An International Investment Portfolio Accounting Essay

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International investment seems to attract many investors resulted from the many benefits of the published international investment portfolio by firms all over the world. Fund investors can play a part in the economic growth of the other countries, able to review their rate of risk, realizing diversification effects and taking advantage of different market segments on a global scale. Globalization reflects the worldwide growth of marketing individual countries. These advantages may seem tempting but the risks involved for international investment must not be overlooked. In an international investment perspective, financial investments are not only subjected to currency and political risk. However, there are many organizations drawbacks and difficulties, one of which is related to tax issues. These weaknesses of an organization usually benefited those investors who are able to manage to overcome those difficulties in a well-organised approach.


1 INTRODUCTION

The international economic activities currently have been increased dramatically due to the investment of business internationally. International economies have become incorporated through a vast network of communication and trade with the help of globalization. Due to globalization, international flow of financial assets have been improved by many advances in better lower cost of communication and transport, which means that geographical distances are unimportant and thus national economies are closely linked.

Investment portfolio usually involves the purchase of bonds, shares, stocks and assets by foreign international investors, all of them with the key objective of achieving a financial profit. It works in a variety of different ways toward the goal of conserving and generating profits. Money could be made from possibly any investment environment even though international financial markets are very much complex. International portfolio investment is somehow risky. The greatest challenge faced almost by all investors in making an investment portfolio work is by knowing exactly what to do at the right time. The factors that normally affects are foreign exchange rates, interest rates and tax rates on interests. Therefore, a well-diversified portfolio is recommended to mitigate risk. When the investors want to broaden their investment, they will observe the international market and investing in foreign companies. The significant reason why international portfolio investment might enhance stable returns and reduce risk is the broader diversification. One of the benefits of broader diversification is risk-return tradeoff, which is more profitable while investing internationally. Another potential benefit is the diversification of currency, which means it not only exposed a foreign company's operation, but also to this foreign currency. As an investment fund manager, the management includes deciding what assets to purchase, how many to purchase, and when is the best time to purchase. These decisions must have some analysis of measurements, which typically involves expected return on the portfolio and the risk on the return.


2 Evaluation of the listed firms

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2.1 Brief introduction of the 3 list firms

    DASHANG GROUP 'A'

    Code: CN:DDS(P) explain

    SPECIALTY FASH.GP. (BER)

    Code: D:MVJ(P) explain

    PACIFIC NET

    Code: J:PNET(P) explain

2.2 Asses the VaR exposure of the investments

2.3 Analyzing the economic exposure of each company

Figure2.3.1:

Figure 2.3.2:

Figure 2.3.3:

2.3.1 Regression Analysis

Regression analysis is a widely used statistical tool means on focusing on analyzing the relationship between a dependent variable, Y, and independent variable, X, using the simple linear model Y = a + bX. Regression analysis gives an understanding on how the dependent variable Y changes with varying independent variable X. The values of X and Y are inputted into Microsoft Excel and by using the regression approach, values of a and b are calculated. Excel will then output a summary consisted of a regression statistics table and ANOVA.

The R2 is a degree of variation, measured in percentage, in the dependent variable that can be accounted for by the independent variables. Multiple R is basically the square root of R2. The standard error is an estimated value that is determined by Excel in conjunction with the estimated coefficient. Adjusted R2 is calculated using All calculated values are as shown in table 2.3.1.1. explain observations (n).

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Regression Statistics

DASHANG GROUP 'A'

SPECIALTY FASH.GP.

PACIFIC NET

Multiple R

0.59815775

0.608295486

0.907217302

R2

0.357792693

0.370023398

0.823043232

Adjusted R2

0.356552911

0.368816546

0.822704235

Standard Error

2300.212343

38.18072501

118.5093191

Observations

520

524

524

Table 2.3.1.1: Regression Statistics

2.3.2 ANOVA (Analysis of Variance)

There are two tables in ANOVA.

2.3.2.1 ANOVA OUTPUT I

The components of the ANOVA were tabulated using the following equations:

p represents the number of coefficients and k represents the total number of coefficients which in this case k=p+1= 2.

    Regression df = k - 1.

    Residual df = n - k.

    Total df = n - 1.

    Total SS = Regression SS + Residual SS.

    Regression MS = Regression SS/(k - 1).

    Residual MS = Residual SS/(n - k).

    F =Regression MS/Residual MS.

    Significance F = FDIST(F, k - 1, n - k).

The results are as shown in tables 2.3.2.1, 2.3.2.2 and 2.3.2.3.

ANOVA

df

SS

MS

F

Significance F

Regression

1

1526939549

1526939549

288.59

8.99811×10-52

Residual

518

2740725995

5290976

N/A

N/A

Total

519

4267665544

N/A

N/A

N/A

Table 2.3.2.1: ANOVA output I - DASHANG GROUP 'A'

ANOVA

df

SS

MS

F

Significance F

Regression

1

446954.807

446954.807

306.6

2.40467×10-54

Residual

522

760954.772

1457.8

N/A

N/A

Total

523

1207909.58

N/A

N/A

N/A

Table 2.3.2.2: ANOVA output I - SPECIALTY FASH.GP.

ANOVA

df

SS

MS

F

Significance F

Regression

1

34098162.8

34098162.8

2427.9

1.8982×10-198

Residual

522

7331207.45

14044

N/A

N/A

Total

523

41429370.2

N/A

N/A

N/A

Table 2.3.2.3: ANOVA output I - PACIFIC NET

2.3.2.2 ANOVA OUTPUT II

The next stage is the coefficients. (Note that the numbers have been converter to 3 decimal places to save space). It gives the coefficient for each parameter, including the intercept. T-stat value is the ratio of the estimated coefficient value divided by the standard error value. T-stat value can be compared across all variables in comparison with the standard error.. The p-value is associated with the variable, and the confidence intervals of the parameter estimates as evaluated by Excel.

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ANOVA

Coefficients

Std. Error

t stat

P-value

Lower 95%

Upper 95%

Intercept

-4642.803

833.091

-5.573

4×10-8

-6279.455

-3006.151

X Var 1

1212.5559

71.377

16.988

9×10-52

1072.332

1352.78

Table 2.3.3.1: ANOVA output II - DASHANG GROUP 'A'

ANOVA

Coefficients

Std. Error

t stat

P-value

Lower 95%

Upper 95%

Intercept

424.128

19.535

21.711

6×10-75

385.751

462.505

X Var 1

-165.397

9.446

-17.51

2×10-54

-183.953

-146.84

Table 2.3.3.2: ANOVA output II - SPECIALTY FASH.GP.

ANOVA

Coefficients

Std. Error

t stat

P-value

Lower 95%

Upper 95%

Intercept

-412.872

30.206

-13.67

1×10-36

-472.213

-353.532

X Var 1

9.125

0.185

49.273

2×10-198

8.761

9.488

Table 2.3.3.3: ANOVA output II - PACIFIC NET

2.3.3 Confidence Intervals for Slope Coefficients

95% confidence interval for slope coefficient ß2 is from Excel output (-1.4823, 2.1552).

Excel computes this as

b2 ± t_.025(3) - se(b2)
= 0.33647 ± TINV(0.05, 2) - 0.42270
= 0.33647 ± 4.303 - 0.42270
= 0.33647 ± 1.8189
= (-1.4823, 2.1552).

Other confidence intervals can be obtained.
For example, to find 99% confidence intervals: in the Regression dialog box (in the Data Analysis Add-in), check the Confidence Level box and set the level to 99%.

2.3.4 Test of Statistical Significance

The coefficient of HH SIZE has estimated standard error of 0.4227, t-statistic of 0.7960 and p-value of 0.5095.
It is therefore statistically insignificant at significance level a = .05 as p > 0.05.

The coefficient of CUBED HH SIZE has estimated standard error of 0.0131, t-statistic of 0.1594 and p-value of 0.8880.
It is therefore statistically insignificant at significance level a = .05 as p > 0.05.

There are 5 observations and 3 regressors (intercept and x) so we use t(5-3)=t(2).
For example, for HH SIZE p = =TDIST(0.796,2,2) = 0.5095.

2.3.5 Test Hypothesis on a Regression Parameter

Here we test whether HH SIZE has coefficient ß2 = 1.0.

Example: H0: ß2 = 1.0 against Ha: ß2 ? 1.0 at significance level a = .05.

Then

    t = (b2 - H0 value of ß2) / (standard error of b2 )

    = (0.33647 - 1.0) / 0.42270

    = -1.569.

2.3.5.1 Using the p-value approach

  • p-value = TDIST(1.569, 2, 2) = 0.257. [Here n=5 and k=3 so n-k=2].

  • Do not reject the null hypothesis at level .05 since the p-value is > 0.05.

2.3.5.2 Using the critical value approach

  • We computed t = -1.569

  • The critical value is t_.025(2) = TINV(0.05,2) = 4.303. [Here n=5 and k=3 so n-k=2].

  • So do not reject null hypothesis at level .05 since t = |-1.569| < 4.303.

2.3.6 Overall Test of Significance of the Regression Parameters

We test H0: &szlig;2 = 0 and &szlig;3 = 0 versus Ha: at least one of &szlig;2 and &szlig;3 does not equal zero.

From the ANOVA table the F-test statistic is 4.0635 with p-value of 0.1975.

Since the p-value is not less than 0.05 we do not reject the null hypothesis that the regression parameters are zero at significance level 0.05.

Conclude that the parameters are jointly statistically insignificant at significance level 0.05.

Note: Significance F in general = FINV(F, k-1, n-k) where k is the number of regressors including the intercept.

Here FINV(4.0635,2,2) = 0.1975.

2.3.7 Predicted Value of Y Given Regressors

Consider case where x = 4 in which case CUBED HH SIZE = x^3 = 4^3 = 64.

yhat = b1 + b2 x2 + b3 x3 = 0.88966 + 0.3365-4 + 0.0021-64 = 2.37006

2.3.8 Excel Limitations

Regression in Excel has a number of limitations:

  • No standardised coefficients. It was very difficult to interpret unstandardised coefficients. The standardised coefficients could be calculated using the unstandardised coefficient if it is needed.

  • Lack of diagnostic graphs. The standard diagnostic graphs were not available in Excel, such as the plot of the residuals, the scatter-plot or residuals against predicted values.

  • Lack of Diagnostic statistics. There were no co-linearity diagnostics, which would provide a more understanding of the data that was analyzed.

  • Excel standard errors and t-statistics and p-values are based on the assumption that the error is independent with constant variable. Excel does not provide alternaties, such autocorrelation standard errors and t-statistics and p-values.

3 CONCLUSION

4 REFERENCE

  1. http://www.qimacros.com/qiwizard/regression.html

  2. http://mallit.fr.umn.edu/fr4218/assigns/excel_reg.html

  3. http://www.jeremymiles.co.uk/regressionbook/extras/appendix2/excel/