One aspect ultimately significant in making investment and financial decisions in a company; it is systematic risk. It is vital because it indicates the systematic risk of the company in relation to the market risk. Systematic risk changes are reflected in stock prices. They can decrease or increase stock prices thus increasing or decreasing the value of the shareholders. Therefore to understand the determinants of systematic risk is vital for companies' financial managers to be able to increase shareholders value and to attract investors by controlling systematic risk of the firm. Following this, the goal of the current study is to explore the financial determinants of systematic risk. To achieve the goal 457 non-financial companies have been selected from the Tehran Stock Exchange. Based on the financial information the dependent and independent variables have been estimated. Then, the hypothesis have been formulated and tested by the corresponding statistical tools. The results indicated that there are four variables that are significant for determining systematic risk.
Literature Review
According to the Sharp, 1963 there are two types of risks associated with all companies; systematic risk and unsystematic risk. Systematic risk is a market risk. Unsystematic risk is a risk that an individual firm faces [13]. Systematic risk measures change in stock in relation to change in market. It is covariance of stock returns of capital market [6]. Unlike unsystematic risk that can be removed or decreased with the help of diversification systematic risk is non-diversifiable. CAPM (Capital asset pricing model) describes the relationship between systematic risk and expected return. Mathematical representation of CAPM is as follows:
Ri = Rfr + ßo ( Rm - Rfr)
The idea underlying CAPM is that investors need to be compensated in two ways: time value of money and risk. The time value of money is represented by the risk-free (rf) rate in the formula and compensates the investors for putting money in any investment over a period of time. The amount of compensation the investor needs for taking on additional risk is calculated by a risk measure (beta) that compares the returns of the asset to the market over a period of time and to the market premium (Rm-rf).
Beta is measured according to relationship between the market return and expected return on security [7], and mathematically can be represented as:
E(Ri)= ßo + ßi Rm + ei
E(Ri) indicates expected return of a company that is expressed as a linear function of market return Rm and ei indicates the errors. The equation of beta can be derived from this formula in the following way:
ßi = Cov (E(Ri) , Rm) / Var (Rm)
As it can be seen there is a direct association between risk, expected return and market return. Hence, the higher the uncertainty is, the higher is the expected return of the investment. Information of systematic risk is useful for investors to analyze the nature of risk associated with investment [6]. In other words it is essential to understand the determinants of systematic risk to able to control it through these determinants. There have been number of researches that aimed to identify the determinants of systematic risk. Most of them have focused on financial determinants of systematic risk [6, 7]. Scherrer and Mathison, (1996); Gu and Kim, (2002); Lee and Jang (2006); Rowe and Kim (2010) have indicated a negative relationship between profitability and systematic risk in some industries and in others vice versa [6]. Borde et al. (1994) concluded positive relationship between profitability and systematic risk in insurance companies [1]. The underlying idea is that in financial institutions more profit leads towards greater risk. According to Logue and Merville (1972) profitability, debt ratio and company size play significant role in determining systematic risk [8]. Damodaram (2009) suggests that the degree of financial leverage, operating leverage and company size are among the main variables which affect the beta values of companies [2]. Jensen (1984) has estimated a positive relationship between systematic risk and liquidity [5]. However, most studies have found out a negative relationship between systematic risk and liquidity. Logue and Merville, (1972); Moyer and Charlfield, (1983); Gu and Kim, (1998) and (2002); Lee and Jang, (2006) have found negative relationship among systematic risk (Beta) and liquidity [4, 6, 7, 8, 11]. They argued that with increase in liquidity of the firm, the systematic risk decreases. According to Milicher (1974) there is a positive and nonlinear relationship between leverage and systematic risk. Olib et al., (2008) have used leverage in their study as control variable and found positive relationship between leverage and systematic risk [12]. Then, they [12] argued that large firms should have lower systematic risk due to economies of scale. Slliven, (1978) has stated that in large companies systematic risk is low because the large firms have the ability to decrease the effect of economic changes. Another argument was given by Titman and Wessels (1998) that large companies have more opportunities for diversification and due to diversification systematic risk reduces [14]. Further, researchers show that operating efficiency has negative impact on risk [4, 6]. Mandelbrot (2004) and Malkiel (2003) have applied time series data to estimate future beta values based on past periods [9, 10 . There are also some researchers who studied the impact of macroeconomic factors on systematic risk. Dilip Patro et al (2000) have found out that several variables including inflation, imports, exports significantly affect systematic risk [3].
Methodology
The sample of the current study includes 457 listed companies in Tehran Stock Exchange (http://www.tse.ir/). The data covers 2001-2011. The statistical package used is SPSS 16 and statistical tools used are common effect model and descriptive statistics. To understand which of the selected determinants are important for reducing systematic risk the following hypothesis have been put forward and tested:
H1: Liquidity is negatively related to systematic risk (Beta).
H2: Leverage is positively related to systematic risk (Beta).
H3: Operating efficiency is negatively related with systematic risk (Beta).
H4: Profitability is positively related with systematic risk (Beta).
H5: Firm size is negatively related with systematic risk (Beta).
Panel data applied in the current research has combined effect of time series and cross sectional data. To test the hypotheses the following common effect model has been used:
βit = α0 + α LIQit + α LIVit + α OEit + α PROFit + α FSit
Dependent variable, systematic risk for each firm has been estimated by linear regression model for 10 years in the following way:
Rc = β0 + β1 Rm β1= Rc- β0/ Rm
Where Rc is monthly average returns of company; Rm is monthly average returns of market while coefficient β1 is estimated beta on yearly bases.
The selection of the determinants for the current study is based on the fact that they can help managers to assess systematic risk and control it by means of firm specific factors. Independent variables are described in the table below.
Table 1- Independent Variables
Name of Variable
Measurement
Liquidity (LIQ)
Quick Ratio = Current asset - Inventory / Current liability
Leverage(LEV)
Debt ratio = Total Debt / Total Assets
Operating Efficiency
Asset Turnover = Total revenue / Total Asset
(OE)
Profitability (PROF)
Return on Assets = Net income / Total Assets
Firm Size(FS)
LN(Total Asset)
Data Analyses
Table 2 demonstrates the descriptive statistics of systematic risk (beta) and five independent variables for 457 listed companies for ten year period of 2001- 2011. Mean value of beta is 0.825. This means that the beta of selected companies is less than market beta that is always equal to 1 and thus the listed companies are less risky than market. Liquidity has mean score of 1.072 with std. deviation of ±0.856 which indicates the listed companies have enough cash to cover their current liabilities. Leverage has mean of 0.721 with deviation of ±0.374 indicating that on average 72.1% of the assets are financed by debt. Operating efficiency indicates that the average return on capital invested in the total assets is 12.3% from sales revenue. Finally, profitability measures indicate that average rate of return on investment is 12.5%.
Table 2-Descriptive Statistics
Beta
LIQ
LEV
OE
PROF
FS
Mean
0.825
1.072
0.721
1.123
0.125
8.02
SD
0.674
0.856
0.374
0.642
0.346
1.02
N
457
457
457
457
457
457
Table 3 indicates the relationship between financial variables and systematic risk. Common effect model is significant at the level of 5 percent with all variables significant but size. As the results indicate size does not have significant impact on risk. According to first hypothesis of study, liquidity is negatively related to beta. The results support our hypothesis indicating that one unit increase of liquidity will decrease systematic risk by 0.4581 units and vice versa. Second hypothesis states that there is positive relationship between leverage and risk. The results support the second hypothesis as well pointing out that with 95% confidence leverage increases systematic risk. As to third hypothesis, it is also supported by the results, i.e. increase of operating efficiency will decrease risk and vice versa. Finally, the fourth hypothesis also is accepted, concluding that the higher is the profitability the higher is the risk. The R-square coefficient is not very high which indicates that there are other factors apart from the five factors studied here that can influence systematic risk. Still, it shows that 58% of the variability in systematic risk is explained by the variability of the four significant financial factors.
Table 3-Results of Common Effect Model
Coefficients
Standard Error
t Stat
Intercept
0.8420
0.6520
1.24
LIQ
-0.4581
0.0426
-7.86*
LEV
0.5620
0.1405
2.40*
OE
-0.3248
0.0286
-4.58*
PROF
1.8622
0.2324
6.28*
FS
-0.5460
0.1458
-0.58
R Square
0.58
Adjusted R Square
0.565
F Statistics
2.186*
Observations
457
*Significant at the level of 5%.
Conclusion
The findings of the current research support previous researches. Financial variables do play significant role in determining systematic risk. The main goal of a company is to increase shareholders' value. To understand the factors related to systematic risk is very useful for investors and company managers. Though the higher risk promises higher returns, it also promises high losses. To avoid such losses managers should consider these factors not to put the shareholders' value at high risk. Investors should consider them to be able to assess the risk of investment according to the degree of their risk aversion. Current study has analyzed the relationship between systematic risk and financial variables. Four financial variables are found to be the determinants of systematic risk. This study is very useful for investors and managers. The limitation of the current research is perhaps the fact that the study included only non-financial companies. Another limitation is connected with the number of determinants. There can be other factors which may affect systematic risk and future researches can try to find them.
