Portfolio management

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INVESTMENT GROUP ASSIGNMENT2.docx

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Table of content

1.  INTRODUCTION 2

2. INVESTMENT POLICY STATEMENT 2

3. CAPITAL MARKET EXPECTATIONS 4

3.1 Index fluctuation and return analysis 4

3.2 Real Gross Domestic Product (RGDP) 5

3.3 Inflation and unemployment 6

3.4 Gross Government Debt (%GDP) 6

3.5 Currency risk 7

4. ASSET ALLOCATION 8

4.1 Industry analysis 8

4.2 Correlation matrix 9

4.3 Optimization 10

5. CONCLUSION 11

REFERENCE LIST 13

APPENDIX 14

 

 

1.  INTRODUCTION

The purpose of this report is to produce an endowment management plan, providing substantial cash flows for current bursary and scholarship payments. Additionally, it aims to address the problem of rising needs for financing in the future, due to the growing number of students. Specifically, great attention is paid to the development of an investment policy statement (IPS), and asset allocation. All the recommendations are underpinned by the economic, industry and individual share analysis. The report structure is presented as follows: the first part covers the questions of the IPS development, including a spending policy; the second part aims at capital market expectations analysis; the third part focuses on asset allocation; finally, some conclusions are drawn.

2. INVESTMENT POLICY STATEMENT

The IPS is likely to be the cornerstone of the portfolio management process since it specifies all the objectives, constrain and other issues required for investment decision-making (Maginn, 2007).

 

As the principal goal of the university endowment is to ensure incremental cash flows, the capital appreciation strategy seems quite reasonable to apply. The required rate of return was calculated so as to include 5% bursary and scholarships payments, 1.5% annual fund team fees and 3.1% inflation rate determined by geometric middling (Appendix 1). Specifically, the 5% spending rate was taken as the common percentage used among the majority of US endowment funds (Swensen, 2000). Additionally, it was specified that 50% of any excess return should be available for spending whereas the rest of it must be attributable to the fund principal for reinvesting. In order to increase the fund tolerance for short-term portfolio risk, the geometric smoothing rule was applied for the annual spending policy (Maginn, 2007) (Appendix 2).

 

As far as risk objectives are concerned, the maximal level of volatility was set as high as 8% for the whole portfolio and 14% for individual shares. Thus, a greater return could be achieved with a lower level of risk. Risk minimization was also guaranteed by the portfolio asset structure, including 30% of gilts. Additionally, no short-selling was permitted.

 

In order to ensure the required level of liquidity needed for providing current bursary payments, £500,000 (2.5% of the total assets) was deposited into the bank. Another restriction was concerned with security selection for acquisition. Thus, no purchase of tobacco, alcohol and weapon companies’ shares was permitted as those businesses often impede human progress. Also, constrain was imposed on acquisition of securities issued or property owned by the University in order to eliminate possible manipulations.

 

Generally, it was stated that any security from main industries and economies (except for those stated above) can be included in the portfolio as long as it leads to the fulfillment of risk/return objectives.

 

 Finally, the jurisdictional division between all the participants involved in the endowment management process was determined: board of trustees, investment committee, investment managers and independent investment consultants (Appendix 3).

 

 

 

3. CAPITAL MARKET EXPECTATIONS

3.1 Index fluctuation and return analysis

Since any fund performance appears highly dependent on future factors affecting investment value, capital market expectations (CME) should be brought to the forefront as a basis of decision-making in a strategic asset allocation (Maginn, 2007).

 

One of the principal reasons for portfolio development is likely to be risk minimization through diversification. Simultaneously, Maginn correctly argues (2007) that systematic risk (unlike unsystematic) cannot be controlled within one market. Hence, it could be reasonable to include different economies in the present CME analysis: USA (America), UK (Europe) and Japan (Asia).

 

As the historical data often contain valuable information, determining, in some degree, future results, the CME analysis would be started with evaluating the past performance of three market indices: Dow Jones (USA), FTSE (UK) and Nikkei (Japan) (Figure 1).

Figure 1 indicates a relatively stable movement of FTSE and Dow Jones indices during 2003-2010. By contrast, during the same period Nikkei demonstrated very high volatility – evidence of high risks concerned with investing in the Japanese stock market. Additionally, it showed the lowest annual return (5.1%) compared with FTSE (6.4%) and Dow Jones (7.7%) indices. Hence, the US and UK economies appear more attractive for investing.

 

However, history often shows direct relationships between asset returns and economy activity (Backus, 2008). Thus, it is essential to conduct a macro analysis of each economy. The following economic indicators, therefore, were applied: real Gross Domestic Product, inflation, unemployment, Gross Government Debt and currency risk.

3.2 Real Gross Domestic Product (RGDP)

RGDP is likely to be a chief measurement of economic activity since it is directly associated with business growth. The more final goods and services are produced during a year, the more prosperous the economy is likely to be. This, in turn, has a great impact on share returns.

 

Figure 2: RGDP

Source: IMF (2010)

Figure 2 shows that the Japanese economy appears to be the weakest one. During 2000-2009 its average GDP growth did not exceed 0.7%. Moreover, in 2009 the Japanese economy experienced a worse decline than either the UK or the USA, as its GDP fell by 5.2%. The British economy was also badly affected by the global recession. Its GDP fell by 4.9% in 2009. However, British average GDP growth for 2000-2009 was 1.7% annually, which was close to the US economy growth (1.9%). Hence, in terms of RGDP growth the UK and the USA seem more attractive for investing.

3.3 Inflation and unemployment

Inflation and unemployment rates are very important indicators of economic activity as they directly relate to not only Government monetary and fiscal policies, but also business and consumer expectations (Maginn, 2007).

Figure 3: Inflation and unemployment

Source: IMF (2010)

It can be seen that in 2009 both US and Japanese economies experienced falling prices – deflation, indicating recession trends. Deflation processes can be very dangerous as they undermine debt-financed investments and dilute the power of the central banks.  Specifically, in the USA, deflation was aggravated by a high level of unemployment (9.3%) doubled since 2000 – further evidence of economic problems.

 

By contrast, the UK economy had a quite reasonable inflation rate (2.2%) and relatively stable level of unemployment (7.5%) in 2009. Additionally, during 2000-2009 its inflation and unemployment rates did not exceed 1.9% and 5.4%, respectively, which seems to make the UK economy quite sound.

3.4 Gross Government Debt (%GDP)

The ratio of Gross Government Debt to GDP is often used as a measure of debt burden. When it exceeds 50%, it puts the economy into the danger zone (Maginn, 2007).

Figure 4: Gross Government Debt

Source: IMF (2010)

Figures 4 illustrates a steady rise in debt burden for all the economies during 2000-2009. Particularly, in 2009 the Japanese economy demonstrated the highest debt level, which was more than twice as much as its GDP (218%). The USA had the second largest level of debt. Specifically, it had passed the safe zone (50%) long before the global downturn started. Therein, the UK economy seems to be more stable since its average debt burden did not exceed 44% during 2000-2009.

3.5 Currency risk

Currency risk becomes a factor of the first magnitude when foreign investments are concerned; high unpredictable currency fluctuations may badly affect asset returns (Arnott, 1999).

 

The analysis of daily exchange rates fluctuations during 1994-2009 shows that the yen and the US dollar appear to be quite risky since their standard deviations are 12.7% and 9.5%, respectively (Appendix 4). The UK pound demonstrates only 6.6% risk. Moreover, the investments in British economy can be considered as currency-risk-free since the university endowment is located in the UK.

 

It can be inferred that the UK economy is likely to be the most attractive one for the University endowment fund’ investments as it appears relatively sound and has no currency risks. Thus, in order to minimize risk, it seems reasonable to allocate all the fund assets in the UK capital market. In the future the endowment asset structure could be changed if the other countries demonstrate sound economic trends.

4. ASSET ALLOCATION

Asset allocation is likely to be crucial for the portfolio management process as it provides an effective tool for controlling risk exposures by diversification (Maginn, 2007). However, the large number of securities available brings the problem of choice to the forefront.

4.1 Industry analysis

In order to group shares, London Stock Exchange’s classification was employed (LSE, 2010). The thirteen sectors of the UK economy were selected. The regression models were created by the application of the industries’ monthly performance data during 2005-2009 (represented by FTSE indices) (Appendix 5). Then, the future trends were assessed. Finally, forecasted annual returns were compared with historical ones (Figure 5).

 

Figure 5: Industries’ trends

Source: Yahoo (2010)

Figure 5 indicates that only four sectors are likely to grow in the future: Chemistry, Electricity, Gas&Water and Mining. Since short-selling is forbidden by the IPS, the other industries seem to be of no interest to the endowment fund. Once the most attractive industries were identified, great attention had to be paid to share selection within each sector.

 

As a level of risk and return is likely to be directly associated with a company’s market capitalization, companies with the highest and lowest market values were selected (Appendix 6). A high market capitalization may be evidence of a strong financial position ensuring low risks whereas a low market value seems to guarantee high returns.

 

Additionally, in order to select the most attractive companies, meeting the IPS requirements, the following ratios were applied: Annual Return (AR), Standard Deviation (SD) and Price to Earnings ratio (P/E). Generally, all the companies having AR lower than 9.6% and SD higher than 14% were excluded from the portfolio. Also, great attention was paid to P/E analysis so as to identify the companies with the lowest ratios (below 30). Low P/E ratios may be evidence of relative share undervaluation (McLeavey, 2007). Thus, the seven companies were chosen from the sixteen companies initially selected (Appendix 6).

 

Also, in order to provide risk minimization, three risk-free assets (UK gilts) showing positive annual return were included in the portfolio. Gilts analysis is presented in Appendix 7.

4.2 Correlation matrix

According to Maginn (2007) there are several principles underlying the effective asset allocation.

First, assets within an asset class should be relatively homogeneous. Second, asset classes should be mutually exclusive. Third, asset classes should be diversifying.

 

Only then could the effectiveness of asset allocation in controlling risk be achieved. In order to conduct such allocation, a correlation matrix can be applied. It helps to identify a high positive correlation between assets, which often indicates a lack of risk minimization.

 

 

Figure 6: Correlation matrix

 

Figure 6 demonstrates a high correlation (above 85%) between the several companies, which indicates duplicating risk exposures already present. Some companies, therefore, should be excluded from the portfolio.

 

The analysis shows that as Jonson Matthey’s and International Power’s shares have an extremely high correlation with several companies, they should be excluded from the portfolio.

4.3 Optimization

Optimization is likely to be one of the most critical steps in asset allocation as it helps to develop the set of efficient portfolios with the highest returns for given levels of risk (M, 2007). In order to speed up and simplify calculations, Microsoft Office software – Excel was used. Specifically, all the objectives and restrictions written in the IPS were included in the model. Thus, the following efficient portfolios were created (Figure 7).

 

 

Figure 7: Efficient portfolios

Finally, the selection of the most attractive portfolio was done by the application of Sharpe ratio, measuring the excess return per unit of risk. The greater it is, the better. It can be seen that portfolio number 10 has the highest level of expected return for the given level of risk. However, its Sharpe ratio is only the second largest. This could be explained by nonlinear relationships between the portfolio’s volatility and expected return. Since portfolio number 9 has the highest Sharpe ratio, it should be chosen as the most efficient (Figure 7).

5. CONCLUSION

As was shown above, the way to effective asset allocation lies, first of all, in the careful development of the Investment Policy Statement, which specifies all the client’s objectives and constrain. The IPS also provides rules for a spending policy and identifies areas of responsibility for all the participants involved in the endowment management process.

 

Once the IPS has been developed, an exhaustive micro and macro analysis should be conducted. This could include historical data evaluations, chief economic ratio calculations, industry and individual share analysis. It provides a basis for creating the most desirable combination of assets, including shares and gilts. At the final stage, the portfolio goes through the process of optimization. This helps to create the set of efficient portfolios with the highest returns for given levels of risk. Additionally, Sharpe ratio could be applied to find the highest excess return per unit of risk.

 

As the present endowment project went through all the steps discussed above, the goals set for the investment management group can be considered as successfully accomplished.

 

 

REFERENCE LIST

Arnott, R., Bisset, A., DeRosa, D., Kritzman, M., Layard-Liesching, R., Lee, A., Levich, R., Montoya, M., O'Grady , T., Solnik, B., Strange, B. and Vecchio, F. (1999). Currency Risk in Investment Portfolios. London: CFA Institute

 

Backus, D., Routledge, B., and Zin, S. (2008). The cyclical component of US asset returns. [online]. Available at < http://www.stern.nyu.edu/> [Accessed 12 May 2010]

 

Bloomberg (2010). Market data. [online]. Available at <http://www.bloomberg.com/> [Accessed 12 May 2010]

 

FT (2010). Markets data. [online]. Available at <http://markets.ft.com/> [Accessed 10 May 2010]

 

International Monetary Fund (IMF) (2010). World Economic Outlook Database: 2009. [online]. Available at <http://www.imf.org/> [Accessed 9 May 2010]

 

London Stock Exchange (LSE) (2010). All the companies on London Stock Exchange. [online]. Available at < http://www.londonstockexchange.com/> [Accessed 12 May 2010]

 

Maginn, J., McLeavey, D., Pinto, J., and Tuttle, D. (2007) Managing Investment Portfolios: A Dynamic Process. 3rd Edition. New Jersey: John Wiley & Sons.

 

McLeavey, D., Pinto, J., Robinson, T. and Stowe, J., 2007. Equity Asset Valuation. New Jersey: John Wiley & Sons

Swensen, D. (2000). Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment. New York: Free Press

 

Wooldridge, J., 2002. Introductory Econometrics: A Modern Approach. Phoenix: South-Western College

 

Yahoo (2010). Markets data. [online]. Available at <http://uk.finsearch.yahoo.com/> [Accessed 14 May 2010]

 

 

APPENDIX

 

Appendix 1: Geometric middling

 

Figure 8: Inflation in the UK (%)

1980

1981

1982

1983

1984

1985

1986

1987

16.849

12.189

8.511

5.198

4.448

5.16

3.626

4.066

1988

1989

1990

1991

1992

1993

1994

1995

4.612

5.197

7.036

7.413

4.297

2.497

2.071

2.625

1996

1997

1998

1999

2000

2001

2002

2003

2.442

1.816

1.561

1.317

0.867

1.182

1.274

1.363

2004

2005

2006

2007

2008

2009

   

1.344

2.041

2.3

2.346

3.629

2.166

   

Source: IMF (2010)

 

 

 

 

 

Appendix 2: Geometric smoothing rule

 

 

 

 

 

Appendix 3: Roles and areas of responsibility

 

Participants

Roles and responsibilities

Board of trustees

The Board of Trustee is responsible for

-approving the IPS developed by the Investment Committee for the investment program;

-establishing and reviewing the investment policies for the Endowment;

-collaborating with Investment Committee and the Investment Consultant so as to review the portfolio’s investment structure and financial performance.

Investment committee

The investment Committee is responsible for making recommendations concerned with the development of investment strategies and guidelines.

Also, it is responsible for reviewing quarterly reports of the operations and results as prepared by the Foundation staff prior to submission to the Board of Trustees.

Investment managers

Investment managers are responsible for the fulfillment of policies and guidelines set by the Board of Trustees.

Also they have to review the Portfolio’s investments at least monthly and maintain a quarterly summary of investment activity.

Independent investment consultants

Independent investment consultants are responsible for providing independent review, analysis, and recommendations concerning the development and revision of policies


 

 

 

Appendix 4: Currency risk

 

 

In order to evaluate currency risk concerned with the US dollar, the UK pound and the yen, daily exchange rates between Special Drawing Rights (international monetary reserve currency) and the three currencies for 03.01.1994-10.05.2010 were used (Figure 9). 

 

 

Figure 9: currency risk calculations

Source: IMF (2010)

 

 

Appendix 5: Regression models

 

Future indices’ returns could be predicted by the application of linear regression models (Wooldridge, 2002):

yt = β0 + β1t + εt , where

yt – expected index return,

β0 and β1 – model parameters,

t – time,

εt –value of the stochastic error.

The monthly indices’ performance data were used to calculate β0 and β1 (Figure 10).

 

Figure 10: Indices’ performance

 Source: Yahoo (2010)

The parameters of the models are presented as follows (Figure 11)

Figure 11: Regression model parameters 

 

Appendix 6: Company selection

Ratio

Gas. Water & Multiutilities

Electricity

Dee Valley Group

Severn Trent

Northumbrian Water

Scottish&Southern Energy

International Power

P/E

11.47

15.16

12.05

27.62

4.97

Expected return

8.36%

3.64%

15.34%

9.04%

22.78%

Standard Deviation

5.49%

6.12%

6.18%

4.48%

9.22%

Market value

41.81m

2.67bn

514.67m

9.99bn

4.77bn

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Описание работы
The purpose of this report is to produce an endowment management plan, providing substantial cash flows for current bursary and scholarship payments. Additionally, it aims to address the problem of rising needs for financing in the future, due to the growing number of students. Specifically, great attention is paid to the development of an investment policy statement (IPS), and asset allocation. All the recommendations are underpinned by the economic, industry and individual share analysis. The report structure is presented as follows: the first part covers the questions of the IPS development, including a spending policy; the second part aims at capital market expectations analysis; the third part focuses on asset allocation; finally, some conclusions are drawn.
Содержание
1. INTRODUCTION 2
2. INVESTMENT POLICY STATEMENT 2
3. CAPITAL MARKET EXPECTATIONS 4
3.1 Index fluctuation and return analysis 4
3.2 Real Gross Domestic Product (RGDP) 5
3.3 Inflation and unemployment 6
3.4 Gross Government Debt (%GDP) 6
3.5 Currency risk 7
4. ASSET ALLOCATION 8
4.1 Industry analysis 8
4.2 Correlation matrix 9
4.3 Optimization 10
5. CONCLUSION 11
REFERENCE LIST 13
APPENDIX 14