Portfolio Creation

The report analyses the different Exchange Traded Funds, Real Estate Investment Trust and Infrastructure funds trading in Australia. It includes the primary assets, suitability of the fund, factors affecting the fund, analysis of excess return and tracking error, comparison with other index and optimal weights of the portfolio.

Running head: PORTFOLIO CREATION
Portfolio Creation
Name of the Student:
Name of the University:
Author Note:

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Table of Contents
Introduction:...............................................................................................................................3
Discussion:.................................................................................................................................3
Primary Assets:......................................................................................................................3
Suitability of the Fund:...........................................................................................................4
Factors affecting the fund:.....................................................................................................4
Analysis of Excess return and Tracking Error:......................................................................5
Comparison with other Index:................................................................................................6
Optimal Weights:...................................................................................................................7
Conclusion:................................................................................................................................8
References and Bibliographies:..................................................................................................9

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Introduction:
The following report highlights the different Exchange Traded Funds, Real Estate
Investment Trust and Infrastructure funds trading in Australia. The Exchange Traded Funds
which are chosen for our portfolio are ETFS Euro Stoxx 50 ETF, Beta Shares FTSE 100 ETF
and the Exchange Traded Commodity is VanEck Vectors Australian Resources ETF. The
infrastructure fund is Spark Infrastructure Group and the A-Reits is Scentre Group. The aim
of this report is to analyse the return of these funds over the last three years and the factors
which would affect the company which these funds represent.
Discussion:
Primary Assets:
Assets owned by infrastructure funds are those which are the main revenue generators
for the company. The primary assets which belong to the infrastructure fund taken for
analysis are SA Power Network and Citi-Power. The company also has other assets like
Powercor and Transgrid. The SA Power Network which is one of the biggest assets of the
infrastructure fund had a total of distribution network of 178200 km in 2017
(http://www.annualreports.com). However, in the year 2018 it fell to 178000 km. Also there
was a fall in the peak demand of electricity from 2017 to 2018. However, the number of
consumers for the company increased in 2018. Citi-Power is one of the biggest supplier of
electricity in the Sydney city. The number of consumers remained the same in both the years.
However, there was a fall in the network distribution in Sydney city from 7526 km to 7500
km. Also the peak demand of electricity demand increased from 2017 to 2018. The other
assets of the company also are a major contributor to the company revenue which cover other
regions covered by the country (https://www.asx.com.au).

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Suitability of the Fund:
The basic necessity for the citizen of any country is electricity and the company
chosen is provider of electricity to various parts in Australia. Thus Australia having one of
the most friendly immigration policy attracting people from various countries for various
opportunities in the country, leads to a rise in population and ultimately the rise in
consumption of electricity. Also the available of various renewable source of energy like the
solar energy and wind energy enables the company to generate electricity at a lower cost and
thus leading to a rise in profits for the company. Thus the infrastructure fund chosen as a part
of the portfolio allocation is justified as the fund has suitable opportunity to grow in future.
Factors affecting the fund:
The factors which might affect the fund are some of the following points highlighted
below,
Rise in Prices: Since the electricity manufactured in Australia is generated by organisation
and distributed from some other organisation. Thus the fall in the production of electricity
will lead to rise in cost of the electricity distributed to the end consumers. Thus this will lead
to less consumption and thus being with all the rising variable cost and fixed cost being the
same. The profit potential of the fund is reduced.
Increased Competition: The electricity providing company in Australia are affected by
various severe competition as the company are mostly privately owned. Thus with the rise in
competition the company with the most cheap and uninterrupted supply of electricity is
dominating in the market. Also there are no stringent policy of foreign investment in
Australia regarding electricity. Thus, this factor can affect the fund in the next 12 months.
New Technology: The technology in the industry is always upgrading to efficient way of
production at a lower cost. Thus the fund would require huge investment in technology to

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adapt the new technology to remain profitable in the long run. Thus this factor can affect the
company in the next 12 months if a competitor creates a more efficient and suitable
distribution network. Thus rendering the company unprofitable in the long run.
Analysis of Excess return and Tracking Error:
The excess return is calculated by subtracting the return of the individual stock with
the 10 year risk free treasury rate. The following table shows the calculation of excess return.
Figure 1: Excess Return
Source: By the Author
Thus the excess return calculated by the Scentre Group is -0.17123% and for the
Spark Infrastructure Group is -0.10382%. While the return for the other two investments is
positive, which is shown in the calculation above. Thus the excess return generated by cash is
0% as the return is same for both the investment.
The tracking error is the active risk of the project, which means that it measures the
difference between the portfolio return and the benchmark return of the investment. The
formula used to measure the tracking error of the project is that the portfolio return subtract
the benchmark return to derive the tracking error of the investment.

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Figure: Tracking Error
Source: By the Author
Thus the tracking error of the project is 0.34056%, which means the portfolio
generates difference in return by 0.34056%. Thus the portfolio deviates from the benchmark
return by 0.34056%.
On observing the data of the excess return and tracking error, it is seen that only two
investment in the portfolio are generating positive excess return while the other two
investment are generating negative excess return. The portfolio which is indexed to the ASX
200 index, is closely following the index with a small tracking error of 0.34056%. Thus, this
indicates that the portfolio is mimicking the benchmark portfolio in respect to returns. Thus
the fund manager has a passive investment strategy of investing in a portfolio.
Comparison with other Index:
The performance of a stock with other index is generally observed to understand the
relation with the fund performance with the stock performance. The Dow Jones Industrial
Average Index is an index which includes the industrial average of various industry. The
relation of the stocks with the index is given below,
Figure 3: Correlation
Source: By the Author
The Correlation between the Fund with the index indicates the strength of the
relationship between the index and the fund. As it is seen the individual funds have a positive

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correlation with the index. The Lowest being of the Scentre Group having the lowest
correlation and the highest of the Vaneck Vector having the highest positive correlation.
Optimal Weights:
The Optimal weights of a portfolio is the weight of the portfolio which generates the
highest return with the lowest risk. To calculate the optimal weights a hypothetical portfolio
with equal weights is created and shown in the table below,
Figure 4: Equal weights
Source: By the Author
The hypothetical portfolio with equal weights is created and the portfolio has a return
of 0.53190% monthly while the Standard deviation or the volatility is 2.2126%. The return by
total risk of the portfolio is at 0.24
Thus the portfolio which is created to reduce the return to total risk by 1%, this
portfolio is formed using the excel solver function which generates the following portfolio
weights.
Figure: Unequal Weights

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Source: By the Author
Thus the portfolio with the optimal weights created to reduce the return to total risk of
the portfolio and generates a return of 0.84079% and S.D of 3.0758%.
Conclusion:
In the above report it is concluded, about the infrastructure fund and the factors which
might affect the fund in future in Australia. The importance and the difference of the excess
return and the tracking error of the portfolio is observed. The portfolio created has a passive
style of returns as it closely follows the benchmark with a small tracking error. The
relationship between the portfolio returns and the Dow Jones Industrial Average index, and
the portfolio funds have a slight positive correlation with the fund. Thus the optimal weights
of the portfolio is 5%, 57%, 2% and 35.53%. Thus by investing the respective stocks at this
weights the optimal portfolio returns are generated.

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References and Bibliographies:
(2019). Annualreports.com. Retrieved 4 October 2019, from
http://www.annualreports.com/HostedData/AnnualReportArchive/S/ASX_SKI_2017.
pdf
Best, M. J., & Grauer, R. R. (2016). Prospect theory and portfolio selection. Journal of
Behavioral and Experimental Finance, 11, 13-17.
Camanho, N., Hau, H., & Rey, H. (2018). Global Portfolio Rebalancing and Exchange
Rates (No. w24320). National Bureau of Economic Research.
Chen, C., & Wang, Y. (2017). Understanding the multifractality in portfolio excess
returns. Physica A: Statistical Mechanics and its Applications, 466, 346-355.
Complete list of exchange-traded products on ASX.. (2011). Asx.com.au. Retrieved 4 October
2019, from https://www.asx.com.au/products/etf/managed-funds-etp-product-list.htm
Daly, M. (2018). Feasible portfolios under tracking error, β, α and utility
constraints. Investment Management & Financial Innovations, 15(1), 141.
Mutunge, P., & Haugland, D. (2018). Minimizing the tracking error of cardinality constrained
portfolios. Computers & Operations Research, 90, 33-41.
Pettenuzzo, D., & Ravazzolo, F. (2016). Optimal Portfolio Choice Under Decision‐Based
Model Combinations. Journal of Applied Econometrics, 31(7), 1312-1332.
Satchell, S. E., & Hwang, S. (2016). Tracking Error: Ex Ante Versus Ex Post Measures.
In Asset Management (pp. 54-62). Palgrave Macmillan, Cham.

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