The document describes the process of creating the Gavekal Knowledge Leaders Indexes to capture companies that invest significantly in knowledge. It begins by outlining the construction of the underlying Gavekal Capital International (GKCI) Indexes, which serve as the selection universe. Key aspects include excluding certain security types and applying liquidity thresholds. It then compares the GKCI Indexes to the Morgan Stanley Capital International (MSCI) Indexes, noting some differences in methodology and constituent weights between the two sets of indexes. The document concludes by outlining the performance and risk profiles of the Gavekal Knowledge Leaders Indexes.

1. 1
Executive Summary
In our first white paper, “The Knowledge Effect: Excess Returns of Highly Innovative Companies,”
we identified a market anomaly that leads to persistent excess returns among highly innovative
companies. We offered two explanations why companies that share a common risk factor—the
Knowledge Factor—historically generate excess returns. First, the introduction of the semiconductor
has enabled humankind to multiply its intellectual strength in a similar way that the steam engine and
electric motor enabled humankind to multiply its physical strength. Corporate knowledge production
takes the form of investment in research and development (R&D), advertising and employee training.
Corporations spend more on knowledge than they do on property, plant and equipment. The second
important root for the Knowledge Effect is the dearth of information about corporate knowledge activi-
ties that has been amplified by the poorly timed implementation of conservative accounting practices
at the start of the greatest period of knowledge production in human history. This information deficien-
cy has led investors to make a systematic error in the way they assess the prospects of companies
that invest significantly in knowledge. Ultimately, this systematic error is reflected in a persistent risk
premium, or excess return, for companies that invest significantly in knowledge.
In this second paper, we describe our process of creating the Gavekal Knowledge Leaders Indexes.
These indexes are designed to capture companies that share a common risk factor: knowledge inten-
sity. We begin with a history and discussion of index construction schemes. Next we review how and
why we created our own Gavekal Capital International (GKCI) Indexes to serve as the selection uni-
verse for the Gavekal Knowledge Leaders Indexes, comparing and contrasting our methodology with
Morgan Stanley Capital International (MSCI) Index model. From there, we discuss how we adjust
company financial statements for knowledge investments and outline the rules we use to identify the
companies in our flagship Gavekal Knowledge Leader Indexes. We follow with a detailed review of
the performance and risk history of each index, comparing and contrasting with the MSCI Indexes.
We conclude with a factor based decomposition of the Gavekal Knowledge Leaders Indexes which
quantifies the alpha specifically attributable to the Knowledge Factor.
Index Weighting Schemes
Any indexing discussion starts with an acknowledgement that the index weighting scheme is crucially
important to the results of the index. Different commonly used indexes use different methodologies,
and it is important for investors to appreciate the differences.
In the United States, the longest running stock index, the Dow Jones Industrial Average, still uses a
price-weighted methodology to calculate its index. This means that a stock that trades at $100 will
comprise 10x more of the total index than a stock that trades at $10. It is well documented that the
disadvantages of this weighting scheme, such as the arbitrary overweighting of a higher priced stock
The Gavekal Knowledge Leaders Indexes: Capturing the Excess Returns of Highly Innovative
Companies
By Steven Vannelli, CFA, Eric Bush, CFA, and Bryce Coward, CFA

2. 2
to lower priced stocks, creates a poor representation of the stock market as a whole.
Eventually in a move to make stock market indexes more representative of the broader market, the
vast majority of stock indexes moved to a pure market-capitalization value weighting scheme. Under
this regime, a stock index’s weights are calculated by taking the market capitalization of each individ-
ual security, adding them all together, and calculating the proportion that the market cap of each indi-
vidual security is to the total market cap of all the securities in the index. This leads to a stock index
where larger companies account for a greater proportion of the index than smaller ones. The S&P
500 used such a weighting methodology until 2004.
As technology made foreign investing easier and more accessible, a movement started in the early
2000s by the largest index providers to move to float-adjusted market capitalization weighting. The
float-adjustment attempts to include only the shares available to purchase on the open market rather
than simply the total number of shares outstanding. MSCI shifted all of its indexes to a float-adjusted
methodology in 2002 and most large index providers followed suit soon thereafter. According to the
index providers, float-adjusted indexes provide a more accurate set of investment opportunities for
investors. They also reduce the cost of running index funds and ETFs because funds with less float,
and consequently less liquidity, are a smaller proportion of the total index.
Academic work in recent years, however, is pushing back against the idea that float-adjusted indexes
are more advantageous than pure-value weighted indexes. In “Pure Versus Float-Adjusted Value
Weighting” Seifried and Zunft found that pure-value weighted indexes exhibit “favorable index proper-
ties” and that “float-adjusted indices fail to improve index practices and enhance distortions.” The
main disadvantage of float-adjusted indexes is that “due to regulatory differences and different defini-
tions of free float” float-adjusted indexes are “subject to a time lag, resulting in incomparability be-
tween different countries and providers and best guesses when analyzing data.” This leads to a
weighting scheme that is more subjective and less objective than a pure-value weighting scheme.
Float-adjusted indexes do offer a more investable universe than basic value weighted indexes. But,
pure-value weighted indices with simple liquidity thresholds are better still as they offer an objective
methodology that is not only more transparent and uniform, but more investable. We employ this val-
ue/liquidity hybrid model in our GKCI Indexes. While the intentions of the entrenched index providers
may seem sound, regulatory and information discrepancies reduce the benefits of float-adjusted in-
dexes and create significant disadvantages for investors.
Construction of the Gavekal Capital International Indexes
We begin the construction of the Gavekal Knowledge Leaders Indexes by first creating their selection
universe indexes, the GKCI Indexes. These indexes are designed to be broad representations of the
investable universe in each country.
We begin by taking the top 85% of stocks that are publicly listed in each country. In order to end up
with a truly investable universe, we create a few rules to exclude hard-to-invest in securities or non-
equity securities before we can take the largest 85% of stocks. The types of securities that we ex-

3. 3
clude are: Treasury Shares, American Depositary Receipt (ADR), Master Limited Partnership (MLP),
Preferred Shares, Non-Equity Securities, Shares that are issued for founders, executives, and family
for a dual share class stock (i.e., we include Berkshire Hathaway Class B shares but not Class A
shares).
In addition we exclude a few other special circumstances. In the GKCI Developed World Index, we
also exclude all stocks trading below $1 in the United States. The rationale behind this decision is the
lack of liquidity in penny stocks. We also exclude all shares that are domiciled in Bermuda because of
the number of non-operating shares traded on this exchange. In the GKCI Emerging Markets Index,
we exclude all companies that trade on the Shanghai or Shenzhen exchange. For now, foreign inves-
tors do not have access to companies that trade on these exchanges, so for the vast majority of the
investing world these securities are un-investable. The MSCI and FTSE index committees are con-
sidering including Chinese A-shares in their emerging market indexes, and if both organizations move
forward with the proposal, after a testing period, we would anticipate adding Chinese A-shares listed
in Shanghai and Shenzhen to the GKCI Emerging Markets Index. After incorporating these exclu-
sions, we take the largest 85% of companies based on market capitalization.
In order to make these indexes as investable as possible we apply one more parameter: a liquidity
test. For the GKCI Developed World Index, we eliminate the bottom 10% of stocks based on average
63-day trading liquidity. This allows us to eliminate shares that are very thinly and rarely traded. For
the GKCI Emerging Markets Index, we eliminate the bottom 50% of stocks based on average 63-day
trading liquidity. We eliminate a much larger portion of emerging markets stocks because of the over-
all lack of liquidity in many emerging markets companies.
There are currently 1,924 companies in the GKCI Developed World Index. There are 22 countries
represented in the index, in regions including North America, Western Europe and Asia. In the GKCI
Emerging Markets Index there are currently 970 companies covering companies from 25 countries, in
regions including Latin America, Europe, Middle East, North Africa, Eastern Europe and Asia.
GKCI Indexes vs. MSCI Indexes
MSCI is one of the leading index providers in the global fund management business with trillions of
dollars of assets benchmarked against its indexes. Because of the shortcomings of MSCI’s float-
adjustment methodology and other crucial differences, we believe higher quality indexes can be cre-
ated much more simply.
The construction of the GKCI Indexes differ from the construction methodology used by MSCI in a
variety of ways. The most obvious difference is complexity. MSCI publishes a 147 page guideline
document explaining how it constructs its indexes and all of the special circumstances that circumvent
rules. Without documenting the agonizingly long list of differences, we do want to point out three im-
portant differences. MSCI includes preferred shares that “exhibit characteristics of equity securities.”
MSCI analyzes every preferred share on a case-by-case basis and excludes preferred shares that
“resemble—and behave like—a fixed income security” but includes preferred shares that are similar

4. 4
to common shares except that they have limited voting power. MSCI also includes “limited partner-
ships, limited liability companies, and business trusts, which are listed in the USA and are not struc-
tured to be taxed as limited partnerships.” MSCI demands a lower liquidity threshold for emerging
markets stocks than it does for developed world stocks, using a combination of data points that look
at trading frequency over the short-term (three months) and volume ratios over the short-term (three
months) and long-term (twelve months) to “select securities with a sound long and short-term liquidi-
ty.” According to MSCI, the EM Minimum Liquidity Requirement has lower thresholds to meet than the
DM Minimum Liquidity Requirement.
In our opinion, the least appreciated aspect of MSCI’s methodology is its free-float adjustment that is
used to calculate the weights of the securities in the MSCI Indexes. The basic market capitalization
equation is the number of shares outstanding multiplied by the price of the shares. MSCI reduces the
number of shares outstanding for a company by using publicly available ownership information. It re-
duces the number of shares outstanding, and consequently a company’s market capitalization, based
on who owns certain shares. All shares owned by governments, other companies, banks, officers and
board members and employees are eliminated from the total amount of shares outstanding. This in-
formation is available in many countries, however, in markets where this information is not available,
MSCI must make estimates to reduce the number of shares outstanding.
MSCI also applies a foreign ownership parameter to its indexes. This is applied at the individual secu-
rity level. Any stock must have at least 15% of its shares available for “purchase in the public equity
markets by international investors.” In order for a company’s stock to be included in the index at “its
entire free-float adjusted market capitalization,” at least 25% of the proportion of shares available to
foreign investors must still be available. This is referred to as “foreign room.” If a security only has
15%-24.99% of its foreign room available, only half of its market capitalization will be included in the
index. For any security that has less than 15% of its foreign room available, it will not be included in
the investable equity universe.
In the developed world, the differences between the market cap weighted GKCI Indexes and the
MSCI Indexes are slight. Starting with sector allocation, no sector is more than 1% different between
the two indexes. Our GKCI Developed World Index has a slightly greater weight in consumer discre-
tionary companies and slightly lower weight in health care companies.

5. 5
Sector Allocation of GKCI Developed World Index vs. MSCI Developed World Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset
From a country standpoint, we have a slightly higher weight in Japan and slightly lower weight in
North America. The US is 53.6% of the GKCI Developed World Index while it represents 57.6% of
the MSCI Developed World Index.
Sector GKCI Weight (%) MSCI Weight (%) Difference (%)
Consumer Discretionary 13.9 12.9 1.0
Consumer Staples 10.5 9.8 0.6
Energy 6.8 7.5 -0.7
Financials 19.9 20.7 -0.8
Health Care 12.3 13.3 -1.0
Industrials 11.2 10.9 0.3
Information Technology 12.9 13.4 -0.4
Materials 5.6 5.1 0.5
Telecommunication Services 3.7 3.2 0.5
Utilities 3.1 3.2 -0.1

6. 6
Country Allocation of GKCI Developed World Index vs. MSCI Developed World Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset
Our GKCI Emerging Markets Index is somewhat different than the MSCI EM Index for one important
reason. We include Hong Kong in our emerging markets index rather than the developed world in-
dex. MSCI includes listed companies on the Hong Kong Stock Exchange in its developed world in-
dex, but it separates out the Chinese H-shares, associating them with China and putting them in the
emerging markets index. This does not make practical sense. For an obvious start, Hong Kong is an
administered region of China and its currency is linked to the Chinese Yuan via common links to the
USD. All companies listed in Hong Kong are subject to the same listing requirements. All companies
conduct the majority of their business in China and are owned and/or controlled by Chinese compa-
nies, individuals or the government. As mentioned earlier, index committees at MSCI and FTSE are
considering including Chinese A-shares as a result of Chinese financial liberalization moves designed
to more fully integrate China into Hong Kong and the rest of the world. This reinforces the idea of
combining China, Hong Kong and Taiwan into the same index. For all these reasons, we include
Country GKCI Weight (%) MSCI Weight (%) Difference (%)
Australia 2.7 2.8 -0.1
Austria 0.1 0.1 0.0
Belgium 0.8 0.5 0.3
Canada 3.8 3.7 0.1
Denmark 0.8 0.6 0.2
Finland 0.4 0.3 0.1
France 4.2 3.8 0.4
Germany 3.9 3.7 0.2
Hong Kong 0.0 1.2 -1.2
Ireland 0.3 0.1 0.2
Israel 0.3 0.2 0.1
Italy 1.2 0.9 0.3
Japan 10.2 8.6 1.6
Netherlands 1.5 1.1 0.4
New Zealand 0.1 0.1 0.0
Norway 0.5 0.2 0.3
Portugal 0.1 0.1 0.0
Singapore 0.9 0.6 0.3
Spain 1.6 1.4 0.2
Sweden 1.6 1.2 0.4
Switzerland 3.4 3.6 -0.2
United Kingdom 7.9 7.7 0.2
United States 53.6 57.6 -4.0

7. 7
Hong Kong in the GKCI Emerging Markets Index.
The inclusion of Hong Kong skews the sector concentration in the GKCI Index more in favor of finan-
cials and away from information technology. The GKCI Index also has a slightly higher weighting in
industrials and energy than the MSCI Index, while having a slightly lower weighting in consumer sta-
ples and materials.
Sector Allocation of GKCI Emerging Markets Index vs. MSCI Emerging Markets Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset
Country differences also are somewhat different than the MSCI World Index. The GKCI Emerging
Markets Index has a larger weight in Hong Kong and China, while having a lower weight in Brazil, In-
dia, South Africa, South Korea and Taiwan. If we combine China, Hong Kong and Taiwan into a
group of China-related countries, the GKCI Emerging Markets Index has a roughly 52% allocation as
compared to the MSCI Emerging Markets Index which has a combined 36% weight in China-related
countries. Given China’s economic base and rapid growth, our index better captures the importance
of China in an investable emerging markets index.
Sector GKCI Weight (%) MSCI Weight (%) Difference (%)
Consumer Discretionary 7.0 9.4 -2.4
Consumer Staples 6.7 8.1 -1.4
Energy 9.8 8.0 1.8
Financials 36.3 28.5 7.8
Health Care 2.1 2.4 -0.2
Industrials 9.0 6.8 2.2
Information Technology 11.9 19.1 -7.1
Materials 4.8 7.0 -2.2
Telecommunication Services 8.3 7.3 1.0
Utilities 4.0 3.3 0.7

8. 8
Country Allocation of GKCI Emerging Markets Index vs. MSCI Emerging Markets Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset
Our GKCI Developed World Index and GKCI Emerging Markets Index offer a better representation of
global equity markets than the MSCI Indexes. We employ a hybrid value/liquidity scheme that has
the benefit of transparency and simplicity, complemented with a liquidity screen that concentrates
GKCI constituents into an investable universe of companies. Our country segregation, allocating
Hong Kong to the emerging markets index, raises the combined weight of China-related countries
(China, Hong Kong and Taiwan) in the GKCI Indexes and makes our index more representative of the
weight of China-related companies in the emerging markets. For these reasons, we use our own
GKCI Indexes as our selection universes for our flagship Gavekal Knowledge Leaders Indexes.
Country GKCI Weight (%) MSCI Weight (%) Difference (%)
Brazil 3.8 7.3 -3.5
Chile 1.1 1.4 -0.3
China 27.9 23.1 4.8
Colombia 0.4 0.6 -0.2
Czech Republic 0.2 0.2 0.0
Egypt 0.1 0.2 -0.1
Greece 0.2 0.3 -0.1
Hong Kong 17.2 0.0 17.2
Hungary 0.1 0.2 -0.1
India 5.3 7.5 -2.2
Indonesia 2.6 2.8 -0.2
Malaysia 2.6 3.5 -0.9
Mexico 3.1 4.7 -1.6
Peru 0.1 0.4 -0.3
Philippines 1.4 1.4 0.0
Poland 1.0 1.5 -0.5
Qatar 1.1 0.8 0.3
Russia 3.5 3.7 -0.2
South Africa 4.5 8.0 -3.4
South Korea 10.2 15.0 -4.8
Taiwan 7.3 12.8 -5.5
Thailand 3.2 2.4 0.8
Turkey 1.7 1.5 0.2
United Arab Emirates 1.3 0.6 0.7

9. 9
Constructing the Gavekal Knowledge Leaders Indexes; Step 1 - Capitalize Knowledge
Investments
After creating a superior selection universe in the GKCI Indexes, the first step in identifying
Knowledge Leaders is to adjust the reported financial statements of approximately 3,000 companies
for investments in knowledge. In order to capture knowledge investments such as research and de-
velopment (R&D), advertising, employee training and the production of firm-specific resources, we
capitalize certain expenses on the income statement.
Once we remove knowledge investments from the income statement, the next step is to account for
these investments on the statement of cash flows. Knowledge investments are categorized in the
same portion of the statement of cash flows as investments in tangible capital such as property, plant
and equipment: cash flows from investment. Since knowledge is a long lived asset similar to an in-
vestment in tangible fixed assets, the cash flow associated with the investment belongs in the invest-
ing section of the statement of cash flows.
The next step in the capitalization process is to create a new long-term asset on the balance sheet. In
year=0 of the capitalization model, a company is assumed to have no knowledge assets. In year=1,
after the company has made a year of knowledge investments, we record an asset on the balance
sheet that reflects the amount invested in knowledge. In keeping with sound accounting principles, as
time progresses we depreciate the asset and carry the asset at net depreciated, historic cost.
Knowledge investments have a shorter useful life than most tangible fixed investments and a lower
residual value. In order to maintain a conservative approach to knowledge-adjusted financial state-
ments, we calculate depreciation of all knowledge investments using a $0 residual value. As our pre-
vious paper and academic research concludes, investments in knowledge are almost always entirely
equity financed. Our model assumes that 100% of all knowledge investments are equity financed and
captured on the balance sheet as cumulative retained earnings.
Also in year=1, we begin to adjust the income statements for knowledge investments by adding back
a non-cash charge to reflect depreciation of the knowledge asset. This annual depreciation reduces
the carrying value of the knowledge assets on the balance sheet.
Constructing the Gavekal Knowledge Leaders Indexes; Step 2 - Screen for Knowledge
Leaders
Once the financial statements of nearly 2,000 developed world companies and 1,000 emerging mar-
kets companies have been adjusted for investments in knowledge, we apply our Knowledge Leader
screen to identify the most highly innovative companies. Our screen looks at three separate compo-
nents: knowledge intensity, financial leverage and profitability.
We begin with knowledge intensity as this is the basis for the Knowledge Effect. After adjusting finan-
cial statements for knowledge investments, we separate the companies that follow an innovation
strategy from those that follow a mimicking strategy by measuring knowledge intensity. In order for a
company to pass this portion of the screen, a company must spend at least 5% of sales on

10. 10
knowledge investments or have at least 5% of assets represented by knowledge assets.
The next step is a balance sheet test. Because knowledge investments are entirely equity financed,
we screen for companies that have low amounts of total leverage and low amounts of net debt. In or-
der for a company to be a Knowledge Leader, it must have less than 3x gross financial leverage and
less than 50% net debt as a percent of total capital.
The last step is to look at three profitability measures. Positive profitability is an important marker of
innovative companies as it measures the productivity with which knowledge is employed. We look for
companies that have industrialized the innovation process, creating an institutional framework to de-
ploy new knowledge. Companies must show a successful track record of turning innovative invest-
ments into profits. The first layer of our profitability screen mandates that a company must have at
least 20% gross margins. High gross margin is a good indicator of the knowledge embedded in a
good or service sold by a company. A company passing our screen must have at least a 10% operat-
ing cash flow margin on average over the last seven years and it must have a positive trailing seven
year average free cash flow. In this last step we look at how a company performs over at least one full
business cycle. Profitability over the medium term is a good marker of the institutional ability to har-
ness and productively employ knowledge.
Once we apply these seven tests, the companies that pass the screen are deemed Knowledge Lead-
ers, and companies that fail the screen are discarded. Roughly 683 companies from 22 countries
pass our screen and comprise the Gavekal Knowledge Leaders Developed World Index (KNLGX).
Roughly 196 companies from 25 countries pass our screen and comprise the Gavekal Knowledge
Leaders Emerging Markets Index (KNLGEX). The total return indexes (KNLGX and KNLGEX) meas-
ure performance assuming that dividends are reinvested in the stock that paid the dividend and all
other cash distributions are reinvested. This is the same total return methodology employed by MSCI
Indexes. The indexes are calculated in USD and there is no currency hedging employed.
The Gavekal Knowledge Leaders Developed World and Emerging Markets Indexes are equal-
weighted and rebalance twice a year, in April and in October. In general, value weighted indexes
have a momentum and large cap bias. We overcome these biases by equal weighting our indexes
and rebalancing only twice per year. This weighting and rebalancing scheme removes the momen-
tum and large cap bias and introduces a small cap bias. Later in this paper we will decompose each
index by the standard Fama-French four factor model and illustrate the exposure to various factors.
At each rebalance, we run the Knowledge Leader screen to ensure that both indexes continually cap-
ture the most innovative companies in the world. The Gavekal Knowledge Leaders Developed World
Index data begins on March 31, 2000, and the Gavekal Knowledge Leaders Emerging Markets Index
data begins on March 31, 2005. Prior to 2005, there were not enough Knowledge Leaders in the
emerging markets to create a broad, well-diversified index.
Both Gavekal Knowledge Leaders Indexes have an active share in excess of 70%, making them high-
ly active strategies. Active share is the extent to which a portfolio and benchmark differ in composi-

11. 11
tion and/or weighting. Our proprietary selection methodology and an equal weighting scheme create
that difference from the benchmark. Academic literature suggests that a high active share is associ-
ated with subsequent outperformance after fees.
The Gavekal Knowledge Leaders Indexes begin in the month of April because we use full, final finan-
cial information for the previously completed fiscal year. It is standard practice for companies to re-
port final fiscal year-end financial statements within 60 days of the end of the quarter. So, by starting
and rebalancing in April, we ensure that we have final and complete information on every company
using the same time period. We also ensure that the data captured by our index existed at the time of
each portfolio rebalance, addressing the “as of” data flaw that exists in some indexes. It is one thing
in 2015 to look back at April 2010 and compile a portfolio with the data that exists in 2015. It is anoth-
er, higher standard, to ensure that the data used in the index construction methodology existed at the
time of the index construction. By lagging our index start and rebalance date three months, we over-
come this “as of” data flaw.
A Closer Look at the Gavekal Knowledge Leaders Developed World Index
Next we take a closer look at the Gavekal Knowledge Leaders Developed World Index characteristics
and compare and contrast them with the MSCI Developed World Index.
In the table below we summarize the pass/fail rate by sector and region. There are 683 companies,
or 35.5% of all stocks in the GKCI Developed World Index (selection universe), that were identified as
Knowledge Leaders in the latest rebalance of the index. Health care (67.3%), information technology
(62.1%), and consumer staples (57.1%) are the three sectors with the highest pass rate. Conversely,
the lowest pass rate can be found in the financial (1.3%), energy (3.6%) and utilities (4.7%) sectors.
From a regional perspective, at least a third of all companies in each region pass our screen. The Pa-
cific region has the highest pass rate at 39.3%, followed by Europe at 34.2% and North America at
33.5%.

12. 12
Gavekal Knowledge Leaders Developed World Index Screening Results: Pass/Fail Rate
Data as of March 31, 2015
Source: Gavekal Capital, Factset; Monthly data; Index Publisher: Solactive
The largest sector in the Gavekal Knowledge Leaders Developed World Index is the consumer dis-
cretionary sector. The consumer discretionary sectors accounts for 21.35% of the market value of the
Gavekal Knowledge Leaders Developed World Index compared to just 12.93% of the MSCI Devel-
oped World Index. The next largest sector is the industrial sector at 19.01%. Again this sector has a
much larger weighting in the Gavekal Knowledge Leaders Developed World Index compared to the
MSCI Developed World Index, where just 10.91% of stocks are industrial stocks. Information technol-
ogy, health care, consumer staples and materials all fall between 10.53% and 17.98%. By far the
greatest difference from a sector allocation perspective between the Gavekal Knowledge Leaders De-
veloped World Index and the MSCI Developed World Index is the weighting to the financial sector:
financial stocks make up only 0.73% of the Gavekal Knowledge Leaders Developed World Index
while they account for 20.69% of the MSCI Developed World Index.
Sector Pass Fail Total % Pass
Consumer Discretionary 146 158 304 48.0%
Consumer Staples 80 60 140 57.1%
Energy 4 108 112 3.6%
Financials 5 388 393 1.3%
Health Care 107 52 159 67.3%
Industrials 130 194 324 40.1%
Information Technology 123 75 198 62.1%
Materials 72 97 169 42.6%
Telecommunication Services 12 28 40 30.0%
Utilities 4 81 85 4.7%
Total 683 1241 1924 35.5%
Region Pass Fail Total % Pass
DM Asia 236 364 600 39.3%
DM EMEA 179 345 524 34.2%
DM Americas 268 532 800 33.5%
Total 683 1241 1924 35.5%

13. 13
Gavekal Knowledge Leaders Developed World Index Sector Allocation Compared to the MSCI World
Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset; Monthly data; Index Publisher: Solactive
From a country standpoint, the Gavekal Knowledge Leaders Developed World Index is fairly similar to
the MSCI Developed World Index with two big exceptions. First, the Gavekal Knowledge Leaders De-
veloped World Index is overweight Japan relative to the MSCI Developed World Index. Japanese
stocks make up only 8.61% of the MSCI World Index while in the Gavekal Knowledge Leaders Devel-
oped World Index, Japanese stocks have the second largest weighting at 31.14%. The second major
difference is the proportion of US stocks in the index. While still the largest overall weighting in the
Gavekal Knowledge Leaders Developed World Index at 36.26%, US stocks are less prominent in our
index relative to the MSCI Developed World Index where 57.57% of the index is US companies. The
third largest country weighting in the Gavekal Knowledge Leaders Developed World Index is the Unit-
ed Kingdom at 6.73%. The rest of the 19 developed world countries represent somewhere between 0-
3.07% of the Gavekal Knowledge Leaders Developed World Index.
Sector Allocation (%)
Economic Sector KNLGX MSCI World Difference
Consumer Discretionary 21.35 12.93 8.42
Consumer Staples 11.70 9.84 1.85
Energy 0.58 7.45 -6.87
Financials 0.73 20.69 -19.96
Health Care 15.79 13.32 2.47
Industrials 19.01 10.91 8.10
Information Technology 17.98 13.36 4.63
Materials 10.53 5.13 5.40
Telecommunication Services 1.75 3.23 -1.47
Utilities 0.58 3.16 -2.58

14. 14
Gavekal Knowledge Leaders Developed World Index Country Allocation Compared to the MSCI
World Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset; Monthly data; Index Publisher: Solactive
In addition, the Gavekal Knowledge Leaders Developed World Index is much less skewed toward the
largest companies in the world than the MSCI Developed World Index. To see this, we measure the
weighted average market capitalization size of the Gavekal Knowledge Leaders Developed World
Index compared to the MSCI Developed World Index. The weighted average market cap of the
Gavekal Knowledge Leaders Developed World Index is about $24 billion while the weighted average
market cap of the MSCI Developed World Index is over $97 billion. The median market cap of the
Gavekal Knowledge Leaders Developed World Index is slightly lower than the median market cap of
the MSCI Developed World Index. Lastly, the Gavekal Knowledge Leaders Developed World Index is
significantly underweight companies that are larger than $15 billion in market cap compared to MSCI
Developed World Index. The largest concentration of companies in the Gavekal Knowledge Leaders
Developed World Index have a market cap between $2.5 billion and $15 billion.
Country Allocation (%)
Country KNLGX MSCI World Difference
Australia 2.34 2.83 -0.49
Austria -- 0.08 -0.08
Belgium 0.73 0.51 0.22
Canada 2.92 3.67 -0.75
Denmark 1.32 0.64 0.68
Finland 1.32 0.34 0.98
France 3.07 3.77 -0.70
Germany 2.78 3.69 -0.91
Hong Kong -- 1.22 -1.22
Ireland 0.44 0.13 0.31
Israel 1.17 0.23 0.94
Italy 1.17 0.91 0.26
Japan 31.14 8.61 22.53
Netherlands 1.02 1.06 -0.03
New Zealand 0.44 0.06 0.38
Norway 0.73 0.25 0.49
Portugal -- 0.06 -0.06
Singapore 0.58 0.56 0.02
Spain 0.58 1.39 -0.80
Sweden 2.78 1.19 1.59
Switzerland 2.49 3.58 -1.10
United Kingdom 6.73 7.66 -0.93
United States 36.26 57.57 -21.31

15. 15
Gavekal Knowledge Leaders Developed World Index Market Capitalization Allocation Compared to
the MSCI World Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset; Monthly data; Index Publisher: Solactive
Download more information, including full holdings, on the Gavekal Knowledge Leaders Developed
World Index.
A Closer Look at the Gavekal Knowledge Leaders Emerging Markets Index
We use a similar analytical framework to compare companies in the Gavekal Knowledge Leaders
Emerging Markets Index to the MSCI Emerging Markets Index.
In aggregate there are fewer innovative companies in the emerging markets so the overall number of
companies and the pass rate percentage is lower in the Gavekal Knowledge Leaders Emerging Mar-
kets Index. There are 196 companies, or 20% of the selection universe, the GKCI Emerging Markets
Index, that pass the Knowledge Leader screen. Only the consumer staples sector in the Gavekal
Knowledge Leaders Emerging Markets Index has a pass rate above 50%. Telecommunications ser-
vices, consumer discretionary, health care and information technology sectors all have pass rates be-
tween 30.1%-47.7%. Conversely, only two companies in the energy sector, one company in the finan-
cial sector and two companies in the utility sector pass the Knowledge Leader screen.
Market Capitalization - Millions ($)
Statistic KNLGX MSCI World Difference
Weighted Average 23,969 97,221 -73,252
Median 9,086 11,408 -2,321
Maximum 717,000 717,000 0
Minimum 531 1,507 -977
Market Capitalization (%)
Market Cap KNLGX MSCI World Difference
MC Bin 1: > 50B 10.82 50.48 -39.66
MC Bin 2: 25B - 50B 11.40 20.03 -8.63
MC Bin 3: 15B - 25B 10.53 11.01 -0.49
MC Bin 4: 10B - 15B 13.30 7.84 5.46
MC Bin 5: 7.5B - 10B 11.11 4.69 6.42
MC Bin 6: 7B - 7.5B 2.63 0.82 1.81
MC Bin 7: 5B - 7B 11.84 2.74 9.10
MC Bin 8: 2.5B - 5B 16.52 2.29 14.23
MC Bin 9: 2B - 2.5B 3.51 0.07 3.44
MC Bin 10: 1.5B - 2B 5.26 0.02 5.25
MC Bin 10: 1B - 1.5B 2.63 -- 2.63
MC Bin 10: 0 - 1B 0.44 -- 0.44

16. 16
From a regional perspective, EM Latin America has the highest pass rate at 27% but makes up the
fewest number of companies as there are only 20 companies in the Gavekal Knowledge Leaders
Emerging Markets Index. EM Asia stocks have the second highest pass rate at 20% and account for
146 of the 196 total companies in the Gavekal Knowledge Leaders Emerging Markets Index. EM
EMEA has a 17.8% pass rate and represents 30 companies in the Gavekal Knowledge Leaders
Emerging Markets Index.
Gavekal Knowledge Leaders Emerging Markets Index Screening Results: Pass/Fail Rate
Data as of March 31, 2015
Source: Gavekal Capital, MSCI; Monthly data; Index Publisher: Solactive
The largest sector in the Gavekal Knowledge Leaders Emerging Markets Index is the consumer dis-
cretionary sector. The consumer discretionary sector accounts for 25.26% of the Gavekal Knowledge
Leaders Emerging Markets Index and is substantially larger than the consumer discretionary sector in
the MSCI Emerging Markets Index (9.41%). The second largest sector in the Gavekal Knowledge
Leaders Emerging Markets Index is information technology at 21.13% of the index. This is roughly in
line with the weighting of the information technology sector in the MSCI Emerging Markets Index
(19.09%). The third largest sector in the Gavekal Knowledge Leaders Emerging Markets Index is the
consumer staples sector. This sector accounts for 19.59% of the index while consumer staples stocks
only account for 8.11% of the MSCI Emerging Markets Index. Tied for the smallest sector, and the
sector with the largest underweight compared to the MSCI Emerging Markets Index, is the financial
sector. The financial sector makes up only 0.52% of the Gavekal Knowledge Leaders Emerging Mar-
kets Index while it accounts for a staggering 28.49%, which makes it the largest sector, of the MSCI
Sector Pass Fail Total % Pass
Consumer Discretionary 49 73 122 40.2%
Consumer Staples 38 34 72 52.8%
Energy 2 40 42 4.8%
Financials 1 227 228 0.4%
Health Care 20 31 51 39.2%
Industrials 13 137 150 8.7%
Information Technology 41 95 136 30.1%
Materials 9 75 84 10.7%
Telecommunication Services 21 23 44 47.7%
Utilities 2 39 41 4.9%
Total 196 774 970 20.2%
Region Pass Fail Total % Pass
EM Asia 146 581 729 20.0%
EM EMEA 30 139 169 17.8%
EM Americas 20 54 74 27.0%
Total 196 774 970 20.2%

17. 17
Emerging Markets Index. In general, the Gavekal Knowledge Leaders Emerging Markets Index is
most overweight the consumer sectors in Asia.
Gavekal Knowledge Leaders Emerging Markets Index Sector Allocation Compared to the MSCI
Emerging Markets Index
Data as of March 31, 2015
Source: Gavekal Capital, MSCI; Monthly data; Index Publisher: Solactive
The largest country weight in the Gavekal Knowledge Leaders Emerging Markets Index is Korea
(19.59%), followed by Taiwan (18.56%), and Hong Kong (12.37%). In the MSCI Emerging Markets
Index, China is the largest country weight in the index at 23.11%. China stocks only make up 10.31%
of the Gavekal Knowledge Leaders Emerging Markets Index, but because we include Hong Kong in
our index, the combined weight of Hong Kong plus China is roughly the same as the MSCI Emerging
Markets Index. If we combine China, Hong Kong and Taiwan into a group of China-related countries,
the Gavekal Knowledge Leaders Emerging Markets Index has a combined 41% allocation, roughly
5% higher allocation than the MSCI Emerging Markets Index. We believe the higher China-related
weight in the Gavekal Knowledge Leaders Emerging Markets Index better reflects the investment ori-
entation investors should have toward China.
Sector Allocation (%)
Economic Sector KNLGEX MSCI EM Difference
Consumer Discretionary 25.26 9.41 15.85
Consumer Staples 19.59 8.11 11.48
Energy 0.52 8.03 -7.52
Financials 0.52 28.49 -27.97
Health Care 10.31 2.36 7.95
Industrials 6.70 6.79 -0.09
Information Technology 21.13 19.09 2.04
Materials 4.12 7.04 -2.92
Telecommunication Services 10.82 7.35 3.48
Utilities 1.03 3.33 -2.30

18. 18
Gavekal Knowledge Leaders Emerging Markets Index Country Allocation Compared to the MSCI
Emerging Markets Index
Data as of March 31, 2015
Source: Gavekal Capital, MSCI; Monthly data; Index Publisher: Solactive
The Gavekal Knowledge Leaders Emerging Markets Index is also much less skewed toward the larg-
est companies in the emerging markets compared to the MSCI Emerging Markets Index. The
weighted average market capitalization of the Gavekal Knowledge Leaders Emerging Markets Index
is about $11 billion compared to over $48 billion for the MSCI Emerging Markets Index. The median
market cap size is slightly lower for the Gavekal Knowledge Leaders Emerging Markets Index at $4.3
billion compared to $5.4 billion for the MSCI Emerging Markets Index. The Gavekal Knowledge Lead-
ers Emerging Markets Index is significantly underweight companies with a market cap over $10 billion
compared to the MSCI Emerging Markets Index. Unsurprisingly then, the Gavekal Knowledge Lead-
ers Emerging Markets Index is significantly overweight the smallest companies, those with a market
cap of $0-$5 billion, compared to the MSCI Emerging Markets Index.
Country Allocation (%)
Country KNLGEX MSCI EM Difference
Brazil 6.19 7.32 -1.13
Chile 0.52 1.41 -0.89
China 10.31 23.11 -12.80
Colombia -- 0.63 -0.63
Czech Republic -- 0.20 -0.20
Egypt -- 0.24 -0.24
Greece 1.03 0.33 0.70
Hong Kong 12.37 -- 12.37
Hungary 0.52 0.20 0.31
India 2.06 7.46 -5.40
Indonesia 3.09 2.77 0.33
Korea 19.59 14.99 4.60
Malaysia 1.55 3.53 -1.99
Mexico 3.61 4.70 -1.09
Peru -- 0.42 -0.42
Philippines 1.55 1.39 0.16
Poland 0.52 1.50 -0.98
Qatar -- 0.80 -0.80
Russia 1.55 3.71 -2.16
South Africa 7.73 7.95 -0.22
Taiwan 18.56 12.82 5.73
Thailand 5.67 2.40 3.27
Turkey 3.09 1.51 1.58
United Arab Emirates 0.52 0.60 -0.09

19. 19
Gavekal Knowledge Leaders Emerging Markets Index Market Capitalization Allocation Compared to
the MSCI World Index
Data as of March 31, 2015
Source: Gavekal Capital, MSCI; Monthly data; Index Publisher: Solactive
Download more information, including full holdings, on the Gavekal Knowledge Leaders Emerging
Markets Index.
Performance and Risk History of Gavekal Knowledge Leaders Indexes
We will next compare performance and risk statistics for both of the Gavekal Knowledge Leaders In-
dexes to the relevant MSCI Indexes. We will compare the total return indexes, which assumes divi-
dends are reinvested.
The Gavekal Knowledge Leaders Developed World Index has generated a cumulative return of
234.9% since March 31, 2000, or 8.4% annually. Comparatively, the MSCI World Index has generat-
ed a 60.6% return, or 3.2% annually. The Gavekal Knowledge Leaders Developed World Index has
consequently outperformed the MSCI World Index by 7% on an annual basis in 13 of the past 15
years.
Market Capitalization - Millions ($)
Statistic KNLGEX MSCI EM Difference
Weighted Average 10,833 48,399 -37,567
Median 4,292 5,418 -1,126
Maximum 267,252 267,252 0
Minimum 307 785 -478
Market Capitalization - Millions ($)
Market Cap KNLGEX MSCI EM Difference
MC Bin 1: > 50B 3.61 25.01 -21.40
MC Bin 2: 25B - 50B 3.09 14.41 -11.32
MC Bin 3: 15B - 25B 6.70 16.85 -10.15
MC Bin 4: 10B - 15B 7.73 11.71 -3.98
MC Bin 5: 7.5B - 10B 11.34 7.44 3.90
MC Bin 6: 7B - 7.5B 1.03 1.57 -0.54
MC Bin 7: 5B - 7B 12.37 7.91 4.46
MC Bin 8: 2.5B - 5B 19.07 10.95 8.13
MC Bin 9: 2B - 2.5B 3.61 1.71 1.90
MC Bin 10: 1.5B - 2B 5.15 1.65 3.51
MC Bin 11: 1B - 1.5B 10.31 0.62 9.69
MC Bin 12: 0 - 1B 15.98 0.18 15.80

20. 20
Gavekal Knowledge Leaders Developed World Index Performance Compared to MSCI World Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset; Monthly data; Index Publisher: Solactive
An investor cannot invest directly in an index.
160.65
334.89
Gavekal Knowledge Leaders Developed World Index (TR USD)
MSCI World Index (TR USD)

21. 21
Gavekal Knowledge Leaders Developed World Index Performance by Year Compared to MSCI World
Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset; Monthly data; Index Publisher: Solactive
An investor cannot invest directly in an index.
There are five important differences between the risk metrics of the Gavekal Knowledge Leaders De-
veloped World Index and those of the MSCI World Index: 1) the Gavekal Knowledge Leaders Devel-
oped World Index has a 72.72% active share compared to an active share of zero for the MSCI World
Index. 2) The Gavekal Knowledge Leaders Developed World Index has a beta almost 7% lower than
MSCI World Index. 3) The Gavekal Knowledge Leaders Developed World Index has generated 5.4%
alpha per year while the MSCI World Index has generated no alpha. 4) The Gavekal Knowledge
Leaders Developed World Index has experienced a max drawdown that is more than 5% lower com-
pared to the max drawdown that the MSCI World Index has experienced. 5) The Gavekal Knowledge
Leaders Developed World Index has a Sharpe Ratio that is almost three times as much as that of the
MSCI World Index.
Gavekal Knowledge Leaders Developed World Index - USD (%)
Fiscal Year KNLGX (TR) MSCI World (TR) Difference
2000 -2.6 -14.1 11.5
2001 -8.6 -16.8 8.2
2002 -10.1 -19.9 9.8
2003 39.3 33.1 6.2
2004 17.7 14.7 2.9
2005 13.5 9.5 4.0
2006 16.5 20.1 -3.6
2007 6.3 9.0 -2.7
2008 -34.8 -40.7 5.9
2009 37.2 30.0 7.2
2010 21.3 11.8 9.6
2011 -1.9 -5.5 3.7
2012 16.1 15.8 0.2
2013 30.6 26.7 3.9
2014 5.1 4.9 0.2
2015 YTD 7.0 2.3 4.7
2000-Current Cumulative 234.9 60.6 174.2
Annualized Return 8.4 3.2 7.0

22. 22
Gavekal Knowledge Leaders Developed World Index Risk Metrics Compared to MSCI World Index
Data as of March 31, 2015
Source: Gavekal Capital, Factset; Monthly data; Index Publisher: Solactive
Next, we compare the Gavekal Knowledge Leaders Emerging Markets Index and the MSCI Emerging
Markets Index. Our index has generated a return of 274% since March 31, 2005, or a 14.2% annual-
ized return. In the same time period, the MSCI Emerging Markets Index returned a cumulative
122.6%, or an 8.4% annualized return. The Gavekal Knowledge Leaders Emerging Markets Index
has returned 9.7% more per year than the MSCI Emerging Markets Index and outperformed the
MSCI Emerging Markets Index in seven out of the last 10 years. The three years that our index un-
derperformed occurred during the run up to the financial crisis and in the commodity bubble in 2005-
2007. The index has since outperformed the MSCI Emerging Markets Index every year.
Gavekal Knowledge Leaders Developed World Index
Risk Statistics (Annualized, Monthly)
Risk Metric KNLGX (TR) MSCI World (TR)
Standard Deviation 15.1% 15.8%
Correlation 96.6% -
Sharpe Ratio 0.53 0.18
Tracking Error 4.1% -
Beta 0.93 1.00
Alpha 5.4% -
Information Ratio 1.71 -
Active Share 72.72 -
Max Yearly Drawdown -34.82% -40.71%

23. 23
Gavekal Knowledge Leaders Emerging Markets Index Performance Compared to MSCI Emerging
Markets Index
Data as of March 31, 2015
Source: Gavekal Capital, MSCI; Monthly data; Index Publisher: Solactive
An investor cannot invest directly in an index.
Gavekal Knowledge Leaders Emerging Markets Index Performance by Year Compared to MSCI
Emerging Markets Index
Data as of March 31, 2015
Source: Gavekal Capital, MSCI; Monthly data; Index Publisher: Solactive
An investor cannot invest directly in an index.
Gavekal Knowledge Leaders Emerging Markets Index - USD (%)
Fiscal Year KNLGEX (TR) MSCI EM (TR) Difference
2005 27.2 31.6 -4.4
2006 24.0 32.1 -8.1
2007 25.3 39.4 -14.1
2008 -45.4 -53.3 7.9
2009 95.2 78.5 16.7
2010 31.9 18.9 13.0
2011 -9.1 -18.4 9.3
2012 27.0 18.2 8.7
2013 11.6 -2.6 14.2
2014 -0.7 -2.2 1.5
2015 YTD 5.3 2.2 3.1
2005-Current Cumulative 274.0 122.6 151.5
Annualized Return 14.2 8.4 9.7

24. 24
There are six important differences between the risk metrics of the Gavekal Knowledge Leaders
Emerging Markets Index and those of the MSCI Emerging Markets Index: 1) the Gavekal Knowledge
Leaders Emerging Markets Index has a 85.15% active share compared to an active share of zero for
the MSCI Emerging Markets Index. 2) The Gavekal Knowledge Leaders Emerging Markets Index has
a beta that is 13% lower than the MSCI Emerging Markets Index. 3) The Gavekal Knowledge Leaders
Emerging Markets Index has generated 6.9% alpha per year compared to the MSCI Emerging Mar-
kets Index which has generated zero alpha. 4) The Gavekal Knowledge Leaders Emerging Markets
Index has had a roughly 2.2% lower annualized volatility compared to the MSCI Emerging Markets
Index. 5) The Gavekal Knowledge Leaders Emerging Markets Index experienced nearly an 8% lower
max drawdown compared to the max drawdown experienced by the MSCI Emerging Markets Index.
6) The Gavekal Knowledge Leaders Emerging Markets Index has a Sharpe Ratio double that of the
MSCI Emerging Markets Index.
Gavekal Knowledge Leaders Emerging Markets Risk Metrics Compared to MSCI Emerging Markets
Index
Data as of March 31, 2015
Source: Gavekal Capital, MSCI; Monthly data; Index Publisher: Solactive
Capturing the Knowledge Factor
A factor is a characteristic possessed by a group of securities which helps to explain the risk and re-
turn. A rich academic history suggests long-term portfolio returns can be explained by factors. Wil-
liam Sharpe identified the first factor—exposure to the equity market itself—in his 1964 Capital Asset
Pricing Model (CAPM). In 1993, Kenneth Fama and Eugene French overturned conventional wisdom
by identifying two additional factors—the small cap factor and value factor. Later, in 1997, Mark Car-
hart expanded on the work of Fama and French identifying the momentum factor. More recently, aca-
demic research has uncovered the low volatility, dividend yield and quality factors. In our recent white
paper “The Knowledge Effect: Excess Returns of Highly Innovative Companies,” we identified a new
effect that explains equity returns. We attempt to capture this effect by constructing portfolios of com-
panies that possess certain characteristics related to their knowledge activities. These portfolios rep-
Gavekal Knowledge Leaders Emerging Markets Index
Risk Statistics (Annualized, Monthly)
Risk Metric KNLGEX (TR) MSCI EM (TR)
Standard Deviation 21.4% 23.6%
Correlation 95.8% -
Sharpe Ratio 0.60 0.30
Tracking Error 6.9% -
Beta 0.87 1.00
Alpha 6.9% -
Information Ratio 1.41 -
Active Share 85.15 -
Max Yearly Drawdown -45.41% -53.18%

25. 25
resent exposure to the Knowledge Factor, and our Gavekal Knowledge Leaders Indexes are built to
capitalize on the Knowledge Factor.
Certain factors historically have earned a risk premium and represent exposures to specific risks. In-
vestors commonly employ factor based investments to tilt a portfolio toward a certain desired factor or
set of factors. Some investors use factor exposures as the basis for asset allocation, seeking to man-
age risk by managing exposure to a variety of factors.
The smart beta industry has grown on the back of factor investing. Numerous iShares ETFs are
based on MSCI factor indexes and seek to isolate a single factor such as size or quality. A recent
wave of ETF products, so-called “multi-factor” funds, seek to capture multiple factors at the same
time. Research Affiliates (RAFI) takes another approach with its fundamentally weighted indexes
which represent a third of the smart beta industry and employ weighting schemes tied to revenues,
earnings or dividends (rather than market cap weighting) which are applied to common indexes, like
the S&P 500. The Gavekal Knowledge Leaders Indexes are similar to the RAFI fundamentally
weighted indexes with two important differences: we apply a proprietary selection scheme and an
equal weighting methodology.
In an effort to reveal the Knowledge Factor and better understand the sources of systemic risk and
return in the Gavekal Knowledge Leaders Indexes, we next decompose our indexes down to factor
exposures. We use the standard Fama-French four factor model that includes the following factors:
market, firm size, value and momentum. Data history on the Fama-French model can be found on
Kenneth French’s Dartmouth College research website.
In the table below are the summary statistics of the Gavekal Knowledge Leaders Developed World
Index. The Fama-French four factor model explains roughly 95% of the returns indicating the model
is quite robust.
The multiple regression equation takes the form:
The Gavekal Knowledge Leaders Developed World Index generates a 4.22% annualized alpha, and
with a T-statistic of 4.69, this indicates that the alpha has a high level of statistical significance. It has
a good sensitivity (.89) to the market factor, and this factor has a very high T-statistic as well. The
coefficients for the size, value and momentum factor are small, and only the size and momentum fac-
tor are statistically significant. The results suggest the Gavekal Knowledge Leaders Developed
Summary Statistics of Multiple Regression
Adjusted R Square 0.95
Observations 180
Standard Error 0.96
Regression Equation (Annualized)
Y = 4.22 + (.89) Market + (.20) Size + (-.03) Value + (-.05) Momentum

26. 26
World Index generates excess returns, with a positive exposure to the market and size factor, and no
meaningful exposure to the value and momentum factor.
In the table below, we show the Gavekal Knowledge Leaders Emerging Markets Index regression
summary statistics. Because we are applying this model to an emerging markets group of compa-
nies, the explanatory power of the model is somewhat lower, but the model still explains 78% of the
return history.
The multiple regression takes the form:
The Gavekal Knowledge Leaders Emerging Markets Index generates a 7.17% annualized alpha, and
with a T-statistic of 2.19, this indicates that the alpha has a high level of statistical significance. It has
a good sensitivity (1.14) to the market factor, and this factor has a very high T-statistic as well. The
coefficients for the size and value are reasonably high (.57 for size, and -.57 for value) and they are
both statistically significant variables. The sensitivity to momentum is low (-.11) but the T-statistic of
only 1.39 suggests the factor is not statistically significant. The results suggest the Gavekal
Knowledge Leaders Emerging Markets Index generates excess returns, with a positive exposure to
the market and size factor, a negative exposure to the value factor and no meaningful exposure to the
momentum factor.
Regression Statistics (Annualized) Coefficient T-Statistic
Alpha 4.22 4.69
Market Factor 0.89 53.05
Size Factor 0.20 4.72
Value/Growth Factor -0.03 1.09
Momentum Factor -0.05 2.68
Summary Statistics of Multiple Regression
Adjusted R Square 0.78
Observations 119
Standard Error 2.91
Regression Equation (Annualized)
Y = 7.20 + (1.14) Market + (0.57) Size + (-0.57) Value + (-0.11) Momentum
Regression Statistics (Annualized) Coefficient T-Statistic
Alpha 7.17 2.19
Market Factor 1.14 18.30
Size Factor 0.57 3.11
Value/Growth Factor -0.57 3.11
Momentum Factor -0.11 1.39

27. 27
Both the Gavekal Knowledge Leaders Developed World Index and the Gavekal Knowledge Leaders
Emerging Markets Index generate statistically significant alpha after regressing against the basic
Fama-French four factor model.
Both indexes have a positive exposure to the market factor that is statistically significant. The
Gavekal Knowledge Leaders Developed World Index is tilted toward lower beta stocks, while the
Gavekal Knowledge Leaders Emerging Markets Index is tilted toward higher beta stocks.
The size factor is statistically significant for both indexes. The coefficient for the Gavekal Knowledge
Leaders Developed World Index is fairly low (.2) and the coefficient for the Gavekal Knowledge Lead-
ers Emerging Markets Index (.57) is somewhat higher. This means both indexes have a tilt toward
smaller stocks.
While both indexes have a negative exposure to the value factor, the Gavekal Knowledge Leaders
Emerging Markets Index has a much larger coefficient to the value factor (-.57) and the factor is sta-
tistically significant. For the Gavekal Knowledge Leaders Developed World Index, the coefficient is
very small (-.03) and the T-statistic suggests the variable is not statistically significant. The data sug-
gests that only the Gavekal Knowledge Leaders Emerging Markets Index has a meaningful tilt toward
growth stocks.
They both appear to have negative exposure to the momentum factor, but the coefficients to the mo-
mentum factor are very low. Furthermore, the momentum factor is not statistically significant for the
Gavekal Knowledge Leaders Emerging Markets Index (meaning it is not statistically different than ze-
ro). We conclude there is no meaningful exposure to the momentum factor for either index.
A summary of factor exposures is detailed in the table below.
For investors, these results are important because they indicate that the excess returns of the
Gavekal Knowledge Leaders Indexes are not the product of common risk factors. The Knowledge
Factor, represented by the residual in each regression (alpha), is statistically significant after account-
ing for the basic Fama-French four factors that drive equity returns. The Gavekal Knowledge Leaders
Indexes are truly differentiated, and the Knowledge Factor stands up to rigorous statistical testing.
Conclusion
Index based investing continues to increase in popularity. A new strand referred to as “smart beta” or
“strategic beta” is attracting new assets in large part due to its promise of efficiently capturing some
risk factor. Investors are becoming increasingly aware of the benefits that these products can bring to
portfolio efficiency. With the ability to tilt portfolios toward or away from specific risk factors, investors
can fine tune expected portfolio risk exposure, diversification and returns.
Factor Exposure Market Size Value Momentum
Gavekal Knowledge Leaders Developed World Index + + - =
Gavekal Knowledge Leaders Emerging Markets Index ++ ++ -- =

28. 28
While asset allocation is traditionally considered on the basis of various asset classes, such as
stocks, bonds or real estate, many practitioners now employ an approach to asset allocation that in-
stead focuses on risk factors. Asset allocation based on risk factors seeks to diversify across various
factors, with deliberate tilts toward or away from certain factors.
A practitioner of traditional asset allocation forms a portfolio that is overweight/underweight one asset
or another due to the perceived risk/return tradeoff. A practitioner of factor based asset allocation
forms a portfolio that is overweight/underweight one factor or another. For example, let’s say that a
recession is expected and investors want to bring down portfolio risk. The traditional asset allocator
would think about increasing his weighting in bonds relative to stocks. The factor based allocator
might think about decreasing his exposure to the market and momentum factor while increasing his
exposure to the value factor.
An additional benefit of the factor based approach is that it has given investors a new perspective and
set of tools with which to evaluate fund performance. Investors traditionally have evaluated the mer-
its of an investment fund based on whether or not it outperforms a benchmark after considering its
exposure to the market factor. Investors can now evaluate the merits of an investment fund based on
whether or not it outperforms a benchmark after accounting for not just the market factor but also the
size, value and momentum factors. It is becoming standard among practitioners to evaluate whether
an investment fund generates excess returns after accounting for multiple factors. Since factor expo-
sure can be cheaply and easily achieved, investors are becoming increasingly discerning in selecting
funds for investment, requiring that a strategy deliver alpha against not just the market factor, but the
size, value and momentum factor as well.
The Gavekal Knowledge Leaders Indexes represent a new evolution in smart/strategic beta indexing.
Our indexes have a long track record of capturing the Knowledge Factor, a unique risk exposure not
well related to other well established factors. Our indexes convert the excess returns of highly inno-
vative companies into multi-dimensional alpha. This multi-dimensional alpha is statistically significant
and represents an opportunity for investors to improve portfolio efficiency. Does your portfolio have
exposure to the Knowledge Factor?

29. 29
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Petajisto, Antti. “Active Share and Mutual Fund Performance.” Working Paper, January 2013.
Seifried, Sebastian, and Claudia Zunft. “Pure Versus Float-Adjusted Value Weighting.” ETF.com, May
22, 2015.
Definitions
Active Share is the percentage of stock holdings in a portfolio that differ from the benchmark index.
Active Share determines the extent of active management being employed by mutual fund managers:
the higher the Active Share, the more likely a fund is to outperform the benchmark index. Research-
ers in a 2006 Yale School of Management study determined that funds with a higher Active Share will
tend to be more consistent in generating high returns against the benchmark indexes.
Adjusted R Squared represents the percentage of a fund or security’s movements that can be ex-
plained by movements in a benchmark index.

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Alpha is a measure of the portfolio’s risk adjusted performance. When compared to the portfolio’s be-
ta, a positive alpha indicates better-than-expected portfolio performance and a negative alpha worse-
than-expected portfolio performance.
Beta is a measure of the funds sensitivity to market movements. A portfolio with a beta greater than 1
is more volatile than the market and a portfolio with a beta less than 1 is less volatile than the market.
Coefficient is the ratio of the standard deviation to the mean.
Downside Capture is used to evaluate how well or poorly an investment manager performed relative
to an index during periods when the index has dropped.
Market Factor is the sensitivity of an index relative to the overall market.
Max Drawdown is the maximum single period loss incurred over the interval being measured.
Momentum Factor reflects excess returns to stocks with stronger part performance.
The MSCI World Index is a free float-adjusted market capitalization weighted index that is designed to
measure the equity market performance of developed markets.
Sharpe Ratio uses a fund’s standard deviation and its excess return (the difference between the
fund’s return and the risk‐free return of 90‐day Treasury Bills) to determine reward per unit of risk.
Size Factor captures the excess returns of smaller firms relative to their counterparts.
Standard deviation is a calculation used to measure variability of a portfolio’s performance.
Tracking Error is a measure of how closely a portfolio follows the index to which it is benchmarked.
T-Statistic is a ratio of the departure of an estimated parameter from its notional value and its stand-
ard error.
Upside Capture is used to evaluate how well an investment manager performed relative to an index
during periods when that index has risen.
Value/Growth Factor captures excess returns to stocks that have low prices relative to their funda-
mental value.
An investor cannot invest directly in an index.
Disclaimer
This document does not constitute an offer of services in jurisdictions where Gavekal Capital, LLC is
not authorized to conduct business. All information provided herein by Gavekal Capital is impersonal
and not tailored to the needs of any person, entity or group of persons. Past performance of an index
is not a guarantee of future results. It is not possible to invest directly in an index. Exposure to
an asset class represented by an index is available through investable instruments based on that in-
dex. Gavekal Capital makes no assurance that investment products based on the index will accurate-
ly track index performance or provide positive investment returns. A decision to invest in any such

31. 31
investment fund or other investment vehicle should not be made in reliance on any of the statements
set forth in this document. Prospective investors are advised to make an investment in any such fund
or other vehicle only after carefully considering the risks associated with investing in such funds, as
detailed in an offering memorandum or similar document that is prepared by or on behalf of the issuer
of the investment fund or other vehicle. Inclusion of a security within an index is not a recommenda-
tion by Gavekal Capital to buy, sell or hold such a security, nor is it considered to be investment ad-
vice. Closing prices for the Gavekal Knowledge Leaders Indexes are calculated by Solactive AG
based on the closing price of the individual constituents of the index as set by their primary exchange.
These materials have been prepared solely for informational purposes based upon information gener-
ally available to the public from sources believed to be reliable. No content contained in these materi-
als (including index data, ratings, credit-related analyses and data, model, software or other applica-
tion or output therefrom) or any part there of (Content) may be modified, reverse-engineered, repro-
duced or distributed in any form by any means, or stored in a database or retrieval system, without
the prior written permission of Gavekal Capital. The Content shall not be used for any unlawful or un-
authorized purposes. Gavekal Capital and its third-party data providers and licensors do not guaran-
tee the accuracy, completeness, timeliness or availability of the Content. Gavekal Capital Parties are
not responsible for any errors or omissions, regardless of the cause, for the results obtained from the
use of the Content. The Content is provided on an “as is” basis.
The Gavekal Knowledge Leaders Developed World Index and the Gavekal Knowledge Leaders
Emerging Markets Index (Indexes) claim to be the longest running, real time test of the innovation
leaders. This claim was determined via an internal search of all indexes offered by the following list of
index providers, which we believe to be comprehensive: S&P Dow Jones Indices, MSCI, FTSE,
FTSE/TMX Canada, Solactive, Research Affiliates, NASDAQ OMB Global Indices, Morningstar, Rus-
sell Investments, Auspice eBeta Enhanced Indices, BNY Mellon Indices, CME Group/Dow Jones,
Barclays Capital Indices, Zacks Investment Research, Alphashares, Cohen & Steers and Sustainable
Wealth Management. None of these providers offer indexes compiling global innovation leader stocks
nor do they offer indexes that have a quantitative process to measure a company’s innovation.
Gavekal will continue to monitor the above mentioned landscape with the goal of provide accurate
and non-misleading information.
The Indexes are calculated and published by Solactive AG. Solactive AG uses its best efforts to en-
sure that the Indexes are calculated correctly. Irrespective of its obligations towards Gavekal Capital,
Solactive AG has no obligation to point out errors in the Indexes to third parties including but not lim-
ited to investors and/or financial intermediaries of the financial instrument. Neither publication of the
Indexes by Solactive AG nor the licensing of the Indexes or Indexes trademark for the purpose of use
in connection with the financial instrument constitutes a recommendation by Solactive AG to invest
capital in said financial instrument nor does it in any way represent an assurance or opinion of Solac-
tive AG with regard to any investment in any financial instrument.
For full information including any named holdings that may have been mentioned in the document as
well as additional policies and full disclosures on the Advisor, please visit our website gavekalcapi-
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