Capital market

4.3.1.2 Homoskedasticity, serial correlation & endogeneity

As it was mentioned before the GMM estimators doesn't require homoscedasticity. However, we provide the Sargan test to see the instruments' validity. It is vital for them to be exogenous.

The assumption lacking with which the analysis would not be sufficient is the second-order autocorrelation which is taken into consideration in both IV and GMM estimators. Due to the correlation between the errors and the instruments the estimators will be invalid. But we can test it using m-statistics provided by Arellano & Bond which would indicate whether the autocorrelation is present or not.

Though the majority of papers regarding capital structure miss the discussion of the endogeneity we suppose this issue to be a concern. In terms of corporate finance it results from the measurement errors or omitted variables. Measurement errors arise from the corporate finance assumptions when we decide to use the proxies extensively, for instance, when we suppose the market-book ratio to represent the firm growth. We are going to run an endogeneity test suggested in 2010 by Wooldridge. However, even in case of endogeneity presence the GMM estimators' validity is not suffered.

Chapter 5. Empirical results, discussion and analysis

In this chapter we are going to focus on the results achieved through the analysis of retail companies run in terms of specifications mentioned in the previous chapter. Here we provide the comparison of literature review and the figures obtained from the regressions. The proxies' descriptive statistics is represented along with the collinearity tests. More than that, in this chapter we discuss the trade-off theory and its validity for the retail industry by adjustment speed and target capital structure.

5.1 Descriptive statistics

In the table below there is the sample descriptive statistics. The whole sample seems to be quite stable for the chosen period and it is true both for leverage ratio and for the proxies. As we can see, the mean for the leverage is equal to 0.73 for the entire period. It means that in retailing sector firms' equity exceeds debt by 27% on average. There is such a tendency that debt share in the capital structure is to drop (1.01 in 2010 and 0.53 in 2015) which inevitably confirms what was said in the introduction that retailers tend to be financed within internal cash flows. As for the proxies, they are quite stable as well having almost the same average meanings during the period.

Table 2. Descriptive statistics (Stata, own contribution).

Statistics for selected years (the beginning and the end of the chosen period) and for the overall period. Leverage stands for debt-equity ratio. Equity is measured as market cap. Size is a natural logarithm of total assets of the firm. Tangibility is the ratio of PP&E to total assets. Growth represents the market-book ratio of equity. Risk stands for the standard deviation logarithm of EBIT through 6 years period. NDTS is measured as depreciation to total assets ratio. Profitability is EBIT to total assets ratio. Macro stands for GDP growth. USA is a dummy - 1 for the USA and 0 for the other countries. REP is Debt/EBITDA ratio. PROM is goodwill/EBITDA ratio.

	2010-2015	2010	2015
	Mean	Std	Min	Max	Mean	Std	Mean	Std
LEVERAGE	0.73	1.17	0.03	9.76	1.01	1.73	0.53	0.55
SIZE	8.55	1.39	4.52	12.23	8.51	1.38	8.53	1.43
TANG	0.37	0.20	0.03	0.96	0.37	0.21	0.38	0.19
GROW	3.43	2.40	0.19	10.75	3.15	2.33	3.63	2.73
RISK	5.09	1.29	0.01	7.86	5.04	1.39	5.06	1.34
NDTS	0.05	0.02	0.01	0.10	0.05	0.02	0.05	0.02
PROF	0.14	0.07	0.02	0.39	0.14	0.07	0.15	0.08
MACRO	0.02	0.01	-0.01	0.03	0.02	0.01	0.02	0.01
USA	0.89	0.31	0	1	0.89	0.32	0.89	0.31
REP	3.25	2.01	0.65	10.18	3.45	2.23	3.00	1.53
PROM	0.70	0.82	0.01	3.75	0.75	0.85	0.66	0.89

There are 60 retailers analyzed in this research paper. The data was collected mostly from their financial statements, so, this indicates that they are mature ones as they should have had IPO in 2005 at least as we need data since 2005 for Risk calculation.

Though we mitigate the collinearity effect using the panel data still we need to keep in mind that the one aspiring to be perfect can cause some negative issues (for instance, Size and Risk might be correlated negatively and strongly as larger firms tend to be less risky). In the table below there is correlation summarized for the variables. There is no point to say that we have collinearity as correlation between explaining variables level overall is low. On the other hand, we see a strong correlation between Size & Risk and it is positive, however; Growth & Profitability; Repayment & Growth. It is not a concern as we just orthogonalize the set of explanation variables and get rid of the correlation.

Table 3. Correlation Matrix (Stata, own contribution).

Here is the correlation between variables used in the analysis.

	LEV	SIZE	TANG	GRO	RISK	NDTS	PROF	MACR	USA	RE	PRO
LEV	1.00
SIZE	-0.11	1.00
TANG	-0.13	0.44	1.00
GROW	-0.32	0.13	0.17	1.00
RISK	-0.19	0.75	0.35	0.27	1.00
NDTS	-0.19	-0.14	0.39	0.11	0.04	1.00
PROF	-0.36	-0.03	0.08	0.63	0.21	0.08	1.00
MACR	-0.03	-0.08	-0.05	0.09	-0.11	-0.11	0.10	1.00
USA	0.01	-0.24	-0.10	0.13	-0.13	-0.07	0.12	0.37	1.00
REP	0.56	0.18	-0.11	-0.19	-0.01	-0.37	-0.66	-0.04	-0.08	1.00
PROM	0.13	0.24	-0.33	-0.26	0.15	-0.30	-0.39	-0.11	-0.10	0.40	1.00

5.2 Trade-off theory

In this section there are results for trade-off theory presented. At first, we examine the target capital structure and compare it with the observed data for the overall sample. Here we use the partial adjustment model estimation.

5.2.1 Target capital structure

There are the results reported for the target capital structure estimation. There are two random-effects estimations: including time effect and without them. According to the preliminary results we should say that the larger retailer is the more debt it will use in the capital structure and it goes in line with the trade-off theory predictions. The opposite effect is observed for the profitability which is inconsistent with the trade-off theory predictions and findings. As for growth, we say that it has a positive impact on the leverage level but not that much like size does. The further analysis will show the trustworthy estimations. Risk and NDTS appeared to be insignificant for this model but, on the other hand, it doesn't mean it is true as we shall use stronger estimators as it follows. Profitability as it was found is related positively to the debt share in retailer's capital structure. A preliminary conclusion might be that the more profitable retailer is the more accouts payable it can accumulate as it appears to be trustworthy.Also, though it is found contradictive towards the trade-off theory the dynamic one suggests a possible explanation for it to be a sign of financial flexibility (Strebulaev&Whited(2012); DeAngelo, et al. (2011)). As for proxy for debt repayment, it appeared to have a negative correlation with leverage. Capital structure of retailers doesn't' depend on market conjuncture and allocation. Also, according to our predictions good advertising campaigns are financed with debt.

There are the results for the target capital structure of retailing companies estimation. The model is specified the following way:

Table 4. Target Capital Structure (Stata, own contribution)

	Leverage	Leverage, Time Effects
SIZE	0.52*	0.65*
TANG	0.72*	Not Significant
GROW	Not Significant	0.10**
RISK	Not Significant	Not Significant
NDTS	Not Significant	Not Significant
PROF	0.20*	0.15*
MACRO	Not Significant	Not Significant
USA	Not Significant	Not significant
REP	-0.16**	-0.08**
PROM	0.34*	0.25*
Cons	Not Significant	0.50**
Obs.	256	256
R2	0.2123	0.3008
Wald-statistics	37.12	62.18

Herewehavepositive and significant growth measured as the market-book ratio. However, as Welch argued in 2011 this ratio might be a poor proxy for the book value of debt. As for the risk and NDTS, their coefficients are found to be negative but, on the other hand, insignificant (0.617 and 0.826 p-value, respectively). These results go in line with Alves& Ferreira(2011) and Jong, et al. (2008) research papers which had the same findings. The correlation between factors couldn't have been the reason for these proxies to be insignificant as before running the regression the sample was orthgonalized. Initially, we can state that promos increase debt level which is consistent with what we have stated before.

5.2.2 Target vs observed capital structure

To see whether retailing firms over the observed period were under- or overleveraged we look at the target capital structure which was defined in the previous chapter and compare it to the observed level of debt-equity ratio. The bar chart below represents data for mean values of observed and target leverage levels. Though there are deviations between them they are negligible, except for the 6th period (2015). According to the paper written in 2011 by Welch these little deviations are in common with the concept that when deciding upon the capital structure firms consider their leverage having book debt. For the periods 2-5 (2011 - 2014) companies were overleveraged as their actual debt-equity ratio exceeded target one. The same is truefor the 1st (2010) and 6th (2016) periods when they were highlyoverleveraged. In the 2nd - 3rd periods (2011 - 2012) retailers were close to their target leverage ratios which might be a sign that they have focused on their target ratios. The dynamics shown in the bar chart contains a proof for the trade-off theory in retail sector. The adjustment speed is going to be analyzed in the next part.

Figure 2. Target leverage versus actual leverage (Stata, own contribution).

5.2.3 Speed of adjustment

The preliminary results of the analysis have shown that trade-off theory is partially proved for the retail sector and we fit the data to define the adjustment speed of the firms. Using more complex estimators might give better results.

The table given below represents the results we got from the partial adjustment model which is set in the equation discussed in section 5.2. The analysis is provided within the estimators highlighted in section 5.3.1.1. Unfortunately, Anderson & Hsiao IV estimators have appeared to be quite sensitive to the sample size. So, the adjustment speed coefficient (lagged leverage) came out being biased and according to the research paper written in 2008 by Verbeek it is a concern given the coefficient approaches are coherent.

There are the results obtained from the partial adjustment model estimation represented in this table: . The results are for three estimators: OLS, FE (fixed effects) and two-step system GMM estimator of Blundell & Bond (see chapter 5).

Table 5. Target Adjustment Model (Stata, own contribution).

	OLS	RE	BB - twostep
Di,t-1	0.7824*	0.7703*	0.5861*
SIZE	Not Significant	-0.0384*	-0.0501*
TANG	Not Significant	Not Significant	0.7306**
GROW	0.0205**	0.1525**	0.0365*
RISK	Not Significant	Not Significant	0.0243**
NDTS	Not Significant	Not Significant	-7.7896*
PROF	0.1969*	0.0180*	0.0475*
MACRO	Not Significant	Not Significant	Not Significant
USA	Not significant	Not Significant	Not Significant
REP	Not significant	Not Significant	Not Significant
PROM	0.0039*	0.0256*	0.1567*
Cons.	Not Significant	Not Significant	Not Significant
F-statistics	259.77	N/A	N/A
Wald	N/A	1544.70	4637.64
R2	0.9389	0.9082	N/A
Obs.	197	197	197

All in all, determinants'coefficie

nts have not much common sense with the ones obtained in the previous section and they don't provide a proof for the dynamic trade-off theory, as well. Size has appeared to be insignificant for OLS estimators but for RE and GMM it is negative indicating that the larger retailer is the less it suffers from information asymmetry and thelessdebt financing as a less risky source it needs. Tangibility is significant in terms of influencing on the leverage level and it is positive which goes in line with trade-off predictions. Growth is positive and is seen as the faster company is growing the more attractive it is for investors. NDTS are significant and negative for the leverage. As for profitability, it has a positive coefficient indicating debt increase. Retail capital structure doesn't depend on market condition and business allocation. Ability to repay debt appeared to be insignificant going in contradiction with our assumptions. Also, promos appeared being significant and indicating debt financing. The system GMM estimator has provided the adjustment speed of 41.39% (1-coefficient).

The adjustment speed is interpreted as a result of balancing between the pros (tax benefits) and cons (costs) of adjusting. A high adjustment speed would occur in case of high taxes given the firm is underleveraged. On the other hand, the costs increase would, also, push them to adjust faster towards the target capital structure. To see that we divide our sample into two categories: over- and underleveraged firms - and we apply our model towards them.

In the table below there are the results for B&B two-step GMM estimation. It is an intriguing fact that we have almost no difference between these subgroups. Overleveraged companies appeared to have 61.64% adjustment speed while underleveraged ones have 39.42%.

This table summarizes the results for comparison the adjustment speed of over - and underleveraged firms. The analysis is performed in terms of book data with a two-step B&B GMM estimator.

Table 6. Target Adjustment Model - Overleveraged vs Underleveraged (Stata, own contribution).

	Underleveraged	Overleveraged
Di,t-1	0.7058*	0.3836*
SIZE	0.0334*	-0.0573*
TANG	Not Significant	Not Significant
GROW	-0.0138**	Not Significant
RISK	-0.0169**	Not Significant
NDTS	-0.8075*	-16.2168*
PROF	Not Significant	-0.0239**
MACRO	Not Significant	Not Significant
USA	Not Significant	Not Significant
REP	Not Significant	Not Significant
PROM	0.0106*	0.7968*
Cons.	Not Significant	Not Significant
WALD	2046.38	4168.23
R2	N/A	N/A
Obs.	91	106

Even though we should be careful when estimating small volume samples within a two-step GMM the coefficients generated are in line with OLS which means that the results are valid.

A higher level of the adjustment speed for overleveraged firms indicates that they adjust their leverage level more actively. The reason for that is that comparing with underleveraged ones they have lower adjustment costs and they benefit more being at their target capital structure. But comparing the results of the whole sample and the subgroups we see that there is a big difference in adjustment speed for subgroups, for instance, overleveraged ones have it almost two times lower. A possible reason for that might be a liquidity constraint in case the firm is unable to acquit or to issue equity. On the other hand, considering other features of the firms suggests some attractive perspectives. On average overleveraged firms are bigger and they have less growth opportunities and their leverage levels are approximately 30% higher.

The results obtained show that retailers having robust capital structures and aspiring to be financially flexible due to damping adjust their capital structure in a more active way than other industries do. However, this contradicts with the concept that in case a company wants to be flexible in finance terms it should adjust the capital structure slowly. This case suggests further considerations. In general, the results show that the determinants are mostlyinsignificant and they need to be investigated further.

5.3 Summary of findings

This paragraph contains the results of analysis towards capital structure determinants of retailers. The trade-off model examination was divided into several parts and the results obtained have not lot in common with the empirical research papers and they are not supportive towards trade-off theory. Profitability is found to have a positive impact on leverage level. Thus, the 1st hypothesis is declined. However, asset tangibility has a positive correlation with leverage level. It is, also, the case for size when using target adjustment model in most regressions, so, we confirm the 3rd hypothesis and partly the 2nd one. As for NDTS, it was found being significant using a two-step GMM estimator only and it has a negative coefficient, thus, the 6th hypothesis is confirmed. The size appeared to be significantfor strong estimators but, however, negative. Thus, it is inconsistent with the trade-off model, so we decline the 2nd hypothesis. Also, we should reject the 4th hypothesis for growth as the coefficient obtained is positive which contradicts with the theory and, also, the 5th one as risk is positive. The 8th hypothesis is confirmed as it is found that leverage of retailers is irrelevant to macroeconomics and business allocation. As for promos, this determinant's coefficient is positive which goes in line with our predictions that they are financed through debt. But we have to reject the 9th hypothesis as this proxy appeared being insignificant when estimated with GMM.

The target adjustment model research confirms the presence of the adjustment speed in retailsector. Due to the fact that overleveraged retailers adjust towards their target leverage level at the speed being significantly higher than underleveraged ones have we suggest that there is a lower barrier of leverage. Thus, we confirm the 7th hypothesis.

This finding contradicts with the results obtained by Gaud, et al. in 2007 who found an upper barrier to leverage. Comparison with the finding saying that small retailers have a higher level of adjustment speed suggests that industry characteristics have influence significantly on adjustment speeds level. However, the analysis has shown that growth appeared to have positive effect. Underleveraged and small retailers face less financial constraint and therefore they incur lower adjustment costs. Another possible explanation of adjustment speed to vary might be the risk faced by retailers. The sample of little firms had negative operating profit, on average, due to damping indicating a higher risk. A rapidly performed adjustment and low leverage levels could be the ways to minimize the risk. Larger retailers are more diversified and they have more stable earnings at the positive level and therefore the probability to face a financial distress is much lower.

Part three. Conclusion

Chapter 6. Conclusion and evaluation

This chapter concludes current research and represents the results. Also, it provides the study limitations. Finally, it finishes this paper by highlighting the contributions and making recommendations to conduct future research.

6.1 Conclusions

This degree paper contains the analysis of the capital structure determinants of retailers dating 2010 - 2015. The test of the capital structure has evolved around the pecking order theory and the trade-off theory. The literature review revealed a gap in empirical capital structure research in retail companies, supporting the need for this thesis.

The support for trade-off theory was partly found here. Some capital structure determinants were found almost going in line with empirical research papers focusing on other industries and even countries, the others were not. For example, growth and profitability are positively correlated with leverage level while size has a negative impact on this variable.

Here we have found an evidence of a target capital structure and led to adjustment speed investigation in terms of partial adjustment model. The analysis showed that retailers have a 41% annual adjustment speed towards their target. Such a high level might be an industry risk indicator as damping is quite common in retailing. A deeper analysis revealed the overleveraged firms having a two-times-faster adjustment speed than the underleveraged do indicating high deviation-from-target costs for overleveraged.

6.2 Limitations of study

As well as most empirical research papers this thesis is limited. One of the primary concerns is the multitude of econometric estimators. According to analyzed papers we should say that the adjustment speed obtained is quite sensitive to the estimator chosen. For instance, Fama& French paper contains results about a low adjustment speed level as it was biased due to the estimator. The fact that the results produced by OLS estimator are downwards biased was accepted just after the paper has gone public. The issue towards the estimator choice is still a highly controversial one and it's difficult to compare results obtained within different estimators. In this paper we have used several of them and obtained a variety of adjustment speed levels. The closer result is to the one gained by OLS the more valid it is.

Also, one of the possible limitations could be the sample size as according to other corporate finance research papers some estimators are influenced a lot with it. But we have considered this issue and performed the validity test for the estimators.

6.3 Contributions of study and recommendations

This thesis contains the first in-depth capital structure determinants dynamic analysis of the retail sector. Even though it is built upon classic models and research methods which have been widely used in research papers the findings are unique.

As it is the first paper to investigate in depth the retailers' capital structure there are a lot of possible ways to investigate further. It could provide insights into the retailers' CFO's minds and, also, a valuable input to compare the results with the other studies. Another focus of this research paper is to expand the time span to see if the results are robust and to examine how the capital structure determinants and adjustment speed level vary over the time period.

Although a lot of empirical research papers focused on the other industries are similar to this one this study provides the adjustment speed analysis a bit further investigating its sensitivity to firm characteristics. However, in 2006 Flannery &Rangan conducted similar research towards adjustment speeds investigation in terms of overleveraged and underleveraged firms. The fact that overleveraged retailers have the adjustment speed level a lot higher than underleveraged ones might be the most interesting finding of this research paper. A future research might focus on more detailed analysis of the adjustment speed level.

Finally, we should provide further development within the econometric part to create valid and consistent results. The paper written in 2013 by Flannery & Hankins is an interesting contribution to this area.

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