finance

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The use of FINDINGS AND ANALYSIS in Business

As mentioned in the earlier chapter, the objective of this study is to identify performance for KLCI financial sector price index. The factors used to test the relationship were between the interest rate, inflation rate and the exchange rate. Therefore, a multiple linear regression is used as a method in order to see the relationship between the dependent variable (financial sector price index) and the independent variables (interest rate, inflation rate, and exchange rate).

A multiple regression model use monthly data from the year 2005 to 2009. The relationship between the price index and the sectors which represent the dependent variable, and several macroeconomic factors that represent the independent variables will be shown by the regression model. Besides that, it also shows the result from the regression by analyzed the data based on the independent variables used. (Interest rate, inflation rate, and exchange rate).

The data gather from 2005 to 2009 shows a pattern of pricing in every index included. This is shown drafty on table 4.1.1. Every information gathers are to analysis (analyze) using SPSS software system to get a linear regression result.

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Table 4.1.1: Multiple data from 2005 to 2009 (where do you get this sources) please state sources below the table..

CPI (inflation rate)

exchange rate

interest rate

KLCI

2005

Jan

98.6000

3.8000

6.3700

922.5000

Feb

98.7000

3.8000

6.4200

911.8935

Mac

98.7000

3.8000

6.3900

890.2843

Apr

98.9000

3.8000

6.3800

870.8490

May

99.7000

3.8000

6.4100

886.7777

Jun

99.9000

3.8000

6.4400

887.8041

Jul

99.9000

3.7881

6.4400

917.9843

Aug

100.6000

3.7587

6.3300

930.7530

Sep

100.8000

3.7686

6.3100

919.4141

Oct

101.0000

3.7730

6.3100

918.3614

Nov

101.4000

3.7790

6.3300

903.4995

Dec

101.5000

3.7780

6.4600

894.1109

2006

Jan

101.8000

3.7527

6.6000

907.5573

Feb

101.9000

3.7253

6.6700

924.2125

Mac

103.4000

3.7046

6.7400

922.9735

Apr

103.4000

3.6628

6.8300

942.0945

May

103.6000

3.6154

6.9900

947.5422

Jun

103.8000

3.6659

7.3400

908.8041

Jul

104.0000

3.6687

7.4000

923.2481

Aug

103.9000

3.6750

7.3500

944.1057

Sep

104.1000

3.6721

7.3200

961.5624

Oct

104.1000

3.6789

7.0800

976.7782

Nov

104.4000

3.6438

7.0800

1,030.7541

Dec

104.6000

3.5529

6.8700

1,084.6219

2007

Jan

105.1000

3.5076

6.9400

1,140.0261

Feb

105.1000

3.4961

6.9600

1,244.7895

Mac

105.0000

3.4916

6.8300

1,196.6182

Apr

105.0000

3.4389

6.7000

1,303.7376

May

105.1000

3.4013

6.7900

1,349.0278

Jun

105.3000

3.4451

6.8100

1,366.1776

Jul

105.7000

3.4422

6.8300

1,373.6982

Aug

105.9000

3.4839

6.8700

1,277.4161

Sep

106.0000

3.4740

6.8700

1,300.0010

Oct

106.1000

3.3785

6.8600

1,375.8887

Nov

106.8000

3.3575

6.8900

1,382.4473

Dec

107.1000

3.3342

6.8300

1,418.4929

2008

Jan

107.5000

3.2679

6.7000

1,437.4826

Feb

107.9000

3.2223

6.8100

1,403.3243

Mac

107.9000

3.1863

6.8900

1,232.6410

Apr

108.2000

3.1621

7.1000

1,256.8191

May

109.1000

3.2180

6.9200

1,281.1255

Jun

113.4000

3.2584

7.4000

1,221.8424

Jul

114.7000

3.2499

7.5100

1,137.8922

Aug

114.9000

3.3273

7.4900

1,102.6290

Sep

114.7000

3.4416

7.5400

1,043.2714

Oct

114.2000

3.5264

7.4400

927.5513

Nov

112.9000

3.5870

7.4200

882.3275

Dec

111.8000

3.5518

7.4500

859.3691

2009

Jan

111.7000

3.5662

6.9300

895.6477

Feb

111.9000

3.6386

7.0900

893.5965

Mac

111.7000

3.6730

7.3400

862.4045

Apr

111.5000

3.6100

7.1400

948.3705

May

111.7000

3.5224

7.0900

1,027.4995

Jun

111.8000

3.5182

7.0100

1,069.3805

Jul

111.9000

3.5475

6.9800

1,075.9425

Table 4.1.2: Multiple Linear Regression Analysis for KLCI Financial Sector (2005-2009)

Model Summary

Model

R

R Square

Std. Error of the Estimate

1

.909a

.826

.816

80.3897920

a. Predictors: (Constant), x3, x1, x2

Coefficients

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

7062.354

534.091

13.223

.000

x1

-86.855

50.393

-.168

-1.724

.091

x2

-13.425

4.433

-.341

-3.028

.004

x3

-1116.498

76.421

-1.120

-14.610

.000

a. Dependent Variable: y

From the Table 4.1.2 above, it shows the result of correlation between the dependent variable and the independent variables. The information represents monthly data from the year 2005 to 2009 in term of finance index, Base Lending Rate, Consumer Price Index and Exchange rate. (actually what is your IV? State clearly.. It must be same with IV in previous chapters) Alpha (a) in the Table 4.1.2 above represents a constant number of equation and it referred as Y intercept in the equation.

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The alpha (a) value stand at 7062.354 and it means that when all independent variables equal to zero, the Y is at. 7062.354. The F-Statistic value is significant at 13.223 at the 0.00 level The R square in the Table 4.1.2 represent as a proportion of variance in the dependent variable, which can be predicted from independent variables. Based on the result, the R square value was stand at 0.826 and this value indicates that 83 percent of the variance in finance sector index can be explained from the independent variables used which is Interest Rate, Inflation Rate and Exchange Rate (USD/RM)

4.2.0 Analysis Statement

Thus, following findings and analysis will interpret the result for the independent variables and test on the hypotheses either reject or accept the hypotheses. (have you state the hypothesis statement in this chapter or in previous chapter?)

Result Equation

From table 4.1.2, this study concludes that the result can be explained by the following equation. The equation was stated as below:

Y = 7062.354 - 86.855x1 -13.425x2 -1116.498x3

( so it means negative relationship for all variables, right? ) state clearly…. Why it happens ?

4.2.1 Interest Rate

Based on the analysis between financial price index and interest rate from the table 4.1.2 above, is shows that the Beta value for the Interest Rate was stand at 86.855. It means that for every one percent increase in Interest Rate, the performance of the sector will decrease by 86.855. The result for t-significant value was stands at 0.091, which is not significant since the value was below the level of significant. ( state what is the level of significant value? )

From the equation above it is been analyze that interest rate(x1) has a negative correlation with financial price(y) in normal economy time. With every changes of one percent in interest rate will affect (positively or negatively affected?) financial price by 86.855. The higher the changes will bring much more effect to financial price performance in term of low price.

Therefore, the findings hereby accept the hypothesis statement for Interest Rate, which explained that interest rate, had influence the performance of KLCI financial sector. And it also rejects the null hypothesis statement which explained interest rate does not influence the performance of KLCI financial sector price index.

.

4.2.2 Inflation Rate

For the result on the Inflation rate, it shows that the beta value for the inflation rate was stand at -13.425. It means that for every one percent increase in inflation rate will influence (decrease or increase) the performance of the sector by 13.425. Since it was negative relationship, for every one percent increase in inflation rate will result the decrease in financial performance by 13.425. The result for t-significant value was stands at 0.04, and it means that it was statistically significant.

From the equation above it is been analyze that inflation rate(x2) has a negative correlation with financial price(y) in normal economy time. With every changes of one percent in interest rate will affect financial price by13.425. The higher the changes will bring much more effect to financial price performance in term of low price.

Therefore, the finding hereby rejects the null hypothesis statement for inflation rate, which explained that inflation rate, is not influences the performance of finance sector. Since it was statistically significant, therefore it also accepts the alternatives hypothesis statement which explained that relationship between interest rate and the performance of finance sector.

4.2.3 Exchange Rate

For the result on the exchange rate factor which used US Dollar (USD) against Ringgit Malaysia (RM), it shows that the beta value for the exchange rate was stand at -1116.498. It means that for every one percent increase in exchange rate will influence the performance of the sector by 1116.498. Since it was negative relationship, therefore for every one percent increase in exchange rate will result the decreasing in sector performance by 1116.498. Based on the result for t-significant, the value was stands at 0.00 and it means that it was statistically significant.

From the equation above it is been analyze that exchange rate(x3) has a negative correlation with financial price(y) in normal economy time. With every changes of one percent in exchange rate will affect financial price by 13.425. The higher the changes will bring much more effect to financial price performance in term of low price.

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Therefore, the finding hereby rejects the hypothesis statement for exchange rate, which explained that exchange rate, is not influence the performance in finance sector. Since it was statistically significant, therefore it also accepts the alternatives hypothesis statement which explained that there is a relationship between the exchange rate and the performance of finance sector.

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