task report on financial modelling module

5/28/2018 Task Report on Financial Modelling Module

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Full name : La Ode Sabaruddin

Student ID Number : 139040727

Program : Accounting and Finance MSc

Module : Financial Modeling (MN7024)

Word Count : 1965 (References and appendix are not included)

1. Introduction

This report aims to investigate stock performance of new listed companies , the

companies in which launched initial public offering (IPO) in stock market. Based on

theoretical review and empirical studies, five variables are considered as determinants

of IPO companies stock returns, namely: age of company, total assets, ownership

concentration, founder, rate of return on capital employed (ROCE), and industry or

sectors where the company operates1. The report, therefore, will assess the notion of

these relationships through both descriptive and inferential statistics. 300 samples are

selected to conduct the analysis.

2. Model Specification, Variables Definitions and Measurements

Model specification is the process of converting theories into a regression

model (Lee, et. al., 1999 p. 178). Referring to prior studies, stock returns of IPO

companies are primarily determined by a linear combination of five explanatory

variables, as mentioned before. The theoretical model can be written as follows:

(Adapted from Wooldridge, 2002 p.48)

Variables definitions and measurements:

IPOreturn = rate of return three years after IPO launched, P t / Pt-0, where Pt-0

represents the IPO price, and Pt the trading price three years

subsequently (%)

age = age of company (years)

size = total assets of the company ( million)

con = ownership concentration, percentage shares of pre-IPO owners post

floatation (%).

founder = CEO is also founder of the company:

1 if yes

0 if no

ROCE = average return on capital employed over the year prior to IPO (%)

1Other determinants of IPO return exist in literature, see, for example, Bansal and Khanna(2012); Bessler and Thies (2007); and Ritter and Welch (2002).


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industry = Sector in which company operates:

1 if computer hardware/electronics

2 if pharmaceuticals

3 if other manufacturing

4 if software development

5 if other servicesu = unobservable random disturbance of error

3. Statistical Analysis

3.1. Univariate AnalysisDescriptive Statistics

Descriptive statistics describes characteristics and spread of data through

graph and numerical summaries. Table 1 presents characteristics of IPO sample

companies for continues variables, while categorical variables are presented in table

2.

Table 1

Characteristics of IPO Sample CompaniesContinues Variables

Continues Variables

IPOreturn Age Size Con ROCE

Mean 20.241 14.63 18355.28 28.85 35.48

Standard deviation 27.910 2.793 9600.135 17.082 24.625

Normality tests (p-values):- Kolmogorov-Smirnov- Shapiro-walk

0.000

0.000

0.000

0.001

0.004

0.001

0.000

0.000

0.200

0.268

From the above table we can see that:

- IPO return, size, ownership concentration, and ROCE variables have relativelywider data spreads, indicated by high values of standard deviation. On the other

hand, data of age variable are closer to its mean, indicated by lower value of

standard deviation.

- Normality tests indicate that ROCE variable has normal distribution, while othersnot. Central limit theorem, however, says that random variables with large samples

(i.e. >30) will be normally distributed (Watsham and Parramore, 1997 p.136-137).

For graphical figures of continues variables, see appendix 1 section a.


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Table 2

Characteristics of IPO Sample Companies Categorical Variables

Frequency Percentage

Industry Sectors:

Computer hardware/electronics 53 17.7Pharmaceuticals 17 5.7

Other manufacturing 113 37.7

Software development 9 3.0

Other services 108 36.0

Total 300 100

Founder:

CEO is also founder of the company 88 29.3

CEO is not founder of the company 212 70.7

Total 300 100

In total of 300 IPO sample companies, other manufacturing and other services sectorsare the two largest sample companies with 37.7% and 36% respectively, whereas

software development sector is the least which occupied only 3% of the total samples.

In terms of founder, 70.7% of IPO sample companies have CEOs, which also the

founder, whereas CEOs of 29.3% companies are not the founder. For graphical

figures of categorical variables, see appendix 1 section b.

3.2. Bivariate Analysis - Correlations

Correlation indicates the strength of relationship between two variables. We

assess correlations among continues variables using Pearson correlation and

scatterplot, while association between continues variables and categorical variables

are assessed through analysis of variance (ANOVA) and boxplots. Association among

categorical variables is examined through chi-square test.

Correlations between Explanatory Variables and Dependent Variable

Based on statistical outputs in appendix 2 section a, correlations between

explanatory variables and dependent variable can be summarized as follows:

- IPO return has positive relationship and moderate in strength with ROCE, meansthat higher values of ROCE associate with higher values of IPO return (r=0.638).

Similarly, ownership concentration and size of company have positive

relationships with IPO return, but very weak in strength (r=0.136 and r=0.120

respectively). On the other hand, IPO return tends not to correlate with age of

company (r=-0.056). For the graphical figures, see scatterplots.


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- Company in which the CEO is also the founder tends to associate with higher IPOreturns, while company in which the CEO is not the founder tends to have lower

values of IPO returns (ANOVA test shows that these two groups of companies

have different means, p=0.000). On the other hand, higher or lower IPO returns

tends not to associate with particular industries or sectors where the companies

operate (ANOVA test has p=0.868). For the graphical figures, see boxplots.

Correlations Within Explanatory Variables

Correlations statistics within explanatory variables as shown in appendix 2,

section (b) indicate that most of explanatory variables are not correlate each other.

The exceptions are ownership concentration which has a weak negative relationship

with age of company (r=-0.318, see also scatterplot), and ROCE which has positive

association with founder where higher values on the ROCE tends to associate with

company in which the CEO is also the founder (ANOVA test has p=0.000, see also

boxplot). Later on, we will assess these correlations in multivariate analysis whether it

violates multicollinearity assumption or not.

3.3. Multivariate Analysis - Multiple Linear Regression

Since the model consists of several explanatory variables (see equation 2 in

model specification), we run multiple linear regressions to conduct the analysis.

Statistical outputs as shown in appendix 3, section (a) highlight that p-value for

regression model F-test is .000 and adjusted R-square=0.519, means explanatory

variables in the model are simultaneously account for 51.9% to explain variability in

IPO returns. Partially, size of company, ownership concentration, and founder are

statistically significant to predict IPO returns (p-values0.05),

means we find no support that different ages of company or different sectors of

industry will result in different future stock performance of IPO companies.

To capture differences in industry sectors, we create dummy variables for each

sectors of industry, as shown in equation (3).


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Again, the results (see appendix 3, section b) confirmed that all sectors or industry

have identic average value of IPO returns, indicated by insignificant p-values of all

dummy variables for industry. These insignificant outcomes, however, cannot be

interpreted in a strict sense since this report employs cross-sectional data, which has

less power to capture such differences.

Then, we rerun the model while omitting insignificant variables. The result shows

higher adjusted R-square (0.521) than the previous value (0.519), indicates that

insignificant variables are irrelevant to predict IPO returns. Hence, estimated model of

the relationships between IPO return and its explanatory variables can be expressed:

As IPO return is a linear combination of four explanatory variables in equation (4),

the model can be interpreted as follows:

- Intercept: If values of all explanatory variables in the model are zero, then IPOreturn is -29.201%.

- Slope of continues variables: If other variables in the model are fixed, then foreach change of 1 million in assets (size), then the model predicts that IPO

returns increases by 0.0004 %; If the ownership concentration increases by 1 %,

then IPO returns will increase by approximately 0.248% while holding other

variables constant; For each change of 1 % in ROCE, average increase in the

mean of IPO returns is about 0.678 % while controlling other variables constant.

- Slope of categorical variable: Company in which the CEO is also the founder hashigher IPO return average of 16.004% than company in which the CEO is not the

founder.

Testing the Assumptions of OLS Linear Regression Model

Assumption testing of OLS regression is necessary to obtain a valid model. In

this report, assumption tests include multicollinearity, heteroscedasticity, normality of

residuals and model specification. The tests use STATA program and the results are

summarized as follows (Appendix 4):

- Collinearity statistics show that all variables have VIF values around 1 andtolerance values around 0.9, means multicollinearity doesnt existin the model.

- Heteroscedasticity is assessed through Whites test and Breusch-Pagan test. Bothtests confirmed that the model has heteroscedasticity problem (p-values < 0.05),


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means the variance is not homogenous. The plotted residuals is also supported the

arguments (see rvfplot).

- Normality of residuals is examined by plotting ordered values of standardizedresiduals against expected values from standard normal distribution. The plots

include kernel density plot, P-P plot (pnorm plot), and quantiles plot (qnorm plot).

The results show a slight deviation from normality. These results are also

confirmed by Shapiro-Wilk W test (p-value=0.00571). However, since we have

large samples, central limit theorem can be applied.

- Model specification is evaluated by linktest and Ramsey Reset test. Linktest showsthat predicted values squares (hatsq) is significant, means the model has a

specification error. Likewise, the Ramsey Reset test is significant, indicates that

there could be omitted variable in the model, which it should. In conclusion, both

tests indicate that the model may not be correctly specified.

Interaction Terms

Refer to estimated model in equation (4), we check whether interaction terms

between dummy variable and other explanatory variables in the model exist or not

through regress equation (5).

The results show that the effects of company size, ownership concentration, and

ROCE are higher in a company where the CEO is also the founder (p-values < 0.05).

4. Conclusion and Limitations

This report examines whether age of company, size, ownership concentration,

founder, ROCE, and industry or sectors where the company operates can predict stock

return performance of IPO companies. The major findings are summarized as follows:

- Size of company, ownership concentration, founder and ROCE are statisticallysignificant to influence the stock return of IPO companies, which account for

52.1%, while 47.9 remains unexplained. On the other hand, we find no support

that age of company and industry sectors can predict future stock performance of

IPO companies.


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- The company in which the CEO is also the founder has higheraverage of IPOreturns than the company in which the CEO is not the founder.

- The effects of company size, ownership concentration and ROCE to stock returnof IPO companies are higher in a company where the CEO is also the founder.

Having said that, this report has some limitations:

1. There may be a problem of error specification and potential omitted variable biasin the model, as the model doesnt satisfy the specification test. In addition, the

model also has a problem of heteroscedasticity.

2. Coefficient determination of the model is relatively low, and therefore it isadvisable to include other powerful explanatory variables in future empirical

research.

3. The model only employs linear approach, while real-world financial time series arelikely to have both linear and nonlinear patterns, and therefore an approach, which

combines linear-nonlinear method will produce a more precise result.

4. As the employed data is cross section, it may less accurate to capture inter-company or industry sector differences as well as intra-company or sector

dynamics. Using panel data and larger sample size will give greater capacity to

capture the complexity of the relationships, include controlling the impact of

omitted variables.

References

Bansal, R. and Khanna A., 2012. Determinants of IPOs Initial Return: Extreme

Analysis of Indian Market. Journal of Financial Risk Management, 1 (4), pp. 68-

74.

Bessler, W. and Thies, S., 2007. The long-run performance of initial public offerings

in Germany.Managerial Finance, 33 (6), pp. 420-441.

Lee, C.F., Lee, J.C. and Lee, A. C., 1999. Statistics for Business and FinancialEconomics.Singapore: World Scientific Publishing.

Ritter, J.R. and Welch, I., 2002. A Review of IPO activity, pricing and allocations.

Journal of Finance, 57(4), pp. 1795-1828.

Watsham, T.J. and Parramore, K., 1997. Quantitative Methods in Finance. Singapore:

South Western Cengage Learning.

Wooldridge, J.M., 2002. Econometric Analysis of Cross Section and Panel Data.

Massachusetts: MIT Press.


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Appendix 1 - Univariate Analysis

a. Continues VariablesData Distribution


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b. Categorical Variables Data Distribution

Appendix 2Bivariate Analysis

a. Relationships between explanatory variables and dependent variable

Pearson Correlation - Continues explanatory variables and dependent variable

Correlations

Iporeturn age size con Roce

iporeturn

Pearson Correlation 1 -.056 .120* .136

* .638

**

Sig. (2-tailed) .338 .038 .018 .000

N 300 300 300 300 300

Age

Pearson Correlation -.056 1 -.009 -.318**

-.007

Sig. (2-tailed) .338 .874 .000 .910

N 300 300 300 300 300

Size

Pearson Correlation .120* -.009 1 .019 -.065

Sig. (2-tailed) .038 .874 .748 .258

N 300 300 300 300 300

Con

Pearson Correlation .136* -.318

** .019 1 -.059

Sig. (2-tailed) .018 .000 .748 .306

N 300 300 300 300 300

Roce

Pearson Correlation .638** -.007 -.065 -.059 1

Sig. (2-tailed) .000 .910 .258 .306

N 300 300 300 300 300

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).


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Scatterplots - Continues explanatory variables and dependent variable

Analysis of Variance (ANOVA):

Categorical explanatory variables and Continues dependent Variable

ANOVA - IPO return and Founder

Iporeturn

Sum of Squares df Mean Square F Sig.

Between Groups 40475.336 1 40475.336 62.679 .000

Within Groups 192433.990 298 645.752

Total 232909.327 299

ANOVAIPO return and Industry

Iporeturn


Between Groups 988.124 4 247.031 .314 .868

Within Groups 231921.202 295 786.174

Total 232909.327 299


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BoxplotsCategorical explanatory variables and continues dependent variable

b. Relationships within explanatory variables

Pearson CorrelationCorrelations between Continues explanatory variables

Correlations

age size con roce

age

Pearson Correlation 1 -.009 -.318**

-.007

Sig. (2-tailed) .874 .000 .910

N 300 300 300 300

size

Pearson Correlation -.009 1 .019 -.065

Sig. (2-tailed) .874 .748 .258

N 300 300 300 300

con

Pearson Correlation -.318**

.019 1 -.059

Sig. (2-tailed) .000 .748 .306

N 300 300 300 300

roce

Pearson Correlation -.007 -.065 -.059 1

Sig. (2-tailed) .910 .258 .306

N 300 300 300 300

**. Correlation is significant at the 0.01 level (2-tailed).

Scatterplots Correlations between continues explanatory variables


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Analysis of Variance (ANOVA):

Categorical explanatory variables and Continues explanatory Variables

ANOVA Founder and Continues Explanatory Variables


age

Between Groups 10.748 1 10.748 1.380 .241

Within Groups 2321.438 298 7.790

Total 2332.187 299

size

Between Groups 284679531.259 1 284679531.259 3.111 .079

Within Groups 27271932811.221 298 91516553.058

Total 27556612342.480 299

roce

Between Groups 8842.276 1 8842.276 15.278 .000

Within Groups 172464.640 298 578.740

Total 181306.917 299

con

Between Groups 385.340 1 385.340 1.322 .251

Within Groups 86856.910 298 291.466

Total 87242.250 299


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ANOVAIndustry and Continues Explanatory Variables


age

Between Groups 32.294 4 8.073 1.036 .389

Within Groups 2299.893 295 7.796Total 2332.187 299

size

Between Groups 609291110.615 4 152322777.654 1.668 .158

Within Groups26947321231.86

5295 91346851.633

Total27556612342.48

0299

roce

Between Groups 1513.115 4 378.279 .621 .648

Within Groups 179793.802 295 609.471

Total 181306.917 299

con

Between Groups 2055.916 4 513.979 1.780 .133

Within Groups 85186.334 295 288.767

Total 87242.250 299

BoxplotsCategorical explanatory variables and continues explanatory variables


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Pearson Chi-Square

Categorical explanatory variables

Chi-Square TestsFounder and Industry

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 1.561a 4 .816

Likelihood Ratio 1.483 4 .830

Linear-by-Linear Association .126 1 .723

N of Valid Cases 300

a. 2 cells (20.0%) have expected count less than 5. The minimum expected count is2.64.


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Appendix 3Multivariate Analysis

a. Multiple Linear RegressionAll variables

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 .727a .529 .519 19.34867

a. Predictors: (Constant), industry Industry, founder Founder, con, size, roce, age

b. Dependent Variable: iporeturn

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1Regression 123218.578 6 20536.430 54.856 .000

b

Residual 109690.748 293 374.371

Total 232909.327 299

a. Dependent Variable: iporeturn

b. Predictors: (Constant), industry Industry, founder Founder, con, size, roce, age

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig. Correlations Collinearity

Statistics

B Std. Error Beta Zero-order Partial Part Tolerance VIF

1

(Constant) -36.015 8.735 -4.123 .000

Age .228 .426 .023 .534 .593 -.056 .031 .021 .883 1.133

Size .000 .000 .133 3.279 .001 .120 .188 .131 .976 1.025

Con .263 .070 .161 3.779 .000 .136 .216 .151 .885 1.130

founder 16.051 2.545 .262 6.308 .000 .417 .346 .253 .930 1.076

Roce .681 .047 .601 14.490 .000 .638 .646 .581 .934 1.071

industry .810 .778 .042 1.041 .299 -.017 .061 .042 .973 1.027


b. Multiple Linear RegressionAll variables + Dummy variables for industry sectors

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .729a .531 .516 19.41089

a. Predictors: (Constant), d4_sector4, age, roce, d2_sector2, size,d1_sector1, founder Founder, con, d3_sector3


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ANOVAa


1

Regression 123642.326 9 13738.036 36.461 .000b

Residual 109267.001 290 376.783

Total 232909.327 299


b. Predictors: (Constant), d4_sector4, age, roce, d2_sector2, size, d1_sector1, founder Founder, con,

d3_sector3

Coefficientsa


Coefficients

Standardized

Coefficients


Statistics

B Std. Error Beta Zero-order Partial Part Tolerance VIF

1

(Constant) -31.614 7.888 -4.008 .000

Age .211 .429 .021 .490 .624 -.056 .029 .020 .876 1.141

Size .000 .000 .131 3.178 .002 .120 .183 .128 .958 1.043

Con .253 .071 .155 3.569 .000 .136 .205 .144 .861 1.161

founder 16.276 2.564 .266 6.348 .000 .417 .349 .255 .922 1.085

Roce .679 .047 .599 14.362 .000 .638 .645 .578 .929 1.077

d1_sector1 -3.003 3.287 -.041 -.914 .362 .018 -.054 -.037 .799 1.251

d2_sector2 -1.263 5.131 -.010 -.246 .806 .031 -.014 -.010 .893 1.120

d3_sector3 -1.791 2.662 -.031 -.673 .501 -.032 -.039 -.027 .755 1.324

d4_sector4 6.061 6.846 .037 .885 .377 .049 .052 .036 .921 1.086


c. Multiple Linear RegressionInsignificant variables are excluded

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 .726a .527 .521 19.32410

a. Predictors: (Constant), roce, con, size, founder Founder



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ANOVAa


1

Regression 122750.220 4 30687.555 82.180 .000b

Residual 110159.106 295 373.421

Total 232909.327 299


b. Predictors: (Constant), roce, con, size, founder Founder

Coefficientsa


Coefficients

Standardized

Coefficients


Statistics

B Std.

Error

Beta Zero-order Partial Part Tolerance VIF

1

(Constant) -29.201 3.701 -7.890 .000

Size .0003774 .000 .130 3.212 .001 .120 .184 .129 .982 1.019

Con .248 .066 .152 3.770 .000 .136 .214 .151 .990 1.010

founder 16.004 2.539 .262 6.304 .000 .417 .345 .252 .932 1.073

Roce .678 .047 .598 14.467 .000 .638 .644 .579 .938 1.066



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Appendix 4 REGRESSION ASSUMPTION TESTS (STATA OUTPUT)

NORMALITY TESTS OF RESIDUALS

. predict r, resid

. kdensity r, normal

_cons -29.20125 3.700855 -7.89 0.000 -36.48468 -21.91783

roce .6778655 .0468565 14.47 0.000 .58565 .7700809 founder 16.00441 2.538823 6.30 0.000 11.00791 21.00091

con .2478889 .0657597 3.77 0.000 .1184714 .3773064

size .0003774 .0001175 3.21 0.001 .0001462 .0006087

iporeturn Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 232909.325 299 778.960954 Root MSE = 19.324

Adj R-squared = 0.5206

Residual 110159.105 295 373.420696 R-squared = 0.5270

Model 122750.22 4 30687.555 Prob > F = 0.0000

F( 4, 295) = 82.18

Source SS df MS Number of obs = 300

. regress iporeturn size con founder roce

_cons -36.01547 8.734635 -4.12 0.000 -53.20605 -18.82489

industry .8097259 .7780957 1.04 0.299 -.7216392 2.341091

roce .6814775 .0470294 14.49 0.000 .5889192 .7740358

founder 16.0508 2.544542 6.31 0.000 11.04291 21.0587

con .2631583 .0696444 3.78 0.000 .1260916 .400225

size .000387 .000118 3.28 0.001 .0001547 .0006192

age .2278899 .4263863 0.53 0.593 -.6112783 1.067058


Total 232909.325 299 778.960954 Root MSE = 19.349



Model 123218.578 6 20536.4297 Prob > F = 0.0000 F( 6, 293) = 54.86


. regress iporeturn age size con founder roce industry

0

.005

.01

.015

.02

.0

25

-50 0 50 100Residuals

Kernel density estimate

Normal density

kernel = epanechnikov, bandwidth = 4.9020

Kernel density estimate


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. pnorm r

. qnorm r

. swilk r

0.0

0

0.2

5

0.5

0

0.7

5

1.0

0

0.00 0.25 0.50 0.75 1.00

Empirical P[i] = i/(N+1)

-50

0

50

100

i

l

-50 0 50

Inverse Normal

r 300 0.98621 2.938 2.529 0.00571

Variable Obs W V z Prob>z

Shapiro-Wilk W test for normal data


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HOMOSCEDASTICITY TESTS

. rvfplot, yline(0)

Prob > chi2 = 0.0000

chi2(1) = 22.82

Variables: fitted values of iporeturn

Ho: Constant variance

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

. estat hettest

Total 42.94 18 0.0008

Kurtosis 2.40 1 0.1214

Skewness 13.58 4 0.0088

Heteroskedasticity 26.96 13 0.0126

Source chi2 df p

Cameron & Trivedi's decomposition of IM-test

. estat imtest

-50

0

50

100

-40 -20 0 20 40 60Fitted values


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MULTICOLLINEARITY TEST

MODEL SPECIFICATION TEST

. linktest

. ovtest

_cons -.8786877 1.531233 -0.57 0.567 -3.89213 2.134755

_hatsq .0092055 .001993 4.62 0.000 .0052834 .0131276

_hat .6709956 .0888404 7.55 0.000 .4961591 .845832


Total 232909.325 299 778.960954 Root MSE = 18.602



Model 130133.203 2 65066.6016 Prob > F = 0.0000

F( 2, 297) = 188.03


Prob > F = 0.0001

F(3, 292) = 7.23

Ho: model has no omitted variables

Ramsey RESET test using powers of the fitted values of iporeturn

Mean VIF 1.04

con 1.01 0.989811

size 1.02 0.981521

roce 1.07 0.938088

founder 1.07 0.931615

Variable VIF 1/VIF

. vif

_cons -29.20125 3.700855 -7.89 0.000 -36.48468 -21.91783 roce .6778655 .0468565 14.47 0.000 .58565 .7700809

founder 16.00441 2.538823 6.30 0.000 11.00791 21.00091

con .2478889 .0657597 3.77 0.000 .1184714 .3773064

size .0003774 .0001175 3.21 0.001 .0001462 .0006087


Total 232909.325 299 778.960954 Root MSE = 19.324



Model 122750.22 4 30687.555 Prob > F = 0.0000

F( 4, 295) = 82.18


. regress iporeturn size con founder roce


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.

Prob > F = 0.0001

F(3, 290) = 7.29

Ho: model has no omitted variables

Ramsey RESET test using powers of the fitted values of iporeturn

. ovtest

_cons -.8816762 1.52568 -0.58 0.564 -3.88419 2.120838

_hatsq .0091313 .0019613 4.66 0.000 .0052716 .0129911

_hat .6734393 .087824 7.67 0.000 .5006033 .8462754


Total 232909.325 299 778.960954 Root MSE = 18.553



Model 130679.95 2 65339.9752 Prob > F = 0.0000 F( 2, 297) = 189.83


. linktest

_cons -36.01547 8.734635 -4.12 0.000 -53.20605 -18.82489

size .000387 .000118 3.28 0.001 .0001547 .0006192

roce .6814775 .0470294 14.49 0.000 .5889192 .7740358

industry .8097259 .7780957 1.04 0.299 -.7216392 2.341091 founder 16.0508 2.544542 6.31 0.000 11.04291 21.0587

con .2631583 .0696444 3.78 0.000 .1260916 .400225

age .2278899 .4263863 0.53 0.593 -.6112783 1.067058


Total 232909.325 299 778.960954 Root MSE = 19.349



Model 123218.578 6 20536.4297 Prob > F = 0.0000

F( 6, 293) = 54.86


. regress iporeturn age con founder industry roce size

task report on financial modelling module

Documents

ipo price

age of company

determinants of ipo

roce variables

variables definitions

categorical variables

rate of return

company millioncon