The document describes a proposed low carbon mutual fund investment product. It outlines how the fund will be constructed by selecting stocks from the MSCI Low Carbon Target Index that have low exposure to carbon price risk factors. Three portfolio variants will be created with different weightings and their performance will be evaluated against a benchmark index. The results show that all three funds exhibited higher risk-adjusted returns than the benchmark, demonstrating the viability and profitability of the proposed low carbon investment strategy.

1. Low Carbon Mutual
Fund
InvestmentProduct
Student ID: 21024026
Lecturer:Dr Andreas Hoepner

2. 2
Executive summary
1. Research purpose
The main purpose our this study is to construct a applicable and profitable investment product
which is based on carbon price and emission scheme. Therefore, the investment theory and
performance evaluation will be presented in the study. In the course of the research, several
questions have been answered. The first is what are the price drivers have impact on carbon
pricing. Furthermore, selected portfolio will be classified with different weights to assess our
investment strategyaccording to the performance evaluation.
2. Research design
To initiate our research, the first step to obtain the relevant data. The data in our study is mainly
from Datastream terminal and MSCI Low Carbon Target Index. After having retrieving the data, a
regression will be run to test what variables are explanatory to carbon price. Next, further
regressions are applied to each of the selected top 10% weighting stocks in MSCI Low Carbon
Target Index with the purpose to eliminate the stocks with significant relevance to carbon price.
Finally, portfolio will be sorted into three funds with different weights. Return and risk
performance measure will be used to assess and evaluate the three funds relative to benchmark.
3. Findings
Based on the result given by performance evaluation, it turns out that our investment strategyis
applicable and profitable. All the three funds exhibit high and well risk-adjusted return compared
with benchmark.
4. Research limitations
As most carbon emission data towards the corporate level is confidential with limited access to
them, the data used in this study is mainly based on MSCI Low Carbon Target Index. In addition,
as the index contains large constituent companies, this study only selected a sample of top 10%
weighting stocks and these companies tend to have medium or large capitalisation. It could be
questionable of the representative of these companies.
5. Research Implications
As mentioned in research limitations, the stocks selected tend to be medium and large
companies with high value of capitalisation, which means high cost of investment. It is advised to
improve this problem by looking into other securities with this strategy.
6. Originality/Value
This investment product is mainly based on the theory of mitigating and eliminating the risk
exposure of carbon emission and trading. It is constructed on account of the immaturity and
unstableness of carbon price. This investment strategyis not set to stone. To fully utilise and gain
profits from this trading strategy, understanding the carbon pricing system and relevant policies is
necessary.

3. 3
Table of Contents
Executive summary ······················································································· 2
1. Introduction ·························································································· 5
2. Study outline ·························································································· 5
3. Data description and preparation ·································································· 6
4. Investment strategy··················································································· 7
5. Performance evaluation·············································································· 8
5.1 Test for normality ················································································ 8
5.2 Portfolio Returns ················································································ 9
5.3 Correlation ························································································ 10
5.4 Carhart Model ···················································································· 11
5.5 Risk measures ····················································································12
5.51 Return standard deviation and variance ················································ 12
5.52 Advanced absolute risk measures ························································ 12
5.6 Advanced risk-adjusted performance measures ············································· 14
6. Discussion and conclusion ············································································· 16
7. Sources ·································································································· 17
8. Appendix ································································································· 18
Tables
Table 1. Regressionresult of carbonprice andits explanatoryvariables fromEviews············································ 7
Table 2. Kurtosis, skewness and test statistic of four funds····················································································· 9
Table 3. Covariance matrix of four funds················································································································ 10
Table 4. Correlationmatrix of four funds············································································································ 10

4. 4
Table 5. Regressionresult of coefficients ofCarhart modelfor four funds···························································· 11
Table 6. Summarytable of advancedriskmeasures······························································································· 13
Table 7. Summarytable of advancedrisk-adjustedperformance measures·························································· 14
Table 8:Regressionresult of Carhart Modelfor Equallyweighted Fund ······························································ 18
Table 9: Regression result of Carhart Model for Market Cap Weighted Fund ·································································· 19
Table 10: Regression result of Carhart Model for Carbon Exposure Weighted Fund························································· 19
Figures
Figure 1. Return distributionof equallyweightedfund··························································································· 8
Figure 2.:Returndistributionof market capweightedfund···················································································· 8
Figure 3. Return distributionof carbonexposure weighted fund············································································ 8
Figure 4. Return distributionof benchmark—MSCI WorldIndex············································································· 8
Figure 5:Arithmetic dailyreturns of four funds····································································································· 10·
Figure 6:Arithmetic dailyreturns of four funds······································································································ 10
Figure 7:Correlationbetweenthree funds andbenchmark ·················································································· 10
Figure 8:Annualisedstandarddeviation offour funds ························································································ 12
Figure 9:Annualisedvariance of four funds········································································································· 12
Figure 10:Sector weighting ···································································································································· 18

5. 5
1.Introduction
A series of environment friendly oriented activities and legislation have been taken by
government and authorities since the commitment made in Kyoto Protocol. In the meanwhile, a
new investment field has been explored --- carbon finance, followed by emission trading scheme
and greeninvestment funds.
Based on the preceding literature review on carbon pricing, many specialised researchers have
come to same conclusion that several price drivers have significant impact on carbon emission
price and the emission would subsequently influence the stock return. The preceding literature
reviews also imply that carbon emission trading is still immature and the movement of carbon
price is peculiar, which means the companies involved in carbon trading are exposed to
tremendous risk. On account of this finding, a new carbon investment product has been created,
which contributes to invest in the “good quality” assets with the minimum exposure to carbon
risk. The skeleton of this study is:
--- Study outline
--- Data description and preparation
--- Investment strategy
--- Performance evaluation
--- Discussion and conclusion
2. Study outline
Based on the findings in literature review that emission allowance price is significantly explained
by several price drivers, namely coal price, natural gas price, crude oil price and Baltic Dry Index,
a regression function is run firstly in order to assess to what extent allowance price can be
explained by these price drivers in the time period of Jan 2013 to March 2015. The reason why
this duration is selected is that carbon market is growing rapidly and most up-to-date information
is preferable. Next step is to obtain the raw data which, in this case, is the top 10% weighting
companies in MSCI Low Carbon Target Index. After all relevant data having been retrieved and
prepared, regression functions towards these top 10% weighting companies are run to evaluate
and screen the companies that do not fit investment strategy which is defined and elaborated
after data collection. In order to compare and contrast, three funds are established with different
weights in assets. Once the portfolios are constructed, a vast number of return and risk
performance measures are utilised to evaluate the fund performance. Followed by the return and
risk measures, the findings of this trading strategywill be discussed in details in section 6.

6. 6
3.Data
3.1 Data description
Due to the scarcity of most carbon index constituents, data of this investment product is achieved
from MSCI Low Carbon Target Index. This index is initially designed to address the carbon
exposure of carbon emission. It is a subset of MSCI Index, which measures the performance of
equity market in developed and emerging markets. What differs MSCI Low Carbon Target Index
from MSCI Index is that the former aims to reflect low carbon exposure and overweight the
companies with low carbon emission relative to sales.
In course of the establishment of MSCI Low Carbon Target Index, some constraints have been
applied: (i) minimize emission exposure subject to the constraint of 30 basis points relative to the
Parent Index; (ii) the maximum weight of an Index constituent is not allowed to be greater than
15 times its weight in the Parent Index; (iii) country weights in the Index may only deviate within
the range of 2% from the country weights in the Parent Index; and (iv) exception is granted
merely to the energy sector, where there is no weight constraint. The index includes around
1,300 companies, among which the top 10% weighting have eventually been selected as raw
data.
3.2 Data preparation
Several variables have been recognised to have influence on emission price. Keppler and
Mansanet-Batallet (2008) discovered that, coal and gasprices impacted on carbon future prices.
Bredin and Muckley (2011) found the relevance of crude oil and emission price. In addition, level
of activity of the economy is natural influence on emission price. Coleman (2012) pointed out
that Baltic Dry Index could also be a price indicator as it measures the price of shipping goods by
sea. The following daily frequency model is used by Deeney et al.(2014) ,which aims to capture
the allowance price movement and its potential explanatory variables,
APt = α+β1△Oilt+β2△Gast+β3△Coalt+β4△BDIt+εt (3.21)
(where AP stands forcarbonallowance price, εt is error term)
Explanatory variables
Oil: Brent crude oil price from the Intercontinental Commodities Exchange is used in this case.
Hintermann (2010) discovered that fuel switching behaviour between coal and gas of power
providers impacted on emission price. However, this behaviour will not be influenced to any
degree by oil price.
Gas: National Balance Point naturalis employed. NBP gas price is used by Chevallier (2011) and it
is the main hub for international gas market.
Coal: this is the ARA coal, which is major coal indicator in international coal market and it is
expected to affect the allowance price through fuel switching behaviour.

7. 7
Baltic Dry Index: it is the measure of shipping cost and is able to capture the world economic
activity.
DependentVariable:DCARBON
Method: LeastSquares
Date: 04/01/15 Time:17:21
Sample (adjusted):1/03/2013 3/20/2015
Included observations:441 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
C 0.001697 0.002151 0.788972 0.4306
DCOAL 0.188842 0.206656 0.913797 0.3613
DNATURAL 0.150307 0.087805 1.711839 0.0876
DBALTIC 0.052494 0.085550 0.613605 0.5398
DCRUDE 0.300396 0.160556 1.870973 0.0620
Table 1: Regression result of carbon price and its explanatory variables from Eviews
Above is the result of the regression function in the time period of Jan 2013 and Mar 2015. As
can been seen, with a 90% confidence level, only the price of natural gas and crude oil are
explanatory variables to carbon allowance price. Therefore, model is converted into
APt = α+β1△Oilt+β2△Gast+ εt (3.22)
(where AP stands for allowance price, εt is error term)
4. Investmentstrategy
In order to gain excess return over the market, a reliable and reasonable investment strategyis
necessary. Previous literature reviews significantly indicate relevance between explanatory
variables and carbon emission allowance price. A multiple regression is run to assess and
evaluate the findings and according to the result, only crude oil and natural gas are explanatory
factors with respect to carbon emission allowance price. Having integrating the regression
function and the MSCI Low Carbon Target Index, a new investment strategy is created.
In this study, the fund invests in the stocks which are exposed to carbon risk to the minimum. By
applying this tactics, further regression function with respect to the 130 selected companies will
be done to exclude the companies with significant impact by crude oil or natural gas prices. After
this process, it gives a portfolio comprised of 46 companies. Therefore, our strategy is simply the
buy-and-hold investment strategybecause we believe that after ruling out the companies with
high carbon exposure and investing in the assets that arechosen by MSCI index, profits under
our portfolio is lucrative. In order to compare and analyse this trading strategy, the fund will be
assessed and evaluated in three investment forms—different weights.

8. 8
5.Performanceevaluation
In this section, 3 funds will be evaluated and assessed with respect to their returns and relative
risk exposure over the time period of Jan 2013 and Mar 2015. The first fund is equally weighted
portfolio where all the containing stocks share the same weight. The second fund is weighted
based on the value of market capitalization and the third fund is to apply the weight in MSCI Low
Carbon Target Index after adjustment by totalnumber of observations as well as carbon
exposure.
5.1 Test for normality
Before proceeding to the return and risk performance analysis, test for normality needs to be
done to test if the returns of the funds are normally distributed because it will influence the
measurement of returns and risk. The following histograms visually present the frequency of
returns with identical intervals.
Figure 1: Return distribution of equally weighted fund Figure 2: Return distribution of market capweighted fund
Figure 3: Return distributionofcarbon exposure weighted fund Figure 4: Return distributionof benchmark—MSCI World Index
Judging from the tendency line and histograms, all funds tend to be centralisedly distributed,
which satisfies the definition of normal distribution. However, equally weighted fund and carbon
exposure fund seem to have more peaked shape than normal distribution. For that reason,
Jarque Bera test has been performed to test whether the kurtosis and skewness exist.
Two hypotheses for Jarque Bera test are :
H0= Returnis normally distributed vs H1=Returnis not normally distributed
The test statistic is :

9. 9
(where n stands for number of observations;S is skewnessandKis kurtosis.)
Table 2:Kurtosis, skewness and test statistic offour funds
Table above presents the specified kurtosis and skewness of the funds and benchmark returns
and more specific analysis of these two will be done in section 5.4. Substituting the kurtosis and
skewness into the formula gives the four test statistic. Jarque Bera test is used to examine if the
sample data matches the normal distribution. With a confidence level of 95% given a critical
value of 5.99 derived from X2 distribution, the test statistic which exceedthis value will fall in the
rejection area of null hypothesis. In this case, all the funds and benchmark dramatically exceed
the critical value, which implies the non-normality of the funds and benchmark returns.
5.2 Portfolio Returns
Returns express relative changes in price and they are useful as they make it easier to compare
to compare the variations of a portfolio of assets. Generally, two returns are widely used in
financial asset pricing, which are arithmetic return and geometric return. Since the assumption is
based on the theory that returns are normally distributed and geometric return is consistent
with this property, in this study, geometric return is applied to all relevant assets with the
following equation,
In terms of the returns over multiple time periods, there are also two general ways to compute
named as arithmetic and geometric mean return with the same logic as mentioned. As there is
uncertainty of reinvestment and we have a sequence of geometric returns over identical
successive periods, the use of arithmetic mean averagereturn is preferable and the formula is as
follows,
In order to visualise the excess return histogram, rateof U.S 2-year Treasury rate of 0.55% is
introduced. As this is annual data, the process of transforming into daily number is done as
shown below,
Rf,t,1d = ln (1 + SRf,t,4w
728
365.25
)
1
TDW∗104
(where SR stands for treasury rate, TDW=Observation in sample/Weeks in sample)
After obtaining the daily risk-free rate, two histograms are generated as below,

10. 10
Figure 5: Arithmetic daily returns of four funds Figure 6: Arithmetic daily returns of four funds
As can be seen, arithmetic daily mean returns of all three funds substantially outperform the
benchmark—MSCI World Index. Similarly, taking the risk-free rateinto consideration, the funds
still exhibit high return between 0.094% to 0.101% while conversely, MSCI World Index presents
a different picture of negative 0.07663% excess return on a daily basis. It is needless to say that
investment in either of the three funds will generate lucrative profits.
However, every coin has two sides. Although return is a significant measurement of portfolio
performance, it cannot tell the whole story. There is always an expense of high return – risk.
5.3 Correlation
Correlation is a statistic tool to quantify the dependence between two variables. In finance, it is
widely used to test the co-movement of assets. In order to compute correlation, derivation of
covariance is required and the formula aregiven below,
Table 3: Covariance matrix of four funds Table 4: Correlation matrix of four funds
Figure 7: Correlation between three funds and benchmark

11. 11
According to the result of correlation figures, all three funds are highly correlated to MSCI world
benchmark. This result is reasonable because the sample MSCI Low Carbon Target Index is a
subset of MSCI Index. Our funds areconstructed based on MSCI Low Carbon Index and that can
explain the high correlation coefficient with MSCI World Index.
5.4 Carhart Model
Carhart model was developed in 1997 and it is based on the well-known Fama-French three
factor model. What differs Carhart model from Fama-French model is that Carhart model adds
another explanatory factor called momentum to capture the phenomenon that previous winner
stocks outperform previous loser stocks. Chi-Hsiou Hung(2006) provides evidence that Cartart
models have relatively lower errors than CAPM and Fama-French model in in explaining the cross
section of returns of momentum and size portfolios. In this study, this model is applied to find
the relevant explanatory factors towards our fund return and it is shown below,
Rit − Rft =∝i+ βi(Rmt − Rft) + γiSMBt + ζiHMLi + φiMOMt + εit
Rit − Rft refers to the excess portfolio return over risk-free rate; ∝i stands for the systematic
return; βi(Rmt − Rft) is the market risk premium; SMBt represents the return of a portfolio
investing long in small stocks and short selling large stocks; HMLi represents the return of a
portfolio investing high Book-to-Market and shorting selling low Book-to-Market stocks; MOMt
represents the return of a portfolio investing long in winner stocks and shorting selling loser
stocks.
Due to the one month data lagging of SMB, HML and MOM, the time period of the data for
regression is from Jan 2013 to Feb 2015. In addition, the data of MOM is not available and thus it
is computed by the following formula,
MOM =
1
2
(small high + big high) −
1
2
(small low + big low)
Table 5: Regression result of coefficients of Carhart model for four funds
The regression result( in appendix) illustrates that all three funds are significantly explained by
market risk premium, SMB and HML with a confidence level of 99%. For the reason, MOM will
not be covered in the following discussion. All three funds exhibit positive ∝, which means that
they all generate excess return even if the return is not significantly explained by any variable.
Three funds generateexcess return in the rangebetween 4.93% to 5.29%, which significantly
outperforms the benchmark (-0.33%). With respect to β，three weighted funds and MSCI have a
positive β from 0.32 to 0.52, which provides the information that return of three funds move in
the same direction as the market does and they are nearly half less riskier than the market.

12. 12
Given the result regarding γ and ζ, it is obvious that they are negatively relatedto fund return. γ
signifies that most stocks in our fund have large capitalization and ζ implies our fund tend to be
a growth fund with low book-to-market ratio.
5.5 Risk measures
In the course of finance study and research, risk measures has become exceedingly important
since the lessons from the previous financial crisis. Generally, risk in finance investment refers to
uncertainty adherent which could possibly lead to certain extent of financial loss and to be more
specific, a single portfolio comes with a series of risk factors. Therefore, in this section, a number
of risk management methods will be used to assess and evaluate the funds performance.
5.51 Return standard deviation and variance
In finance, the standard deviation and variance of returns are widely used measures of
investment risk. Both of them are measures of the variation of a distribution of returns about its
mean over a specified time periods. We caculate them as follows,
As the standard deviation and variance of sample observation are assessed on a daily basis, they
have been annualized for analysis and evaluation purpose by multiplying the square root of 12.
Figure 8: Annualised standard deviation of four funds Figure 9: Annualised variance of four funds
The two bar charts reveal that market cap weighted fund is exposed to the most risk up to
11.04%, followed by equally weighted and carbon exposure weighted fund with the standard
deviation of 9.51% and 8.99% respectively. MSCI World Index is in line with the expectation,
owning the least standard deviation of 5%. Given the return and risk measures so far, most of the
funds are consistent with the financial theory that high return comes with high risk. The only
exception is market cap weighted fund, which is exposed to the highest volatility with rare
compensation for its return. What is also worthwhile to note is that carbon exposure fund
performs well in this case with the least volatility compared to the other two funds, which
accords with our trading strategy.

13. 13
5.52 Advanced absolute risk measures
Although standard deviation is the simplest and most efficient way to quantity the adherent risk,
it inevitably has limitations and drawbacks on risk analysis. The most notable drawback is that
standard deviation is a poor tool if the return is asymmetric, which tends to be most common
condition of most assets and portfolios. In section 5.1, Jarque Bera test is performed to test the
normality and none of the funds and benchmark meet the conditions of symmetric return. For
that reason, more advanced risk measures are required to assess the risk.
The first advanced risk measure is drawdown risk and it measures the largest single drop from
peak to bottom in the value of a portfolio. It is computed by the formula below,
Apart from one limitation mentioned regarding standard deviation, there remains another due
to the uncertainty of the standard deviation. In other words, measurement of standard deviation
considers downside risk as well as upside chances and as a matter of fact, more concentration is
paid on downside risk in risk evaluation. Lower partial moment only considers downside risk and
the general formula for LPM is ,
Another risk measure of downside risk is semi-variance, which is based on Lower Partial
Moment.
The last risk measure is the well-known Value at Risk(VaR) and it was developed in the late 1980s
as a systematic method to segregate extreme events. VaR quantifies the possible maximum loss
given a period of time and specified confidence level. Three ways are normally used to compute
VaR with distinct properties. In this study, as all distribution of fund returns exhibit
non-normality, historical VaR method is applied on a daily basis with a confidence level of 95%.
Table 6:Summarytable of advanced riskmeasures
Table above summarises the result of five advanced risk measures. Three funds all exhibit high
maximum drawdown risk within the range of negative 4.3% to 5.2% relative to benchmark of
negative 2.5%.This result is consistent with the expectation and reveal the fact that our funds are

14. 14
exposed to high volatility risk at a specific point. As for shortfall risk measure, three funds and
benchmark exhibit similar risk exposure within the range between 0.46 and 0.49. Based on this
result, it implies that three funds and benchmark are expected to have the similar extreme price
movement. The calculation of semi-standard deviation is based on lower partial moment. By
this reason, the following analysis will take semi-standard deviation as example. Benchmark with
no surprise has the least daily volatility of 0.197%. Carbon exposure fund performs best out of
the three funds with the value of 0.349% daily volatility. Market cap weighted funds still presents
high daily volatility with the value slightly over 0.42%.VaR values give the similar implication. The
three different weighting based funds are distributed between the maximum loss from 0.73% to
0.83%, which indicates that the maximum loss in one day will not exceed the calculated
percentage with the chances of 95%.
5.6 Advanced risk-adjusted performance measures
On the basis of the risk measures presented above, a number of relevant ratio analysis will be
used to further assess and evaluate performance of the funds. A table of statistical summary is
presented below,
Table 7: Summary table of advanced risk-adjusted performance measures
Sharpe ratio is one of the most commonly used risk-adjusted return measures and it measures
the average return in excess of risk-free raterelative to the portfolio’s volatility.
Generally, the higher the value of Sharpe ratio, the more attractive the investment is. Among our
three funds, their Sharpe ratios are twice as much as it of benchmark, which, to certain extent,
proves our funds are well risk diversified. However, the application of Sharpe ratio has been
questioned as it uses standard deviation in the denominator. Due to that reason, Sharpe ratio
can be a pool tool for risk-adjusted return when returns are not normally distributed. In our
study, it turns out that our fund returns do not match normal distribution and thus Sharpe ratio
is only introduced but not a significant element in our performance evaluation.
To conquer the inaccuracy caused by non-symmetric returns, other risk-adjusted return
measures have been developed and introduced. Treynor ratio replaces standard deviation with
systematic risk(β)in the denominator.

15. 15
In this case, the result is different from what Sharpe ratio shows. Market cap weighted fund
performs best with the highest value of 0.074%, closely followed by carbon exposure fund with a
value up to 0.073%. All three funds beat the benchmark up to 0.045-0.055%.
Another risk-adjusted measure is Sortino ratio, which removes the effect of upward price
movements in standard deviation and only measures the downward returns.
Since sortino ratio removes the effect of upward price movement,the ratios of three funds are
apparently smaller than the values of Sharpe ratio. Despite the fact of that, the result given by
Sortino ratio mirror the result of Sharpe ratio with equally weighted fund performing best while
benchmark given a lowest risk-adjusted return.
Apart from Treynor ratio and Sortino ratio, there is another risk-adjusted measure similar to
Sortino ratio ---RoPS( return on probability of shortfall). RoPS also only measures the downward
return but it measures the mean excess return relative to shortfall risk instead of semi-standard
deviation in Sortino ratio.
In this case, carbon exposure weighted fund yields 0.21% on a daily basis and equally weighted
and market cap weighted funds arrive at the returns around 0.1%. Benchmark stands at 0.03%.
Sterling ratio and Burke ratio are two ratios based on maximum drawdown. These two ratios aim
to the capture the returns when the portfolio is at the most loss periods. In contrast to other
return and risk measures, these two ratios tend to highlight the point of reducing the losses at
the expense of profits. These ratios are of extreme significance in measuring risk-adjusted return
when the world economy is in distress.
𝑆𝑇𝑃 =
𝑟̅ 𝑥𝑝
𝐷𝐷1𝑇𝑝
& 𝐵𝑢𝑟𝑘𝑒 𝑝 =
𝑟̅ 𝑥𝑝
√ 𝐷𝐷2𝑇𝑝
In our study, the two result of these two ratios are identical and it is also in line with the result
from Sharpe ratio. Three funds are within the range between 1.83% and 2.29% and benchmark
generates a return of 0.89%.
Similar to Sterling and Burke ratio, RoVaRxp(return on Value at Risk) measures the excess return
over the maximum loss in the given periods. Higher return indicates higher investor utility.
𝑅𝑜𝑉𝑎𝑅 𝑥𝑝 =
𝑟̅ 𝑥𝑝
| 𝑉𝑎𝑅 𝑥𝑝|

16. 16
6. Discussion and conclusion
This study aims to create a reasonable and reliable investment product and based on the result
of performance evaluation in section 5, our low carbon trading strategy is applicable and
profitable.
The principle of our trading strategy is to minimise the carbon related risk. To follow through this
concept, the sample data retrieved are all from MSCI Low Carbon Target Index and our three
portfolio are established based on the MSCI index. On account of the return, the three funds all
outperform the benchmark to a large extent. Taking the risk factors into consideration, the three
funds still perform better than benchmark. What is also worthwhile to note is that carbon
exposure weighted fund exhibits high investment value. Asset weighting in this fund is adjusted
according to the carbon emission or trading level. Although the expected return of carbon
exposure weighted fund is slightly lower than the other two funds, the advantage of carbon
exposure weighted fund can be seen in risk-adjusted measures. This result is in line with the
findings of influential literature review that carbon market is still immature and not well
structured. Companies that have positive relevance with energy price(crude oil, fuel, gas and etc.)
tend to be exposed to higher carbon risk and accordingly impact on stock return.
There still remain some potential issues and limitations in the research. As the carbon emission
data towards the company level is of high confidentiality, the access to it is limited and the
adjusted weights depend heavily on the MSCI Low Carbon Target Index weighting. There is no
way to assess the weight system of MSCI Low Carbon Target Index. In addition, we only select
top 10% with the highest weighting in the index as the entire portfolio of MSCI Low Carbon
Target Index owns 1,300 companies. The top 10% weighting constituents tend to be large-cap
companies and it is reasonable to question whether the sample can represent the whole MSCI
Low Carbon Target Index. Last but not least, the portfolio contains the companies on a
worldwide scope. As carbon trading and pricing scheme differs from region to region, the extent
of impact on carbon price can be distinct. In our study, this factor has not been considered due
to the complexity of carbon policies and pricing. Gary Koop and Lise Tole(2012) develop a carbon
pricing model but the result is not satisfactory.

17. 17
7. Sources
Bredin,D.,&, Muckley,C.(2010).An emerging equilibrium in the EU emissions trading
scheme.Energy Economics,33(2),353-362.
Chevallier, J. (2011). Econometric Analysis of Carbon Markets. Springer.
Coleman, L. (2012). Explaining crude oil prices using fundamental measures. Energy
Policy.10,318-324.
Deeney,P., Cummins, M.,Dowling,M.,&Bermingham,A.(2014). Sentiment in the EU Emissions
Trading Scheme(Working paper). Dublin City University business school.
Hintermann, B. (2010).Allowance price drivers in the first phase of the EU ETS. Journal of
Environmental Economics and Management, 59(1), 43–56.
Hung, C, H.(2006). Momentum, Size and Value Factors versus Systematic Co-moments in Stock
Returns(Working paper). Durham University.
Keppler, J.,& Mansanet-Bataller, M.(2008). Casualties between CO2, electricity, and other energy
variables during phase I and phase II of the EU ETS. En. Poly, 38, 3329–3341.
Koop,G .,&Tole,L.(2012). Forecasting the European carbon market. Journal of the Royal
Statistical Society,176(3).723-741.

18. 18
8. Appendix
Figure 10: Sector weighting
DependentVariable:Equally Weighted Fund
Method: LeastSquares
Date: 04/12/15 Time:22:30
Sample:1/02/2013 2/27/2015
Included observations:543
Variable Coefficient Std. Error t-Statistic Prob.
C 0.049349 0.014218 3.470895 0.0006
MARKET_RISK_PREMIU
M 0.513396 0.020868 24.60219 0.0000
SMB -0.163950 0.033197 -4.938739 0.0000
HML -0.253424 0.045317 -5.592221 0.0000
MOMENTUM 0.022775 0.036248 0.628301 0.5301
Table 8: Regression result of Carhart Model for Equally weighted Fund
DependentVariable:Market Cap Weighted Fund
Method: LeastSquares
Date: 04/12/15 Time:22:33
Sample:1/02/2013 2/27/2015
Included observations:543
Variable Coefficient Std. Error t-Statistic Prob.
C 0.057882 0.022853 2.532854 0.0116
MARKET_RISK_PREMIU
M 0.360573 0.033541 10.75025 0.0000
SMB -0.165080 0.053357 -3.093894 0.0021
HML -0.148680 0.072838 -2.041237 0.0417

19. 19
MOMENTUM -0.073790 0.058261 -1.266542 0.2059
Table 9: Regression result of Carhart Model for Market Cap WeightedFund
DependentVariable:Carbon Exposure Weighted
Fund
Method: LeastSquares
Date: 04/12/15 Time:22:38
Sample: 1/02/2013 2/27/2015
Included observations:543
Variable Coefficient Std. Error t-Statistic Prob.
C 0.052916 0.015297 3.459183 0.0006
MARKET_RISK_PREMIU
M 0.439305 0.022452 19.56664 0.0000
SMB -0.163082 0.035716 -4.566061 0.0000
HML -0.231612 0.048757 -4.750355 0.0000
MOMENTUM -0.013121 0.038999 -0.336449 0.7367
Table 10: Regression result of Carhart Model for Carbon Exposure Weighted Fund
List of portfolio companies
1. Microsoft
2. Johnson&Johnson
3. Wells Fargo
4. General Electric
5. Nestle “R”
6. Procter&Gamble
7. Novartis “R”
8. Toyota Motor
9. Roche Holding
10. Coco Cola
11. Merck &Co.
12. City Group
13. Intel
14. Home Deposit
15. Facebook “A”
16. Pepsico
17. CVS Health
18. Samsung Electronics
19. Amazon.com
20. Walmart
21. Philip Morris Intl
22. Amgen
23. Taiwan Semicon. Mnf
24. Bristol Myers Squibb
25. Biogen
26. Fanuc
27. Mitsubishi
28. Infosys
29. Celgene
30. Westpac Banking
31. Abbvie
32. Central Japan Railway
33. Astrazenca
34. Tencent Holdings
35. Monsanto
36. Walgreens
37. Starbucks
38. Abbott Laboratories
39. National AUS. Bank
40. BT Group
41. Nike “B”
42. Unilever Nigeria
43. Sun Hung Kai Properties
44. Lowe’s Companies
45. UBS Group
46. Reckitt Benckiser