State Ownership at the Oslo Stock Exchange

Transcription

1 State Ownership at the Oslo Stock Exchange Bernt Arne Ødegaard 1 Introduction We ask whether there is a state rebate on companies listed on the Oslo Stock Exchange, i.e. whether companies where the state is a major owner are priced lower than they would be had the state not been there as an owner. We first ask whether there are potential reasons for this to happen, before looking at two empirical investigations that speaks to this. 2 Theory What are we looking at: Regular companies, typically listed on a stock exchange. Such companies have an implicit goal: Maximizing the value of their equity stake. Enter the state (or more generally, government) as an owner some fraction of a company s stocks are owned by a governmental owners. One view: This does not make any difference. The company is run by its management to maximize value, the state as an owner does not affect that. More complicated view: Agency view, Jensen and Meckling (1976). Firm is run as a result of interaction between various groups. In particular, the three groups Equity owners Creditors Management are all interested in the running of the firm, but have different objectives. They also have different saying in the running of the firm. Objectives: Shareholders: Maximize the value of outstanding equity. Creditors: Maximize probability of full repayment of debt. Managers: High salaries (Can be justified by increasing firm size) Easy life Minimize probability of bankruptcy (Their human capital to some (more or less) degree bound to their current employment.) These different preferences may impose nonvalue maximizing behaviour. Why? Informational asymmetries makes it impossible for providers of capital to write contracts that make managers always act in their best interest. Mechanisms that indirectly prevent the managers of the firm from deviating too far Equity ownership concentration If ownership concentrated with few investors. 1

2 Each investor has incentives to monitor management. The investor also has power, both voting power representation on board of directors Cost of large stake in a firm: Limited diversification. Debt ownership concentration: Suppose debt is concentrated with a few investors/banks. Debtholders have incentives to monitor firms performance. Can threathen to withold funds, gives leverage over management s decisions. But debtholders only care about lower part of cashflow distribution. This is the main features of the corporate governance problem. Where does the state as an owner fit? As a large, external, owner. (Concentrated ownership). Issue: Who influences how the state uses its power to affect running of the firm (voting/investor representation on board). Bureaucrats (e.g. ministry of industry)? Do these have incentives to monitor the running of the firm? Politicians? May have different (political) objectives that conflict with efficient running of the firm. 3 Estimation How can one estimate whether the presence of the state as a large owner affects firm value? Look at two types of estimation. 3.1 Governance modelling Consider the regression Firm value = f(corporate Governance) Here it is impossible to observe directly the corporate governcance mechanism, so one specifies a bunch of proxy variables believed to be part of the governance of a firm. 1 Typical Governance variables in such studies Ownership concentration (external owners) Type of the large owners Inside concentration Corporate Financing (Debt/Equity) Product market... The estimate of firm value is usually Tobin s Q. In this type of study add fraction owned by state. See Table 1 for results. So for the whole period, the state ownership is not a significant determinant of Q. 1 Classical studies of this is Demsetz and Lehn (1985), Morck, Shleifer, and Vishny (1988), McConnell and Servaes (1990) 2

3 Table 1 Governance Performance regression with state Q Variable coeff constant (0.00) Herfindahl index (0.00) Insider ownership fraction (0.00) Kvadrert(Insider ownership fraction) (0.00) Fraction owned directly by state (0.22) Fraction owned by foreign owners (0.00) Fraction owned by financials (0.56) Fraction owned by nonfinancials (0.88) ln(company Size) (0.01) Product market: 10 Energy (0.00) Product market: 15 Material (0.00) Product market: 20 Industry (0.00) Product market: 25 Consumer Discretionary (0.00) Product market: 30 Consumer Staples (0.00) Product market: 45 IT (0.00) n 1327 R Performance analysis We want to test whether The state policy is that listed companies should act so that they maximize the stock value, ie. act in the shareholders interest. The state is not supposed to pursue political objectives that makes companies where they are majority owners deviate from value maximization. An obvious prediction we can look at is whether the companies on the exhange with state majority ownership earns less returns than they should. This is a question we can ask as a portfolio performance question. We evaluate the state portfolio using standard methods of portfolio performance. 3.3 Doing a performance analysis First doing the obvious descriptive analysis, using octave We find that the annualized return on this portfolio is 13.6% per year. Another nice way to vizualize such data is to plot the evolution of the implied wealth from investing in this portfolio. 3

4 Now, whether this is a good or bad return is not something we can say without more information. In particular we need to compare this return to something else. An obvious first try is to look at an alternative investment, such as a market portfolio. Generating similar wealth series for the market, we can then plot comparisons

5 (EW on the left, VW on the right) Compare the state portfolio with the market portfolios. Observe that the state s portfolio evolution is lower than either of the market portfolios. 60 state ew market vw market But just comparing things to the market is not what we should be doing. Instead, this is of course when we need a model saying: What should the portfolio return have been? In particular, we need to formulate this as a finance question: What is the expected return on a portfolio with the same risk as the state portfolio? The classical performance measure is the calculation of an alpha relative to the CAPM. Let r p be the return on the state portfolio, r f be the risk free rate, r m the return on a market portfolio. Consider the CAPM relationship r pt = r ft + b p (r mt r ft ) Rewriting in excess return terms er pt = r pt r ft er mt = r mt r ft 5

6 We see that the CAPM relationship is or To get a testable model we consider the regression r pt r ft = β p (r mt r ft ) er pt = β p (er mt ) er pt = a p + b p er mt + e pt If the CAPM holds a p = 0. This a p is the object of interest, and is typically called Jensens alpha. Our next step is to estimate this. To return to the question of the state s portfolio performance, this is answered by asking whether the alpha is significantly negative. If it is, it is consistent with the state ownership influencing these companies in a negative fashion. > lm(formula="ers ~ ermew") (Intercept) ermew > lm("ers ~ ermvw") (Intercept) ermvw Once we have done this, we can also ask for the complete results of the analysis > summary(runs.ew) Call: lm(formula = "ers ~ ermew") Residuals: Min 1Q Median 3Q Max Estimate Std. Error t value Pr(> t ) (Intercept) ermew <2e-16 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 214 degrees of freedom Multiple R-squared: ,Adjusted R-squared: F-statistic: on 1 and 214 DF, p-value: < 2.2e-16 > runs.vw=lm(formula="ers ~ ermvw") > summary(runs.vw) Call: lm(formula = "ers ~ ermvw") 6

7 Residuals: Min 1Q Median 3Q Max Estimate Std. Error t value Pr(> t ) (Intercept) ** ermvw < 2e-16 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 214 degrees of freedom Multiple R-squared: ,Adjusted R-squared: F-statistic: on 1 and 214 DF, p-value: < 2.2e-16 Note the beta estimates. In both cases the beta is less than one. So the return on this portfolio should be less than the return on the market portfolio (according to the CAPM) The economically interesting numbers here are the alpha estimates, when we use the ew index, and when we use the value weighted index. In both cases the estimates are negative. So on first glance there is some evidence that the returns on this portfolio is less than it should. But this ignores the uncertainty in the parameter estimate. Before we can conclude anything we need to estimate the uncertainty about the parameter estimate. Although negative estimate of alpha, the t stat is nowhere near significant. This result relies on the CAPM as the true model of returns. Now, an alternative model of returns than CAPM is very popular among acadmics is the Fama French 3 factor model. Essentially, this model uses two additional factors SM B and HM L to explain asset returns Let us estimate the alpha in this setting. r pt = r ft + b p (r mt r ft ) + b HML HML + b SMB SMB > runs.ew=lm(formula="ers ~ ermew + SMB + HML ") > summary(runs.ew) Call: lm(formula = "ers ~ ermew + SMB + HML ") Residuals: Min 1Q Median 3Q Max Estimate Std. Error t value Pr(> t ) (Intercept) ermew <2e-16 *** SMB <2e-16 *** HML Signif. codes: 0 *** ** 0.01 * Residual standard error: on 212 degrees of freedom Multiple R-squared: 0.66,Adjusted R-squared: F-statistic: on 3 and 212 DF, p-value: < 2.2e-16 and > runs.vw=lm(formula="ers ~ ermvw + SMB + HML ") > summary(runs.vw) 7

8 Call: lm(formula = "ers ~ ermvw + SMB + HML ") Residuals: Min 1Q Median 3Q Max Estimate Std. Error t value Pr(> t ) (Intercept) ermvw < 2e-16 *** SMB *** HML * --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 212 degrees of freedom Multiple R-squared: ,Adjusted R-squared: F-statistic: on 3 and 212 DF, p-value: < 2.2e-16 Again, the interesting numbers are the alpha estimates and their t-stats ew: (0.918) vw: (-1.86) Using the ew portfolio as the market, we have a positive (albeit not significant) alpha. Using the vw portfolio we have a negative alpha. Whether it is significant depends on the significance level. If we use a value weighted index we would reject that alpha is zero at the 5% level but not at the 2.5% level. Once the data is in R it is also simple to do additional statistical analysis. Let us for example calculate a confidence interval > confint(runs.ew) 2.5 % 97.5 % (Intercept) ermew SMB HML The default is a 95 % confidence interval. If we want to relax it specify the level > confint(runs.ew,level=0.9) 5 % 95 % (Intercept) ermew SMB HML Changing risk levels? When we run the regression er pt = a p + b p er mt + e pt we are assuming that the risk is constant. But the portfolio composition is changing over time. One way deal with that is to let the beta change over time: er pt = α p + β pt er mt 8

9 Figure 1 Time series of estimated beta

10 If we have an estimate of beta we can simply plug in the estimated beta, calculate the ex post alpha, and take the average. In figure 2 we plot the estimates of beta. Doing the calculations we find the ex post average abnormal returns Let us look at the distribution of these estimates Histogram: Ex post excess returns relative to ew portfolio Histogram: Ex post excess returns relative to vw portfolio 10

11 Calculating t-stats for testing whether the estimated excess returns are different from zero: >> t=mean(e_ew)/(std(e_ew)/sqrt(t)) t = >> t=mean(e_vw)/(std(e_vw)/sqrt(t)) t =

12 Appendix A Performance analysis using matlab(octave) We use a dataset which is taken from Ødegaard (2009), with the monthly returns from the government s portfolio (or part of it, Statoil is actually not here since that is owned by a different department). The returns are from 1992 to First doing the obvious descriptive analysis, using octave >> sp = dlmread("../data_set/state_portfolio_returns.txt",";"); >> sprets=sp(:,2); >> mean (sprets) ans = >> annret=(1+mean(sprets))^12-1 annret = We find that the annualized return on this portfolio is 13.6% per year. Another nice way to vizualize such data is to plot the evolution of the implied wealth from investing in this portfolio. We show one way to generate this, by adding the monthly return month by month. >> wealth=[1]; >> w=1; >> for i=1:rows(sprets) > w=w*(1+sprets(i)); > wealth=[wealth;w]; > endfor > plot(wealth) Now, whether this is a good or bad return is not something we can say without more information. In particular we need to compare this return to something else. An obvious first try is to look at an alternative investment, such as a market portfolio. You also have available various other comparable returns, such as the returns on two market portfolios ew and vw, in the period from 1980 onwards. I pull the matching market portfolio from the file with monthly market portfolios: > rm=dlmread("../../asset_pricing_ose/data_set/market_portfolios_monthly.txt",";",1,0); > ew=rm(133:348,2); > vw=rm(133:348,3); Note that I have used the dates to pick the relevant rows, investigate alternative ways of generating a matching set of returns. Generating similar wealth series for the market, we can then plot comparisons (EW on the left, VW on the right) And then we compare the state portfolio with the market portfolios. Observe that the state s portfolio evolution is lower than either of the market portfolios. But just comparing things to the market is not what we should be doing. Instead, this is of course when we need a model saying: What should the portfolio return have been? In particular, we need to formulate this as a finance question: What is the expected return on a portfolio with the same risk as the state portfolio? The classical performance measure is the calculation of an alpha relative to the CAPM. 12

13 Let r p be the return on the state portfolio, r f be the risk free rate, r m the return on a market portfolio. Consider the CAPM relationship r pt = r ft + b p (r mt r ft ) Rewriting in excess return terms We see that the CAPM relationship is er pt = r pt r ft er mt = r mt r ft or To get a testable model we consider the regression r pt r ft = β p (r mt r ft ) er pt = β p (er mt ) er pt = a p + b p er mt + e pt If the CAPM holds a p = 0. This a p is the object of interest, and is typically called Jensens alpha. Our next step is to estimate this. To return to the question of the state s portfolio performance, this is answered by asking whether the alpha is significantly negative. If it is, it is consistent with the state ownership influencing these companies in a negative fashion. Now, step 1 is to input data > sp = dlmread("../data_set/state_portfolio_returns.txt",";",1,0); > sprets=sp(:,2); > rm=dlmread("../../asset_pricing_ose/data_set/market_portfolios_monthly.txt",";",1,0); > ew=rm(133:348,2); > vw=rm(133:348,3); To calculate excess returns we need an estimate of a risk free rate. We use montly observations of NIBOR. > RFmonthly=dlmread("../../asset_pricing_ose/data_set/NIBOR_monthly.txt",";",1,0); > RFmonthly(133,1) ans = > RFmonthly(132,1) ans = > rf=rfmonthly(131:131+rows(sprets)-1,2); Note a particular issue here. The interest rate is the interest rate observed on that date for one month borrowing going forward. We therefore have to lag this one period. We are now ready to do estimation. Here are the simple OLS estimates ols(er_s,[ones(216,1) er_m_ew]) ans = >> ols(er_s,[ones(216,1) er_m_vw]) ans =

14 Note the beta estimates. In both cases the beta is less than one. So the return on this portfolio should be less than the return on the market portfolio (according to t The economically interesting numbers here are the alpha estimates, when we use the ew index, and when we use the value weighted index. In both cases the estimates are negative. So on first glance there is some evidence that the returns on this portfolio is less than it should. But this ignores the uncertainty in the parameter estimate. Before we can conclude anything we need to estimate the uncertainty about the parameter estimate. The typical first step to inference is to calculate the variance covariance matrix of the parameter estimates. In OLS settings the following are the relations we need to crank through Under normality: b ols n y = Xb + e = (X X) 1 X y S 2 = 1 n (e e) b ols n N ( b, S 2 (X X) 1) We need to calculate the covariance matrix of the estimates Let us do this for the ew index. er_s=sprets-rf; er_m_ew=ew-rf; i=ones(rows(sprets),1); X_ew = [i er_m_ew]; b_ew=ols(er_s,x_ew) e_ew=er_s-[i er_m_ew]*b_ew; S2_ew=e_ew *e_ew/t Sigma_ew = S2_ew*inv(X_ew *X_ew) t_ew=b_ew(1)/sqrt(sigma_ew(1,1)) Σ = S 2 (X X) 1 This set of commands produces the following useful results The covariance matrix of the estimates. Sigma_ew = e e e e-03 What we want to base inference on alpha on is the t statistic > t=b_ew(1)/sqrt(sigma_ew(1,1)) t = Although negative estimate of alpha, the t stat is nowhere near significant. This result relies on the CAPM as the true model of returns. Now, an alternative model of returns than CAPM is very popular among acadmics is the Fama French 3 factor model. Essentially, this model uses two additional factors SM B and HM L to explain asset returns r pt = r ft + b p (r mt r ft ) + b HML HML + b SMB SMB Let us estimate the alpha in this setting. The additional work needed is reading in the FF factors 14

15 > FFmonthly=dlmread("../../asset_pricing_ose/data_set/pricing_factors_monthly.txt",";",13,0); > FFmonthly(108,1) ans = >> FFmonthly(109,1) ans = > SMB=FFmonthly(109:109+T-1,2); > mean(smb) ans = > HMB=FFmonthly(109:109+T-1,3); > mean(hmb) ans = Doing the regression is then a matter of the following commands i=ones(rows(sprets),1); X_ew = [i er_m_ew SMB HML]; b_ew=ols(er_s,x_ew) e_ew=er_s-x_ew*b_ew; mean(e_ew) S2_ew=e_ew *e_ew/t Sigma_ew = S2_ew*inv(X_ew *X_ew) t_alpha_ew =b_ew(1)/sqrt(sigma_ew(1,1)) Which produces the following interesting estimates b_ew = S2_ew = Sigma_ew = e e e e e e e e e e e e e e e e-03 t_alpha_ew = and b_vw = S2_vw = Sigma_vw = e e e e e e e e e e e e e e e e-03 t_alpha_vw = Again, the interesting numbers are the alpha estimates and their t-stats ew: (0.918) vw: (-1.86) Using the ew portfolio as the market, we have a positive (albeit not significant) alpha. 15

16 Using the vw portfolio we have a negative alpha. Whether it is significant depends on the significance level. Calculating the probability levels Using the normal distrubution > normcdf(t_alpha_ew) ans = > normcdf(t_alpha_vw) ans = Using the t distribution > tcdf(t_alpha_ew,t-4) ans = > tcdf(t_alpha_vw,t-4) ans = If we use a value weighted index we would reject that alpha is zero at the 5% level but not at the 2.5% level. A.1 Changing risk levels? When we run the regression er pt = a p + b p er mt + e pt we are assuming that the risk is constant. But the portfolio composition is changing over time. (See the data on the shares owned.) One way deal with that is to let the beta change over time: er pt = α p + β pt er mt If we have an estimate of beta we can simply plug in the estimated beta, calculate the ex post alpha, and take the average. On the homepage find portfolio beta estimates. β pt = i w i β it where β it is a rolling beta estimate for the beta of each stock. In figure 2 we plot the estimates of beta. Figure 2 Time series of estimated beta Doing the calculations we find the ex post average abnormal returns Sp = dlmread("../data_set/state_portfolio_returns.txt",";",1,0); sprets=sp(:,2); Spbetas = dlmread("../data_set/state_portfolio_betas.txt",";",1,0); betas=spbetas(:,2); plot(betas) print("beta_evolution_state_portf.eps","-depsc"); T=rows(sprets); rm=dlmread("../data/market_portfolios_monthly.txt",",",1,0); ew=rm(133:133+t-1,2); vw=rm(133:133+t-1,3); RFmonthly=dlmread("../data/NIBOR_monthly.txt",";",1,0); rf=rfmonthly(132:132+t-1,2); e_ew=sprets-(rf+ betas.* (ew-rf)); 16

17 hist(e_ew) print("hist_rolling_beta_e_ew.eps","-depsc"); mean(e_ew) e_vw=sprets-(rf+ betas.* (vw-rf)); mean(e_vw) print("hist_rolling_beta_e_vw.eps","-depsc"); Let us look at the distribution of these estimates Histogram: Ex post excess returns relative to ew portfolio Histogram: Ex post excess returns relative to vw portfolio Calculating t-stats for testing whether the estimated excess returns are different from zero: >> t=mean(e_ew)/(std(e_ew)/sqrt(t)) t = >> t=mean(e_vw)/(std(e_vw)/sqrt(t)) t =

18 B Performance analysis Using R Let us now illustrate using R in this setting. sp <- read.table ("../data_set/state_portfolio_returns.txt",header=true,sep=";"); sprets <-sp[,2]; T=dim(sp)[1] rm <- read.table("../../asset_pricing_ose/data_set/market_portfolios_monthly.txt",header=true,sep=";" ew<-rm[133:(133+t-1),2]; vw<-rm[133:(133+t-1),3]; RFmonthly<-read.table("../../asset_pricing_ose/data_set/NIBOR_monthly.txt",header=TRUE,sep=";"); rf<-rfmonthly[132:(132+t-1),2]; ers <- sprets-rf; ermew <- ew-rf; ermvw <- vw-rf; lm(formula="ers ~ ermew") lm(formula="ers ~ ermvw") Produces the output > lm(formula="ers ~ ermew") (Intercept) ermew > lm("ers ~ ermvw") (Intercept) ermvw Once we have done this, we can also ask for the complete results of the analysis > summary(runs.ew) Call: lm(formula = "ers ~ ermew") Residuals: Min 1Q Median 3Q Max Estimate Std. Error t value Pr(> t ) (Intercept) ermew <2e-16 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 214 degrees of freedom Multiple R-squared: ,Adjusted R-squared: F-statistic: on 1 and 214 DF, p-value: < 2.2e-16 > runs.vw=lm(formula="ers ~ ermvw") > summary(runs.vw) 18

19 Call: lm(formula = "ers ~ ermvw") Residuals: Min 1Q Median 3Q Max Estimate Std. Error t value Pr(> t ) (Intercept) ** ermvw < 2e-16 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 214 degrees of freedom Multiple R-squared: ,Adjusted R-squared: F-statistic: on 1 and 214 DF, p-value: < 2.2e-16 Adding the FF factors is then simply a matter of: > FFmonthly <- read.table("../../asset_pricing_ose/data_set/pricing_factors_monthly.txt",header=true,sep=";",ski > FFmonthly[108,1] [1] SMB <- FFmonthly[109:(109+T-1),2]; HML <- FFmonthly[109:(109+T-1),3]; runs.ew=lm(formula="ers ~ ermew + SMB + HML ") Which produces the following results > runs.ew=lm(formula="ers ~ ermew + SMB + HML ") > summary(runs.ew) Call: lm(formula = "ers ~ ermew + SMB + HML ") Residuals: Min 1Q Median 3Q Max Estimate Std. Error t value Pr(> t ) (Intercept) ermew <2e-16 *** SMB <2e-16 *** HML Signif. codes: 0 *** ** 0.01 * Residual standard error: on 212 degrees of freedom Multiple R-squared: 0.66,Adjusted R-squared: F-statistic: on 3 and 212 DF, p-value: < 2.2e-16 and > runs.vw=lm(formula="ers ~ ermvw + SMB + HML ") > summary(runs.vw) Call: lm(formula = "ers ~ ermvw + SMB + HML ") 19

20 Residuals: Min 1Q Median 3Q Max Estimate Std. Error t value Pr(> t ) (Intercept) ermvw < 2e-16 *** SMB *** HML * --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 212 degrees of freedom Multiple R-squared: ,Adjusted R-squared: F-statistic: on 3 and 212 DF, p-value: < 2.2e-16 So, the gains to going to the statistical framework are substantial, since R actually knows about the relevant statistical methods it is merely a matter of getting the data aligned. Once the data is in R it is also simple to do additional statistical analysis. Let us for example calculate a confidence interval > confint(runs.ew) 2.5 % 97.5 % (Intercept) ermew SMB HML The default is a 95 % confidence interval. If we want to relax it specify the level > confint(runs.ew,level=0.9) 5 % 95 % (Intercept) ermew SMB HML References Harold Demsetz and Kenneth Lehn. The structure of corporate ownership: Causes and consequences. Journal of Political Economy, 93: , Michael C Jensen and William H Meckling. Theory of the Firm: Managerial behavior, Agency costs and Ownership structure. Journal of Financial Economics, 3: , October John J McConnell and Henri Servaes. Additional evidence on equity ownership and corporate value. Journal of Financial Economics, 27: , Randall Morck, Andrei Shleifer, and Robert W Vishny. Mangement ownership and market valuation: An empirical analysis. Journal of Financial Economics, 20: , Bernt Arne Ødegaard. Statlig eierskap på Oslo børs. Praktisk Økonomi og Finans, 25(4),