. In addition some researchers adopt performance criteria such as economic value added-EVA, return on sale-ROS and cash flow return on Investment-CFRI (Yükçü and Ata?an, 2010; Werbeeten and Win, 2010).\par
As a result, it is possible to say that a common consensus has not been reached, although it has been debated for years how to measure the financial performance of businesses. \hyperref[b19]{Knight (1998)} classifies the methods used for measuring financial performance as income-based, cash-based, return-based and valuebased criterions. It is claimed that each of these methods has weaknesses as well as strengths (Young \& O'Byrne, 2001;. \hyperref[b30]{Rogerson, 1997;} {\ref Ottosan \& Weissenrieder, 1996)}.\par
Each new method proposed for financial performance measurement is the result of new requirements that emerge over time. The method chosen may vary depending on how the concept of performance is interpreted and whose benefit is a priority. For example, traditional methods focus on company profitability whereas value based methods focus on shareholder profitability.\par
As Buveneswari and Venkatesh (2013) point out, financial performance should be considered not only as a measure of how much revenue a company generates from operating activities, but also as a measure of how it uses its resources and how good its financial health is. In this framework, the present study considers both long-term financial health of companies and financial ratios that show their profitability. Aforementioned financial ratios are as follows (Table \hyperref[tab_0]{I}).  
\section[{Table I Financial ratios}]{Table I Financial ratios} 
\section[{Topsis Methodology}]{Topsis Methodology}\par
It is Hwang and Yoon those that proposed the TOPSIS method for the first time. \hyperref[b8]{(Cheng-RU et al. 2008}).The standard TOPSIS method attempts to choose alternatives that simultaneously have the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. The positive ideal solution maximizes the benefit criteria and minimizes the cost criteria, whereas the negative ideal solution maximizes the cost criteria and minimizes the benefit criteria. TOPSIS makes full use of attribute information, provides a cardinal ranking of alternatives and does not require attribute preferences to be independent. To apply this technique, attribute values must be numeric, monotonically increasing or decreasing, and have commensurable units \hyperref[b37]{(Wang \& Elhag, 2006;}\hyperref[b43]{Zavadskas et al., 2016)}. The TOPSIS method includes a six-step solution process \hyperref[b20]{(Kobry?, 2016)}. 
\section[{Step1: Creation of a decision matrix}]{Step1: Creation of a decision matrix}\par
The lines of the decision matrix A indicate the decision points, and the columns indicate the evaluation factors used for decision-making. Matrix A is defined as the initial matrix and is illustrated as follows.\par
A=?? 11 ?? 12 ? ?? 1?? ?? 21 ?? 22?. ?? 2?? ?. ?? ?? ?.\par
??. 
\section[{??}]{??} 
\section[{?? ??1 ?? ??2?. ?? ????}]{?? ??1 ?? ??2?. ?? ????}\par
In the ?? ???? matrix, "m" represents the number of decision points and "n" represents the number of evaluation factors. 
\section[{Step 2: Creation of a normalized decision matrix}]{Step 2: Creation of a normalized decision matrix}\par
The normalized " r" matrix obtained from matrix A is calculated using the following formula.?? ???? = ?? ???? ?? ?? ???? 2 ?? ??=1 i= 1,?.,m j=1,?.,n\par
Step 3: Creation of a weighted normalized decision matrix First, the weight of the evaluation factors is determined (?? ?? ). Then the elements in each column of the matrix"r" are multiplied by the value"?? ?? " and matrix V is generated. 
\section[{Step 4: Indication of the positive and negative-ideal solution}]{Step 4: Indication of the positive and negative-ideal solution}\par
In the V matrix, the maximum and minimum values of rows and columns are determined.?? + = \{?? 1 + , ?? 2 + ? ? ? ? , ?? ?? + \} maximum values in each column ?? ? = \{?? 1 ? , ?? 2 ? ? ? ? ? , ?? ?? ? \} 
\section[{minimum values in each column}]{minimum values in each column} 
\section[{Step 5: Calculation of distance of each alternative to positive and negative ideal solution points}]{Step 5: Calculation of distance of each alternative to positive and negative ideal solution points}\par
Maximum-minimum points and distances to ideal points are calculated by the following formulas.?? 1 + = ? ? (?? ???? ? ?? ?? =1 ?? ?? + ) 2 i=1, 2..., m ?? 1 ? = ? ? (?? ???? ? ?? ?? =1 ?? ?? ? ) 2 i=1, 2?, m\par
The numbers of ?? 1 + ???? ?? 1 ? to be calculated are the number of decision points.\par
Step 6: Calculation of the relative closeness of the decision points to the ideal solutionC i + = S i ? S i ? +S i + i=1, 2?, m\par
Volume XX Issue VII Version I 
\section[{( H )}]{( H )}\par
Point ?? ?? + is inthe range of 0 ? ?? ?? + ?1 and indicates proximity to the ideal solution VI. 
\section[{Methodology a) Sampling and data}]{Methodology a) Sampling and data}\par
The data belongs to food companies which are the among Turkey's top 100 listed brands. The data are obtained from the annual brand valuation reports and financial statements published between 2008 and 2018.The companies included in the research are selected based on three basic criteria.1) To operate in the same industry 2) to be within the brand valuation report during the research period 3) to reach the financial statements of the companies on Public Disclosure Platform (PDP). I reduced financial ratios including the period 2008-2018 into a single ratio by using TOPSIS method. The table below displays the brand values published by Brand Finance by years. (Table \hyperref[tab_1]{II}). LBV is an independent variable in all models. LFP, LROS, LROA and LROE are dependent variables for each model. In models where ?? 0 symbolizes constant parameter, "?" is slope parameter; "U" is error term, "i" subscript indicates units (firms) and "t" subscript indicates time (i.e. years).I used software of the stata15 and e-views10 for the statistical analyses i. 
\section[{Cross sectional dependency and unit root tests}]{Cross sectional dependency and unit root tests}\par
Because of the fact that in panel data analyses non-stationary series lead to spurious regression the first step to be taken is to determine whether the series is stationary or not. The relevant literature suggests firstgeneration unit root tests, if not cross-sectional dependency, otherwise second-generation unit root tests.  {\ref (Tato?lu, 2013b)}. According to Pesaran CD test, there is not cross sectional dependency except for LFP and LBV Therefore, I preferred second-generation unit root tests for the LFP, LBV and first generation unit root test for the others.\par
Second-generation unit root tests consisting of three groups aim to reduce the effect of correlation between units. Even though the first group of tests reduces the correlation between units, it may not be applicable in some cases. MADF (Multivariate Augmented Dickey Fuller), which is one of the second group tests requires T> N condition while SURADF (seemingly unrelated regressions augmented Dickey Fuller) is considered more suitable for time series rather than panel data  {\ref (Tato?lu, 2018)}. The tests in the third group eliminate the correlation between the units by estimating the factor loads. I preferred the secondgeneration Pesaran CD unit root, which is preferred for non-stationary series and low number of units. The second and first generation test results of unit root are as follows (Table VI and VII). Stationarity refers to the resistance of a variable's series to the shocks it has been exposed to over the long term. Temporary shock effects imply the stationarity of the series while the permanence of shock effects indicates that the series has lost its stationarity. In other words, its parameters such as arithmetic mean and variance of the series do not change in the long term despite the shocks. The relationship between nonstationary variables may cause spurious regression. To overcome this problem of non-stationarity an econometric analysis of panel data has increasingly moved towards the cointegration model. Nevertheless, traditional Engle and Granger (1987) cointegration analysis cannot be applied if the stationarity level of the series is different (i.e., X series I (0) and Y series I (1).\par
In the first model, although independent variable is stationary, dependent variable (LFP) is not stationary. However, when the first differences method is conducted to LFP series, the series become stationary (Table \hyperref[tab_5]{VIII} ARDL model could be expressed together with error correction models. Error correction models may be grouped into two main categories as first and second generation. Dynamic fixed effects (dfe), pooled mean group estimator (pmg), mean group estimators (mg), and random coefficient model (rcm). General characteristic of first generation estimators is that they do not consider inter-unit correlation. Conversely, second-generation estimators such as common correlated effects (cce), augmented mean group (amg) and dynamic common correlated effects (dcce) consider inter-unit correlation. Additionally, though some of first generation estimators take homogeneity and some heterogeneity into consideration all of secondgeneration estimators consider heterogeneity  {\ref (Tato?lu, 2018;} {\ref 272;} {\ref 373)}. Therefore, determining the most suitable estimation model requires conducting homogeneity and cross section dependency tests. Swammy test results point out that the model established with LFP and LVB variables is heterogeneous \{Chi (2)10=141.02; p=0.000). LM test shows that the remains in model with aforementioned variables include inter-unit correlation (LM-71.32; p=0.000). These results point out that the best estimators for the model-1 are second-generation error correction models.\par
Augmented mean group estimator (AMG), one of the second-generation error correction models is estimated with first difference method by adding T-1 number time dummy variable in the first stage. In the second stage, the estimations made in the previous stage are added to error correction model established for each unit. In the third stage, the AMG estimator adapts the ARDL model proposed by Pesaran and Smith to the MG model. In the third stage, the AMG estimator uses the following estimator by adapting the ARDL model proposed by Pesaran and Smith to the MG model  {\ref (Tato?lu, 2018: 279-303)}. So model-1 can be written as follows. Table \hyperref[tab_5]{VIII}: 
\section[{Volume XX Issue VII Version I}]{Volume XX Issue VII Version I}??????? ???? =? ?? (?????? ???? ?1 -? ?? ? ?????? ???? ?1 )+? ?? =1 ???1 ? ?? * ??????? ???? ??? +? ?? =1 ???1 ? ?? * ??????? ???? ??? +?? ???? +? ???? ? = (1 ? ? ?? =1 ?? ? ?? ), ?=? ?? =1 ?? ?? ?? /(1 ? ? ?? ); ? ?? * = ?? ?? =?? +1 ?? ? ?? ; ? ?? * = ?? ?? =?? +1 ?? ? ??\par
Here "?" represents long period," ?" and "?" are represent short period and "?" is error correction parameter. 
\section[{b. Stationary panel data models and model selection}]{b. Stationary panel data models and model selection}\par
Literature suggests either fixed effects or random effects model in the stationary panel data models, if there is unit or time effect. Otherwise, it suggests classic model. Literature suggests that random effects model should be preferred for estimations conducted for a large mass. Panels with no unit and time effect are defined as homogeneous and others as heterogeneous panels  {\ref (Tato?lu, 2013a)}.\par
I conducted F" test for unit effect and LR (like hood ratio) test for time effect. I also performed Hausman test to choose between fixed effect and random effects. The table below shows test results. (Table \hyperref[tab_7]{IX}). Since the unit effect is not constant in random models, it is shown in the margin of error, not in the fixed parameter (Vit= ? ?? + ?? ???? ). Here the term ?? 0 represents constant, other ? the slope and ?? ???? represent all residual errors. 
\section[{VII.}]{VII.} 
\section[{Assumption Tests}]{Assumption Tests}\par
Consistent estimates depend on whether selected models meet assumptions. General assumptions about our preferred panel data models are summarized below. 
\section[{a) Distribution of error terms}]{a) Distribution of error terms}\par
The null hypothesis states that Uit means are equal to zero. Results of Jarqua Bera tests are as follows (Table \hyperref[tab_8]{X}).  
\section[{b) Heteroscedasticity}]{b) Heteroscedasticity}\par
In panel data models, it is expected from error term to be homoscedastic within unit and inter-units. We employed Breush Pagan Lagrange Multiplier test to examine heteroscedasticity in random effects model and altered Wald test for fixed effect model. Test results Autocorrelation means that there is a significant relationship between the unit values of successive error terms. Annual or seasonal period difference between error terms shows the degree of autocorrelation. In case where there is a significant relation between t period errors and t-1 period in an annual time series, there is first order autocorrelation. We performed LM test for random effect model and DW test for fixed effect model to understand whether there is an autocorrelation. Test results indicate the presence of autocorrelation for all models exclusive of forth model (Table \hyperref[tab_10]{XII}). One of the general assumptions of panel data models is that error terms are not correlated according to the units. Literature suggests Pesaran CD test to investigate cross section dependency for both random and fixed effect models in cases of T?N . Test results are as follows (  
\section[{e) Robust estimator for deviation from the assumption}]{e) Robust estimator for deviation from the assumption}\par
In cases where there is at least one of the heteroscedasticity, autocorrelation or correlation between the units, literature suggests correction of standard errors or using robust estimators without changing parameter . Robust estimators conduct the corrections needed when the panel data models do not meet the assumptions. 
\section[{VIII.}]{VIII.} 
\section[{Findings a) Effects of brand value on financial performance}]{Findings a) Effects of brand value on financial performance}\par
Estimation results of AMG, which is one of the second-generation error correction models and developed to correct heteroscedasticity and correlation between units as is below (Table \hyperref[tab_12]{XIV}). Wald test indicates that model is significant (Chi2 (4) =36.97; p=0.000). Error correction parameter of the model is negative and significant (-1.019). However, it seems that there is no significant relationship between financial performance and brand value in both the short and the long period 
\section[{b) Effects of brand value on profitability rates}]{b) Effects of brand value on profitability rates}\par
Second, third and fourth models aim to examine the effect of LBV on LROS, LROA and LROE. Although third and fourth models do not include cross section dependency (correlation between units), they have heteroscedasticity and autocorrelation. The test results of all three models indicate that the relationship between dependent and independent variables are significant. 
\section[{IX.}]{IX.} 
\section[{Discussion}]{Discussion}\par
Although there are many studies examining the relationships between brand value and financial performance, most of them approach the issue from the different point of views. If we ignore research that measures the relationship between brand equity and financial performance, we can say that the main source of discrepancy at issue is related to the measuring of financial performance. This distinctness makes it difficult to compare directly the results of researches regarding the financial performance. For instance, Yeung and Ramasamy accept market returns and stock market as external financial performance measure (2008) while they accept ROA, ROE and ROI as an internal performance measure. Another example is article of  {\ref Rasti and Gharibvan (2013)}. The authors adopt EBIT (Earnings before interest and tax) and dividend yield as financial performance measure. The results of the aforementioned researches indicate that the brand value relation with the EBIT and stock market, but not to the dividend income and market return.\par
Contrary to the research results pointing out the relationship between brand value and financial performance, the results indicating the relationship between brand value and profitability rates are directly comparable. On the other hand, some of the directly comparable studies support our research results while others do not. For example, the research results of Ceylan (2019) as well as Yeung and Ramasamy (2008) support our research while Chaudhary's (2016) research results do not.\par
The results of the research conducted by \hyperref[b39]{Yeung and Ramasamy (2008)} indicate that the brand value had a positive effect on the internal performance criteria such as ROI, ROA, GPS and PM. \hyperref[b9]{Ceylan (2019)} concludes that the brand value had positive effects on the profitability of the assets. However, she calculates the brand value using the Hirose model. Results of the study in banking sector by \hyperref[b3]{Arora and Chaudhary (2016)} indicate that brand value relation to ROA and ROE however, this relationship is negative. The researchers interpret this result as the fact that the expenses made to increase brand value have reduced the return. However, results of this study point out that brand value positively affects profitability rates (ROS-ROA and ROE).\par
Normally, brand value is expected to make a positive contribution to financial performance and profitability in every condition, as they greatly reduce price flexibility and isolate the competition strategies of competitors. Whereas the results of our research indicate that "brand-value" does not affect financial performance but it affects profitability rates positively. This result may result from the financial rates, which we use to calculate financial performance. When we consider that some of the rates used for financial performance are affected not only by brand strengths but also by management skills, it is possible to say that the result is reasonable. Contrary to financial performance, it is more likely that strong brands affect profitability rates because of generating more profitability of high price.\par
The profitability rate that the brand value contributes the most is ROE. One percent increase in brand value contributes to the ROE at the level of 0.41 percent. These rates are 0.22 percent for ROA and 0.18 percent for ROS. Although strong brands are expected to contribute most to the profitability of sales, it is quite interesting that they contribute to the lowest level.\par
Even if the positive effect of the increase in brand value on the sales is statistically significant, it may be interpreted that the contribution level is extremely low. The reason for this is the compulsory expenditures that companies make to protect the brand strength besides creating a brand. Such a result is not compatible with the importance attributed to the brand. 
\section[{X.}]{X.} 
\section[{Conclusion}]{Conclusion}\par
What conclusion should we draw from this study? In my opinion, I can say this study points out two possible problems. The first possible problem is that Turkish companies do not have a strong brand. For this reason, it is possible to say that Turkish firms need to put much more effort to increase their brand values, which may also enhance their competitive strength.\par
The second possible problem is possibility of investor losing trust in the brand valuation reports and financial statements. The reason is that brand valuation reports and financial statements are not compatible with each other. Where as it is expected that the reports at issue associate with each other's especially in the long term. Otherwise, investors may distrust about the financial statements and brand valuation reports. I can say that the problem is in the financial statements probably, when we consider that the brand valuation companies use the information in the financial statements.\par
In fact, it is known that accounting manipulations in corporate "financial reports" are performed in all countries and in every age. In other words, many companies manipulate their financial reports to some extent to achieve their "budget" goals and in order to show that managers are successful. Additionally, many companies manipulate to some reasons such as greed, desperation, immorality or tax evasion. \hyperref[b5]{(Bhasin, 2016)}. Generally, such manipulative behaviors are prevented by accounting standards. When evaluated in this context; the results also may be interpreted as a sign that accounting standards should still be developed XI. 
\section[{Limitations and Future Research}]{Limitations and Future Research}\par
The findings and insight gained from this research are valid and significant. However, some limitations cannot be overlooked. First, the sample size involved in this research is small because the number of companies meeting the selection criteria as explained in the sampling method is limited. Second, the time dimension is not sufficient for some of the error correction models. Third, the numerator and denominator, which show debt ratio and leverage ratio were inverted to ensure that the rates used to calculate financial performance are in the same direction (e.g., "Assets/Debts" instead of "Debts/Assets"). Finally, even if the data is subjected to logarithmic transformation, LFP and LROS are not normally distributed. Therefore, the results cannot be generalized. Future research may assess these models for the companies other than food industry in order to explore generalizability of the findings. \begin{figure}[htbp]
\noindent\textbf{I} \par 
\begin{longtable}{P{0.5782527881040892\textwidth}P{0.039498141263940516\textwidth}P{0.23224907063197026\textwidth}}
Solvency\tabcellsep CR\tabcellsep Current Ratio\\
\tabcellsep AR\tabcellsep Acid Ratio\\
Turnover\tabcellsep STR\tabcellsep Stock Turnover Ratio\\
\tabcellsep AT\tabcellsep Asset Turnover)\\
\tabcellsep FAT\tabcellsep Fixed Asset Turnover\\
Financial structure\tabcellsep LR\tabcellsep Leverage Ratio\\
\tabcellsep DR\tabcellsep Dept Ratio\\
Profitability\tabcellsep ROE\tabcellsep Return on Equity\\
\tabcellsep ROA\tabcellsep Return on Asset\\
\tabcellsep ROS\tabcellsep Return on Sale\\
\multicolumn{2}{l}{As in other similar studies measuring financial}\tabcellsep \\
\multicolumn{2}{l}{performance by using multiple financial ratios, the}\tabcellsep \\
\multicolumn{2}{l}{present study also employs the TOPSIS (Technique for}\tabcellsep \\
\multicolumn{2}{l}{Order Preference by Similarity to Ideal Solution) method}\tabcellsep \\
\multicolumn{2}{l}{(Inani and Gupta, 2017; Zavadskas, et al.. ;2016; Fenk}\tabcellsep \\
\multicolumn{2}{l}{and Wang, 2000; Yükçü and Ata?an, 2010; Yu-Jie, W.}\tabcellsep \\
2008).\tabcellsep \tabcellsep \\
V.\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table I :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{II} \par 
\begin{longtable}{P{0.35251937984496123\textwidth}P{0.07248062015503876\textwidth}P{0.07083333333333333\textwidth}P{0.17131782945736435\textwidth}P{0.05436046511627907\textwidth}P{0.060949612403100775\textwidth}P{0.06753875968992248\textwidth}}
YILLLAR/FIRMA\tabcellsep MIGROS\tabcellsep BIM\tabcellsep TAT\tabcellsep Kent\tabcellsep BANVIT\tabcellsep ULKER\\
2008\tabcellsep 735\tabcellsep 582\tabcellsep 75\tabcellsep 64\tabcellsep 75\tabcellsep 193\\
2009\tabcellsep 1213\tabcellsep 688\tabcellsep 102\tabcellsep 76\tabcellsep 101\tabcellsep 331\\
2010\tabcellsep 1234\tabcellsep 923\tabcellsep 131\tabcellsep 99\tabcellsep 172\tabcellsep 364\\
2011\tabcellsep 812\tabcellsep 1182\tabcellsep 92\tabcellsep 68\tabcellsep 118\tabcellsep 385\\
2012\tabcellsep 653\tabcellsep 965\tabcellsep 64\tabcellsep 103\tabcellsep 136\tabcellsep 452\\
2013\tabcellsep 680\tabcellsep 1395\tabcellsep 77\tabcellsep 111\tabcellsep 141\tabcellsep 657\\
2014\tabcellsep 610\tabcellsep 1120\tabcellsep 60\tabcellsep 106\tabcellsep 125\tabcellsep 564\\
2015\tabcellsep 547\tabcellsep 1387\tabcellsep 69\tabcellsep 107\tabcellsep 131\tabcellsep 745\\
2016\tabcellsep 512\tabcellsep 668\tabcellsep 82\tabcellsep 111\tabcellsep 88\tabcellsep 522\\
2017\tabcellsep 531\tabcellsep 742\tabcellsep 104\tabcellsep 55\tabcellsep 52\tabcellsep 647\\
2018\tabcellsep 638\tabcellsep 584\tabcellsep 78\tabcellsep 66\tabcellsep 89\tabcellsep 616\\
2019\tabcellsep 235\tabcellsep 308\tabcellsep 38\tabcellsep 41\tabcellsep 37\tabcellsep 401\\
\multicolumn{3}{l}{I formed a total of 11-decision matrix belonging}\tabcellsep \multicolumn{4}{l}{concerns, only the matrix displaying the year 2008 is}\\
\multicolumn{3}{l}{to six firms among 2008-2018, by using the profitability}\tabcellsep \multicolumn{2}{l}{included (Table III).}\tabcellsep \tabcellsep \\
\multicolumn{3}{l}{ratios (a decision matrix per year). Due to space}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table II :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{III} \par 
\begin{longtable}{P{0.14284968684759916\textwidth}P{0.0709812108559499\textwidth}P{0.03637787056367432\textwidth}P{0.11445720250521921\textwidth}P{0.0842901878914405\textwidth}P{0.0842901878914405\textwidth}P{0.029279749478079334\textwidth}P{0.08517745302713987\textwidth}P{0.087839248434238\textwidth}P{0.08606471816283924\textwidth}P{0.028392484342379953\textwidth}}
\tabcellsep 2013\tabcellsep \tabcellsep 1.756\tabcellsep 3.465\tabcellsep 3.555\tabcellsep \tabcellsep 5.646\tabcellsep 2.726\tabcellsep 3.467\tabcellsep \\
\tabcellsep 2014\tabcellsep \tabcellsep 1.914\tabcellsep 3.595\tabcellsep 4.606\tabcellsep \tabcellsep 5.714\tabcellsep 2.816\tabcellsep 3.733\tabcellsep \\
\tabcellsep 2015\tabcellsep \tabcellsep 1.787\tabcellsep 4.823\tabcellsep 7.555\tabcellsep \tabcellsep 8.453\tabcellsep 3.847\tabcellsep 4.972\tabcellsep \\
\tabcellsep 2016\tabcellsep \tabcellsep 1.335\tabcellsep 4.254\tabcellsep 7.27\tabcellsep \tabcellsep 5.93\tabcellsep 3.892\tabcellsep 4.018\tabcellsep \\
\tabcellsep 2017\tabcellsep \tabcellsep 1.724\tabcellsep 3.406\tabcellsep 4.86\tabcellsep \tabcellsep 5.134\tabcellsep 3.583\tabcellsep 3.037\tabcellsep \\
\tabcellsep 2018\tabcellsep \tabcellsep 1.808\tabcellsep 2.669\tabcellsep 1.473\tabcellsep \tabcellsep 1.905\tabcellsep 2.043\tabcellsep 1.899\tabcellsep \\
\tabcellsep 2019\tabcellsep \tabcellsep 1.940\tabcellsep 2.665\tabcellsep 1.469\tabcellsep \tabcellsep 1.702\tabcellsep 2.085\tabcellsep 1.887\tabcellsep \\
\multicolumn{3}{l}{b) Developing econometric model}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
2008\tabcellsep CR\tabcellsep STR\tabcellsep AR\tabcellsep AT\tabcellsep FAT\tabcellsep ROS\tabcellsep ROE\tabcellsep ROA\tabcellsep DR\tabcellsep LR\\
Migros\tabcellsep 1.354\tabcellsep 6.946\tabcellsep 0.948\tabcellsep 0.593\tabcellsep 3.045\tabcellsep 0.095\tabcellsep 0.076\tabcellsep 0.056\tabcellsep 1.284\tabcellsep 1.745\\
Bim\tabcellsep 0.761\tabcellsep 20.151\tabcellsep 0.346\tabcellsep 5.057\tabcellsep 10.496\tabcellsep 0.035\tabcellsep 0.564\tabcellsep 0.179\tabcellsep 0.465\tabcellsep 1.465\\
Tat\tabcellsep 0.973\tabcellsep 5.206\tabcellsep 0.582\tabcellsep 1.195\tabcellsep 4.033\tabcellsep 0.020\tabcellsep 0.078\tabcellsep 0.024\tabcellsep 0.431\tabcellsep 1.431\\
Kent\tabcellsep 0.797\tabcellsep 7.228\tabcellsep 0.575\tabcellsep 0.991\tabcellsep 2.844\tabcellsep 0.052\tabcellsep 0.160\tabcellsep 0.051\tabcellsep 0.469\tabcellsep 1.469\\
Banvit\tabcellsep 1.354\tabcellsep 11.038\tabcellsep 1.011\tabcellsep 1.591\tabcellsep 5.085\tabcellsep 0.005\tabcellsep 0.035\tabcellsep 0.007\tabcellsep 0.266\tabcellsep 1.266\\
Ülker\tabcellsep 1.245\tabcellsep 2.399\tabcellsep 1.089\tabcellsep 0.151\tabcellsep 1.077\tabcellsep 0.367\tabcellsep 0.149\tabcellsep 0.055\tabcellsep 0.590\tabcellsep 1.590\\
\multicolumn{10}{l}{Table IV indicates the financial performance scores calculated for 11 years using the TOPSIS method}\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{5}{l}{Table IV: Financial performance scores}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{MIGROS}\tabcellsep BIM\tabcellsep TAT\tabcellsep \tabcellsep KENT\tabcellsep BANVIT\tabcellsep ULKER\tabcellsep \\
\tabcellsep 2008\tabcellsep \tabcellsep 2.714\tabcellsep 5.062\tabcellsep 4.965\tabcellsep \tabcellsep 5.077\tabcellsep 4.496\tabcellsep 5.422\tabcellsep \\
\tabcellsep 2009\tabcellsep \tabcellsep 1.874\tabcellsep 3.878\tabcellsep 3.956\tabcellsep \tabcellsep 5.798\tabcellsep 3.546\tabcellsep 4.318\tabcellsep \\
\tabcellsep 2010\tabcellsep \tabcellsep 1.92\tabcellsep 3.873\tabcellsep 4.159\tabcellsep \tabcellsep 5.355\tabcellsep 3.895\tabcellsep 4.947\tabcellsep \\
\tabcellsep 2011\tabcellsep \tabcellsep 2.092\tabcellsep 4.757\tabcellsep 4.974\tabcellsep \tabcellsep 6.573\tabcellsep 4.069\tabcellsep 5.127\tabcellsep \\
\tabcellsep 2012\tabcellsep \tabcellsep 2.047\tabcellsep 4.457\tabcellsep 4.663\tabcellsep \tabcellsep 7.548\tabcellsep 3.717\tabcellsep 4.391\tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table III :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{V} \par 
\begin{longtable}{P{0.25575221238938056\textwidth}P{0.36106194690265486\textwidth}P{0.23318584070796458\textwidth}}
Data set\tabcellsep Pesaran CD test\tabcellsep p\\
LFP\tabcellsep 8.4511\tabcellsep 0.0000\\
LBV\tabcellsep 5.7085\tabcellsep 0.0000\\
LROS\tabcellsep -1.6223\tabcellsep 0.1047\\
LROA\tabcellsep -1.1075\tabcellsep 0.2681\\
LROE\tabcellsep -1.4374\tabcellsep 0.1506\\
* p<0.05\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table V :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{VI} \par 
\begin{longtable}{P{0.05909090909090909\textwidth}P{0.07727272727272727\textwidth}P{0.07272727272727272\textwidth}P{0.06818181818181818\textwidth}P{0.06818181818181818\textwidth}P{0.08636363636363636\textwidth}P{0.05909090909090909\textwidth}P{0.3590909090909091\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 59\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Volume XX Issue VII Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ( H )\\
\tabcellsep t-bar\tabcellsep cv10\tabcellsep cv5\tabcellsep cv1\tabcellsep Z[t] bar\tabcellsep p\tabcellsep \\
LFP\tabcellsep -1.381\tabcellsep -2.220\tabcellsep -2.370\tabcellsep -2.260\tabcellsep 0.790\tabcellsep 0.770*\tabcellsep non-stationary series\\
LBV\tabcellsep -1.843\tabcellsep -2.220\tabcellsep -2.370\tabcellsep -2.260\tabcellsep -0.299\tabcellsep 0.383*\tabcellsep non -stationary series\\
*P>0.05\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: T]} 
\caption{\label{tab_4}Table VI :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{VII} \par 
\begin{longtable}{P{0.38779840848806363\textwidth}P{0.0856763925729443\textwidth}P{0.0676392572944297\textwidth}P{0.09018567639257295\textwidth}P{0.06538461538461539\textwidth}P{0.08793103448275862\textwidth}P{0.06538461538461539\textwidth}}
\tabcellsep \multicolumn{2}{l}{LROS}\tabcellsep LROA\tabcellsep \tabcellsep \multicolumn{2}{l}{LROE}\\
\tabcellsep statisitic\tabcellsep p\tabcellsep statisitic\tabcellsep p\tabcellsep statisitic\tabcellsep p\\
Levin Li Chu (t)\tabcellsep -5.652\tabcellsep 0.0000*\tabcellsep -9.9634\tabcellsep 0.0000*\tabcellsep -10.352\tabcellsep 0.0000*\\
Im pesaran Shin (W)\tabcellsep -3.606\tabcellsep 0.0002*\tabcellsep -7.4259\tabcellsep 0.0000*\tabcellsep -7.781\tabcellsep 0.0000*\\
ADF -Fisher ( chi squre)\tabcellsep 34.138\tabcellsep 0.0006 *\tabcellsep 63.998\tabcellsep 0.0000*\tabcellsep 63.998\tabcellsep 0.0000*\\
PP Fisher (chi square)\tabcellsep 34.001\tabcellsep 0.0007*\tabcellsep 70.034\tabcellsep 0.0000*\tabcellsep 73.829\tabcellsep 0.0000*\\
P<0.05;\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
ii. Panel data model selection\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{a. Panel data model selection in non-stationary series}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table VII :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.7046468401486988\textwidth}P{0.10269516728624535\textwidth}P{0.02053903345724907\textwidth}P{0.014219330855018586\textwidth}P{0.007899628252788104\textwidth}}
\tabcellsep \multicolumn{3}{l}{Unit root test for LFP series (First generation)}\tabcellsep \\
Series: D(LFP) Method\tabcellsep Statistic\tabcellsep Prob.**\tabcellsep sections\tabcellsep Obs\\
\multicolumn{2}{l}{Null: Unitroot (assumescommonunitrootprocess)}\tabcellsep \tabcellsep \tabcellsep \\
Levin, Lin \&Chu t*\tabcellsep -2.36571\tabcellsep 0.0090\tabcellsep 6\tabcellsep 48\\
\multicolumn{2}{l}{Pesaran Smith and Shin suggest (2001) Auto}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Regressive Distributed Lag model (ARDL) which is a}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{special type of cointegration test for cases when}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{stationarity level of series are different I(0) and I(1). While}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{cointegration tests estimate long-term relationships}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{between the variables, error correction models (ECM)}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{estimate both long term and short-term relationships.}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{IX} \par 
\begin{longtable}{P{0.05862068965517241\textwidth}P{0.09183908045977011\textwidth}P{0.2852873563218391\textwidth}P{0.2794252873563218\textwidth}P{0.13482758620689655\textwidth}}
Models\tabcellsep Test ad?\tabcellsep Null Hypotesis\tabcellsep Test ?statisti?i\tabcellsep p\\
Model -2\tabcellsep F\tabcellsep ?? 0 : ??? = 0\tabcellsep F (5,59)=14.28\tabcellsep 0.0000*\\
\tabcellsep LR\tabcellsep ?? 0 : ?? ?? = 0\tabcellsep Chi2 (01)=0.00\tabcellsep 1.000\\
\tabcellsep Hausman\tabcellsep ?? 0 : ??? = r\tabcellsep Chi2 (1)=1.50\tabcellsep 2.201(re)\\
Model -3\tabcellsep F\tabcellsep ?? 0 : ??? = 0\tabcellsep F (5,59)=4.480\tabcellsep 0.0009*\\
\tabcellsep LR\tabcellsep ?? 0 : ?? ?? = 0\tabcellsep Chi2 (01)=2.8e-14\tabcellsep 1.0000\\
\tabcellsep Hausman\tabcellsep ?? 0 : ??? = r\tabcellsep Chi2 (1)=2.98\tabcellsep 0.0840 (re)\\
Model -4\tabcellsep F\tabcellsep ?? 0 : ??? = 0\tabcellsep F (5,59)=11.98\tabcellsep 0.0000*\\
\tabcellsep LR\tabcellsep ?? 0 : ?? ?? = 0\tabcellsep Chi2 (01)=0.000\tabcellsep 1.0000\\
\tabcellsep Hausman\tabcellsep ?? 0 : ??? = r\tabcellsep Chi2 (1)=8.35\tabcellsep 0.0039(fe)\\
\tabcellsep Model -2:\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: (One way random effects model) Model -3: ?????? ???? = ?? 0?? + ?? ?? ???? ???? + ?? ???? (One way random effects model) Model -4: ?????? ???? = ?? 0?? + ?????? ???? + ?? ???? (One way fixed effects model)]} 
\caption{\label{tab_7}Table IX :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{X} \par 
\begin{longtable}{P{0.4138157894736842\textwidth}P{0.07269736842105264\textwidth}P{0.36348684210526316\textwidth}}
\multicolumn{2}{l}{Are Brand Value Reports Compatible with Financial Reports?}\tabcellsep \\
\tabcellsep \tabcellsep Year 2020\\
\tabcellsep \tabcellsep 61\\
\tabcellsep \tabcellsep Volume XX Issue VII Version I\\
\tabcellsep \tabcellsep ( H )\\
\tabcellsep \tabcellsep Global Journal of Human Social Science -\\
Modeller\tabcellsep Jarkue Bera\tabcellsep P\\
?? ???? = 0; (Model 2)\tabcellsep 0.891\tabcellsep 0.9230*\\
?? ???? = 0 (Model 3)\tabcellsep 0.746\tabcellsep 0.0580*\\
?? ???? = 0 (Model 4)\tabcellsep 3.098\tabcellsep 0.0143**\\
*P> 0,05; **p>0.01\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep © 2020 Global Journals\end{longtable} \par
 
\caption{\label{tab_8}Table X :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{XI} \par 
\begin{longtable}{P{0.248068669527897\textwidth}P{0.13862660944206007\textwidth}P{0.39399141630901285\textwidth}P{0.06931330472103003\textwidth}}
Model\tabcellsep Null Hyp\tabcellsep Test Statistic\tabcellsep p\\
Model 2 (re)\tabcellsep Var(u)=0\tabcellsep Breush Pagan LM, Chibar2 = 71,84\tabcellsep 0.0000\\
Model 3 ( re)\tabcellsep Var(u)=0\tabcellsep Breush Pagan LM,. Chibar2(01)=9.61\tabcellsep 0.0010\\
Model 4 (fe)\tabcellsep ?? ?? 2 = ?? 2\tabcellsep Wald testi, Chi2 (6)= 978.04\tabcellsep 0.0000\\
* p<0,05\tabcellsep \tabcellsep \tabcellsep \\
c) Autocorrelation\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table XI :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{XII} \par 
\begin{longtable}{P{0.17484725050916497\textwidth}P{0.3929735234215886\textwidth}P{0.15580448065173116\textwidth}P{0.12637474541751528\textwidth}}
Model\tabcellsep Test ?statisiti?i\tabcellsep \tabcellsep p\\
Model 2 ( re)\tabcellsep LM (lambda=0)\tabcellsep = 24.97 Pr>chi2 (1)\tabcellsep 0.0000*\\
\tabcellsep ALM (lambda=0)\tabcellsep = 2.34 Pr>chi2 (1)\tabcellsep 0.1262\\
\tabcellsep \multicolumn{2}{l}{Joint (var(u)=0, lambda=0) =74.18 Pr>chi2 (2)}\tabcellsep 0.0000*\\
Model 3 ( re)\tabcellsep LM (lambda=0)\tabcellsep = 7.20 Pr>chi2 (1)\tabcellsep 0.0425*\\
\tabcellsep ALM (lambda=0)\tabcellsep =2.27 Pr>chi2 (1)\tabcellsep 0.1323\\
\tabcellsep \multicolumn{2}{l}{Joint (var(u)=0, lambda=0) =11.88 Pr>chi2 (2)}\tabcellsep 0.0000*\\
Model 4 (fe)\tabcellsep \multicolumn{2}{l}{Modified Bhargava et al. DW=1.89}\tabcellsep n= 66 için db=1.37\\
\tabcellsep (?f db<DW<4-db\tabcellsep No aotocorelation)\tabcellsep 1.37<1.89<2.63\\
\tabcellsep Baltagi Wu LBI= 2.09\tabcellsep \tabcellsep \\
P<0.05\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{d) Cross section dependency (Correlation among}\tabcellsep \\
units)\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_10}Table XII :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{XIIXIII} \par 
\begin{longtable}{P{0.2738888888888889\textwidth}P{0.29277777777777775\textwidth}P{0.2833333333333333\textwidth}}
Model 2\tabcellsep Model 3\tabcellsep Model 4\\
Pesaran= -1.402\tabcellsep Pesaran = -1.329\tabcellsep Pesaran: -0.748\\
P=1.839\tabcellsep P= 1,816\tabcellsep P= 1,816\end{longtable} \par
 
\caption{\label{tab_11}Table XII Table XIII :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{XIV} \par 
\begin{longtable}{P{0.1554176072234763\textwidth}P{0.0019187358916478553\textwidth}P{0.11320541760722347\textwidth}P{0.12663656884875846\textwidth}P{0.09785553047404062\textwidth}P{0.06715575620767494\textwidth}P{0.06331828442437923\textwidth}P{0.13047404063205417\textwidth}P{0.09401805869074492\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{xtmg dLFP dLBV dlLFP lLFP lLBV, aug}\\
dLFP\tabcellsep \tabcellsep Coef\tabcellsep Std Err\tabcellsep \tabcellsep z\tabcellsep p>Z\tabcellsep {}[95\% Conf. Interval]\\
dLBV\tabcellsep \tabcellsep .0578541\tabcellsep .0780104\tabcellsep \tabcellsep 0.74\tabcellsep 0.458\tabcellsep .0950435\tabcellsep .2107518\\
dlLFP\tabcellsep \tabcellsep .5636441\tabcellsep .1037567\tabcellsep \tabcellsep 5.43\tabcellsep 0.000\tabcellsep .3602847\tabcellsep .7670036\\
lLFP\tabcellsep \tabcellsep -1.018913\tabcellsep .2006249\tabcellsep \tabcellsep -5.08\tabcellsep 0.000\tabcellsep 1.412131\tabcellsep -.6256959\\
lLBV\tabcellsep \tabcellsep -.0434217\tabcellsep .1250677\tabcellsep \tabcellsep -0.35\tabcellsep 0.728\tabcellsep -.28855\tabcellsep .2117066\\
00000Rc\tabcellsep \tabcellsep 1.024834\tabcellsep .2012054\tabcellsep \tabcellsep 5.09\tabcellsep 0.000\tabcellsep .6304785\tabcellsep 1.419189\\
cons\tabcellsep \tabcellsep .8251058\tabcellsep .8490087\tabcellsep \tabcellsep 0.97\tabcellsep 0.331\tabcellsep .8389207\tabcellsep 2.489132\\
Wald chi2(4)\tabcellsep =\tabcellsep 36.97\tabcellsep \multicolumn{2}{l}{Prob > chi2}\tabcellsep = 0.0000\tabcellsep \\
\multicolumn{4}{l}{Variable 00000Rc refers to the common}\tabcellsep \multicolumn{2}{l}{dynamic process.}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_12}Table XIV :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{XV} \par 
\begin{longtable}{P{0.1265267175572519\textwidth}P{0.14923664122137403\textwidth}P{0.30388040712468195\textwidth}P{0.0010814249363867686\textwidth}P{0.051908396946564885\textwidth}P{0.04866412213740458\textwidth}P{0.11354961832061068\textwidth}P{0.055152671755725184\textwidth}}
\tabcellsep \tabcellsep \multicolumn{4}{l}{xtgls LROS LBV, i(id) t(t) panels (correlated)}\tabcellsep \\
LROS\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep \tabcellsep z\tabcellsep P>z\tabcellsep \multicolumn{2}{l}{[95\% ConfInterval]}\\
LBV\tabcellsep .1891839\tabcellsep .0310992\tabcellsep \tabcellsep 6.08\tabcellsep 0.000\tabcellsep .1282306\tabcellsep .2501371\\
\textunderscore cons\tabcellsep -4.005708\tabcellsep .1962197\tabcellsep \tabcellsep -20.41\tabcellsep 0.000\tabcellsep -4.390292\tabcellsep -3.621125\\
Wald chi2(1)\tabcellsep = 37.01\tabcellsep Prob> chi2\tabcellsep \tabcellsep = 0.0000\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{5}{l}{Table XVI: Relation between BV and ROA (Model 3)}\\
\tabcellsep \tabcellsep \multicolumn{4}{l}{xtgls LROA LBV, i(id) t(t) panels (correlated)}\tabcellsep \\
LROA\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep \tabcellsep z\tabcellsep P>z\tabcellsep \multicolumn{2}{l}{95\% ConfInterval]}\\
LBV\tabcellsep .2209399\tabcellsep .0517723\tabcellsep \tabcellsep 4.27\tabcellsep 0.000\tabcellsep .119468\tabcellsep .3224118\\
\textunderscore cons\tabcellsep -4.003274\tabcellsep .3248985\tabcellsep \tabcellsep -12.32\tabcellsep 0.000\tabcellsep -4.640063\tabcellsep -3.366485\\
Wald chi2(1)\tabcellsep = 18.21\tabcellsep \multicolumn{2}{l}{Prob> chi2}\tabcellsep =\tabcellsep 0.0000\tabcellsep \\
\tabcellsep \multicolumn{6}{l}{Table XVII: Relation between LBV and LROE (Model 4)}\\
\tabcellsep \tabcellsep \multicolumn{4}{l}{xtgls LROE LBV, i(id) panels(hetero)}\tabcellsep \\
LROE\tabcellsep Coef.\tabcellsep Std. Err.\tabcellsep \tabcellsep z\tabcellsep P>z\tabcellsep \multicolumn{2}{l}{95\% ConfInterval]}\\
LBV\tabcellsep .4122762\tabcellsep .0687638\tabcellsep \tabcellsep 6.00\tabcellsep 0.000\tabcellsep .2775016\tabcellsep .5470508\\
\textunderscore cons\tabcellsep -3.891073\tabcellsep .3830425\tabcellsep \tabcellsep -10.16\tabcellsep 0.000\tabcellsep -4.641823\tabcellsep -3.140323\\
Wald chi2(1)\tabcellsep = 35.95\tabcellsep Prob> chi2\tabcellsep =\tabcellsep 0.0000\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_13}Table XV :}\end{figure}
 			\footnote{Year 2020 © 2020 Global Journals Are Brand Value Reports Compatible with Financial Reports?} 			\footnote{© 2020 Global Journals} 		 		\backmatter  			 
\subsection[{Year 2020}]{Year 2020}\par
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\end{document}
