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Mean–Variance Approach and Portfolio Selection

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Part of the book series: India Studies in Business and Economics ((ISBE))

Abstract

We make an attempt to examine the performance of portfolios formulated on the basis of Mean–Variance approach . For the analysis, monthly adjusted opening and closing prices of composite portfolio of BSE 100 companies have been taken for the period ranging from June 1996 to May 2005. The study has wide-ranging implications for finance professionals and policy makers. Ten portfolios have, first, been formulated and then evaluated by using Sharpe’s excess return to beta approach. Nine portfolios’ expected returns out of ten are significant at 5% level of significance. A cross-sectional analysis of the same set of ten portfolios carried out for three non-overlapping sub-periods (June 1996–December 1999, Jan 2000–December 2002, and Jan 2003–May 2005). The three sub-periods exhibit successive different economic conditions in the Indian economy, viz. decline , recession and growth , respectively. The results so obtained exhibit that portfolio -expected return of all ten portfolios, in three different economic conditions, are optimal.

How many millionaires do you know who have become wealthy by investing in savings accounts? I rest my case.

Robert G. Allen

This chapter draws from the author’s previous publication (Dhankar & Kumar, 2006) Rakesh Kumar, Assistant Professor in the Department of Business Studies, Deen Dayal Upadhyaya College, University of Delhi, New Delhi, and re-used here with permission.

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References

  • Dhankar, R. S. (1996). An empirical testing of capital asset pricing model in the Indian context. Journal of Financial Management and Analysis.

    Google Scholar 

  • Dhankar, R. S., & Kumar, R. (2006). Mean-variance approach in portfolio selection: A test of optimization under different economic conditions. Asia Pacific Business Review, 2(2), 13–24. https://doi.org/10.1177/097324700600200203.

  • Grinold, R. C. (1999). Mean-variance approach to portfolio selection. The Journal of Portfolio Management.

    Google Scholar 

  • Gupta, L. C. (2000). Return on Indian equity shares. The ICFAI Journal of Applied Finance, 6(4).

    Google Scholar 

  • Lee, W. Y. (1990). Diversification and time: Do investment horizons matter. The Journal of Portfolio Management.

    Google Scholar 

  • Levitz, G. D. (1974). Market risk and management of institutional equity portfolios. Financial Analysts Journal.

    Google Scholar 

  • Lintner, J. (1965). Security prices, risk and maximum gains from diversification. Journal of Finance, 20(4).

    Google Scholar 

  • Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 12.

    Google Scholar 

  • Madhusoodanan, T. P. (1996). Portfolio management and beta: An analysis of the Indian stock market. Indian Journal of Finance and Research.

    Google Scholar 

  • Madhusoodanan, T. P. (1996). Optimal portfolio selection: A performance analysis with indian stock returns. The ICFAI Journal of Applied Finance, 2(2).

    Google Scholar 

  • Mehta, S. K. (2005). Markowitz revisited in Indian context. The ICFAI Journal of Applied Finance.

    Google Scholar 

  • Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4).

    Google Scholar 

  • Rao, C. U., Nath, G. C., & Malhotra, M. (1998). Capital asset pricing model and Indian stocks. The ICFAI Journal of Applied Finance, 4(1).

    Google Scholar 

  • Seghal, S. (1997). An empirical testing of capital asset pricing model in India. Finance India, 11(4).

    Google Scholar 

  • Sharpe, W. F. (1995). Risk, market sensitivity and diversification. Financial Analysts Journal.

    Google Scholar 

  • Sharpe, W. F., & Cooper, G. M. (1972). Risk return classes of New York stock exchange common stocks 1931–1967. Financial Analysts Journal.

    Google Scholar 

  • Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3).

    Google Scholar 

  • Srinivasan, S. (1998). Testing of capital asset pricing model in indian environment. Decision, 15(1).

    Google Scholar 

Download references

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Correspondence to Raj S. Dhankar .

Appendices

Appendix 1

List of companies of BSE 100

Code

Company

Code

Company

Code

Company

Code

Company

c1

ABB Ltd.

c20

Colgate Pamolive Ltd.

c39

ICICI Bank

c58

Lupin Ltd.

c2

Andhra Bank

c21

Container Corporation

c40

I-Flex Solution

c59

Patni Computers

c3

Arvind Mill

c22

Corporation Bank

c41

Kochi Refinery

c60

Pfizer Ltd.

c4

Ashok Leyland

c23

Cummins Ltd.

c42

Kotak Mahindra Bank

c61

Polaris Software Ltd.

c5

Asian Paints

c24

Divi’s Laboratory

c43

Larsen & Turbo

c62

Punjab National Bank

c6

ACC

c25

Dr. Reddy Lab

c44

MTNL

c63

Ranbaxy Ltd.

c7

Bajaj Auto Ltd.

c26

GAIL

c45

Mahindra & Mahindra

c64

Rashtriya Chemical

c8

Bank of Baroda

c27

Glaxosmithkline

c46

Mangalore Refinery

c65

Raymond Ltd.

c9

Bank of India

c28

Grasim Industries Ltd.

c47

Maruti Udoyg Ltd.

c66

Reliance Capital

c10

Bharat Electronics

c29

Great Eastern shipping Ltd.

c48

Matrix Laboratory

c67

Reliance Energy Ltd.

c11

Bharat Forge Ltd.

c30

Gujarat Ambuja Cement

c49

Moser Baer

c68

Reliance Industries

c12

BHEL

c31

HCL Infosystem

c50

MICO

c69

Satyam Computers

c13

Bharat Petroleum

c32

HCL Technologies

c51

National Aluminium

c70

Shipping Corporation

c14

Bharati Televenture

c33

HDFC Bank

c52

Nestle India

c71

Siemens Ltd.

c15

Biocon Ltd.

c34

Hero Honda

c53

Neyveli Lignite

c72

State Bank of India

c16

Cadila Health care Ltd.

c35

Hindalco

c54

Nicholas Piramal

c73

SAIL

c17

Canara Bank

c36

Hindustan Lever Ltd.

c55

Novartis India

c74

Sterlite Industries Ltd.

c18

Chennai Petroleum Ltd.

c37

Hindustan Petroleum

c56

ONGC

c75

Sun Pharmaceuticals

c19

Cipla Ltd.

c38

HDFC

c57

Oriental Bank of Commerce

c76

Tata Chemicals

c77

Tata Iron & Steel

c83

Indian Hotels

c89

Jaiprakash Associate

c96

VSNL

c78

Tata Motor

c84

Indian Oil Corporation

c90

Jammu & Kashmir Bank

c97

Vijaya Bank

c79

Tata Power

c85

Indian Overseas Bank

c91

TVS Motors

c98

Wipro Ltd.

c80

Tata Tea Ltd.

c86

Indian Petrochemicals

c92

Tata Teleservices

c99

Wockhardt

c81

Indian Rayon Ltd.

c87

IDBI c93 UTI Bank

c94

Union Bank of India

c100

Zee Telefilm

c82

ITC Ltd.

c88

Infosys Technologies

c95

United Phosphorus Ltd.

  

Appendix 2

Correlation Matrix for Portfolio 1

  • c95c42 = 0.18, c95c88 = 0.24, c95c54 = 0.12, c95c1 = 0.1, c95c100 = 0.12, c95c0.05 = –0.01, c95c98 = 0.13, c95c89 = 0.16, c95c31 = 0.14

  • c42c88 = 0.58, c42c54 = –0.28, c42c1 = 0.08, c42c100 = 0.05 c42c69 = 0.34, c42c98 = 0.08, c42c89 = –0.03, c42c31 = 0.17

  • c88c54 = –0.28, c88c1 = 0.08, c88c100 = 0.05, c88c69 = –0.13, c88c98 = 0.33, c88c89 = 0.08, c88c31 = –0.11

  • c54c1 = 0.17, c54c100 = 0.22, c54c69 = 0.18, c54c98 = 0.10, c54c89 = 0.18, c54c31 = 0.27

  • c1c100 = 0.25, c1c69 = 0.23, c1c98 = 0.24, c1c89 = 0.19, c1c31 = 0.23

  • c100c69 = 0.29, c100c98 = 0.02, c100c89 = 0.16, c100c31 = 0.36

  • c69c98 = –0.02, c69c89 = 0.08, c69c31 = 0.35

  • c98c89 = 0.15, c98c31 = 0.16

  • c89c31 = 0.32

Correlation Matrix for Portfolio 2

  • c33c71 = 0.13, c33c68 = 0.18, c33c52 = 0.12, c33c19 = 0.12, c33c47 = 0.25, c33c45 = 0.10, c33c50 = 0.21, c33c27 = 0.11, c33c7 = 0.18

  • c71c68 = 0.34, c71c52 = 0.10, c71c19 = 0.24, c71c47 = 0.30, c71c45 = 0.61, c71c50 = 0.13, c71c27 = 0.29, c71c7 = 25

  • c68c52 = 0.18, c68c19 = 0.41, c68c47 = 0.29, c68c45 = 0.37, c68c50 = 0.21, c68c27 = 0.38, c68c7 = 0.43

  • c52c19 = 0.24, c52c47 = 0.1, c52c45 = 0.09, c52c50 = 0.19, c52c27 = 0.35, c52c7 = 0.26

  • c19c47 = 0.35, c19c45 = 0.36, c19c50 = 0.09, c19c27 = 0.4, c19c7 = 0.29

  • c47c45 = 0.61, c47c50 = 0.09, c47c27 = 0.05, c47c7 = 0.13

  • c45c50 = 0.06, c45c27 = 0.05, c45c7 = 0.29

  • c50c27 = 0.24, c50c7 = 0.16

  • c27c7 = 0.39

Correlation Matrix for Portfolio 3

  • c77c44 = 0.31, c77c28 = 0.48, c77c61 = 0.06, c77c36 = 0.12, c77c32 = 0.28, c77c24 = 0.04, c77c25 = 0.41, c77c6 = 0.43, c77c63 = 0.27

  • c44c28 = 0.31, c44c61 = 0.19, c44c36 = 0.14, c44c32 = 0.33, c44c24 = –0.33, c44c25 = 0.19, c44c6 = 0.52, c44c63 = 0.28

  • c28c61 = 0.27, c28c36 = 0.22, c28c32 = 0.50, c28c24 = –0.04, c28c25 = 0.35, c28c6 = 0.51, c28c63 = 0.39

  • c61c36 = 0.28, c61c32 = 0.53, c61c24 = 0.35, c61c25 = 0.20, c61c6 = 0.28, c61c63 = 0.31

  • c36c32 = 0.34, c36c24 = 0.14, c36c25 = 0.28, c36c6 = 0.14, c36c63 = 0.33

  • c32c24 = 0.27, c32c25 = 0.36, c32c6 = 0.43, c32c63 = 0.32

  • c24c25 = 0.41, c24c6 = 0.18, c24c63 = 0.40

  • c25c6 = 0.37, c25c63 = 0.54

  • c6c63 = 0.43

Correlation Matrix for Portfolio 4

  • c82c74 = 0.01, c82c62 = -0.06, c82c43 = 0.23, c82c94 = 0.31, c82c10 = 0.28, c82c30 = 0.21, c82c26 = 0.12, c82c34 = 0.06, c82c35 = 0.27

  • c74c62 = 0.12, c74c43 = 0.33, c74c94 = 0.03, c74c10 = 0.25, c74c30 = 0.29, c74c26 = 0.13, c74c34 = 0.20, c74c35 = 0.34

  • c62c43 = 0.37, c62c94 = 0.07, c62c10 = 0.24, c62c30 = 0.10, c62c26 = 0.16, c62c34 = 0.20, c62c35 = 0.15

  • c43c94 = 0.12, c43c10 = 0.41, c43c30 = 0.41, c43c26 = 0.38, c43c34 = 0.26, c43c35 = 0.36

  • c94c10 = 0.22, c94c30 = 0.16, c94c26 = 0.-0.05, c94c34 = –0.03, c94c35 = 0.16

  • c10c30 = 0.36, c10c26 = 0.35, c10c34 = 0.24, c10c35 = 0.38

  • c30c26 = 0.24, c30c34 = 0.21, c30c35 = 0.32

  • c26c34 = 0.27, c26c35 = 0.16

  • c34 c35 = 0.16

Correlation Matrix for Portfolio 5

  • c72c86 = 0.21, c72c78 = 0.51, c72c80 = 0.02, c72c14 = 0.50, c72c23 = 0.31, c72c11 = 0.28, c72c55 = 0.45, c72c91 = 0.44, c72c81 = 0.18

  • c86c78 = 0.05, c86c80 = 0.08, c86c14 = 0, c86c23 = –0.02, c86c11 = 0.03, c86c55 = 0.12, c86c91 = –-0.12, c86c81 = 0.18

  • c78c80 = 0.27, c78c14 = 0.41, c78c23 = 0.30, c78c11 = 0.08, c78c55 = 0.28, c78c91 = 0.21, c78c81 = 0.31

  • c80c14 = 0.01, c80c23 = 0.12, c80c11 = 0.11, c80c55 = 0.09, c80c91 = –0.05, c80c28 = 0.28

  • c14c23 = 0.12, c14c11 = 0.15, c14c55 = 0.43, c14c91 = 0.51, c14c81 = –0.36

  • c23c11 = 0.18, c14c55 = 0.17, c23c91 = 0.20, c23c81 = 0.10

  • c11c55 = 0.23, c11c91 = 0.23, c11c81 = 0

  • c55c91 = 0.44, c55c81 = 0.20

  • c91c81 = –0.26

Correlation Matrix for Portfolio 6

  • c83c57 = –0.08, c83c53 = 0.10, c83c22 = 0.10, c83c56 = 0.26, c83c8 = 0.23, c83c38 = 0.16, c83c51 = 0.14, c83c17 = 0.26, c83c90 = 0.06

  • c57c53 = 0.18, c57c22 = 0.24, c57c56 = 0.21, c57c8 = 0.41, c57c38 = 0.22, c57c51 = 0.19, c57c17 = 0.29, c57c90 = 0.37

  • c53c22 = 0.15, c53c56 = 0.36, c53c8 = 0.27, c53c38 = 0.10, c53c51 = 0.07, c53c17 = –0.11, c53c90 = 0.35

  • c22c56 = 0.07, c22c8 = 0.53, c22c38 = 0.09, c22c51 = 0.30, c22c17 = 0.44, c22c90 = 0.38

  • c56c8 = 0.32, c56c38 = 0.20, c56c51 = 0.22, c56c17 = 0.42, c56c90 = 0.16

  • c8c38 = 0.31, c8c51 = 0.21, c8c17 = 0.63, c8c90 = 0.24

  • c38c51 = 0.12, c38c17 = 0.42, c38c90 = 0.22

  • c51c17 = 0.32, c51c90 = 0.18

  • c17c90 = 0.39

Correlation Matrix for Portfolio 7

  • c67c65 = 0.28, c67c39 = 0.14, c67c9 = 0.27, c67c2 = 0.46, c67c75 = 0.29, c67c13 = 0.22, c67c37 = 0.31, c67c40 = 0.02, c67c84 = –0.11

  • c5c39 = 0.05, c5c9 = 0, c5c2 = –0.08, c5c75 = 0.27, c5c13 = 0.16, c5c37 = 0.26, c5c40 = –0.14, c5c84 = –0.28

  • c39c9 = 0.38, c39c2 = 0.18, c39c75 = 0.13, c39c13 = 0.33, c39c37 = 0.22, c39c40 = 0.23, c39c84 = –0.18

  • c9c2 = 0.58, c9c75 = 0.32, c9c13 = 0.36, c9c37 = 0.24, c9c40 = 0.12, c9c84 = 0.43

  • c2c75 = 0.14, c2c13 = 0.23, c2c37 = 0.18, c2c40 = –0.06, c2c84 = 0.30

  • c75c13 = 0.24, c75c37 = 0.23, c75c40 = 0.32, c75c84 = –0.13

  • c13c37 = 0.69, c13c40 = –0.09, c13c84 = 0.15

  • c37c40 = –0.05, c37c84 = 0.03

  • c40c84 = 0.28

Correlation Matrix of Portfolio 8

  • c12c79 = 0.22, c12c49 = 0.28, c12c20 = 0.51, c12c18 = 0.33, c12c48 = 0.33, c12c93 = 0.50, c12c70 = 0.42, c12c73 = 0.29, c12c3 = 0.17

  • c79c49 = 0.35, c79c20 = 0.36, c79c18 = 0.08, c79c48 = 0.35, c79c93 = 0.06, c79c70 = –0.12, c79c73 = 0.14, c79c3 = 0.31

  • c49c20 = 0.32, c49c18 = 0.21, c49c48 = 0.10, c49c93 = 0.06, c49c70 = –0.013, c49c73 = 0.10, c49c3 = 0.18

  • c20c18 = 0.29, c20c48 = 0.05, c20c93 = –0.15, c20c70 = 0.19, c20c73 = 0.35, c20c3 = 0.36

  • c18c48 = 0.17, c18c93 = 0.21, c20c70 = 0.07, c18c73 = 0.43, c18c3 = 0.27

  • c48c93 = 0.26, c48c70 = 0.02, c48c73 = 0.14, c48c3 = –0.01

  • c93c70 = 0.18, c93c73 = 0.22, c93c3 = 0.28

  • c70c73 = 0.16, c70c3 = 0.22

  • c73c3 = 0.37

Correlation Matrix of Portfolio 9

  • c58c41 = 0.01, c58c60 = 0.15, c58c76 = 0.18, c58c59 = 0.41, c58c96 = 0.09, c58c64 = 0.29, c58c4 = 0.21, c58c29 = 0.41, c58c29 = 0.22

  • c41c60 = 0.09, c41c76 = 0.43, c41c59 = –0.05, c41c96 = 0.18, c41c64 = 0.21, c41c4 = 0.36, c41c29 = 0.29, c41c92 = 0.30

  • c60c76 = 0.22, c60c59 = –0.25, c60c96 = –0.03, c60c64 = 0.03, c60c4 = 0.08, c64c29 = –0.01, c60c92 = –0.04

  • c76c59 = 0.04, c76c96 = 0.19, c76c64 = 0.20, c76c4 = 0.34, c76c29 = 0.37, c76c92 = 0.19

  • c59c96 = 0.40, c59c64 = –0.35, c59c4 = –0.48, c59c29 = 0.08, c59c92 = –0.51

  • c96c64 = 0.03, c96c4 = 0.15, c96c29 = 0.21, c96c92 = 0.58

  • c64c4 = 0.02, c64c29 = 0.15, c64c92 = 0.08

  • c4c29 = 0.35, c4c92 = 0.30

  • c29c92 = 0.43

Correlation Matrix of Portfolio 10

  • c65c15 = 0.47, c65c99 = 0.09, c65c16 = 0.20, c65c21 = –0.06, c65c85 = 0.39, c65c46 = 0.04, c65c87 = 0.13, c65c97 = 0.14, c65c66 = 0.46

  • c15c99 = –0.20, c15c16 = 0.01, c15c21 = –0.40, c15c85 = 0.26, c15c46 = 0.56, c15c87 = –0.15, c15c97 = 0.00, c15c66 = 0.61

  • c99c16 = 0.01, c99c21 = 0.05c61, c99c85 = 0.22, c99c46 = 0.0, c99c87 = 0.48, c99c97 = 0.45, c99c66 = 0.33

  • c16c21 = 0.26, c16c85 = 0.16, c16c46 = –0.13, c16c87 = 0.07, c16c97 = 0.33, c16c66 = 0.20

  • c21c85 = 0.04, c21c46 = 0.08, c21c87 = 0.11, c21c97 = 0.23, c21c66 = 0.20

  • c85c46 = 0.35, c85c87 = 0.16, c85c97 = 0.19, c85c66 = 0.32

  • c46c87 = -0.06, c46c97 = 0.02, c46c66 = 0.29

  • c87c97 = 0.56, c87c66 = 0.35

  • c97c66 = 0.

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Dhankar, R.S. (2019). Mean–Variance Approach and Portfolio Selection. In: Risk-Return Relationship and Portfolio Management. India Studies in Business and Economics. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3950-5_16

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