helsinki capital partners · helsinki capital partners . 2! ... greenbla , mohnish"pabrai,guy...

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HCP Quant Fund A Non-‐UCITS fund inves9ng in global small-‐ and mid-‐cap stocks

June 2015

HELSINKI CAPITAL PARTNERS

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§  First fully transparent fee-‐only asset manager in Finland, offering service for ins9tu9onal and private wealth clients, including ar9sts and professional athletes

§  Specialising in global mul9-‐strategy and value driven equity strategies

§  Asset management to be proud of. Founded in 2007, built and owned by HCP team.

§  Firm total AUM €68.9m (as of 30th of June 2015)

§  The company has 7 full 9me employees with 3 investment professionals with 31 years of combined investment experience

§  Fastest growing Asset Manager in Finland since founda9on

Helsinki Capital Partners (HCP)

22,3

20,7

15,9

10,0

Total AUM €68.9M

Black Focus Quant Managed Accounts

0 10 20 30 40 50 60 70 80

Firm Total AUM in M €

HCP Quant Fund Basics

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§  Focusing on Value inves9ng in small-‐cap stocks in both the developed and emerging markets using quan9ta9ve tools to aid in selec9ng companies with strong financials and low stock market valua9ons

§  Strategy launched 4th of October 2010 with current AUM of €14.6m (30st of June 2015)

§  Fund vehicle characteris9cs –  Maximum two investment with 20% weight, rest with maximum 10% weight

–  Quarterly liquid with a one month no9ce

–  Bloomberg 9cker: QUANT4U FH, ISIN Code: FI4000090451

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§  Value driven approach focusing on the fundamentals of companies –  Returns from pricing inefficiencies –  Buy when sufficiently below fair value as assessed by quant models that short lists and orders buy targets –  Holding period of 6 months

§  Mid-‐small market focus –  Under-‐researched and greater risk/return profile (where sufficient liquidity exists)

§  Quan9ta9ve approach to filtering down and ordering a buy list targets –  The algorithm looks through 9,960 listed companies to find a buy list of ~ 30 stocks with favorable

characteris9cs –  Data quality cross checks –  Discre9onary trading –  20% Stop loss on the company level –  2 X annual poreolio turnover

1. 2. 3. 4. Quant models filter small-‐cap universe for undervalued companies with strong fundamentals

Quant model further filters out companies with possible repor9ng manipula9ons

Model orders a buy list of 30 stocks. Trades to porUolio are discre9onary and equally weighted.

Ac9ve risk management with stop loss and primary holding period of 6 months

HCP Quant Fund Sources of Alpha

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What has worked in equity inves9ng? Successful investment strategies

What causes undervalua9on? Influencing factors

How to improve investment returns? U9liza9on of quan9ta9ve methods

”It’s a bumpy road ahead” Nothing is perfect

HCP Quant Fund Investment System

Small companies have returned across the world bejer than the market

Small-‐cap stocks

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Country Time Period Excess return (p.a.) Research

Australia 1985-‐1996 5.9 % Liew & Vassalou (2000)

Austria 1978-‐1995 5.8 % Heston et al. (1999)

Belgium 1978-‐1995 -‐1.2 % Heston et al. (1999)

Canada 1960-‐2001 5.0 % L’Her et al. (2003)

Denmark 1978-‐1995 3.5 % Heston et al. (1999)

France 1978-‐1995 3.1 % Heston et al. (1999)

Germany 1978-‐1995 1.3 % Heston et al. (1999)

Greece 1982-‐1997 0.5 % Rouwenhorst (1999)

Ireland 1977-‐1986 5.6 % Coghlan (1988)

Source: ”Interna9onal Evidence on the Size Effect” (Savina Rizova, 2006)

Small-‐cap stocks

Country Time Period Excess Return (p.a.) Research

Italy 1978-‐1995 -‐0.2 % Heston et al. (1999)

Japan 1975-‐1994 4.2 % Hawanini & Keim (2000)

The Netherlands 1978-‐1995 3.5 % Heston et al. (1999)

New-‐Zealand 1977-‐1984 6.1 % Gillan (1990)

Norway 1978-‐1995 5.6 % Heston et al. (1999)

Portugal 1989-‐1997 -‐8.9 % Rouwenhorst (1999)

Singapore 1975-‐1985 4.9 % Wong et al. (1990)

Spain 1978-‐1995 9.0 % Heston et al. (1999)

Sweden 1978-‐1995 4.1 % Heston et al. (1999)

Switzerland 1978-‐1995 1.7 % Heston et al. (1999)

UK 1955-‐2001 1.8 % Dimson et al. (2003)

7 Source: ”Interna9onal Evidence on the Size Effect” (Savina Rizova, 2006)

Small-‐cap stocks

Country Time Period Excess Return (p.a.) Research

Argen9na 1982-‐1997 46.1 % Rouwenhorst (1999)

Brazil 1982-‐1997 15.8 % Rouwenhorst (1999)

Chile 1982-‐1997 3.7 % Rouwenhorst (1999)

China 1994-‐2001 11.1 % Drew et al. (2003)

Korea 1982-‐1997 3.8 % Rouwenhorst (1999)

Mexico 1982-‐1997 28.7 % Rouwenhorst (1999)

Taiwan 1986-‐1997 8.2 % Rouwenhorst (1999)

India 1982-‐1997 -‐4.2 % Rouwenhorst (1999)

Turkey 1989-‐1997 8.6 % Rouwenhorst (1999)

8 Source: ”Interna9onal Evidence on the Size Effect” (Savina Rizova, 2006)

Small-‐cap stocks

Focusing on small-‐cap stocks has worked well in both the developed and emerging markets

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Market Time Period Excess return (p.a) Research

Europe 1978-‐1995 3.5 % Heston et al. (1999)

USA 1962-‐1989 ~8 % Fama et al. (1992)

Developed markets 1986-‐1996 11.2 % Bauman et al. (1998)

Emerging markets 1982-‐1997 8.2 % Rouwenhorst (1999)

Sources: ”International Evidence on the Size Effect” (Savina Rizova, 2006), ”Is Size Dead? A review of the Size Effect in Equity Returns” (Mathijs A. Van Dijk, 2011)

Value inves9ng

§  Value inves9ng has outperformed the market in the long-‐run §  Several of the world’s most successful investors are value investors: Warren Buffej, Joel

Greenblaj, Mohnish Pabrai, Guy Spier etc. §  Value inves9ng refers to inves9ng in undervalued companies §  Value investor values companies using different ra9os, for example:

–  P/E (Price to Earnings,– market price per share divided by earnings per share) –  P/B (Price to Book value, market price divided by book value of equity) –  P/S (Price to Sales, market price divided by sales) –  Dividend yield (dividends divided by market price) –  FCF (Free Cash Flow)

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Value inves9ng

Value stocks outperformed growth stocks in Europe by 10 % p.a. between years 1985-‐2007

11 Source: ”Value Investing: Tools and Techniques for Intelligent Investment” (James Montier, 2009)

Value inves9ng

In the developed markets, value stocks have returned 12 % p.a. bejer than growth stocks between 1985 and 2007*


* In the sample, stocks have been arranged in an order based on their mul9ples: P/E, P/B, P/CF, P/S and EBIT/EV. A stock’s ranking in each mul9ple is added together and stocks are put in order based on the total sums. Value stocks are the cheapest 20 % of the stocks based on their summa9on value and growth stocks are the expensive 20 %. The minimum market capitaliza9on in the sample is 250 million dollars. Returns are calculated in U.S. Dollars.

Value inves9ng

In the emerging markets, value stocks have returned over 18 % p.a. more than growth stocks and 11 % p.a. more than the market between 1985 and 2007


* In the sample, stocks have been arranged in an order based on their multiples: P/E, P/B, P/CF, P/S ja EBIT/EV. A stock’s ranking in each multiple is added together and stocks are put in order based on the total sums. Value stocks are the cheapest 20 % of the stocks based on their summation value and growth stocks are the expensive 20 %. The minimum market capitalization in the sample is 250 million dollars. Returns are calculated in U.S. Dollars.

Value inves9ng

§  A poreolio consis9ng of stocks from the cheapest fiuh in developed and emerging markets returned on average 18 % p.a.*

§  Growth stocks returned less than 3 % p.a.* §  The excess return of value stocks over growth stocks globally was 15 % p.a.*


* In the sample, stocks have been arranged in an order based on their mul9ples: P/E, P/B, P/CF, P/S ja EBIT/EV. A stock’s ranking in each mul9ple is added together and stocks are put in order based on the total sums. Value stocks are the cheapest 20 % of the stocks based on their summa9on value and growth stocks are the expensive 20 %. The minimum market capitaliza9on in the sample is 250 million dollars. Returns are calculated in U.S. Dollars.

Value inves9ng

§  The fall of the Japanese stock market has been record long §  Between 1990 and 2011 the Japanese stock market dropped -‐62.21 % §  Value inves9ng generated excellent returns even in this difficult market environment §  Low P/E ra9o stocks returned 16.9 % p.a. between 1990 and 2011 §  Low P/B ra9o stocks on the other hand returned 10.6 % p.a. §  Between 1975 and 2011 low P/E ra9o stocks returned 23.6 % p.a. and low P/B ra9o stocks

19.3 % p.a.

15 Source: ”Performance of Value Investing Strategies in Japan’s Stock Market” (Hong Kong University of Science and Technology Value Partners Center for Investing, 2013)

Value inves9ng

16 Source: ”Performance of Value Investing Strategies in Japan’s Stock Market” (Hong Kong University of Science and Technology Value Partners Center for Investing, 2013)

Value inves9ng

Regardless of good returns, risk in value inves9ng has been lower


Value inves9ng

During market distress, value stocks have lost less of their value than the market or growth stocks


Value inves9ng

§  Between 1975 and 2007, value stocks in the U.S. returned 13 % p.a. during a recession and

22 % p.a. during a expansion §  Growth stocks returned 5 % p.a. during a recession and 17 % p.a. during a expansion §  Value stocks outperformed growth stocks in both recession and expansion


Small-‐cap value inves9ng

§  Small-‐cap and value inves9ng have performed well in the past §  How would value inves9ng in small-‐cap stocks have worked in the past? §  In the U.S. small-‐cap value stocks returned 5.28 – 8.40 % p.a. more than growth stocks

between 1979 and 1997

20 Source: ”The Value Premium for Small-‐Capitaliza9on Stocks” (Manjeet S. Dhaj, Yong H. Kim & Sandip Mukherji, 1999)


§  The risk (vola9lity) of small-‐cap value stocks was lower than the risk of growth stocks

§  The amount of excess returns generated varied depending on the ra9o used (e.g. P/E or P/S)

§  Using different ra9os simultaneously improved the risk-‐adjusted returns related to individual ra9os

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Between 1926-‐2004 small-‐cap value stocks in the U.S. returned 15.9 % p.a. while large-‐cap growth stocks returned 9.26 % p.a.*

22 Source: ”Value Inves9ng: Tools and Techniques for Intelligent Investment” (James Mon9er, 2009), * The New York Times used as the source of the graph Eugene S. Fama ja Kenneth R. French’s research and alloca9on of stocks.


§  Small-‐cap value stocks have returned bejer than large bluechip stocks up to today §  Russell 2000 Value ETF, which began trading in 2000 invests in small-‐cap value stocks in the

U.S.

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RUJ (blue) = Russell 2000 Value, SPY (red) = S&P 500. Source: Google Finance


§  In the developed countries, one of the most recent study is the research done by Nobel Prize Laureate Eugene Fama and Kenneth French ”Size, Value, and Momentum in Interna9onal Stock Returns”, which was released in 2012

§  Fama and French state the following: ”Our new evidence centers on how interna'onal value and momentum returns vary with firm size. Except for Japan, value premiums are larger for small stocks.”

§  According to the studies, inves9ng in small-‐cap value stocks has worked globally §  Using several different ra9os simultaneously has improved the chances to generate even

higher returns when inves9ng in small-‐cap value stocks

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25 Source: ”What Works on Wall Street” (James P. O’Shaughnessy, 2005)


§  Similar results can be found in Europe. For example, below is a table of returns between 1991 and 2011, where P/B ra9o was used as a primary factor among other ra9os

§  STOXX Europe 600 returns in the same period were 0.91 % p.a.

26 Source: ”Quan9ta9ve Value Inves9ng In Europe: What Works for Achieving Alpha” (Philip Vanstraceele & Tim du Toit, 2012)

Focused poreolio

§  Diversifica9on lowers poreolio’s total risk §  In an equity poreolio, diversifying to approximately 30 stocks gives already a close to

maximum benefit from diversifica9on

27 Source: ”Value Inves9ng: Tools and Techniques for Intelligent Investment” (James Mon9er, 2009)

Focused poreolio

§  Warren Buffej: ”DiversificaAon is protecAon against ignorance. It makes liEle sense if you know what you are doing.”

§  Worldwide, the cheapest fiuh of stocks returned 18 % p.a. between 1985 and 2007* §  That group includes 1800 stocks §  That kind of diversifica9on is too broad when considering the benefits received from

diversifica9on §  How would a poreolio invested in the 30 cheapest stocks in the world have performed?

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* In the sample, stocks have been arranged in an order based on their mul9ples: P/E, P/B, P/CF, P/S ja EBIT/EV. A stock’s ranking in each mul9ple is added together and stocks are put in order based on the total sums. Value stocks are the cheapest 20 % of the stocks based on their summa9on value and growth stocks are the expensive 20 %. The minimum market capitaliza9on in the sample is 250 million dollars. Returns are calculated in U.S. Dollars.

Focused poreolio

The cheapest 30 stocks worldwide returned 25 % p.a.


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Majority of companies are small-‐cap companies

There are a lot more small-‐cap stocks than large-‐cap stocks

31 Sources: Montanaro, FactSet. As of 3.1.2012.

Majority of companies are small-‐cap companies

32 Source: ”Analyzing the Analysts: A Survey of the State of Wall Street Equity Research 10 Years after the Global Settlement” (Timothy J. Keating, 2013)

Companies in the U.S. stock market divided into groups based on market capitaliza9on. Apple’s market cap is more than 2.6 9mes the combined value of all 2021 micro-‐cap stocks

Minimal coverage

Even though there are a lot more small-‐cap stocks, fewer analysts cover them

33 Source: ”The herd vs the reward, or in praise of contratian investing” (Stacy-Marie Ishmael, Financial Times, 4.11.2009)

Minimal coverage

§  The U.S. Stock market is considered to be the world’s most efficient §  S9ll, at the end of 2012 almost 29 % of listed companies didn’t have an analyst covering

them or the coverage was inadequate*

34 Source: ”Analyzing the Analysts: A Survey of the State of Wall Street Equity Research 10 Years auer the Global Sejlement” (Timothy J. Kea9ng, 2013)

* The tables last column represents how big percentage of the companies doesn’t have analyst coverage at all. For example, 30 % of 74-‐248 million dollar market cap companies weren’t covered by an analyst.

Minimal coverage

§  The analyst coverage of small companies is minimal, which makes it possible for small-‐cap stocks to be trading at a different level than the companies’ true values

§  This makes it easier for investors to find more undervalued stocks among small-‐cap value stocks

§  Analyzing small companies would require more resources §  Analyzing them isn’t profitable for analysts, because small-‐cap stocks cannot be offered to as

many investors as large-‐cap stocks §  Therefore small-‐cap stocks will remain less covered in the future §  This also leads to undervalued small-‐cap stocks returning more than the market in the future

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What color are A and B squares?

36 The copyright holder of this work allows anyone to use it for any purpose including unrestricted redistribu9on, commercial use, and modifica9on.

Answer: They are both grey

37 The copyright holder of this work allows anyone to use it for any purpose including unrestricted redistribu9on, commercial use, and modifica9on.

Human decision-‐making process is flawed

§  In the previous example, our brain tells us that square B is white => the informa9on is incorrect, even though our brain is telling us the opposite

§  A computer would have answered the ques9on correctly => the RGB values of both of the squares are the same

§  The human ability to process informa9on is limited and decision-‐making process is imperfect §  In tradi9onal economics people are assumed the act ra9onally §  In reality, people ouen behave irra9onally

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§  An example of a coin flipping game: –  Heads: you win 10 euros –  Tails: you lose 10 euros

§  Would you play the game? §  Let’s assume that you have just won 100 euros. Would you now play the game? §  What if you had just lost. Would that change the game’s ajrac9veness? §  Majority of people are ready to alter their behaviour based on previous results §  This is despite the fact that each round’s expected value is the same §  Completely ra9onal person would act the same way every 9me

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§  In the past few years behavioral finance has gained popularity §  Behavioral finance studies unsystema9c behavior in inves9ng §  Research findings explain why investors don’t act op9mally

–  A loss hurts twice as much as an equal size gain –  The holding period of a profitable investment is shorter than the holding period of an

unprofitable –  Anchoring to a paid price of a stock –  Investors are ac9ve in a bull market (high risk) and paralyzed in a bear market (low risk) –  Etc.

§  Knowing investment psychology is a step forward, but alone it is not enough to change investor behaviour => causes of behaviour are rooted deeper

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§  The same reasons explain why it is difficult for investors to take advantage of profitable investment prac9ces

§  The most profitable investments are ouen ”ugly” and ”doomed” companies §  It is easier to invest in a company with a good ”story” behind it as other investors are excited

about the company as well §  Due to herd behaviour, it is difficult to act against the general view => it is easier to be wrong

in a group rather than alone §  Sta9s9cs show that the bright futures of many growth companies are shajered => investors

overpay for future expecta9ons

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§  On the other hand, the distressed 9mes of ”ugly” companies don’t last as long as investors expect => future expecta9ons are overly pessimis9c and therefore stock prices are too low

§  The idea of u9lizing quan9ta9ve investment methods is to eliminate the human error from the investment process preven9ng good returns

§  Analyzing tens of thousands of small-‐cap companies is also impossible by using a tradi9onal way of analyzing each company separately

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Quan9ta9ve methods

§  An investor is his own worst enemy => people are prisoners of their feelings §  In several situa9ons a simple computer model works more reliably and more accurately than

a person with a subjec9ve view §  As an example; there is a standardized test called MMPI-‐test (Minnesota Mul9phasic

Personality Inventory) to iden9fy neuro9cism and psycho9cism §  Iden9fying them is important because they are treated differently §  In 1968 Lewis Goldberg went through 1,000 pa9ents’ MMPI test results and formed a model

based on the data, which gave a correct diagnosis 70 % of the 9me §  Goldberg then asked psychologists to diagnose the same pa9ents

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Quan9ta9ve methods

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§  By using the model, the accuracy of diagnosis increased significantly §  S9ll, the accuracy of the diagnoses done by people was worse than the model’s §  There are similar results for iden9fying brain injuries and probability of repea9ng proba9on

viola9ons etc.

Source: ”Pain9ng by numbers: an ode to quant” (James Mon9er, 2006)

Quan9ta9ve methods

§  The model was clearly more accurate than even the best psychologist §  Next, Goldberg gave the model to the psychologists who could use the model to help their

own diagnoses

46 Source: ”Pain9ng by numbers: an ode to quant” (James Mon9er, 2006)

Quan9ta9ve methods

§  Grove et al. went through 136 different studies, which dealt with decision-‐making ranging from heart ajack diagnosis to evalua9ng job compa9bility

§  Only in eight of the studies the quan9ta9ve method lost to people §  The simililarity in all eight of these studies was that the quan9ta9ve methods didn’t have

access to the same informa9on as people did §  In every study where the quan9ta9ve method had access to the same informa9on as people,

quan9ta9ve method was bejer

47 Sources: ”Clinical versus mechanical predic9on: a meta-‐analysis (Grove, Zald, Lebow, Snitz & Nelson, 2000)” and ”Pain9ng by numbers: an ode to quant” (James Mon9er, 2006)

Quan9ta9ve methods

§  Quan9ta9ve models are useful and widely used: –  Airplane pilots’ check list before take off –  Weather forecasts –  Car insurance –  Etc.

§  Simply put, a quan9ta9ve method means using a computer to analyze a large amounts of data => peoples’ ability to process large amounts of data is weak

§  It is possible to screen the most poten9al stocks out of tens of thousands of stocks using quan9ta9ve methods => it is impossible for a person to go through the same number of stocks one by one when every quarter there’s new informa9on coming out

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§  Quan9ta9ve methods work the best when used to analyse small and medium size companies, because of lack of analyst coverage

§  So many professionals analyse large companies that quan9ta9ve methods don’t give a compe99ve advantage when analysing them

§  Quan9ta9ve value inves9ng strategy combines value inves9ng and efficient use of numerical informa9on

§  Companies’ annual and quarterly reports are numerical informa9on §  Tradi9onal ra9os used by value investors are well suited for automated data processing (P/E,

P/B, dividend yield, etc.) §  Quan9ta9ve methods enable the combining of different value inves9ng strategies

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§  Research shows that the mul9-‐factor models produce bejer risk-‐adjusted returns than when only one factor is used

§  Using quan9ta9ve methods can protect against fraudulent accoun9ng methods: –  Dechow F-‐score –  Altman Z-‐score –  Beneish M-‐score

§  For example Beneish M-‐score measures a company’s revenue growth, gross profit, accounts receivables and debt level

§  76 % of companies with an M-‐score below -‐2.22 used fraudulent accoun9ng methods §  Only 17.5 % of companies with fraudulent accoun9ng received an M-‐score above -‐2.22

50 Source: ”The Detec9on of Earnings Manipula9on” (Messod D. Beneish, 1999)

Quan9ta9ve value inves9ng strategy

§  Quan9ta9ve methods can be used to efficiently u9lize different value inves9ng strategies based on fundamental informa9on:

–  F-‐score (Piotroski) –  Magic Formula (Greenblaj) –  ERP5 (Vanstraceele & Allaeys) –  Net Current Asset Value (Benjamin Graham) –  Tiny Titans (O’Shaughnessy) –  Value Composite One (O’Shaughnessy)

§  For example, Piotroski F-‐score measures a company’s profitability, leverage and opera9on efficiency by u9lizing nine different factors (e.g. ROA, OCF, profit margin, long-‐term debt development)

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Nothing works all the 9me

§  One must keep in mind that even though a good investment strategy works most of the 9me, it doesn’t mean it will work all the 9me

§  Even the best investment methods encounter periods of lower returns, some9mes con9nuing for years

§  The fact that an investment strategy doesn’t always work makes it possible that it will work in the future

–  All investors would use the same strategy if one strategy would always work without a risk

–  During periods of poor returns, some investors abandon the strategy §  Ouen this occurs right before the strategy starts producing excess returns again

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70 % of poreolio managers who have generated excess returns of over 3 % p.a. have underperformed the markets for three years or longer


* Figure 14.6 uses a constructed universe where all the fund managers have 3 % alpha and 6 % tracking error. I then let the make-‐believe managers run money for 50 years. The chart illustrates the frequency of years of back-‐to-‐back underperformance. Around 70 % of the make-‐believe fund managers displayed 3 or more years of underperformance! –James Mon9er


Goyal and Wahal studied in 2005 the hiring and firing of four thousand poreolio managers managing funds for pension funds in the U.S. between 1993 and 2003*


* More accurate informa9on can be found in ”The Selec9on and Termina9on of Investment Management Firms by Plan Sponsors” (Amit Goyal & Sunil Waha, 2005)


§  A pension fund tradi9onally hired a poreolio manager with 14 % excess returns in the last three years

§  Poreolio managers who got fired due to poor performance generated 5 % excess returns in three years auer being fired

§  This indicates well how painful periods of underperformance are §  A period of underperformance has historically always been followed by outperformance,

which compensates for the previous poorer returns §  Even HCP Quant fund can be expected to have periods of underperformance, which have

historically been two to three years at their worst

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§  The best performing fund of the decade in the U.S. at the end of 2009 was CGM Focus Fund §  The fund returned on average 18.2 % p.a. over the decade §  A typical fund investor lost -‐11 % p.a. §  This is because investors invested in the fund auer a good period and took their money out

during a poor period §  Timing is extremely difficult, if not impossible §  According to different studies, investors who try to 9me the markets perform worse than

others §  Even the best fund doesn’t help the investor, if the investor cannot tolerate price

fluctua9ons

57 Source: ”Best Stock Fund of the Decade: CGM Focus” (Eleanor Laise, The Wall Street Journal, 31.12.2009)

Individual investor’s tax shield

§  HCP Quant fund’s investment strategy’s stock turnover ra9o is high §  A typical stock is held in the poreolio from six months to a year §  An individual investor paying taxes in Finland has a capital gains tax of 30/33 % §  The funds returns are tax-‐free §  In a fund-‐based investment strategy compounding effect is emphasized because the investor

pays taxes only when withdrawing money §  The same investment strategy is more profitable for a long term individual investor as a fund

than if invested individually

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Summary of HCP Quant

§  A non-‐UCITS fund inves9ng in small-‐ and mid-‐cap stocks §  Invests in global developed and emerging market stocks (Europe, USA, Asia, Australia) §  U9lizes quan9ta9ve investment methods in inves9ng, with a goal to avoid human limita9ons §  Focuses investments in a few dozen stocks analyzed to be the best of class §  Doesn’t use the typical market weighted poreolio weigh9ng §  Is built on a strong scien9fic research; uses proven successful investment strategies, which

can be assumed to perform well in the long run

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Poreolio manager’s realized returns

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Blue = HCP Quant strategy’s realized returns Red = S&P 500 Total Return index in euros Purple = MSCI ACWI SMID Value Total Return index in euros (global small-‐ and mid-‐cap value stock index, HCP Quant fund’s benchmark index) Green = S&P 350 Europe Total Return index in euros

HCP Quant Sta9s9cs (in EUR terms)

MONTHLY PERFORMANCE (AFTER ALL FEES AND COSTS)

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC YTD

2015 4.85% 3.20% 7.65% (0.43)% 4.08% (8.39)% 10.59%

2014 3.73% 9.60% 5.85% 3.08% (0.17)% (1.65)% (0.36)% 2.05% (5.55)% 1.42% 0.16% 3.18% 22.59%

2013 8.00% 9.43% 7.73% (1.81)% (1.15)% (7.73)% 3.05% (8.24)% 9.33% 7.43% 9.06% 0.27% 38.49%

2012 16.11% 9.82% (6.20)% 1.11% (10.11)% 3.49% 16.66% (0.75)% 0.10% (2.10)% 9.55% 5.08% 46.94%

2011 7.73% 0.87% 1.74% 1.97% 3.42% (4.67)% (6.00)% (11.41)% (10.77)% 10.17% (7.78)% 3.11% (13.48)%

2010 17.65% (4.73)% 18.10% 32.37%

ANNUALISED RETURN

STANDARD DEVIATION

SHARPE RATIO

SORTINO RATIO

31.0% 24.5% 1.1 2.1

Past performance is not indica9ve of future performance. HCP Quant strategy commenced opera9ons 4.10.2010. The strategy has been opera9ng under a mutual fund structure since 30.6.2014. The returns for the period 4.10.2010 – 30.6.2014 are gross returns (returns before HCP fees). The returns auer 30.6.2014 are net returns (returns auer all fees). The return numbers are calculated in euros. Libor EUR 3-‐months was used to calculate the risk free rate. As at 30.6.2015

12 MONTH 6 MONTH 3 MONTH 1 MONTH

11.3% 10.6% (5.1)% (8.4)%

Independent Risk Management

§  Investment risk –  Poreolio manager controls risk in line with poreolio strategy –  HCP Investment commijee with two persons independent from the poreolio manager

controlls risk with self-‐standing methodology §  Maximum two investment with 20% weight, rest with maximum 10% weight §  Liquidity management for quarterly liquidity with a one month no9ce

§  Monitoring of counterparty risk

§  Monitoring of vola9lity on different 9mescales

§  Internal opera9onal risk –  Independent Prime Broker (SEB, Interac9ve Brokers) –  Auditor given real 9me access to auditable transac9on and poreolio data. Regular

checks into NAV, at a minimum every two months (EY). Annual audit. –  Custodian given real 9me access to auditable transac9on and poreolio data. Regular

checks into NAV, at a minimum one overall audit annually (SEB)

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The Fund Management Team Pasi Havia – PorUolio Manager of Quant Fund §  Pasi joined Helsinki Capital Partners in 2013 where he manages the quan9ta9ve strategy fund, HCP

Quant. Previously he hold senior souware engineering roles in companies such as Nokia, Mobilespot and Travelbuddy Ltd and started inves9ng through self-‐built quan9ta9ve investment models in 2010.

§  Pasi has also published a book about personal wealth management and writes widely read investment blog in Finland. He studied Informa9on Processing Science and Finance at the University of Oulu, Finland

§  He has 10 years of investment experience. §  Poreolio manager has personally used the strategy since 4.10.2010 §  Realized returns net of transac9on fees is +205 % (as of 30.6.2015) §  “I had worked over a decade with soGware development before I became part of the HCP team. Joining

both the soGware and invesAng industry happened because of a genuine and deep interest in the subjects. I found my way to HCP when I realised how my and the company’s values are aligned. I respect HCP’s way to operate openly and honestly; things are said as they truly are. I want to be part of building an asset management company that puts the customers’ needs first”.

Phone +358 9 68988 472 [email protected]

Kaapelitehdas, Tallberginkatu 1 D 5th floor, 00180 Helsinki, Finland Tel +358 9 689 88 481 Fax +358 9 689 88 475 hjp://www.helsinkicapitalpartners.fi/

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Service Providers

§  Legal structure Alterna9ve Investment Fund (AIF) §  Domicile Finland §  Fund Manager Helsinki Capital Partners Fund Management Company Ltd §  Custodian Skandinaviska Enskilda Banken (SEB), Finland §  Auditor Ernst & Young, Finland (EY) §  Legal Advisor EY, Finland §  Fund Administrator Real Time Access by EY and SEB into auditable transac9on and

poreolio data of HCP Fund administra9on. Regular, at minimum bi-‐monthly, checks into NAV by EY. Annual audit (SEB and EY).

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Asset management to be proud of.

June 2015

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