# FTSE-100(England) and TOPIX-100(Japan)

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Introduction:The purpose of this paper is to interpret the results of two main share indices, namely FTSE-100(England) and TOPIX-100(Japan) obtained from the SPSS report and to adopt and discuss the measures of the benefits of the international diversification. The first chapter discusses the approach used to calculate returns and covariance of the two share indices and second chapter comments critically on the usefulness of this type of calculations obtained from SPSS report. The question addressed in this paper on International Diversification will be discussed in chapter three.

1. To obtain data of two main share indices, namely FTSE-100(England) and TOPIX-100(Japan) from database Thomson Analytic into an Excel sheet:

This chapter describes briefly the approach how to find returns, covariance beta, correlation and Linear Regression for the two main share indices.

First, the author selected two main share indices,FTSE-100(England) and TOPIX-100(Japan) from the database Thomson Analytic (see Appendix 1) and imported this into the programme SPSS via Excel. In SPSS, the function ‘analysis regression' was used to calculate the diagnostic statistics data. The, data obtained from SPSS report is given in (Appendix 2).

2. Analysis of diagnostic statistics data obtained from the SPSS:

The author's calculation is based on monthly data for five years. Descriptive Statistics table consists of columns Mean, Standard Deviation, Variance, Skewness and Kurtosis. Mean value is the arithmetic average of all the values obtained from the indices. Mean should not be used alone, but well with other indicators that will give an idea of the dispersion of the data around the mean.The Standard Deviation is a measure of dispersion that is calculated based on the values of the data. It allows us to see how widely the data are dispersed around the mean. Generally, the larger the standard deviation, the greater the dispersal of scores and the smaller the standard deviation, the smaller the spread of scores,i.e Standard deviation increases in proportion to the spread of the scores around mean as the marker point. (Burns & Burns, 2008) The standard deviation has the desirable property that, when the data are normally distributed, 68.3 % of the observations lie within +/- 1 standard deviation from the mean, 95.4% within +/- 2 standard deviations from the mean and 99.7 % within 3 standard deviations. Variance is defined as average of squared difference from the mean.Skewness is a measure of whether the peak is centered in the middle of the distribution. A positive value means that the peak is off to the left, and a negative value suggests that it is off to the right. Kurtosis is a measure of the extent to which data are concentrated in the peak versus the tail. A positive value indicates that data are concentrated in the peak; a negative value indicates that data are concentrated in the tail. A rule of thumb is that the absolute value of the ratio of skewness to its standard error and of kurtosis to its standard error should be less than 2.For normal distribution, the values of Skewness and Kurtosis should be zero.The Pearson's correlation is used to find a correlation between at least two continuous variables. The value for a Pearson's can fall between 0.00 (no correlation) and 1.00 (perfect correlation). Other factors such as group size will determine if the correlation is significant. Generally, correlations above 0.80 are considered pretty high. When a variable interacts with itself, the correlation will obviously be 1.00.In our case the significance is zero, which is in the limit under 0.05.The correlation value in our case is 0.742.

The Model summary table displays ‘R' as +0.742 and adjusted R2 as 0.543, so 54.3% of the variation in the outcome is determined by the predictor variable (and 45.7% of the variation is caused by something else. Adjusted R2 is used as this refers to sample data. Here R is the correlation coefficient and R2 is the coefficient of determination. The standard error of estimate is a measure of error of prediction. Standard error of estimate is about 0.0297 units. The Durbin-Watson statistic tests for serial correlation of error terms for adjacent cases. This test is mainly used in relation to time series data, where adjacent cases are sequential years. The Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation in the residuals from a regression analysis.

The next subtable indicates that the regression equation is significant with an F=69.821, p<0.001.So, in terms of variance explained and significance the regression equation (often called ‘model') is excellent. Should F not be significant then the regression as a whole has failed and no more interpretation is necessary. The F ratio is calculated by taking ratio of mean sum of squares between the samples and the mean sum of squares within the samples. In addition, a hypothesis gives a specific statement of prediction and can be used to make statistical decisions (Trochim, 2006). Thus, the null hypothesis test can be used.

H0: p = 0

at 5% significance level

H1: p ≠ 0

The regression statement from the SPSS gives a signification of 0.00, which compiles 0 %. If the signification is lower than 5%, which rejects to H0: p = 0 and means that the correlation is significant. The closer the value is to zero, the more statistically significant (Kozak, 2008; Cramer and Howitt, 2004). The "Sig. F Change" column tests the null hypothesis that the regressions for the two groups are equal.

The coefficient subtable is also crucial and displays the values for constant and beta from which the regression equation can be derived. The constant or intercept, b0 in our regression formula(Y= b0 - b1X), is referred to as constant in SPSS and has value of 0.004. The Unstandardized or raw score regression coefficient or slope (b1) is displayed in SPSS under B as the second line and is 0.529.The t value for B was significant and implies that this variable (Return-2) is a significant predictor. It is useful to think of B (or b1 in our symbols) as the change in outcome associated with a unit change in the predictor. This means for every one unit rise (Return-2) in B, (Return-1) rise by 529.Management can now determine whether the cost of increasing (Return-2) will bring sufficient returns over a defined time span.

The column headed Beta gives a value of 0.742.This is the Pearson correlation between the two variables. In other words, if one turns the scores into standard scores (Z-scores) the slope and the correlation coefficients are the same thing. The histogram (Appendix-2) and standardized residual scattergraph (Appendix-1) are essential and address the issue of whether the assumptions for the linear regression were met.

The residual plots shows a reference line where residuals are 0.This is a near perfect plot and very acceptable with data spread reasonably equally around the line with no apparent pattern, i.e.It is homoscedastistic.If there is a pattern such as a curve or funnel distribution rather than randomness then we have heteroscedasticity, which suggests that there is no homogeneity of variance.

The Regression line sloping from bottom right to top left indicates positive relationship between the two Returns. The point seem relatively close to this line, which suggests that the correlation should be a high positive one, and therefore prediction estimates will be close to true values.

### 3. International Diversification:

In these days of high stock market volatility, the question of how to reduce risk is foremost in portfolio managers' minds. Since the classic studies of Grubel (1968), Levy and Sarnat (1970), and Solnik (1974), numerous papers have documented the gains from international portfolio diversification. As companies become more internationally diversified, their operations naturally become more multifaceted. Consistent with such increased complexity, prior research provides evidence that expansion into international markets increases the complexity of information processing for investors (Thomas 1999; Callen, Hope, and Segal 2005), managers (Kim and Mauborgne 1995;Birkinshaw,Toulan, and Arnold 2001), and financial analysts (Duru and Reeb 2002; Khurana, Pereira, and Raman 2003; Tihanyi and Thomas 2005). They show that the gains from international diversification stem mostly from the relatively low correlation among international securities when compared to domestic securities. Further, previous studies, e.g., Heston and Rouwenhorst (1994) and Griffin and Karolyi (1998), have shown that industrial structure explains relatively little of the cross-country difference in stock market volatility, and that the low international correlation is mostly due to country-specific sources of return variation. Also, they show that the dominance of country factors in an international return is robust to differing definitions of industry classifications. Relatively low international correlations, together with the gradual liberalization of capital markets, are indeed responsible for the rising volume of cross-border investments and the proliferation of international mutual funds in the U.S. and abroad.

As international capital markets become more integrated, however, stock market correlations have risen, diminishing the potential gains from international diversification. Longin and Solnik (1995), for example, document that international correlation among stock market indices have indeed increased over the 30-year period 1960-1990. Goetzmann, Li, and Rouwenhorst (2005) also show that international correlations tend to be higher during the periods of higher economic and financial integration. Higher international correlations observed in recent years clearly cast doubt on the strength and validity of the case for international diversification argued by the classic studies. [1]

[1] A few recent studies, e.g., Cavaglia, Brightman, and Aked (2000) and Baca, Garbe, and Weiss (2000), suggest that the rising international correlations may be associated with the declining importance of country factors relative to industry factors. This view, however, is not unanimously held. Brooks and Del Negro (2004), for instance, argue that the rising importance of industry factors relative to country factors does not reflect the ongoing financial integration, but rather a temporary phenomenon associated with the recent stock market fluctuations. Although the relative importance of country vs. industry factors is an important, unsettled issue; we do not address this issue in our paper. Rather, we focus on the merits of country-specific stocks in international diverse market.

To the extent that investors diversify internationally, large-cap stocks have received the dominant share of overseas investments. This ‘large-cap bias' is understandable as investors naturally gravitate toward well-known, large foreign companies that are highly visible and often multinational. [2] As discussed by Foerster and Karolyi (1999) and others, companies often use the cross-listings of shares to enhance the level of investor recognition and expand the shareholder base.

It is possible for multinational firms to reduce the risk of their profits by engaging in foreign operations (F/T). Empirical tests show that the (F/T) variable is inversely related to risk after allowing for size, industry classification, and other factors. This implies that international diversification offers to a multinational firm significant risk reduction advantages that are not available to a nonmultinational. (Rugman). The first such application of the theory of portfolio selection under conditions of uncertainty, as developed by Tobin (1958) and Markowitz (1959) was by Herbert Grubel (1968). He demonstrated that it was possible for individual asset holders to reduce risk by holding an efficiently diversified portfolio of international assets. His analysis considered financial capital flows, as did work by Levy and Sarnat (1970), Miller and Whitman (1970), and Grubel and Fadner (1971). It can be extended in a new direction by consideration of direct investment, as attempted by Cohen (1972) and Severn (1974). The characteristic of direct investment is that the investor retains control over the investment. For example, the parent firm of a multinational corporation may both invest in a foreign subsidiary and control the operations of that subsidiary. Recent work in the field of international investment has shown that direct investment is motivated by the existence of market imperfections: Kindleberger (1969), Johnson (1970), Caves (1971), Knickerbocker (1973). [For an interpretation of these studies see Parry (1973) and Rugman (1975).] These studies show that the motivation of direct investment occurs at the firm level owing to an imperfect international market. The advantage of risk reduction exists due to the possibility of diversification of sales in various national economies, provided that the fluctuations of these economies are not perfectly positively correlated. Theory of portfolio analysis is used to justify variance as a suitable proxy measure of risk.

[2] In their study of foreigners' equity holdings in Japan, Kang and Stulz (1997) show that foreign investors prefer large, export-oriented, liquid and U.S. cross-listed firms. Ferreira and Matos (2006) also report that institutional investors strongly prefer large and liquid stocks with good governance practices. In addition, institutional investors prefer those stocks that are cross-listed in the U.S. market and members of the MSCI all-country world index.

Although a foreign investment variable is desirable when portfolio theory is used to determine the actions of individual asset holders in an international context, a foreign operations variable is suitable when the activities of multinational firms are being analyzed. Foreign operations must be used instead of direct investment because of data limitations; there are no published data on foreign investment of firms-this information is available only at the industry level.

Conclusion: Several policy implications arise from the fact that the foreign operations reduce risk. First, the individual risk-averting investor can purchase shares in multinational corporations order to achieve the benefits of international diversification in an indirect manner will do this if there are barriers to free trade in capital, as, for example, when the cost of acquiring shares in foreign economies was prohibitive due to the tax payments required under the Interest Equalization Tax imposed from 1963 to 1974. These stockholders of a multinational firm can benefit from the more stable earnings in such a firm and thereby enjoy the benefits of international diversification in an indirect manner. Secondly, foreign governments might attempt to measure the gains from diversification that are reaped by multinational firms selling in their own economies. It may then be possible for national governments to impose an "optimum" tax, such that, at the margin, the multinational firm still benefits from foreign investment and sales by subsidiaries in the host economy, but that any excess profits in the form of more stable earnings have been eliminated. Finally, a look into the future may reveal increasing world economic integration. The pace of this interdependence may be speeded up by the operations of multinational firms themselves, as they sell similar products, and introduce similar preferences in world markets. Increased economic integration will also tend to increase the correlation between fluctuations in the domestic economy and fluctuations in foreign economies. As the integration of economies proceeds, market imperfections will become less important as a determinant of direct investment. In the long run, a world market may develop in which knowledge, research, and technology can be freely bought and sold so that direct ownership of these techniques is no longer required. Then the benefits of international diversification via the multinational firm will eventually fade away.

### References:

### Books and Articles:

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Birkinshaw, J., O.Toulan, and D.Arnold. 2001. Global account management in multinational corporations: Theory and evidence. Journal of International Business Studies 32 (2):231-238.

Bruck, Nicholas K. and Lees, Francis A., (1968) "Foreign Investment, Capital Controls and the Balance of Payments." The Bulletin, New York University Graduate School of Business Administration Institute of Finance, No. 48-49 (April 1968).

Burns, Robert and Burns, Richard, (2008) “Business Research methods and statistics using SPSS”.

Callen, J., O.-K. Hope and D. Segal. 2005. Domestic and foreign earnings, stock return variability and the impact of investor sophistication. Journal of Accounting Research Vol. 43 No. 3: 377-412.

Caves, Richard E. (1971) "International Corporations: The Industrial Economics of Foreign Investment." Economica (February 1971), pp. 1-27.

Cohen, Benjamin I. (1972) "Foreign Investment by U.S. Corporations As a Way of Reducing Risk." Yale University Economic Growth Center Discussion Paper No. 151 (September 1972).

Duru, A., and D. M. Reeb. 2002. International diversification and analysts' forecast accuracy and bias. The Accounting Review (April): 415-433.

Goetzmann, William N., Lingfeng Li, and K. Geert Rouwenhorst, 2005, Long-term global market correlations, Journal of Business 78, 1-38.

Griffin, J. and A. Karolyi, 1998. Another Look at the Role of Industrial Structure of Markets for International Diversification Strategies. Journal of Financial Economics 50, 351-373.

Grubel, Herbert G. (1968) "Internationally Diversified Portfolios: Welfare Gains and Capital Flows." American Economic Review (December), pp. 1299-1314.

Grubel, Herbert G. and Fadner, Kenneth, (1971) "The Interdependence of International Equity Markets." Journal of Finance (March 1971), pp. 89-94.

Heston, S. and G. Rouwenhorst, 1994. Does Industrial Structure Explain the Benefits of International Diversification? Journal of Financial Economics 36, 3-27.

Heston, S. and G. Rouwenhorst, 1995. Industry and Country Effects in International Stock Returns. The Journal of Portfolio Management, Spring, 53-58.

Heston, S., Rouwenhorst, G. and R.Wessels. 1995. The Structure of International Stock Returns and the Integration of Capital Markets. Journal of Empirical Finance 2, 173-197

Hope, O.-K., and W.B. Thomas. 2008. Empire building and firm disclosure. Forthcoming, Journal of Accounting Research.

Johnson, Harry G. (1970) "The Efficiency and Welfare Implications of the International Corporation," in C.P. Kindleberger, Ed., The International Corporation. Cambridge: M.I.T. Press, pp. 35-56.

Khurana, I.K., R. Pereira, and K.K. Raman. 2003. Does analyst behavior explain market mispricing of foreign earnings for U.S. multinational firms? Journal of Accounting, Auditing and Finance Vol. 18 No. 4: 453-478.

Kim, W. C., and R. A. Mauborgne. 1995. A procedural justice model of strategic decision making: Strategy content implications in the multinational. Organization Science Vol. 6, (1): 44-61.

Kindleberger, Charles P. (1968) American Business Abroad. New Haven: Yale University Press.

Knickerbocker, Frederick T. (1973) Oligopolistic Reaction and Multinational Enterprises. Boston: Harvard University.

Lessard, Donald R. (1974) "World, National, and Industry Factors in Equity Returns." Journal of Finance (May 1974), pp. 379-391.

Levy, H. and Sarnat, M., (1970) "International Diversification of Investment Portfolios." American Economic Review (September 1970) pp. 668-692.

Longin, F. and B. Solnik. 1995. Is the Correlation in International Equity Returns Constant: 1960-1990? Journal of International Money and Finance, 14. 3-26

Lintner, John (1965) "The Valuation of Risk Assets and Selection of Risky Investments in Stock Portfolios and Capital Budgets." Review of Economics and Statistics (February 1965), pp. 13-37.

Markowitz, Harry (1959) Portfolio Selection: Efficient Diversification of Investments. New York: Wiley.

Miller, Norman C. and Whitman, Marina V.N., (1970) "A Mean-Variance Analysis of United States Direct Foreign Investment in the European Economic Community." Quarterly Journal of Economics (May 1970), pp. 175-192.

Parry, Thomas G. (1973) "The International Firm and National Economic Policy: A Survey of Some Issues." The Economic Journal (December 1973), pp. 1201-1221. Rugman, Alan (1974) "Foreign Operations and the Stability of U.S. Corporate Earnings: Risk Reduction by International Diversification." Ph.D. Thesis, Simon Fraser University.

Rugman, Alan (1975) "Motives for Foreign Investment: The Market Imperfections and Risk Diversification Hypotheses." Journal of World Trade Law (September-October 1975), pp. 567-573.

Severn, Alan (1974) "Investor Evaluation of Foreign and Domestic Risk." Journal of Finance (May 1974), pp. 545-550.

Sharpe, William F. (1964) "Capital Asset Prices; A Theory of Market Equilibrium Under Conditions of Risk." Journal of Finance (September 1964), pp. 425-442.

Solnik, Bruno H. (1974a) "International Pricing of Risk." Journal of Finance (May 1974), pp. 365-378.

Solnik, Bruno H. (1974b) "An Equilibrium Model of the International Capital Market." Journal of Economic Theory (8 pp. 500-524).

Solnik, Bruno H. 1974. Why Not Diversifying Internationally Rather Than Domestically. Financial Analyst Journal. July-August.

Tobin, James (1958) "Liquidity Preference as Behavior Towards Risk." The Review of Economic Studies (February 1958), pp. 65-86.

Thomas, W. B. 1999. A test of the market's mispricing of domestic and foreign earnings.

Journal of Accounting and Economics 28 (December): 243-267.

Tihanyi, L., and W.B. Thomas. 2005. Information-processing Demands and the multinational enterprise: A comparison of foreign and domestic earnings estimates. Journal of Business Research 58: 285-292.

### Websites:

1) http://www1.american.edu/cte/docs_pdfs/training/SPSS_desstats.pdf

2) 2) http://academic.uofs.edu/department/psych/methods/cannon99/level2a.html

3) http://www.microbiologybytes.com/maths/spss3.html

4) 4) http://www.statisticssolutions.com/blog/#anova

5) http://academic.udayton.edu/gregelvers/psy216/SPSS/1wayanova.htm

6) http://www.mathsisfun.com/standard-deviation.html

7) http://www1.american.edu/cte/docs_pdfs/training/SPSS_desstats.pdf

### Appendix-1:

PERIOD

FTSE-100

TOPIX- 100

RETURN-1

RETURN-2

COVARIANCE

BETA

CORRELATION

LINEAR REGRESSION

31/12/2004

4,814.30

826.11

0.01

-0.02

0.0019871428

0.52

0.74

0.53

31/01/2005

4,852.31

806.57

0.02

0.03

28/02/2005

4,968.50

828.10

-0.02

-0.01

31/03/2005

4,894.37

822.85

-0.02

-0.05

29/04/2005

4,801.68

782.67

0.03

0.02

31/05/2005

4,963.97

799.39

0.03

0.02

30/06/2005

5,113.16

817.45

0.03

0.02

29/07/2005

5,282.30

835.18

0.00

0.06

31/08/2005

5,296.92

887.38

0.03

0.12

30/09/2005

5,477.71

995.74

-0.03

0.01

31/10/2005

5,317.28

1,002.47

0.02

0.07

30/11/2005

5,423.17

1,073.91

0.04

0.06

30/12/2005

5,618.76

1,143.11

0.02

0.04

31/01/2006

5,760.26

1,185.43

0.01

-0.02

28/02/2006

5,791.54

1,165.83

0.03

0.03

31/03/2006

5,964.57

1,205.57

0.01

0.00

28/04/2006

6,023.14

1,208.06

-0.05

-0.08

31/05/2006

5,723.81

1,113.09

0.02

0.01

30/06/2006

5,833.42

1,126.98

0.02

0.00

31/07/2006

5,928.33

1,130.83

0.00

0.04

31/08/2006

5,906.13

1,172.83

0.01

-0.02

29/09/2006

5,960.81

1,150.43

0.03

0.01

31/10/2006

6,129.22

1,164.81

-0.01

-0.01

30/11/2006

6,048.85

1,158.24

0.03

0.05

29/12/2006

6,220.81

1,222.09

0.00

0.02

31/01/2007

6,203.09

1,249.22

-0.01

0.02

28/02/2007

6,171.47

1,270.35

0.02

-0.02

30/03/2007

6,308.03

1,239.34

0.02

-0.01

30/04/2007

6,449.21

1,226.36

0.03

0.05

31/05/2007

6,621.45

1,285.03

0.00

0.01

29/06/2007

6,607.90

1,295.48

-0.04

-0.04

31/07/2007

6,360.11

1,243.22

-0.01

-0.06

31/08/2007

6,303.30

1,168.14

0.03

0.01

28/09/2007

6,466.79

1,176.97

0.04

0.00

31/10/2007

6,721.57

1,176.45

-0.04

-0.05

30/11/2007

6,432.45

1,116.26

0.00

-0.03

31/12/2007

6,456.91

1,078.96

-0.09

-0.09

31/01/2008

5,879.78

983.44

0.00

-0.02

29/02/2008

5,884.28

966.68

-0.03

-0.10

31/03/2008

5,702.11

871.18

0.07

0.14

30/04/2008

6,087.30

1,001.22

-0.01

0.04

30/05/2008

6,053.50

1,039.21

-0.07

-0.07

30/06/2008

5,625.90

968.52

-0.04

-0.01

31/07/2008

5,411.90

956.31

0.04

-0.04

29/08/2008

5,636.61

917.81

-0.14

-0.15

30/09/2008

4,902.45

788.09

-0.11

-0.24

31/10/2008

4,377.34

619.38

-0.02

-0.07

28/11/2008

4,288.01

577.31

0.03

0.03

31/12/2008

4,434.17

592.21

-0.07

-0.08

30/01/2009

4,149.64

547.94

-0.08

-0.04

27/02/2009

3,830.09

524.11

0.02

0.01

31/03/2009

3,926.14

529.01

0.08

0.10

30/04/2009

4,243.71

583.73

0.04

0.07

29/05/2009

4,417.94

625.89

-0.04

0.02

30/06/2009

4,249.21

636.04

0.08

0.03

31/07/2009

4,608.36

655.61

0.06

0.01

31/08/2009

4,908.90

661.74

0.04

-0.07

30/09/2009

5,133.90

619.86

-0.02

-0.01

30/10/2009

5,044.55

614.52

0.03

-0.05

30/11/2009

5,190.68

582.01

31/12/2009

#N/A

#N/A

### Appendix-2:

GET DATA

/TYPE=XLS

/FILE='H:\Desktop\TAChartData(FTSE-100).xls'

/SHEET=name 'Chart Data'

/CELLRANGE=full

/READNAMES=on

/ASSUMEDSTRWIDTH=32767.

DATASET NAME DataSet1 WINDOW=FRONT.

REGRESSION

/DESCRIPTIVES MEAN STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS CI BCOV R ANOVA COLLIN TOL CHANGE ZPP

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT RETURN1

/METHOD=ENTER RETURN2

/RESIDUALS DURBIN HIST(ZRESID) NORM(ZRESID)

/CASEWISE PLOT(ZRESID) OUTLIERS(3).

Regression

Notes

Output Created

11-Dec-2009 00:59:50

Comments

Input

Active Dataset

DataSet1

Filter

<none>

Weight

<none>

Split File

<none>

N of Rows in Working Data File

61

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics are based on cases with no missing values for any variable used.

Syntax

REGRESSION

/DESCRIPTIVES MEAN STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS CI BCOV R ANOVA COLLIN TOL CHANGE ZPP

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT RETURN1

/METHOD=ENTER RETURN2

/RESIDUALS DURBIN HIST(ZRESID) NORM(ZRESID)

/CASEWISE PLOT(ZRESID) OUTLIERS(3).

Resources

Processor Time

00:00:01.109

Elapsed Time

00:00:01.435

Memory Required

1492 bytes

Additional Memory Required for Residual Plots

656 bytes

[DataSet1]

Descriptive Statistics

N

Range

Minimum

Maximum

Mean

Std. Deviation

Variance

Skewness

Kurtosis

Statistic

Statistic

Statistic

Statistic

Statistic

Std. Error

Statistic

Statistic

Statistic

Std. Error

Statistic

Std. Error

RETURN-1

59

.220686702848

-1.395477408107E-1

.081138962037

.00127583142597

.005736069820550

.044059588311598

.002

-1.012

.311

1.363

.613

RETURN-2

59

.380019239701

-2.408933188285E-1

.139125920873

-5.93627638733573E-3

.008049867511471

.061832205606639

.004

-.875

.311

3.007

.613

Valid N (listwise)

59

Descriptive Statistics

Mean

Std. Deviation

N

RETURN-1

.00127583142597

.044059588311598

59

RETURN-2

-5.93627638733573E-3

.061832205606639

59

Correlations

RETURN-1

RETURN-2

Pearson Correlation

RETURN-1

1.000

.742

RETURN-2

.742

1.000

Sig. (1-tailed)

RETURN-1

.

.000

RETURN-2

.000

.

N

RETURN-1

59

59

RETURN-2

59

59

Variables Entered/Removed

Model

Variables Entered

Variables Removed

Method

1

RETURN-2a

.

Enter

a. All requested variables entered.

b. Dependent Variable: RETURN-1

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

Durbin-Watson

R Square Change

F Change

df1

df2

Sig. F Change

1

.742a

.551

.543

.029796019571595

.551

69.821

1

57

.000

1.898

a. Predictors: (Constant), RETURN-2

b. Dependent Variable: RETURN-1

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

.062

1

.062

69.821

.000a

Residual

.051

57

.001

Total

.113

58

a. Predictors: (Constant), RETURN-2

b. Dependent Variable: RETURN-1

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

95% Confidence Interval for B

Correlations

Collinearity Statistics

B

Std. Error

Beta

Lower Bound

Upper Bound

Zero-order

Partial

Part

Tolerance

VIF

1

(Constant)

.004

.004

1.133

.262

-.003

.012

RETURN-2

.529

.063

.742

8.356

.000

.402

.655

.742

.742

.742

1.000

1.000

a. Dependent Variable: RETURN-1

Coefficient Correlationsa

Model

RETURN-2

1

Correlations

RETURN-2

1.000

Covariances

RETURN-2

.004

a. Dependent Variable: RETURN-1

Collinearity Diagnosticsa

Model

Dimension

Eigenvalue

Condition Index

Variance Proportions

(Constant)

RETURN-2

1

1

1.096

1.000

.45

.45

2

.904

1.102

.55

.55

a. Dependent Variable: RETURN-1

Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

-1.22950054705143E-1

.07797273993492

.00127583142597

.032691764779516

59

Residual

-6.339722871780396E-2

.074968338012695

.000000000000000

.029538040520459

59

Std. Predicted Value

-3.800

2.346

.000

1.000

59

Std. Residual

-2.128

2.516

.000

.991

59

a. Dependent Variable: RETURN-1

Charts

GRAPH

/SCATTERPLOT(BIVAR)=RETURN2 WITH RETURN1

/MISSING=LISTWISE.

Graph

Notes

Output Created

11-Dec-2009 01:01:19

Comments

Input

Active Dataset

DataSet1

Filter

<none>

Weight

<none>

Split File

<none>

N of Rows in Working Data File

61

Syntax

GRAPH

/SCATTERPLOT(BIVAR)=RETURN2 WITH RETURN1

/MISSING=LISTWISE.

Resources

Processor Time

00:00:00.531

Elapsed Time

00:00:00.499

[DataSet1]