**3. Data and methodology**

daily trading volume? Third, do free float ratios affect price volatility? For that research, 194 firms were selected from Istanbul Stock Exchange Market for the period from 25.02.2011 to 09.03.2012. The statistical method applied was linear regression. Results showed that there is no evidence of relationship between price return and free float ratio. In other words, investors did not pay more or less for stocks depending on whether free float ratio was considered to be high or low. On the other hand, there seems to be a positive relationship between free float ratio and price volatility. Finally, free float ratio is directly related to trading volumes. In other

Bostancı and Kılıç examined the free float ratios effects on market performance of stocks in Turkey. Their research includes 199 listed firms on Istanbul Stock Exchange Market for the year 2007. The relationship between free float ratio and the dependent variables average daily closing price, price volatility and average daily trading activity is measured by regression models. Findings suggest that the market rewards higher floating ratio, that is, average daily closing price and trading activity is significantly higher for stocks with higher free float ratio. They also notice that price volatility, which is associated with the risk of a stock, increases with free float ratio. Finally, the effect of free float ratio on these variables is measured by controlling size of firms through a multivariable regression model. According to regression results, effects of floating ratio do not increase or decrease as the firm size increases or decreases. As a conclusion, these results are compatible with the previous studies and prove that free float ratio does matter for investors. Higher floating ratio implies higher market value for stocks, higher liquidity in the market and lower financial costs for corporations. They support suggesting initiatives to corporations and policy makers to increase floating ratios that will result on the decrease of financial costs and ensure capital market development. Although the regression results of the study are robust, the regressions depend on 1 year data, which

words, higher free float ratio results in higher liquidity in the market.

36 Firm Value - Theory and Empirical Evidence

contain all the sectors and eliminate the free float variations within a stock.

ship across thousands of securities.

Chan et al. asserted that the intervention of the Hong Kong government offers a clear case for examining how market liquidity is affected by a substantial decline in free float. For many companies listed in Asian and emerging markets, government, controlling companies affiliated companies, and majority owners control a large percentage of the shares. As a result, the amount of shares outstanding considered available for trading could be relatively small. When investigating the liquidity of these markets, it is possible to determine the amount of free-floating shares available. The author also indicates that the amount of free-floating shares is often difficult to define, as it is not easy to determine the identity of ultimate beneficial owners. Sometimes, the trail becomes tangled and it is not possible to accurately monitor owner-

The same topic is addressed by other authors [8, 9] saying that in August 1998, after an intervention in the stock market by the Hong Kong government, there was a dramatic decrease in the amount of the shares in the market and this caused a decline on the free floats. The intervention of the Hong Kong government in the stock market offers a natural experiment to examining how the market liquidity was adversely affected by a substantial decline in free float in the market. The trading volume of the stocks listed in the Hang Seng Index (HSI) decreased substantially in 1999, while trading volumes of the group of control stocks did not decline. Also, stocks listed on the HSI experienced price increases. This showed that the government

The objective of this chapter is to show if there is a relationship between floating capital ratios and volumes operated in stock markets, volatility of prices and performance of shares. For this, data compiled include Latin American companies at the end 2016. The total number of enterprises studied is 181 and they belong to Argentina, Brazil, Chile, Peru and Colombia. Data were obtained from Thomson Reuters Eikon platform. (For detail, see Appendices).

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, their volatility and the volume traded during financial year 2016.

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 currency of each country (VOLAM).

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 recent daily trading closing prices, expressed as a percentage.

The volume was calculated based on the annual accumulated volume, divided by the number of working days of the year. The applied model is:

$$\text{LREA}\_{\text{ik}} = \beta\_0 + \beta\_1 \text{ FREF}\_{\text{ik}} + \varepsilon\_i \tag{1}$$

The explanatory variable in the developed regressions is floating capital ratio (FREF). Floating capital can be used as a representative measure of market size, understood as the value of all shares outstanding for trading. The explained variable in the first regression (Eq.(1)) of the proposed model is "annual percentage returns" which is defined as the annual variation of prices of each share that compound the selected indices of each country. It is constructed as the Neperian logarithm of annual returns for each company that conforms the sample in each country. The annual returns are obtained as the quotient of the homogeneous prices at the end of 2016, "Price Ait", of the i-th company and those that correspond to the homogeneous price of the same company at the annual close of the previous year (2015), "Price A i(t-1)". In order to

Effect of Free Float Ratio on the Behavior of Shares Valuation in Companies Listed in Latin…

\_\_\_\_\_\_\_\_ *PriceAit PriceAi*(*t*−1)

The next variable is annual typical deviation of the variation in prices (Eq. (2)) of shares from

A measure of the risk of price variation for a security is calculated from the standard deviation of 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 recent daily

Regarding the standard deviation, it must be borne in mind that it is a measure of risk in absolute terms. The higher the standard deviation, the greater variability of the assets price and therefore the greater its risk. It is a very useful statistical measure as long as the distribution

The third proposal uses the variable average daily traded volume of each i-th company (VOLAMik), (Eq. (3)) and is considered in millions of the currency of each country. Its calculation is performed as the accumulated annual volume divided by the days susceptible of

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 pro-

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

) ∗ 100 (4)

http://dx.doi.org/10.5772/intechopen.76421

39

obtain the percentages of variation, this result is multiplied by 100.

each country, which is used as a response variable. (DESVik).

LREAik = ln (

trading closing price, expressed as a percentage.

of probability of the asset's performance is normal.

duces knowledge and support decision-making.

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

the variables used in the model proposed in this chapter are carried out.

negotiation between both dates.

**4. Result**

$$\text{DESV}\_{\text{it}} = \beta\_0 + \beta\_1 \text{ FREF}\_{\text{it}} + \varepsilon\_{\text{i}} \tag{2}$$

$$\text{VOLAM}\_{\text{ul}} = \beta\_0 + \beta\_1 \text{ FREF}\_{\text{ul}} + \varepsilon\_i \tag{3}$$

The variables are:

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

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

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

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


**Table 1.** Size of sample.

The explanatory variable in the developed regressions is floating capital ratio (FREF). Floating capital can be used as a representative measure of market size, understood as the value of all shares outstanding for trading. The explained variable in the first regression (Eq.(1)) of the proposed model is "annual percentage returns" which is defined as the annual variation of prices of each share that compound the selected indices of each country. It is constructed as the Neperian logarithm of annual returns for each company that conforms the sample in each country. The annual returns are obtained as the quotient of the homogeneous prices at the end of 2016, "Price Ait", of the i-th company and those that correspond to the homogeneous price of the same company at the annual close of the previous year (2015), "Price A i(t-1)". In order to obtain the percentages of variation, this result is multiplied by 100.

$$\text{LREA}\_{\text{it}} = \ln \left( \frac{PriceA\_{\text{it}}}{PriceA\_{\text{it}+1}} \right) \ast 100 \tag{4}$$

The next variable is annual typical deviation of the variation in prices (Eq. (2)) of shares from each country, which is used as a response variable. (DESVik).

A measure of the risk of price variation for a security is calculated from the standard deviation of 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 recent daily trading closing price, expressed as a percentage.

Regarding the standard deviation, it must be borne in mind that it is a measure of risk in absolute terms. The higher the standard deviation, the greater variability of the assets price and therefore the greater its risk. It is a very useful statistical measure as long as the distribution of probability of the asset's performance is normal.

The third proposal uses the variable average daily traded volume of each i-th company (VOLAMik), (Eq. (3)) and is considered in millions of the currency of each country. Its calculation is performed as the accumulated annual volume divided by the days susceptible of negotiation between both dates.
