52221_Eugene_Ang_BEFM015_Coursework_Group_7_480751_1117953625

ABBAS, YAZAN 650008751

ACHU CLINTON, FRU 650056810

ANG, EUGENE 620023714

HONSCHA GARCIA, LUCAS 640061995

XU, JIEYING 650048695

Portfolio Management and Asset Allocation (BEFM015) Coursework

2

Contents Methodology ........................................................................................................................................... 4

Country Selection ................................................................................................................................ 4

Sector Allocation ................................................................................................................................. 4

Porter’s Five Forces ......................................................................................................................... 5

Characteristics of Representative Industries .................................................................................. 5

Influence of Systemic Forces........................................................................................................... 7

Stock Selection .................................................................................................................................... 8

Performance Evaluation........................................................................................................................ 10

Performance ratios analysis .............................................................................................................. 10

Attributive Effects ............................................................................................................................. 10

Relation Between Results and Relevant Financial Theories and Models ............................................. 12

EMH ................................................................................................................................................... 12

Two-Fund Separation Theorem ........................................................................................................ 12

Black-Litterman Model...................................................................................................................... 13

References ............................................................................................................................................ 14

Appendix ............................................................................................................................................... 15

Aims .................................................................................................................................................. 15

Investment Policy Statement (IPS) .................................................................................................... 16

Tables ................................................................................................................................................ 17

Table1 Country Selection ............................................................................................................ 17

Table2 Economic Indicators for Countries .................................................................................. 17

Table3 Bloomberg Consensus Rating ............................................................................................ 18

Table4 Correlation between Exchanges of Countries in Portfolio .............................................. 19

Table5 Sector Allocation for Portfolio ........................................................................................... 20

Table6 Stock Selection .................................................................................................................. 21

Table7 Ratios for Performance Evaluation ................................................................................... 21

Table8 Portfolio Attribution before Rebalance ............................................................................ 22

Table9 Portfolio Attribution after Rebalance ............................................................................... 22

Figures ............................................................................................................................................... 23

Figure1 Country Weight Allocation ............................................................................................. 23

Figure2 Porter’s Five Forces-Consumer Discretionary ................................................................. 23

Figure3 Porter’s Five Forces-Consumer Staples .......................................................................... 24

Figure4 Porter’s Five Forces—Energy ......................................................................................... 24

3

Figure5 Porter’s Five Forces-Financials ....................................................................................... 25

Figure6 Porter’s Five Forces-Health Care .................................................................................... 25

Figure7 Porter’s Five Forces-Industrials ...................................................................................... 26

Figure8 Porter’s Five Forces- InfoTech ........................................................................................ 26

Figure9 Porter’s Five Forces-Materials....................................................................................... 27

Figure10 Porter’s Five Forces-Telecommunication Services....................................................... 27

Figure11 Porter’s Five Forces- Utilities ......................................................................................... 28

Figure12 Stock Allocation Before Rebalance ................................................................................ 28

Figure13 Stock Allocation After Rebalance ................................................................................... 29

Figure14 Stock Correlation for Portfolio ....................................................................................... 29

Figure15 Portfolio Return Pre-rebalance .................................................................................... 30

Figure16 Portfolio Return Post-rebalance ................................................................................... 30

Figure17 Performance Differences between Portfolio and Benchmark ...................................... 31

Figure18 Sharpe Ratio before Rebalance ................................................................................... 31

Figure19 Sharpe Ratio after Rebalance ........................................................................................ 31

Figure20 Treynor Measure before Rebalance .............................................................................. 32

Figure21 Treynor Measure after Rebalance ................................................................................. 32

Figure22 Information Ratio before Rebalance ............................................................................. 32

Figure23 Information Ratio after Rebalance ................................................................................ 33

Figure24 Jensen’s Alpha before Rebalance ................................................................................... 33

Figure25 Jensen’s Alpha after Rebalance ..................................................................................... 33

4

Methodology

As an active-managed portfolio we will select our assets using a top-down approach. After selecting the

countries and sectors we would like to invest in through macroeconomic indicators, we will select assets based

on their forecasted growth potential. We will then use the Black-Litterman model to allocate weights to our pool

of assets.

Country Selection

We developed our shortlist of countries based on criteria that suited our strategy of focusing on growth stocks.

We also had to diversify our portfolio, to minimize systemic risk. Therefore, we had decided on a mix of both

developed countries, and developing countries. This would offer us a variety of securities which would produce

stable, low risk returns, together with other stocks that held the potential for higher returns, albeit at elevated

risk levels (See Figure1).

Our fund selected the following countries based on the above criteria: The G7 group of developed nations,

which represents the leading industrial countries, plus 5 developing nations which comprise the biggest

economies in developing regions. We also included securities from Hong Kong and Singapore, for reasons that

shall be elaborated upon below (See table1 and 2).

Countries from five continents are represented in our portfolio, including the biggest economies in Latin

America and Africa. To bolster and justify our choice of countries, we looked at the following economic

indicators for each country in our portfolio (See table2).

Sector Allocation

The next step in our top-down approach to stock selection was to select the industries from which our securities

would be chosen. For this step, we took a more quantitative approach, based on three steps. First, we conducted

strategic analyses using Porter’s Five Forces. Second, we compared the characteristics of representative

industries from the various economic sectors. Finally, we analysed how the influence of macroeconomic,

demographic, governmental, social, and technological influences on industry growth, profitability, and risk. We

then selected eight sectors which best represented our portfolio aims, which is to maximise returns for a given

level of risk. Also, the choice of eight sectors was selected based on our need to diversify away unsystematic

risk. Out of the ten industrial sectors classified under the S&P Global 1200, we chose all but two:

Telecommunication Services, and Utilities, as they were the poorest-performing sectors. This was determined

through detailed analysis of the results obtained using the methods listed above. They were then filtered further

using the following criteria:

• Debt/Equity Ratio: Lower than 150%

• ROE: Greater than 5

5

• Price/Earnings Ratio: Greater than 10

• 12-Month Forward PEG Ratio: Lower than 4

• Market Cap: Reasonably low

• Institutional Ownership: Reasonably low levels

See Table5 for a full outline of the sector allocation results.

Porter’s Five Forces

See Figures2-11 in appendix.

Characteristics of Representative Industries

6

7

Influence of Systemic Forces

8

Stock Selection

After the sectors were chosen, we used a certain set of criteria, which is a refinement of the criteria used to filter

the sectors, to choose our stocks. They are as follows:

• Debt/Equity Ratio: Lower than 100%

• ROE: Greater than 10

• 12-Month Forward ROE: Greater than 15

• Price/Earnings Ratio: Between 15 and 50

• 12-Month Forward PE Ratio: Between 15 to 50

• 12-Month Forward PEG Ratio: Lower than 1.2

• 12-Month Revenue Growth: More than 15%

• Institutional Ownership: Reasonably low levels

Next, the Black-Litterman model was used to optimise the weights of each security in our portfolio. Firstly, we

ran regressions of each security on our benchmark index. We used the prices of the S&P Global 1200 index as

9

the independent variables, and the prices of each individual security as our dependent variable. The resulting

equations derived from the regressions formed the basis of our optimisation model.

We used Bloomberg analyst consensus ratings as our inputs to our regression equations. Bloomberg provides

the consensus by standardising buy/hold/sell recommendations from different sources, and the average results

form the consensus ratings (See table3). We then obtain the expected returns using our regression equations,

and these formed our target deltas. To rebalance, we will evaluate the performance of our portfolio, and then

optimise the weights of our portfolio again.

Additionally, during rebalancing, we added a certain proportion of US Treasury bonds to the portfolio, as part of

our risk-free asset. This is in accordance to the basic tenants of the two-fund separation theorem. This gives us

both a risk-free asset, and a risky portfolio which is highly similar to the market portfolio.

See table6 and figure12-14 for a full outline of the stock selection results.

10

Performance Evaluation

Performance ratios analysis

As is shown in the graphs15-17, the beta for our portfolio is above the benchmark, allowing our portfolio to

outperform the benchmark when the market is booming.

We used the Sharpe ratio, Treynor measure, Jensen’s alpha and information ratio to evaluate how well our

portfolio performed during the specified time horizon (See table7).

Before rebalancing, our Sharpe ratio for our portfolio was negative, but higher than the benchmark. After

rebalancing, our Sharpe ratio improved from -0.75 to 3.64, partly due to the market as a whole rising after the

rebalance. Compared to the benchmark, the portfolio still exhibited a better Sharpe ratio. (See figures18-19)

This is the case with Treynor ratio as well, which improves from 0.05 to 0.53. As bonds were added during the

rebalance, some volatility was avoided, leading to a higher Treynor ratio thereafter. (See figures20-21)

As is shown in figures 22 to 23, the performance of our portfolio, based on the information ratio, went up from -

0.85 to 1.25 after rebalancing. This is because we are generating excess returns over the benchmark after

rebalancing (see figures 15-16). This excess return also contributed to the increase of our Jensen’s alpha from -

0.03 to 7.72. In Jensen’s paper (1968), it is shown that the positive alpha indicates that active portfolio selection

plays a role in achieving higher returns over the market. Our portfolio selection methods successfully helped us

achieve this (See figures24-25).

Attributive Effects

In the second stage, we detail performance attribution effects, including the allocation, selection, interaction

effect, and currency effects (See tables8-9).

The allocation effect for our portfolio did not change after rebalancing because the weights for the industries

were not changed. The effect remained at 0.61, indicating that we did quite a good on initially allocating stocks

from different sectors.

As is shown in tables 7 to 8, the selection effect improved from -316.86% to 76.92%, for the reason that our

portfolio has excess return to the benchmark, indicating that those outperforming sectors were successfully

given higher weights during the stock selection.

Since we chose an international portfolio, changes in currency values against the dollar played a role in our

overall returns. The currency effect stayed at 3.9, signifying the additional gains to our returns that we have

made due to our foreign investments.

The interaction effect for the portfolio is positive, implying that certain sectors in our portfolio received higher

weights than those in the benchmark, and that we over-weighted the parts with excess return, relative to the

benchmark. After rebalancing, the figure changed from 68.72% to 351.69%, the reason being that the over-

weighted segments have higher excess returns during the post-rebalance period.

11

It can be observed from the portfolio attribution table (see tables 8-9) for the time period from February to April,

that the total attribution rose from -244.33% to 472.93%. This implies that we made superior asset selections,

and that with active management we successfully beat the market.

12

Relation Between Results and Relevant Financial

Theories and Models

EMH

The Efficient Market Hypothesis, first developed in 1970 by Professor Eugene Fama, states that assets, and by

extension stock prices, fully reflect all available information. It further asserts that stocks invariably trade at fair

value, making it hard, if not impossible, for investors to earn abnormal returns on the market. Thus, it should

hypothetically be impossible to outperform the market (Fama, 1970).

Under the EMH, markets are assumed to be efficient, and no information asymmetry exists. But there is

mounting evidence that markets are not efficient, and do in fact, trend (Stout, 2002-2003). This also applies to

market-capitalization portfolios (Haugen and Baker, 1991). Markets have also been demonstrated to be

informationally-asymmetric systems1 (Akerlof, 1970).

Investors are also assumed to be rational. In fact, there have been ample evidence put forward that they are not

(Shiller, 2000). The irrationality of investors, together with market inefficiency, has been alluded to as one of

the main causes of market bubbles (Cassidy, 2009).

Based on the evidence above, we have endeavoured to actively manage our portfolio, as we believe that markets

are not fully efficient, investors are not fully rational, and that positive returns over the market is possible. This

was proven to be correct when we achieved excess returns over the market portfolio.

Two-Fund Separation Theorem

This theorem states that, if the conditions which allow an investor to both lend and borrow at the risk-free rate

are met, the investors will only choose to hold a combination of the risk-free asset, and the market portfolio

(Cass and Stiglitz, 1970). This market following lies on the tangent of the capital market line and the efficient

frontier, which is in turn based on the assumption that the EMH holds.

Using our knowledge of this theorem, we allocated a significant portion of our portfolio to 3-month US

Treasury Bonds, representing our relatively risk-free asset. This allowed us to reduce our idiosyncratic risk, and

allowed to us to achieve a higher Sharpe ratio after rebalancing our portfolio, and contributing positively to our

returns

1 There has been evidence, based on research on scatter diagrams and regression, that supports the idea, first put forward by

Paul A. Samuelson, that the EMH applies more for individual securities, than for the stock markets as a whole (Jung and

Shiller, 2005).

13

Black-Litterman Model

A prominent method that investors use to attempt to realise abnormal returns over market is asset allocation.

Modern Portfolio Theory has provided a way for investors to adopt this method, if expected returns and asset

co-variances are known. But while co-variances are easily discovered, estimates of expected returns can be

challenging to determine.

The Black-Litterman model (Black and Litterman, 1992), whereby the investor is not required to input estimated

expected returns, resolves this problem2. The investor is required to input the difference between his

assumptions of expected returns, and the market’s assumptions, and he would thus be able to optimise his

portfolio based on any constraints he might face, such as liquidity, and risk3.

The use of this model allowed us to optimise the weights allocated to each asset in our portfolio. This is the key

to the ‘active’ portion of our actively-managed portfolio, as we are able to input our opinions on the under or

over-performance of each asset. With the weights optimised, we were able to achieve excess returns over the

benchmark.

2 Basically a combination of theories from both the Capital Asset Pricing Model and Modern Portfolio Theory, it was

devised by Fischer Black and Robert Litterman in 1990, while they were working for Goldman Sachs.

3 Under the Black-Litterman model, current market conditions and subsequent prospects of the manifold sectors of the

economy are evaluated. The appraisal of the likelihood of individual sectors of the market to outperform or to underperform

others can be embedded in the model, and expressed as linear inequalities among stock alphas, and adapted to guide sector

rotation strategies (Chiarawongse, Kiatsupaibul, Tirapat, & Roy, 2012).

14

References

Bacon, C. (2008). Practical portfolio performance. Chichester, England: Wiley.

Baker, N. L., & Haugen , R. A. (1991). “The Efficient Market Inefficiency of Capitalization-Weighted Stock

Portfolios”. Retrieved from http://www.efalken.com/LowVolClassics/HaugenBaker991.pdf

Black, F., & Litterman, R. (1992). “Global portfolio optimisation”. Financial Analysts Journal, 28-43.

Cassidy, J. (2010). “How markets fail: The logic of economic calamities”. New York: Picador. Print.

Cass, David and Joseph E. Stiglitz (1970), “The structure of investor preferences and asset returns, and

separability in portfolio allocation: A contribution to the pure theory of mutual funds.” Journal of Economic

Theory, 2, 122–160. [135, 136, 137, 141, 144, 149, 152]

Chiarawongse, A., Kiatsupaibul, S., Tirapat, S., & Roy, B. V. (2012). Portfolio selection with qualitative input.

Journal of Banking & Finance, 489-496.

Jensen, M. (1968). THE PERFORMANCE OF MUTUAL FUNDS IN THE PERIOD 1945-1964. The Journal

of Finance, 23(2), pp.389-416.

Jung, J. and Shiller, R. J. (2005), SAMUELSON'S DICTUM AND THE STOCK MARKET. Economic Inquiry,

43: 221–228. doi: 10.1093/ei/cbi015

Sharpe, W. (1994). The Sharpe Ratio. The Journal of Portfolio Management, 21(1), pp.49-58.

Shiller, R. (2000). “Irrational exuberance”. Princeton, NJ: Princeton University Press. Print.

Stout, Lynn A (2003). "Mechanisms of Market Inefficiency: An Introduction to the New Finance, The". Journal

of Corporation Law 28.4 (2002-2003): 635-670. Retrieved from

http://scholarship.law.cornell.edu/cgi/viewcontent.cgi?article=1831&context=facpub

15

Appendix

Aims

In this fund we aimed to construct an active portfolio that combines a mixture of global stocks and 3-month

treasury bills. We also used the Global S&P 1200 and USD 50 million 3-month Treasury Bonds as our

benchmark portfolio to evaluate our performance during our investment period.

Our portfolio as allocated based on a top-down approach where we selected potential countries/markets to invest

in, allocated weights to different industries and choose stocks based on a group of indicators that would help us

identify potential growth stocks. We aim to benefit from growth stocks that have the potential in making

abnormal returns.

The reason behind choosing global stock as opposed to specializing in a particular market is because we believe

that by diversifying our portfolio and choosing the appropriately we will be able to outperform the market. We

choose to invest in emerging economies that tend to have higher growth rates that European economies, due to

the fact than most European countries are either already developed, or are highly correlated to developed

countries.

16

Investment Policy Statement (IPS)

Target Client:

- Mid-Career with medium-high income

- High risk tolerance

Fund Size and Currency:

- USD 100 Million

- US Dollar

Fund Objective:

- Investing in growth stocks to generate positive alpha

- Aim: Beat the benchmark

Investment Guidelines and Constraints:

- Time horizon

- Legal and Regulatory

- Unique Circumstances

Allocation and Investment Strategy:

- Active Strategy with Top-down approach

- Assets selected based on forecasted growth potential

- Weights allocated based on the Black-Litterman model

Time Horizon and Rebalancing:

- 3 month horizon

- Black-Litterman model usage dependent on the portfolio performance by week 7

Performance Measurement:

- Benchmark: S&P Global 1200 Index + US 3-month Treasury Bill

- Time period: 25 Jan to 9 Feb 2016

- Ratios: Sharpe ratio, Treynor measure, Jensen’s alpha, Information ratio, Beta, and Correlation

17

Tables

Table1 Country Selection

G7 + 5 Developing Countries +

Canada Brazil Hong Kong

France China Singapore

Germany India

Italy Mexico

Japan South Africa

U.K.

U.S.

Table2 Economic Indicators for Countries

As we can see, there are certain trends present in our country analysis. Developing countries such as India,

China, and Mexico exhibit grow rates that are higher than those of any of the developed countries in our

selection. The 10 year bond yields for the developing countries were also significantly higher, representing a

higher risk of default, compared to developed nations. The inflation rate was also higher in developing countries,

18

reflecting the tendency for developing nations to have lower interest rates in order to boost exports, as most of

the developing countries continue to rely heavily on manufacturing.

Even though Brazil experienced a shrinking economy in 2015, it remains the largest country in Latin America,

and the fifth largest country in the world, by area and total population. Hence, it represents a very large market

with enduring potential for growth. Its forecasted growth for the next two years is positive, and this fund

believes that if it manages so solve its problems with currency, crime, and governance, it will emerge to become

a powerhouse of the Latin American region.

Hong Kong and Singapore were chosen for two main reasons. Firstly, their proximity to, and status as financial

centres for major emerging markets, such as India, China, and South-east Asia, makes them ideal proxies and

indicators of the health of these markets. Secondly, their market capitalisation-to-GDP ratios are within the top 3

highest in the world, with the market cap of shares traded in Hong Kong about 15 times the size of the economy.

Singapore has a market-cap-to-GDP ratio of 188.8%, the third highest in the world, almost twice of the size of

its economy4. This implies that in spite of their small size, the shares traded on their exchanges represent a

significant proportion of the world stock market.

Next, we took into account the correlation between each country’s largest stock exchanges. Certain countries

exhibited negative correlation with each other, including South Africa and China, and Germany, where the

service sector comprises 70% of the economy, also with China, where manufacturing and agriculture are still

dominant. Others, such as the U.S. and Mexico, are highly correlated. This is not unexpected, due to their

geographical closeness, as well as the fact that Mexico exports 71% of its products and derives 51% of its

imports from the U.S.

Table3 Bloomberg Consensus Rating

Bloomberg Consensus Rating (1=sell, 3=hold, 5=buy)

Company Analyst Rating Company Analyst Rating

NORDEX SE 3.72 BUFFALO WILD WIN 3.89

SOMANY CERAMICS 4.85 CYBERAGENT INC 4.11

YESTAR INTERNATI 4.75 FIVE BELOW 4.14

ALIBABA GRP-ADR 4.72 LOJAS RENNER SA 3.59

COGNIZANT TECH-A 4.75 MONCLER SPA 4.67

4 Switzerland has the second highest market-cap-to-GDP ratio

19

CRITEO SA-ADR 4.61 NASPERS LTD-N 4.88

EPAM SYSTEMS INC 4.39 SUOFEIYA HOME-A 4.93

FLEETCOR TECHNOL 4.29 TAL EDUCATIO-ADR 4.69

HEXAWARE TECHNOL 3.55 GUANGDONG HAID-A 5.00

INSPUR ELECTRO-A 4.11 VALERO ENERGY PA 4.29

LUXOFT HOLDING I 4.27 ANICOM HD 4.33

MAXIMUS INC 4.60 AUROBINDO PHARMA 4.37

SHENZHEN EVERW-A 4.82 CADILA HEALTHCAR 3.51

TATA CONSULTANCY 3.73 CAMBREX CORP 4.60

WANGSU SCIENCE-A 4.93 DIVI LABS LTD 3.95

ZOOPLA PROPERTY 3.73 JINYU BIO-TECH-A 5.00

POLARIS INDS 3.82 SINGAPORE O&G LT 5.00

SYMRISE AG 3.65

Table4 Correlation between Exchanges of Countries in Portfolio

20

Table5 Sector Allocation for Portfolio

21

Table6 Stock Selection

Table7 Ratios for Performance Evaluation

Before Rebalance After Rebalance

Portfolio Benchmark Portfolio Benchmark

Sharpe Ratio -0.75 -3.65 3.64 3.63

Jensen Alpha -0.03 0.00 7.72 0.00

Treynor Measure 0.05 -0.03 0.53 0.00

Information Ratio -0.85 0.00 1.25 0.00

Beta -1.30 1.00 1.17 1.00

Correlation 0.94 1.00 0.88 1.00

22

Table8 Portfolio Attribution before Rebalance

Portfolio Attribution (26/02/2016)

Industries Allocation Effect (%)

Selection Effect (%)

Currency Effect (%)

Interaction Effect (%)

Total Attribution (%)

Energy -0.18 5.05 0.00 17.91 22.78

Materials -0.17 -10.83 0.08 -78.81 -89.73

Industrials -0.16 -103.24 0.17 91.77 -11.45

Consumer Discretionary

1.64 49.34 2.34 139.97 193.29

Consumer Staples 0.10 55.32 0.00 -46.91 8.51

Health Care -0.45 -119.53 0.29 0.09 -119.60

Financials -0.25 -135.47 0.11 90.35 -45.26

Information Technology

0.08 -57.51 0.20 -145.63 -202.87

Total 0.61 -316.86 3.19 68.72 -244.33

Table9 Portfolio Attribution after Rebalance

Portfolio Attribution (15/04/2016)

Industries Allocation Effect (%)

Selection Effect (%)

Currency Effect (%)

Interaction Effect (%)

Total Attribution (%)

Energy -0.18 -37.56 0.00 61.23 23.49

Materials -0.17 19.56 0.08 -11.03 8.45

Industrials -0.16 61.90 0.17 -18.68 43.23


1.64 47.19 2.34 421.76 472.93

Consumer Staples 0.10 52.48 0.00 -48.00 4.58

Health Care -0.45 -67.89 0.29 -2.61 -70.67

Financials -0.25 8.56 0.11 -12.54 -4.13

Information Technology

0.08 -7.32 0.20 -38.44 -45.48

Total 0.61 76.92 3.19 351.69 432.41

23

Figures

Figure1 Country Weight Allocation

Figure2 Porter’s Five Forces-Consumer Discretionary

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

32.84%

5.87%2.40% 1.83%

7.65%

2.15% 2.91%

8.10%

1.64%

20.33%

11.48%

57.80%

7.80% 7.40%3.60% 3.20%

0.90% 0.40% 1.20% 0.30% 0.00% 0.00%

Portfolio Weights S&P Global 1200 Weights

012345

Threat of NewEntrants

Threat ofSubstitutes

Bargaining Power ofCustomers

Bargaining Power ofSuppliers

Intensity ofCompetition


24

Figure3 Porter’s Five Forces-Consumer Staples

Figure4 Porter’s Five Forces—Energy

012345






Consumer Staples

012345






Energy

25

Figure5 Porter’s Five Forces-Financials

Figure6 Porter’s Five Forces-Health Care

0

1

2

3

4






Financials

012345






Health Care

26

Figure7 Porter’s Five Forces-Industrials

Figure8 Porter’s Five Forces- InfoTech

0

1

2

3

4






Industrials

012345






InfoTech

27

Figure9 Porter’s Five Forces-Materials

Figure10 Porter’s Five Forces-Telecommunication Services

0

1

2

3

4






Materials

012345






Telecommunication Services

28

Figure11 Porter’s Five Forces- Utilities

Figure12 Stock Allocation Before Rebalance

0

1

2

3

4






Utilities

29

Figure13 Stock Allocation After Rebalance

Figure14 Stock Correlation for Portfolio

30

Figure15 Portfolio Return Pre-rebalance

Figure16 Portfolio Return Post-rebalance

31

Figure17 Performance Differences between Portfolio and Benchmark

Figure18 Sharpe Ratio before Rebalance

Figure19 Sharpe Ratio after Rebalance

32

Figure20 Treynor Measure before Rebalance

Figure21 Treynor Measure after Rebalance

Figure22 Information Ratio before Rebalance

33

Figure23 Information Ratio after Rebalance

Figure24 Jensen’s Alpha before Rebalance

Figure25 Jensen’s Alpha after Rebalance

Documents

52221_Eugene_Ang_BEFM015_Coursework_Group_7_480751_1117953625