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Investment Manager Analyzing ASX 200 Companies

Executive summary

This case study aims to critically examine the potential of an investment manager to closely abide by a specific benchmark index and thus concentrate on a few assets to keep the tracking error low while maintaining the portfolio high. The major endeavor will entail determining if the effect of diversification of shares of companies.

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The study also looks at the various principle of share and portfolio risk analysis imperatively applied in the task of construction of the portfolio. Construction of a portfolio entails choosing the proportion of the share which will be invested in each identified class of the assets. There is a range of classification on which each given asset will fall. These large assets can still be subdivided further to create room for diversification of risks. This diversification is important because it allows for a much better compromise of the risk of the respective portfolio and the accompanying return. Strategic share selection for the portfolio is an integral part of the construction of the portfolio and it is concerned with the long-term in addition to tactical allocation. The process of tactical allocation entails modification of class weights of the portfolio asset class and this is typically carried out on a short-term basis. The entire process of tactical allocation adds value to the return of the investment and this can finally be measured by the information available.

Introduction

To have an appreciation of the basic problems associated with portfolio construction and it is risk, it is useful to understand how risk is calculated. An appreciation of some elementary approaches aimed at providing a very rough feel for risk also aids in gaining an understanding of why differences in share risk must be considered in portfolio selection.

Selection of companies

The companies selected are at ASX 200 and they include TLS, GPT AWC, PNA, SBM, BSL, IPL, BHP, LGL, and CFX These companies have various market performances and their historical prices have been downloaded into excel

which is sent as the main document. Each company has its worksheet where various calculations are carried out.

The Standard deviation, average return, Beta, and variances of the companies

The standard deviation of a distribution of stocks returns represents the square root of the average squared deviations of the individual observation from the expected value.

The coefficient of variation, V, is calculated simply by dividing the standard deviation, σ, for a project by the expected value, E, for the project

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Correlation is a statistical measure that, when combined with other statistical measures such as the standard deviation and expected value of returns, provides a framework within which the decision-maker can view the risk-return trade-offs associated with

various projects in order to select those that align best with his (or the firm’s) risk – return disposition

The standard deviation average return beta and variances for the companies are represented in the table below:

COMPANY AVERAGE RETURN STANDARD DEVIATION VARIANCE BETA
Telstra Corporation Limited(TLS) 0.04% 1.4% 0.025% 0.026
GTP GroupGPT 0.3% 4.7% 0.22% 0.198
Alumina Limited(AWC) -0.000% 1.26% 0.015 1.001
Pan Australian resources limited(PNA) 0.09 5.6% 0.3% 0.13
ST. Barbara Limited(SBM) 0.05% 6.1% 0.367 -0.139
BlueScope steel Limited (BSL) 0.26% 4.3% 0.185% 0.249
Incitec Pivot Limited (IPL) 1.92% 72.5% 52.59% 1.32
Lihir Gold Limited (LGL) -0.041% 3,19% 0.1025 0.173
BHP Billiton (BHP) -0.052% 2.422% 0.059% 0.166
CFS Retail property trust (CFX) 0.014% 1.876 0.035 0.01474

The standard deviation of the companies as calculated using excel is 14%, for TLS, GPT is 4.7%, AWC is -1.26%, Standard deviation for PNA is 5.62%, standard deviation for SBM is 0.61%, standard deviation for PSL is 4.3%, standard deviation for IPL is 72.5%, standard deviation for LGL is -3.19, Standard deviation for BHP is 2.42 and Standard deviation for CFX 1.88.

Combination of the companies to form portfolios

The following are the portfolio that has been created from the shares of the company. This portfolios has its average return

Portfolio 1

This will have one company

Portfolio TLS Weighted
Average return 0.04% 0.04%
Standard deviation 1.4% 1.14%
Variance 0.00020 0.00020
Beta 0.026 0.026

Portfolio 2

This will have two companies

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Portfolio TLS GPT Weighted
Average return 0.04% 0.3% 0.17%
Standard deviation 1.4% 4.7% 3.05%
Variance 0.0002 0.0022 0.0012
Beta 0.026 0.198 0.112
Portfolio 3
Portfolio TLS GPT AWC Weighted
Average return 0.04% 0.30% -0.0002 0.11%
Standard deviation 1. 4% 4.70% 1.26 2.45%
Variance 0.0002 0.0022 0.00015 0.00085
Beta 0.026 0.198 1.001 0.4083
Portfolio 4
Portfolio TLS GPT PNA AWC Weighted
Average return 0.04% 0.30% 0.09% -0.0002 0.10%
Standard deviation 1.4% 4.70% 5.60% 1.26 3.24%
Variance 0.0002 0.0022 0.0003 0.00015 0.00071
Beta 0.026 0.198 0.13 1.001 0.33875
Portfolio 5
Portfolio TLS GPT SBM PNA AWC Weighted
Average return 0.04% 0.30% 0.05% 0.09% -0.0002 0.09%
Standard deviation 1.4% 4.70% 6.1% 5.60% 1.26% 3.81%
Variance 0.0002 0.0022 0.00367 0.0003 0.00015 0.0013
Beta 0.026 0.198 -0.139 0.13 1.001 0.2432
Portfolio 6
Portfolio TLS GPT PSL SBM PNA AWC Weighted
Average return 0.04% 0.30% 0.26% 0.05% 0.09% -0.0002 0.08%
Standard deviation 1.4% 4.70% 4.30% 6.1% 5.60% 1.26 2.98%
Variance 0.0002 0.0022 0.00185 0.00367 0.0003 0.00015 0.0014
Beta 0.026 0.198 0.249 -0.139 0.13 1.001 0.244166667
Portfolio 7
Portfolio TLS IPL GPT BSL SBM PNA AWC Weighted
Average return 0.04% 1.92% 0.30% 0.26% 0.05% 0.09% -0.0002 0.38%
Standard deviation 1.4% 72.50% 4.70% 4.30% 6.1% 5.60% 1.26 13.69%
Variance 0.0002 0.5259 0.0022 0.00185 0.00367 0.0003 0.00015 0.0763
Beta 0.026 1.32 0.198 0.249 -0.139 0.13 1.001 0.398
Portfolio 8
Portfolio TLS IPL LGL GPT BSL SBM PNA AWC Weighted
Average return 0.04% 1.92% —0.04% 0.30% 0.26% 0.05% 0.09% -0.0002 0.33%
Standard deviation 1.4% 72.50% 3.19% 4.70% 4.30% 6.1% 5.60% 1.26 12.38%
Variance 0.0002 0.5259 0.001 0.0022 0.00185 0.00367 0.0003 0.00015 0.0669
Beta 0.026 1.32 0.173 0.198 0.249 -0.139 0.13 1.001 0.36975
Portfolio 9
Portfolio TLS IPL LGL BHP GPT BSL SBM PNA AWC Weighted
Average return 0.04% 1.92% -0.04% -0.05% 0.30% 0.26% 0.05% 0.09% -0.0002 0.28%
Standard deviation 1.4% 72.50% 3.19% 2.42% 4.70% 4.30% 6.1% 5.60% 1.26 11.27%
Variance 0.0002 0.5259 0.001 0.0059 0.002 0.00185 0.00367 0.0003 0.00015 0.0601
Beta 0.026 1.32 0.173 0.166 0.198 0.249 -0.139 0.13 1.001 0.3471
Portfolio 10
Portfolio TLS IPL LGL CFX BHP GPT BSL SBM PNA AWC Weighted
Average return 0.04% 1.92% -0.04% 0.01% -0.05% 0.30% 0.26% 0.05% 0.09% -0.0002 0.26%
Standard deviation 1.4% 72.50% 3.19% 1.88% 2.42% 4.70% 4.30% 6.1% 5.60% 1.26 10.34%
Variance 0.0002 0.5259 0.001 0.00035 0.0059 0.002 0.00185 0.00367 0.0003 0.00015 0.0542
Beta 0.026 1.32 0.173 0.01474 0.166 0.198 0.249 -0.139 0.13 1.001 0.3139

Analysis and discussion of the findings

From the findings above the standard deviation is reducing as diversification increases. Although holding two securities is probably less risky than holding security alone, the portfolio reduces the risk of portfolio by incorporating into a security whose risks is greater than that of any of the investments held initially. From the analysis above it clearly comes out although some portfolio have the same expected return some are riskier. Let me take an example of portfolio three where all companies are equal; the average return of this portfolio can be thought of as the weighted average return of each security in the portfolio; that is

Rp = ∑ Xi Ri Where Rp = expected return on portfolio, Xi = proportion of total portfolio invested in security I , Ri = expected return to security I and N = Total number of securities in portfolio.

Thus by putting part of the money into the riskier stock one is able to reduce risk considerably from what it would have been if one had confined our purchases to the less risky share. If one held only one share expected return would be higher than when in a portfolio. Holding a mixture of many shares the expected return will always be improved. I am not disputing that one can achieve the same expected return for less risk. In this case I am able to eliminate risk of the portfolio. The reduction of risk of a portfolio by blending into a security whose risk is greater than of any of the securities held initially suggests that deducing the riskiness of a portfolio simply by knowing the riskiness of individual securities is not possible. It is vital that we also know the interactive risk between securities!

The crucial point of how to achieve the proper proportions of companies shares in reducing the risk to zero by selecting the portfolio wisely. The risk of the portfolio is reduced by playing off one set of variations against another.

Efforts to spread and minimize risk take the form of diversification. The more traditional forms of diversification have concentrated upon holding a number of security types across industry sectors. The reasons are related to inherent differences in equity contracts, couples with differences in bond and equity contracts, coupled with the notion than an investment in firms in dissimilar industries would most likely do better than in firms within the same industry. Holding one stock each from mining, utility and manufacturing groups is superior to holding three mining stocks. Carried to its extreme this approach leads to the conclusion that the best diversification comes through holding fifty such scattered stocks is five times more diversified than holding ten scattered stocks.

Most people would agree that a portfolio consisting of two stocks is probably less risky than one holding either stock alone. However, experts disagree with regard to the right kind of diversification and the right reason. The discussion that follows introduces and explores a formal, advanced notion of diversification conceived by the genius of harry Markowitz. Markowitz approach to coming up with good portfolio possibilities has its roots in risk return relationships.

This is not at odds with traditional approaches in concept. The key differenced lie in Markowitz assumption that investor attitude toward portfolios depend exclusively upon (1) expected return and risk and (2) quantification of risk. And risk is by proxy, the statistical notion of variance or standard deviation of return.

It is important to note that in diversification the more securities one holds in a portfolio the better. Markowitz-type diversification stresses not the number of securities but the right kinds of securities are those that exhibit less than perfect positive correlation.

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An unfortunate fact is that nearly all securities are positively correlated with each other and the market. King notes that about half the variance in a typical stock results from elements that affect the whole market. The upshot of this is that risk cannot be reduced to zero in portfolios of any size.

The one half of total risk that is not related to market forces can be reduced by proper diversification, but once unsystematic risk is reduced or eliminated we are left with systematic risk, which no one can escape.

Thus beyond some finite of securities adding more is expensive in time and money spent to search them out and monitor their performance and this cost is not balanced by any benefits in the form of additional reduction of risk! Evans and Archers work suggest that unsystematic risk can be reduced by duplicating within industries). This results from simply allowing unsystematic risk on these stocks to average out to near zero. With Markowitz-type diversification, risk can technically be reduced below the systematic level if securities can be found whose rates of return have low enough correlations.

Conclusion

From the two portfolios constructed above a risk averse investor will prefer the first portfolio because its beta is lower than the second portfolio. The higher the portfolio the more volatile the return becomes. Therefore it is prudent for an investor to make an informed decision in relations to the types of beta to be chosen and invested in

References

  1. Brealey R. Myres, S. and Marcus, J.(2001); Fundamentals of corporate Finance, Irwin series in finance, Boston, MA: Irwin/ McGraw – Hill.
  2. DeFusco, R.A., Dennis W. M., Pinto J. E., Anson J. P., and Runkle, D. E. (2007). “Quantitative Investment Analysis” J. Wiley and Sons
  3. Fischer D. E. and Jordan R.J. (2006); Security analysis and Portfolio management, Prentice-Hall, India. Pp559-560
  4. Larcker F. D., and Ittner, D. C., (1998). “Innovations in performance measurement: trends and research implications” International Journal of Management Accounting Research 18, 205–238.
  5. Larcker, F. D., and Ittner, D. C., (2003). “The evidence from a balanced scorecard of Subjectivity and the weighting of performance measures” The Accounting Review 79, 725–758.
  6. McLaney E., (2003); Business finance theory and practice; Prentice Hall ISBN 0-273-67356-4
  7. Pandey I M (2008) financial management; vikas

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