**4. Result**

Information corresponds to companies that are included in the main indices of the countries under study, as shown in **Table 1**. and the data compiled concerns to the evolution of prices,

The applied statistical methodology is a simple linear regression, in which the magnitude of the floating capital ratio is used as the explanatory variable of the model. The explained variables are the Neperian logarithm of the annual percentage return (LREA), the annual volatility (DESV) and the annual average traded volume of shares, expressed in millions of the local

Volatility is a measure of the risk of price movements for a security calculated from the standard deviation of the day-to-day logarithmic historical price changes. The 260-day price volatility equals the annualized standard deviation of the relative price change for the 260 most

The volume was calculated based on the annual accumulated volume, divided by the number

LREAik = β0 + β1 FREFik + εi (1)

DESVik = β0 + β1 FREFik + εi (2)

VOLAMik = β0 + β1 FREFik + εi (3)

FREFik is free float as a percentage of shares outstanding of the company i-th and of the coun-

LREAik is the Neperian logarithm of the annual return in percentage of the company i-th and

VOLAMik is the annual traded volume in millions of pesos of the i-th company in the country

their volatility and the volume traded during financial year 2016.

recent daily trading closing prices, expressed as a percentage.

DESVik is the volatility of the i-th company and the k-th country.

**COUNTRY INDEX Number of companies**

Argentina MERVAL 26 Brazil BOVESPA 57 Chile IPSA 39 Peru I GENERAL 34 Colombia COLCAP 25

of working days of the year. The applied model is:

currency of each country (VOLAM).

38 Firm Value - Theory and Empirical Evidence

The variables are:

the country k-th.

**Table 1.** Size of sample.

try k-th.

k-th.

#### **4.1. Descriptive analysis of the explained and explanatory variables**

The importance of data is potential and it only becomes information when it is associated within a suitable context. Data must be analyzed and transformed; only in this way it produces knowledge and support decision-making.

To begin the analysis of the data the descriptive statistics, although it is very simple, it does become important in many studies. Results allow us to compare experimental evidences with theories and hypotheses, validating empirical arguments from mathematical models designed and adjusted by experts in the corresponding topic. For this reason, descriptive statistics of the variables used in the model proposed in this chapter are carried out.

O'shee et al. mentioned in his article that Latin American companies that are publicly traded are characterized as being highly concentrated. In them, they clearly identified that majority shareholders can be of great strength for the firm due to their active position within it and because they represent a financial source for company in times of crisis.

The first variable to describe is floating capital, which is studied by different authors obtaining interesting results.

In Argentina, the ownership structure changed dramatically in the nineties, when almost all state enterprises were privatized; but even so, high levels of concentration were maintained. This can be seen in the fact that the 20 largest companies show majority shareholders that hold around the 65% of the capital. In Brazil, on average, main shareholder own 41% of the firm, while the most important five hold 61%. In Chile and Peru, it is shown that the first three major shareholders own about three quarters of all shares. Colombia shows the lowest level of concentration and numbers are similar to those held by companies in Europe and Asia [5].

**Table 2** gives the descriptive statistics for Free Float ratio divided into quartiles. It is possible to observe 60.22% (15.47 + 44.75%) of the companies included in the sample have less than 50% of their free float listed in the market.

In the selected temporal space and in this sample, it is possible to affirm that Brazil is the country where stocks that make up the BOVESPA index have the highest percentage of free float on the market. Since 61.40% (17.54 + 43.86%) of its companies have free float higher than 50% and there is no company with less than 25% of their capital as free float.

ity shareholding participation in more than 50%. This is similar to the average mentioned in

**Companies FREF FGS FREF AJUS** Banco Macro SA 61.59 30.97 30.62 San Miguel SA 46.86 26.96 19.90 Edenor SA 51.00 26.81 24.19 Consultatio SA 31.07 26.62 4.45 Distribuidora Gas Cuyana SA 30.00 26.62 3.38 Siderar SA 39.06 26.03 13.03 Telecom SA 96.14 24.99 71.15 Pampa Energía SA 84.31 23.23 61.08 Trans. Gas Sur SA 49.00 23.11 25.89 Mirgor SA 51.74 21.54 30.20 Grupo Financiero Galicia SA 88.40 21.28 67.12 Transener SA 47.35 19.57 27.78

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Investors obtain returns for their investments in shares by two ways: dividens and price appreciation. In most Latin American markets, the payment of dividends is irregular and it is not of significance, therefore price differential is what is important for investors. **Table 4** shows the descriptive statistics for the Neperian logarithm of the annual return (LREA Eq. (4)) in percentage of the companies in different countries. These exposed values are not comparable, since this return is in the countries own currencies but it allows us to observe some

It is observed that in all the indexes analyzed, there were stocks whose prices in 2016 fell compared to the previous year. In the case of Argentina, only 2 shares out of 26 maintained that negative behavior, even including 8 companies whose yields were higher than 100%. The average value of the variable LREA is 52.71 and the standard deviation is 39.33. The country with the smallest range of variation of the variable shown in **Table 4** is Colombia. Only one company has a slight negative variation in its price and only one has yields over

Observing the volatility in **Table 5**, which is a measure of the risk of price movements for a value calculated from the standard deviation of the historical changes in daily logarithmic prices, it can be seen that the highest volatility is found in Peru and Brazil. The variable DESV reached values of 97.53 and 64.75 respectively. The minimum value is also obtained by Brazil

Once the descriptive analysis of the relevant variables has been carried out, an analysis of the

extreme values, average and individual deviation of each country individually.

another article written by different authors [5].

**Table 3.** Details of free float adjusted in different companies in Argentina.

100%.

in a magnitude of 8.43.

results of the regression is accomplished.

The opposite situation is what we found in Chile. In its capital market the 87.18% (20.51 + 66.67%) of the stocks in the 39 companies that make up the IPSA Index are property of the major shareholders.

In Argentina, these numbers change substantially and are more similar to Chile if the holdings of the FGS are incorporated as part of the majority shareholders holdings. **Table 3** shows how the FREF is modified if the FGS holding is considered. It is called FREF adjusted (FREF AJUS) to that difference. In Colombia, 68% of the companies that are part of the index have major-


**Table 2.** Descriptive analysis of FREF in different countries.

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**Table 3.** Details of free float adjusted in different companies in Argentina.

**COUNTRY Sample Companies with** 

of the major shareholders.

50% of their free float listed in the market.

ing interesting results.

40 Firm Value - Theory and Empirical Evidence

**FREF <25%**

**Table 2.** Descriptive analysis of FREF in different countries.

**Companies with FREF >25% ^FREF <50%**

O'shee et al. mentioned in his article that Latin American companies that are publicly traded are characterized as being highly concentrated. In them, they clearly identified that majority shareholders can be of great strength for the firm due to their active position within it and

The first variable to describe is floating capital, which is studied by different authors obtain-

In Argentina, the ownership structure changed dramatically in the nineties, when almost all state enterprises were privatized; but even so, high levels of concentration were maintained. This can be seen in the fact that the 20 largest companies show majority shareholders that hold around the 65% of the capital. In Brazil, on average, main shareholder own 41% of the firm, while the most important five hold 61%. In Chile and Peru, it is shown that the first three major shareholders own about three quarters of all shares. Colombia shows the lowest level of concentration and numbers are similar to those held by companies in Europe and Asia [5]. **Table 2** gives the descriptive statistics for Free Float ratio divided into quartiles. It is possible to observe 60.22% (15.47 + 44.75%) of the companies included in the sample have less than

In the selected temporal space and in this sample, it is possible to affirm that Brazil is the country where stocks that make up the BOVESPA index have the highest percentage of free float on the market. Since 61.40% (17.54 + 43.86%) of its companies have free float higher than

The opposite situation is what we found in Chile. In its capital market the 87.18% (20.51 + 66.67%) of the stocks in the 39 companies that make up the IPSA Index are property

In Argentina, these numbers change substantially and are more similar to Chile if the holdings of the FGS are incorporated as part of the majority shareholders holdings. **Table 3** shows how the FREF is modified if the FGS holding is considered. It is called FREF adjusted (FREF AJUS) to that difference. In Colombia, 68% of the companies that are part of the index have major-

50% and there is no company with less than 25% of their capital as free float.

because they represent a financial source for company in times of crisis.

Argentina 26 5 19.23% 12 46.15% 4 15.38% 5 19.24% Brazil 57 0 0.00% 22 38.60% 10 17.54% 25 43.86% Chile 39 8 20.51% 26 66.67% 3 7.69% 2 5.13% Peru 34 9 26.47% 10 29.41% 3 8.82% 12 35.30% Colombia 25 6 24.00% 11 44.00% 1 4.00% 7 28.00% TOTAL 181 28 15.47% 81 44.75% 21 11.60% 51 28.18%

**Companies with FREF >50% ^FREF <75%**

**Companies with FREF >75%**

ity shareholding participation in more than 50%. This is similar to the average mentioned in another article written by different authors [5].

Investors obtain returns for their investments in shares by two ways: dividens and price appreciation. In most Latin American markets, the payment of dividends is irregular and it is not of significance, therefore price differential is what is important for investors. **Table 4** shows the descriptive statistics for the Neperian logarithm of the annual return (LREA Eq. (4)) in percentage of the companies in different countries. These exposed values are not comparable, since this return is in the countries own currencies but it allows us to observe some extreme values, average and individual deviation of each country individually.

It is observed that in all the indexes analyzed, there were stocks whose prices in 2016 fell compared to the previous year. In the case of Argentina, only 2 shares out of 26 maintained that negative behavior, even including 8 companies whose yields were higher than 100%. The average value of the variable LREA is 52.71 and the standard deviation is 39.33. The country with the smallest range of variation of the variable shown in **Table 4** is Colombia. Only one company has a slight negative variation in its price and only one has yields over 100%.

Observing the volatility in **Table 5**, which is a measure of the risk of price movements for a value calculated from the standard deviation of the historical changes in daily logarithmic prices, it can be seen that the highest volatility is found in Peru and Brazil. The variable DESV reached values of 97.53 and 64.75 respectively. The minimum value is also obtained by Brazil in a magnitude of 8.43.

Once the descriptive analysis of the relevant variables has been carried out, an analysis of the results of the regression is accomplished.


**Table 4.** Descriptive analysis of LREA in different countries.

#### **4.2. Results of the regressions**

The results of this work, based on an empirical study, seek to assess the relationship between the magnitude of the floating capital ratio and the selected market indicators such as the traded volume, annual returns and the standard deviation of price variation. According to Çalişkan and Kerestecioğlu, a high floating capital ratio is positive for investors in case they need to exercise their rights after buying shares. The results of the regression model proposed in the equation (Eq. (1)) are calculated and shown in **Table 6**.

In the case of Peru, this relationship, according to the results obtained, is direct and statistically significant. Therefore, it is shown again that the results obtained are not of equal sense

**Country LREA Coef. Std.Err. t P > |t| [95% Conf.Interval]** Argentina FREF −0.2787 0.3127 −0.89 0.382 −0.9242 0.36671

Brazil FREF 0.1425 0.1959 0.73 0.47 −0.2501 0.5353

Chile FREF 0.2622 0.2181 1.2 0.237 −0.1798 0.704

Perú FREF 0.399 0.175 2.28 0.029 0.04313 0.7565

Colombia FREF 0.2767 0.0939 2.95 0.007 0.08245 0.471144

**Table 6.** Estimated coefficients, standard errors and significance of variables. Variable response LREA.

\_cons 66.68 17.48 3.81 0.001 30.60 102.77

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\_cons 26.91 14.00 1.92 0.06 −1.15 54.98

\_cons 4.00 8.85 0.45 0.654 −13.93 21.95

\_cons 30.86 11.00 2.80 0.008 8.45 53.28

\_cons 4.62 5.37 0.86 0.398 −6.49 15.73

The coefficient of FREF (β1)(Eq. (2)) for Peru is 0.2939 and significant at 5% level as P > |t| is 0.041. This suggests that FREF is significantly positive correlated with price volatility. Higher free float ratio means higher risk for the stock. Peru was the only Latin American country

Finally, the results of the regression proposed in Eq. (3) that seeks to prove whether there is a relationship between the volume traded and the floating capital ratio are shown in **Table 8**.

\_cons 39.14 2.73 14.37 33.52 44.77

\_cons 33.28 4.28 7.78 24.70 41.86

\_cons 23.85 2.94 8.11 17.89 29.81

\_cons 23.61 6.03 3.92 11.33 35.89

\_cons 17.85 2.03 8.78 13.65 22.07

**Country DESV Coef. Std.Err. t P > |t| [95% Conf.Interval]** Argentina FREF −0.0486 0.0487 −1 0.329 −0.1492 0.05198

Brazil FREF 0.0132346 0.0598 0.22 0.826 −0.1067 0.1332

Chile FREF −0.00305 0.0724 −0.04 0.967 −0.1498 0.1437

Peru FREF 0.2939 0.0959 2.13 0.041 0.0084 0.399

Colombia FREF 0.00757 0.0355 0.21 0.833 −0.066 0.081199

**Table 7.** Estimated coefficients, standard errors and significance of the variables. Variable response DESV.

and important in all the countries studied.

analyzed which shows a coefficient of significance.

FREF coefficient (β1) (Eq.(1)) is negative and statistically insignificant for Argentina, so it can be concluded that for the analyzed data there is no relationship between price variation and floating capital ratio. For Brazil and Chile, there is direct relationship but no significance.

For the case of Colombia and Peru this relationship, according to the results obtained, is direct and statistically significant. Therefore, it is concluded that the results obtained are not of equal sense and importance in all the countries analyzed.

Price volatility (DESV) regression is performed for the floating capital ratio. Results are shown in **Table 7**.

FREF coefficients(β1) (Eq.(2)) are negative and statistically insignificant for Argentina and Chile, so it can be concluded that for the analyzed data, there is no relationship between price volatility and floating capital ratio. For Brazil and Colombia, there is direct relationship but no significance.


**Table 5.** Descriptive analysis of DESV in different countries.

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**Table 6.** Estimated coefficients, standard errors and significance of variables. Variable response LREA.

**COUNTRY Sample Mean Std. Dev Min Max** Argentina 26 52.71 39.33 −18.68 129.43 Brazil 57 36.44 37.32 −62.95 137.62 Chile 39 13.63 23.7 −33.15 99.43 Perú 34 51.92 37.15 −8.01 137.63 Colombia 25 17.81 17.01 −0.22 75.32

The results of this work, based on an empirical study, seek to assess the relationship between the magnitude of the floating capital ratio and the selected market indicators such as the traded volume, annual returns and the standard deviation of price variation. According to Çalişkan and Kerestecioğlu, a high floating capital ratio is positive for investors in case they need to exercise their rights after buying shares. The results of the regression model proposed

FREF coefficient (β1) (Eq.(1)) is negative and statistically insignificant for Argentina, so it can be concluded that for the analyzed data there is no relationship between price variation and floating capital ratio. For Brazil and Chile, there is direct relationship but no

For the case of Colombia and Peru this relationship, according to the results obtained, is direct and statistically significant. Therefore, it is concluded that the results obtained are not of equal

Price volatility (DESV) regression is performed for the floating capital ratio. Results are shown

FREF coefficients(β1) (Eq.(2)) are negative and statistically insignificant for Argentina and Chile, so it can be concluded that for the analyzed data, there is no relationship between price volatility and floating capital ratio. For Brazil and Colombia, there is direct relationship but

**COUNTRY Sample Mean Std. Dev Min Max** Argentina 26 36.71 6.15 28.93 48.44 Brazil 57 34.16 11.35 8.43 64.75 Chile 39 23.74 7.72 14.68 47.45 Peru 34 34.35 20.16 12.90 97.53 Colombia 25 18.22 5.5 10.62 35.03

**Table 4.** Descriptive analysis of LREA in different countries.

in the equation (Eq. (1)) are calculated and shown in **Table 6**.

sense and importance in all the countries analyzed.

**Table 5.** Descriptive analysis of DESV in different countries.

**4.2. Results of the regressions**

42 Firm Value - Theory and Empirical Evidence

significance.

in **Table 7**.

no significance.

In the case of Peru, this relationship, according to the results obtained, is direct and statistically significant. Therefore, it is shown again that the results obtained are not of equal sense and important in all the countries studied.

The coefficient of FREF (β1)(Eq. (2)) for Peru is 0.2939 and significant at 5% level as P > |t| is 0.041. This suggests that FREF is significantly positive correlated with price volatility. Higher free float ratio means higher risk for the stock. Peru was the only Latin American country analyzed which shows a coefficient of significance.

Finally, the results of the regression proposed in Eq. (3) that seeks to prove whether there is a relationship between the volume traded and the floating capital ratio are shown in **Table 8**.


**Table 7.** Estimated coefficients, standard errors and significance of the variables. Variable response DESV.


As a conclusion, these findings are compatible with the previous studies and prove that free float ratio does matter for the investors. Higher floating ratio implies higher market value for stocks for the cases of Peru and Colombia. Therefore, these results provide empirical evidence for the growing practice of weighting stocks according to free float ratio for the construction of indexes. They also support designing incentive measures to present to corporations and policy makers for enlarging floating ratios that will decrease cost of capital and ensure capital market development. Although the regression results of this study were robust and clear, it depends on 1-year data, which eliminates the free float variations within a stock. Therefore, examining effects of free float ratio for different sectors or for firms whose floating ratios change substantially within

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The study shows that there is no relationship between floating capital ratio and the traded volume for this temporary space and for the companies selected. It will be possible to repeat the same analysis next year and check whether these conclusions can be different or continue

Nevertheless, this study presents two limitations: the first is the use of data from a crosssectional sample, that is, it takes data corresponding to a set of companies for a moment in time, and the other limitation is that the selected companies are only those that make up the

Future lines of research can be oriented to confirm if the results obtained in the present study (2016 period of analysis) are maintained over the years and to integrate the findings of effects of stock concentration of property (government holdings and majority shareholders)on stock

a time horizon may bring interesting results for further studies [11].

return (7) with the effects of the floating capital ratio in stock markets.

**No. Companies FREF DESV VOLAM LREA** Agrometal SA 45.20 48.00 1.66 129.44 Petrolera Pampa SA 36.90 33.12 1.45 110.63 Autopistas del Sol SA 100.00 39.10 0.86 105.61 Petróleo Brasilero SA 49.58 39.94 41.18 101.46 Holcim SA 20.39 36.54 3.79 101.27 San Miguel SA 46.86 40.44 3.26 89.03 Central Costanera SA 24.32 48.17 2.39 80.22 Central Puerto SA 20.98 33.35 2.58 70.39 Distribuidora Gas Cuyana 30.00 48.45 0.97 65.12 Pampa Energía 84.31 33.19 19.94 64.00 Trans. Gas Norte 20.01 43.14 0.61 63.23 Transener SA 47.35 43.18 4.40 59.87 Trans. Gas Sur 49.00 36.05 2.34 56.05

indexes, due to the availability of public information.

ratifying the current results.

**Appendices**

**Table 8.** Estimated coefficients, standard errors and significance of variables. Variable response VOLAM.

The coefficients (Eq. (3)) that result from the regression for all countries are not statistically significant. Unlike what Caliskan and Kerestecioglu (2013) say, we find that for the countries under study and for this temporary space, it is not possible to demonstrate that there is a relationship between both variables.
