A COMPARATIVE ANALYSIS OF CAPM AND FAMA-FRENCH MODEL

APPLICATION IN THE BALTIC STOCK MARKET

A Thesis

Presented to the Faculty

of Finance Programme at

ISM University of Management and Economics

in Partial Fulfillment of the Requirements for the Degree of

Bachelor of Finance

by

Jakub Gustav Vujčik

Advised by

Dokt. Dmitrij Katkov

December 2020

Vilnius

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 2

Summary

Vujčik, J.G. A Comparative Analysis of CAPM and Fama-French Model Application in the Baltic Stock Market.

Final Bachelor Thesis. Finance studies: Vilnius, ISM University of Management and

Economics, 2020.

This Bachelor thesis investigates two asset pricing models' applicability in the Baltic stock market: CAPM and the Fama-French three-factor model. The research aims to find and compare the relationship between the stock returns and market risk premium, size, and value factors under the respective models. The Baltic stock market presents many challenges in estimating a company's expected returns due to inefficiency and the lack of comparable peers. Therefore, one of the main objectives is to determine whether any particular methodology has a higher explanatory power. The study analyses the data of companies currently listed on the Nasdaq Baltic stock exchange with at least three years of operating history. The analysis uses the average 36-months returns of every company to estimate the betas of each factor. The adjusted R-squared coefficient of determination serves as a proxy for measuring expected returns' replication by the models. The research produces a mixed outcome. Although the adjusted R-squared measures are higher in the Fama-French three- factor model, both asset pricing methodologies have significant statistical data issues related to residuals' normality, heteroskedasticity, and misspecification. The findings imply that investors should apply specific pricing method after modifying an individual company's data.

Keywords: Capital Asset Pricing model, Fama-French three-factor model, coefficient of determination, normality of residuals, heteroskedasticity.

Total word count: 11 949

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Table of Contents

Introduction ...... 7

1. Situation analysis ...... 9

1.1. Economic overview of Baltic countries ...... 9

1.2. Main economic indicators ...... 10

1.3. Household savings and financial literacy ...... 12

1.4. Interest rates ...... 14

1.5. Overview of Baltic stock market ...... 16

1.6. Financial instruments and indexes in the Baltic financial market ...... 18

2. Literature review ...... 21

2.1. Modern portfolio theory ...... 21

2.2. Assumptions of Markowitz Portfolio selection ...... 21

2.3. Risk and Return in Markowitz Portfolio selection ...... 22

2.4. Markowitz portfolio with three assets ...... 23

2.5. Capital Asset Pricing Model ...... 25

2.6. Theory of CAPM ...... 25

2.7. Criticism and testing of CAPM ...... 28

2.8. Fama and French Three-Factor model ...... 31

2.9. Research methodology. CAPM ...... 33

2.10. Research methodology. Fama-French Three factor model ...... 34

3. Empirical Research ...... 36

3.1. Empirical Research description ...... 36

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 4

3.2. Data and descriptive statistics of monthly returns ...... 36

3.3. OMX Baltic All-Share Descriptive statistics and normality ...... 39

3.4. Risk-free rate ...... 40

3.5. Explanatory power of CAPM ...... 41

3.6. Statistical issues of CAPM model...... 43

3.7. Sector Beta CAPM ...... 45

3.8. Fama-French regression ...... 48

3.9. Fama-French regression results ...... 49

3.10. Statistical issues of the Fama-French model ...... 50

3.11. Applicability, limitations, and recommendations for future research ...... 51

Conclusions ...... 54

List of References ...... 57

Appendices ...... 61

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 5

List of Figures

Figure 1. Euro interest rate yield curve at the end of 2019 ...... 16

Figure 2. Baltic Stock market capitalization ...... 17

Figure 3. Attainable E, V combinations ...... 23

Figure 4. Attainable set of portfolios ...... 25

Figure 5. Investment opportunities ...... 26

Figure 6. Median stock returns and standard deviations ...... 37

Figure 7. Distribution of median returns and standard deviations by lists ...... 38

Figure 8. Summary statistics and normality of OMX Baltic All-Share monthly returns ...... 39

Figure 9. Historical returns of OMX Tallinn, OMX Riga, and OMX Vilnius 2010-2020 ...... 40

Figure 10. The weighted Baltic risk-free rate 2010-2020 ...... 41

Figure 11. Stock vs. market returns 2010-2020 ...... 42

Figure 12. Frequency of CAPM market risk premium coefficients ...... 43

Figure 13. Autocorrelation and Partial Autocorrelation functions for market returns ...... 44

Figure 14. Frequency of CAPM market risk premium coefficients with sector beta ...... 46

Figure 15. Frequency of adjusted R-squared in the Fama-French regression ...... 50

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 6

List of Tables

Table 1. Export partners of Baltic countries ...... 9

Table 2. Import partners of Baltic countries ...... 10

Table 3. GDP of Baltic states. Chain linked volumes ...... 11

Table 4. Correlation of macroeconomic variables between the Baltic countries and Europe 11

Table 5. Unemployment rates of Baltic states (%) ...... 12

Table 6. Disposable annual income in Baltic states (Eur) ...... 12

Table 7. Growth of harmonized consumer price index in Baltic states (Eur) ...... 12

Table 8. Households' savings rates in Baltic states (%) ...... 13

Table 9. EMU convergence criterion rates in Latvia and Lithuania (%) ...... 15

Table 10. Baltic Stock market top 10 market capitalization companies ...... 17

Table 11. Companies by sectors ...... 45

Table 12. Sector beta regression results (median) ...... 47

Table 13. Sector beta regression results (mean) ...... 47

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 7

Introduction

Since the groundbreaking work of Harry Markowitz, introducing Modern portfolio theory or mean-variance analysis in 1952, many researchers have tried to create an efficient model that could be applied to calculate the required rate of return. The Capital Asset Pricing

Model (CAPM) developed by Jack Treynor, William F. Sharpe, John Lintner, and Jan

Mossin separately offered the investors a simple yet powerful tool to determine the excess return of the asset. The academic community appraised the simplicity of the model, and practitioners have been using it ever since.

The basis of CAPM is a single factor – systematic risk that explains the overall market volatility. The estimation of market risk, according to the model, is the same for all the companies. Yet elegant, the model does not include any market anomalies like the size or the value of the company. Eugene F. Fama and Kenneth R. French claimed that historically, there are two portfolios with positive alpha – the return higher than predicted by Capital Asset

Pricing Model. Small-cap stocks tended to outperform Large-cap stocks, and high book-to- market stocks outperformed high book-to-market stocks. These findings led financiers to develop a new three-factor model, also known as the Fama-French model, which includes the company's size and value premiums to estimate expected returns.

There has been a lot of research exploring both models; however, most of them focus on developed and efficient markets. This bachelor thesis will test Fama-French versus

CAPM's application in the Baltic Stock market and analyze the implications that appropriate tool selection has on the investors' decisions.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 8

Topic relevance: Research of the models would provide practical benefits for investors estimating expected returns in the Baltic Stock market. The ability to select an appropriate method would help investors make more informed investment decisions.

Problem: Which asset pricing model is better to apply in order to explain the expected return in the Baltic stock market?

Aim: Estimate and compare the statistical significance of expected returns using

Fama-French and CAPM models in the Baltic stock market.

Objectives:

1. Establish the importance of the Baltic Stock market and investigate its

development.

2. Outline the main challenges in estimating the expected returns of company stocks.

3. Compare the arguments for and against the usage of CAPM and Fama-French

methods by analyzing theoretical literature.

4. Apply an empirical model for the estimation of expected returns using CAPM and

Fama-French models.

5. Investigate the results provided by both methods and perform statistical analysis to

find the best approach.

6. Test the potential implications of findings for investors' security selection.

Research methods: The calculation of companies' expected returns in Baltic Stock markets will include CAPM and Fama-French models. Monthly excess return estimates will be regressed against market risk, size, and value premiums to determine which model is more efficient in explaining the companies' stock return variability in the Baltic stock market.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 9

Situation analysis

1.1. Economic overview of Baltic countries

A short economic overview of the countries is necessary to provide an understanding of the selected distinction. Moreover, the economic environment analysis is the first step of the top-down equity analysis approach and is a crucial part of investor decision making.

According to the European Commission (Poissonnier 2017), the Baltic states share similar economic and political features. The countries have common similarities in terms of development goals, geography, and size. Even though some could argue that Baltic states are a single economic area, the asymmetries split them apart. Having strong links with

Scandinavian countries, Estonia's exposure to international growth is the greatest. For example, the primary export partners are Finland and Sweden. According to the World

Integrated Trade Solution (WITS), approximately 30% of all Estonian exports consist of capital goods.

On the contrary, Lithuania and Latvia have connections by being more significant trading partners to one another. Even though Lithuanian imports and exports are almost as big as the other two countries combined, it focuses more on consumer goods, with only 21% of total trading volumes being in capital goods. Latvia, although smaller in scale, follows similar trade patterns as Lithuania.

Table 1. Export partners of Baltic countries Exports Estonia Lithuania Latvia No. 1 Finland (15%) Russian Federation (14%) Lithuania (17%) No. 2 Sweden (10%) Latvia (10%) Estonia (11%) No. 3 Latvia (9%) Poland (8%) Russian Federation (9%) No. 4 Russian Federation (8%) Germany (7%) Sweden (7%) No. 5 United States (7%) United States (5%) Germany (7%) Source: World Integrated Trade Solution

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Table 2. Import partners of Baltic countries

Imports Estonia Lithuania Latvia No. 1 Germany (10%) Russian Federation (14%) Lithuania (17%) No. 2 Russian Federation (10%) Germany (12%) Germany (11%) No. 3 Finland (9%) Poland (12%) Poland (9%) No. 4 China (8%) Latvia (7%) Estonia (9%) No. 5 Lithuania (6%) Netherlands (5%) Russian Federation (8%) Source: World Integrated Trade Solution

Since the Baltic states have similar economic structures and still specialize in producing lower-tech goods, the external factors influencing output have an almost identical effect on the development. Moreover, in the Baltics, the banking sector and monetary policies are very similar. All three countries are part of the European Union and have Central banks monitoring the banking sector following the Maastricht criteria. They are responsible for holding and managing the foreign exchange reserves and taking part in defining the European

Community's mutual monetary policy. The dominance of Nordic banks characterizes the

Baltic's banking sector. Top banks Swedbank, SEB Bank, Luminor, and Danske Bank control a significant share of Baltic financial services provision. Arestis et al. (2001), in their research Financial Development and Economic Growth: The Role of Stock Markets, concluded that bank-based financial systems substantially impact economic growth.

Therefore, the dependency on Scandinavian banks and Baltics' analogical banking structure may drive the three countries' economies to move in the same direction, especially during liquidity shocks or when facing lending constraints.

1.2. Main economic indicators.

In terms of economic development after the financial crisis of 2008, Baltic states have been recovering rapidly with a real GDP growth from 3.31% to 3.85% CAGR (Compound

Annual Growth Rate) (Table 3). Countries had a very similar recovery process after experiencing a double-digit GDP decrease during global economic crumbling. The

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 11

correlation of annual real GDP growth (1996-2016) between Baltic states is equal from 87% to 91% (European Commission, 2017), proving the strong links or countries' similar traits.

Table 3. GDP of Baltic states. Chain linked volumes, index 2010 = 100

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Estonia 100 107.4 110.8 112.3 115.6 117.8 121.5 128.2 133.8 140.5 Latvia 100 106.5 111 113.6 114.8 119.4 122.2 126.2 131.3 134 Lithuania 100 106 110.1 114 118 120.4 123.5 128.7 133.4 138.7 Source: Eurostat

To expand, not only the GDP but also the other macroeconomic variables move in the same manner. According to AMECO data (2017), wages, unemployment, CPI, and private consumption have a strong correlation between countries (Table 4). The correlation of economic variables with European countries, including Russia, is much smaller than inside the Baltic region. The data implies that investments or growth of the country in the market should benefit the whole Baltic region and that states are similar in their economic nature.

Besides, Estonia, Latvia, and Lithuania have historically had near-identical recovery from recessions and possess similar economic policies.

Table 4. Correlation of macroeconomic variables between the Baltic countries and Europe (including Russia) 1996-2016 EE-LV EE-LT LV-LT EE-Europe LV-Europe LT-Europe Private Cons. 85 84 81 43 36 44 CPI 73 81 83 58 41 38 Unemployment 94 91 91 39 42 40 Wages 98 97 95 62 64 66 Source: AMECO

The unemployment rates of all three Baltic counties have been decreasing since 2010

(Table 5). The increasing employment consequently should boost the average households' disposable income, thus creating a higher demand for financial securities as a form of saving.

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Table 5. Unemployment rates of Baltic states (%)

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Estonia 16.7 12.3 10 8.6 7.4 6.2 6.8 5.8 5.4 4.4 Latvia 19.5 16.2 15 11.9 10.8 9.9 9.6 8.7 7.4 6.3 Lithuania 17.8 15.4 13.4 11.8 10.7 9.1 7.9 7.1 6.2 6.3 Source: Eurostat

The data on annual average net earnings confirms the assumption on disposable income. The average income from 2010 to 2018 has grown equally by approximately 42% in all three

Baltic countries (Table 6). In comparison, consumer prices in Estonia, Latvia, and Lithuania have grown by 23%, 14%, and 16%, respectively, during the same period. With increased disposable income, households have an opportunity to either spend their earnings or save.

Spending transforms into rising consumption, which accordingly boosts the stock market's overall value as the demand for products and services increases. Moreover, consumer spending contributes to most of the Gross domestic product.

Table 6. Disposable annual income per person earning the average wage in Baltic states (Eur)

2010 2011 2012 2013 2014 2015 2016 2017 2018

Estonia 10 820 10 898 11 339 11 928 12 503 13 237 14 155 14 984 15 272 Latvia 7 253 7 327 7 403 7 673 8 110 8 786 9 482 10 090 10 357 Lithuania 8 391 8 263 8 448 8 835 9 356 9 986 10 745 11 480 11 920 Source: Eurostat

Table 7. Growth of harmonized consumer price index in Baltic states (Eur)

2011 2012 2013 2014 2015 2016 2017 2018 2019 Estonia 5.1 4.2 3.2 0.5 0.1 0.8 3.7 3.4 2.3 Latvia 4.2 2.3 0.0 0.7 0.2 0.1 2.9 2.6 2.7 Lithuania 4.1 3.2 1.2 0.2 -0.7 0.7 3.7 2.5 2.2 Source: Eurostat

1.3. Household savings and financial literacy

The option to save transforms into the shift of funds into more assets, including financial instruments. Following the assumption, due to the decreasing unemployment, rising disposable income, and economic output, the demand for stocks in the Baltic stock markets should increase, raising the overall market's value and capitalization. However, as seen in

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 13

Table 8, it is untrue. According to OECD data, the saving rate of households has been fluctuating for the last nine years. Although Estonian households' savings currently ranks in

9th place globally, with a rate of 7.5% (OECD, 2020), it is not the case for other Baltic countries. The savings rate in Latvia and Lithuania has been negative for a significant period meaning that the households were spending even more than earning and decreasing their net wealth.

Table 8. Households' savings rates in Baltic states (%)

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Estonia 3.6 4.6 0.7 2.5 4.1 5.7 2.7 5.8 6.2 9.6 Latvia -1.4 -14.0 -13.5 -14.5 -8.7 -4.6 -3.0 -3.3 -1.3 -3.0 Lithuania 4.4 0.6 -1.0 -1.6 -3.7 -3.6 -1.0 -3.7 -3.6 0.6 Source: OECD

The influence of weak financial literacy could be the cause of such savings rates.

According to the OECD's Programme for International Student Assessment (PISA), students' financial literacy scores in Lithuania and Latvia were 498 and 501, respectively. Estonia managed to score a rate of 547, much higher than the sample's average of 504. In terms of the general population, the European Consumer Payment Report (2019) has shown that Lithuania and Latvia have ranked one of the worst countries in financial literacy. Only 50% of respondents could match the basic financial terms with their definitions. The research states that low financial literacy "is impacting consumers' propensity to save" and plan for the future (ibid.). Although the study shows that Estonia also ranks at the bottom in financial literacy, its residents have a habit of saving for unexpected expenses. According to Latvian pollster SKDS (2020), the average Estonian resident has six months' worth of savings but is more likely to see themselves as a subject of potential material deprivation than Latvians or

Lithuanians.

According to Bernheim et al. (2001), households with at least a little exposure to financial education in high schools were much more likely to save later in life. Such

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 14

individuals had a better assessment of their abilities to make investment decisions and appreciated financial advice. Moreover, Van Rooij, Lusardi, and Alessie (2007) have successfully documented that poor household's financial knowledge significantly hinders participation in the stock market. The main takeaway of the research is that even a simple experience, such as the ability to do calculations, impacts stock market participation.

As stated by the research done by central banks of Baltic countries (Lietuvos Bankas

2020, Latvijas Banka 2018, Eesti Pank 2016), the proportion of the households' financial wealth in more advanced financial securities (bonds, mutual funds, and stocks) is insignificant. Advanced financial assets represent only 5.9%, 9.8%, and 5.4% of households' total financial wealth in Estonia, Latvia, and Lithuania. Moreover, approximately 90% of households' total wealth in these countries consists of real assets rather than financial.

1.4. Interest rates

One of the main factors influencing investment decisions is interest rates. It refers to the cost of using someone else's money. In other words, the interest rate is the amount a lender requires for the use of the asset, usually expressed in the percentages. As the interest rates increase, the cost of borrowing also rises, which harms business spending and further economic development. Consequently, in a higher interest rate environment, the stock market value should grow slower.

The interest rates in Estonia, Latvia, and Lithuania have dropped significantly since

2010. Because Estonia has adopted the euro in 2011, it also implemented its key ECB interest rates before the date. Therefore, the country's interest rates had dropped from 0.25% in 2010 to -0.4% in 2019. In terms of Latvia and Lithuania, rates have been decreasing during the same period. The most noticeable difference would be in 2015, generally due to the countries' adopted euro and the new European monetary policy.

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Table 9. EMU convergence criterion rates in Latvia and Lithuania (%)

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Latvia 10.34 5.91 4.57 3.34 2.51 0.96 0.53 0.83 0.9 0.34 Lithuania 5.57 5.16 4.83 3.83 2.79 1.38 0.9 0.31 0.31 0.31 Source: Eurostat

Since March of 2015, the European Central Bank has started to perform quantitative easing as one of its non-standard monetary policy measures in terms of interest rates. The

Bank's goal is to foster the economy and achieve inflation levels close to 2%. Such a policy does decrease the interest rates in the market, offering a cheaper source of capital. Therefore, companies can increase the value of the shareholder's equity more rapidly. Still, at the same time, it may cause difficulties for the banks over a more extended time. Although banks' balance sheets strengthen due to driven bond prices, lending-deposit spread falls, decreasing the interest income from newly issued loans. Such a change might reduce banks' incentive to lend. It could have a detrimental effect on the Baltic economy because bank financing is the most prominent way of raising capital.

In terms of bonds, artificially low interest rates from quantitative easing might push the asset's price too much. Investors, especially the buy-side, such as pension funds, may underperform and start focusing on equity investments, and due to low interest rates, holding funds as deposits becomes unattractive. With only a few higher coupon rate corporate bond issues in the Baltic market per year and government bond yields near zero, the possibility of equity investments becoming more popular is increasing.

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Figure 1. Euro interest rate yield curve at the end of 2019

Source: Eurostat

1.5. Overview of Baltic stock market

Baltic financial markets have a comparably short history. Although the establishment of Tallinn, Riga, and Vilnius stock exchanges was in 1920, 1926, and 1937, they ceased to operate at the beginning of the Second World War (Šarkauskytė, 2012). After regaining independence, Stock Exchanges have reopened, and the formation of a joint list of the three exchanges began. By the end of 2004, the Baltic stock markets became part of OMX Group resulting in cheaper transactions and higher efficiency for traders. After the reopening, the establishment of public laws took a lot of time. However, all of this was possible when in

2010, NASDAQ merged with OMX Group, creating unilateral trading practices worldwide.

The Baltic Regulated Market is composed of three different lists: The Baltic Main List consisting of companies with a market capitalization of more than EUR 4 million, 25% or

EUR 10 million free float, and at least three years of operating history; Baltic Secondary list

– Market cap of at least EUR 1 million without any free float requirements and at least two years of history; and First North Baltic – and alternative marketplace with no prerequisites.

At the time of writing, there are thirty-three companies on the Baltic Main list, twenty-eight companies on the Baltic Secondary list, and seven companies on the First North Baltic list.

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At the end of 2019, the combined annual market capitalization was just above EUR

7.3 billion. The measure was far below the market capitalization peak in 2006 when it approached EUR 14 billion. The Baltic stock market capitalization has been increasing since

2011. At the end of 2019, the ten biggest companies constituted more than 60% of the total market capitalization (Table 10). However, the trade volumes have not been growing even though the stock returns have been high throughout the years. The before mentioned lack of financial literacy and savings could explain such a phenomenon.

Figure 2. Baltic Stock market capitalization

Source: Nasdaq Baltic Stock Exchange

Table 10. Baltic Stock market top 10 market capitalization companies

Telia Energijos Tallink Tallinna Ignitis Tallinna LHV Šiaulių Tallinna Merko Lietuva Skirstymo Grupp Sadam gamyba Kaubamaja Group bankas Vesi Ehitus Operatorius Grupp Cap (mln 743 717 654 522 395 362 341 304 234 166 EUR) Source: Nasdaq Baltic Stock Exchange

Overall, the financial markets have a significant role in allocating capital. Being the bridge between the borrowers and lenders allows the economy to have stable capital inflows to produce goods and services. However, few initial public offerings happened in Baltic stock markets in the last years, and trading volumes have remained consistently low. Companies

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are still primarily relying on the banking system, and the second most preferred method of raising capital is to issue bonds rather than equity.

Even though there is not much development in the Baltic Stock markets, the IPO of

Ignitis, the most significant energy provider in Lithuania, is expected to increase trading volumes. The company managed to raise EUR 450 million during its initial public offering and is currently the highest capitalization company in the Baltic Stock market with almost

EUR 1.5 billion.

1.6. Financial instruments and indexes in the Baltic financial markets.

As mentioned before, there are currently 68 companies traded in the Baltic

Regulated and alternative markets. The companies' stock represents the ownership of a fraction of the entity and is usually attractive to investors for potentially higher returns.

Investment in stock offer equity holders two sources for the rate of return. Firstly an investor could expect a return from the capital gains. For example, from 2010 to 2019, the total increase of the portfolios' value of companies listed in Vilnius, Tallinn, and Riga was 171%,

215%, and 269%, respectively. Collectively, the benchmark index grew by 214%.

In comparison, the S&P 500 index, representing the top 500 companies by capitalization in the United States of America, increased by almost 300% during the same period. However, in terms of the second source of return – dividend, the Baltic stock market has higher measures. The average dividend yield in the US between 2009 and 2019 is just below 2%, whereas, according to INVL asset management (2019), the dividend yield of the

Baltic states stocks exceeds 4.5%. The main reason for such a difference is that companies have to offer higher dividends to attract investors in an underdeveloped and inefficient market. These measures provide compensation to investors for the risk of relative illiquidity.

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OMX Nasdaq Baltics also offers sector comparison indexes based on FTSE Groups'

Industry Classification Benchmark (ICB). There are two methods of index estimation. The first one uses Price (PI) and the other Total return (GI). To estimate the index's total return

(GI), it includes the reinvestment of the dividends. These benchmarks' primary purpose is to offer investors a convenient opportunity to compare companies in a specific industry.

There are five main indexes to measure the return in the Baltic stock market: OMX

Baltic Benchmark GI, OMX Baltic 10, OMX Tallin GI, OMX Riga GI, and OMX Vilnius GI.

OMX Baltic Benchmark index includes the largest and most traded stocks from every industry. The weight of each stock depends on its free-float capitalization. Companies included in the index have lower spreads resulting in cheaper transactions. OMX Baltic 10 index contains companies that are likely to be traded most in the market. Their free-float capitalization also weights the securities; however, one company can not exceed 15% of the index's weight. The other three mentioned indexes are location-based. They represent the respective market and include all stocks except those with a free-float of less than 10%.

These indexes tend to move in the same direction but with different magnitudes.

In terms of debt securities, OMX's regulated market includes only 28 corporate bond issues and 29 government bonds at the time of writing. The maturities of corporate bonds vary from 1 to 10 years, and they offer a coupon rate from 1.3% to 18%. However, most investors can acquire these bonds only at the issuance date because the market is illiquid, and there are almost no trades each day. The government bonds in the market have a near-zero coupon rate, and there is no turnover for most of the days. Moreover, Nasdaq First North includes additional 12 corporate bonds. Although these bonds offer coupon rates from 4.5% to 14%, they do not have to follow legal requirements for listing on a regulated market. All in all, investors in the Baltic financial market do not have many debt instruments to add to their portfolios.

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Finally, there are five funds listed to Nasdaq Baltics: Baltic Horizon Fund, INVL

Baltic Fund, INVL Emerging Europe Bond Subfund, INVL Russia TOP20 Subfund OMX

Baltic Benchmark Fund. The latter focuses on passive investors that aim to replicate the market. The fund includes all Lithuanian, Latvian, and Estonian stocks in the OMX Baltic

Benchmark index. Baltic Horizon fund invests directly into commercial real estate in Baltic countries and focuses on the capital cities. INVL asset management manages three funds listed on Nasdaq that focus on different strategies. The INVL Baltic fund focuses on long- term investment in economic sectors that have potential. Emerging Europe Bond Subfund invests in investment-grade short-term government and corporate bonds of duration up to three years, and Russia TOP20 Subfund contains stocks of most attractive Russian companies.

Even though Baltic countries' growth has been significant throughout the last decade, the financial market did not develop at the same pace. At the moment, companies prefer raising capital through bank loans rather than other debt instruments or equity. Weak investment culture and below-par financial literacy hinder the potential for expansion. Due to low interest in investment subject, there is not much research focused on asset pricing methodology offering practical application of different theories.

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2. Literature review

2.1. Modern portfolio theory

In 1952 Harry Markowitz, in the doctoral dissertation findings Portfolio Selection, published in the Journal of Finance, 7(1), introduced how a rational investor should behave to optimize the investment portfolio. Markowitz argued that the portfolio selection process consists of two stages. The first stage is analyzing and calculating potential future returns of available financial products based on personal observations, experience, and beliefs

(Markowitz, 1952). The second stage begins with the analysis findings mentioned above and ends with applying conclusions made in the first stage to form the portfolio (ibid.). The essential finding was the explanation of diversification and securities' covariance relationship significance on selecting the investment portfolio. The author presents the theory as a mathematical problem with elementary statistical concepts for the second stage of the portfolio selection process.

2.2 Assumptions of Markowitz Portfolio selection

Markowitz built his Portfolio selection theory on explicit and implicit assumptions that focus on the market's efficiency and investors' rationality. The author assumes that investors seek to maximize returns while minimizing variability and only accept the additional risk for additional fair compensation. Moreover, they have full access to the processed information, are correctly informed about investment decisions, and there are no taxes or transaction costs in the market. For the theory to work, markets have to be perfectly efficient. It has to be possible to hold various securities that can perform independently to other assets in the portfolio to achieve the desired outcome of diversification.

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2.3 Risk and Return in Markowitz Portfolio selection

Markowitz defines return as the discounted or capitalized value of an investment's future returns. Because the future can never be known with certainty, the returns must be expected or anticipated. The author divides the concept of risk in Portfolio selection into two parts: systematic and unsystematic. The first one relates to overall market risk, and the latter is also known as the diversifiable risk and does not reward investors. These two risks are assumed to be part of all portfolios. Markowitz in Portfolio selection defines risk as volatility

– uncertainty arising from changes in security's value. The higher the volatility of the portfolio, the greater the risk, described explicitly as the variance.

Markowitz rejects the maxim that investors should choose the security with the highest expected return given the opportunity as it does not include diversification (1952).

The law of large numbers implies that as the amount of financial assets within the portfolio grows, the probability of achieving the portfolio's expected returns increases by mitigating unsystematic risk. However, it is impossible to attain perfect diversification since securities are too intercorrelated. Therefore, investors have to face tradeoffs by either increasing risk or decreasing the portfolio's anticipated returns (ibid.).

According to the author, investors should choose such "E, V" (expected return, variance) combinations that would yield maximum E for given V or less or minimum V for given E or more, as presented in Figure 3 (Markowitz, 1952, p. 82). However, Markowitz's

Portfolio Selection falls short when trying to estimate the set of efficient combinations since it can be applied only to the portfolios of up to four assets.

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Figure 3. Attainable E, V combinations

Source: Markowitz (1952)

2.4 Markowitz portfolio with three assets

Using the Markowitz Portfolio Selection model, it is possible to analyze efficient combinations of the portfolio consisting of three assets. In this case, the model simplifies to:

3 1) 퐸 = ∑푖=1 푋푖휇푖

3 3 2) 푉 = ∑푖=1 ∑푗=1 푋푖푋푗휎푖푗

3 3) ∑푖=1 푋푖 = 1 or 푋3 = 1 − 푋1 − 푋2

4) 푋푖 ≥ 0 for i = 1, 2, 3,

where E is the expected return of the portfolio, and V is the variance. The third and the fourth equations are constraints representing that the sum of assets' weight is equal to one and cannot be negative. Therefore, for simplification purposes, the model does not include the possibility of short-selling. Substitution of the third equation to the first and the second results in E and V functions of X1 and X2:

a) 퐸 = 퐸(푋1, 푋2)

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 24

b) 푉 = 푉(푋1, 푋2)

c) 푋1 ≥ 0, 푋2 ≥ 0, 1 − 푋1 − 푋2 ≥ 0.

The equations a, b, c allows to solve the problem on a two-dimensional plane.

According to Markowitz, all three asset portfolios that can satisfy the constraints (c) and (3) belong to the attainable set. Figure 4 presents the graphical depiction of the problem, where triangle abc is the set of all possible combinations of X1 and X2. The author uses an isomean curve to represent all portfolios with given expected returns and an isovariance line to define all points with the given variance. If E is predetermined, the minimal V value point is where the isomean line is tangent to an isovariance curve. The set of efficient portfolios (denoted as l) starts at point x, where the variance is the least and continues in the direction of increasing expecting returns. The rest of the set begins at the intersection of l with the boundary. It then moves along the borderline towards b until it satisfies the constraints of the attainable set. At any given time, the investor can select such a combination of assets that belongs to the efficient set according to personal risk awareness. It is possible to apply Markowitz's

Portfolio selection for four assets, representing the problem on a three-dimensional plane.

However, the theory remains normative.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 25

Figure 4. Attainable set of portfolios

Source: Markowitz (1952)

2.5 Capital Asset Pricing Model

The Capital Asset Pricing Model (CAPM) was developed by William Sharpe (1964),

Jack Treynor (1962), John Lintner (1965), and Jan Mossin (1966) and is dealing with the question of what impact on expected return should the risk of investment have (Perold, 2004).

The model assumes that unsystematic risk is insignificant because investors can diversify it away (ibid.). Therefore, the only component that explains the expected return of the asset is the market return. This asset pricing theory derives itself from Markowitz's Mean-Variance analysis and is currently widely used to estimate an asset's expected return. The CAPM is a positive economic theory, meaning that it focuses on the description and cause-and-effect behavioral relationship of economic phenomena.

2.6 Theory of CAPM

The Capital Asset Pricing Model, like Markowitz Portfolio Selection, assumes that markets are efficient; however, they introduce a condition that helps determine which

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 26

portfolio combination provides the best mean-variance tradeoff. According to Markowitz, there is a set of efficient portfolios that have the most optimal combinations of risk and return. Therefore, the investor's "propensity to gamble " (Markowitz, 1952, p. 90) should determine the outcome of portfolio selection. Sharpe (1964) and Lintner (1965) simultaneously decided to implement a risk-free rate consideration in their models. In such a case, the assumption that all market participants can borrow unlimited amounts holds.

Because a rate is considered riskless, the portfolio consisting only of risk-free assets should have zero variance. Eugene F. Fama and Kenneth R. French (2004) describe CAPM's essence by presenting the Investment opportunities graph. The curve abc, also called the minimum variance frontier, represents all combinations of risky assets that minimize variance at the given expected return level (ibid). Markowitz stated that all portfolios above point b along the curve maximize the expected return given the variance and are considered efficient combinations (Markowitz, 1952).

Figure 5. Investment opportunities

Source: Fama, French (2004)

Sharpe and Lintner consider a portfolio that consists of portion x of the risk-free asset and another (1 – x) part in risky securities. Denotation g represents the portfolio of risky assets. If an investor places all the funds into riskless assets, they arrive at the point Rf, where the variance is equal to zero, and the return is of the risk-free rate (Fama, French, 2004). The

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 27

portfolios that combine risky portion g and riskless security lay along a line from Rf through g. The expected return and standard deviation of the portfolio that contains risk-free asset and a risky portfolio depend on the proportion x and 1-x of capital invested in either (ibid.):

퐸(푅푝) = 푥푅푓 + (1 − 푥)퐸(푅푔)

휎(푅푝) = (1 − 푥)휎(푅푔)

Accordingly, to achieve the portfolio that lies on the minimum variance curve, one has to push the Rf, g line upward until it becomes tangent with the frontier. The tangency is demonstrated by point T on the graph. Knowing this assumption, everyone should arrive at the same "opportunity set" (Fama, French 2004, p. 28). If there is an agreement about the distribution of returns, all investors will hold the same portfolio of risky and risk-free assets tangent to the minimum variance frontier. The capital asset pricing model is considered a single-period model. Therefore, all market participants are assumed to make matching, rational investment decisions over the same time. Because all investors hold the same portfolio, there is a need for a set risk-free borrowing and lending rate in the market to reach equilibrium. Due to these implications, the market portfolio must be on the minimum variance frontier for the market to clear and be efficient. Accordingly, the estimation of the expected return of an asset in the market portfolio of N risky assets with the minimum variance condition depends on the asset's return covariance relative to the market return

(ibid.):

1) 퐸(푅푖) = 퐸(푅푍푀) + [퐸(푅푀) − 퐸(푅푍푀)]훽푖푀, 푖 = 1, … , 푁.

푐표푣(푅푖,푅푀) 2) 훽푖푀= 2 휎 (푅푀)

In these equations, E(Ri) is the expected return of assets, E(RZM) expected return of assets that are perfectly uncorrelated with the market, and β market beta, which is estimated

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 28

by dividing the covariance of asset's and the market's return and variance of the market return. It represents how sensitive is the investment return to market fluctuations and is the main factor of the model used to predict and estimate the expected return. In the Portfolio

Selection, Markowitz defined an asset's risk to be the variance of its returns over time. The drawback of the theory is that it relies on the historical performance of returns and volatility, which are subject to many factors that one can not determine at the time of estimation. The capital asset pricing model states that an asset's systematic risk is its covariance risk measured relative to the weighted average covariance risk of all assets in the market portfolio. Accordingly, if markets are efficient, the deviation of investors' diversified portfolio return should be explained mostly by the market portfolio's movement.

As mentioned before, E(RZM) represents the return of an asset that is uncorrelated with the market portfolio; in other words, it is the return of securities with zero betas.

Suppose we consider the risk of an asset to be its covariance risk. In that case, the uncorrelated asset is riskless in the portfolio as it does not change the variability of market portfolio returns. Therefore, E(RZM) is equivalent to the risk-free rate that represents riskless lending and borrowing. Incorporating this assumption into the above-written equations yield the following relation:

퐸(푅푖) = 푅푓 + [퐸(푅푀) − 푅푓]훽푖푀, 푖 = 1, … , 푁.

Consequently, an asset's expected return is equal to the risk-free rate plus market risk premium times the beta.

2.7 Criticism and testing of CAPM

Jensen (1968) noted that the relation between expected return and market beta in

Sharpe-Lintner CAPM implies a time-series regression test. According to the initial CAPM, the estimated risk premium thoroughly explains the asset's excess return. However, the author

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 29

argued that sometimes the actual performance differs from the one anticipated by CAPM.

The measure showing the return above or below the predicted, therefore, is called Jensen's alpha. The early empirical tests have rejected the Sharpe-Lintner CAPM. Two central problems of testing became apparent. Firstly, estimating betas for individual assets was imprecise. Secondly, tests have shown a correlation of residuals; therefore, as Jensen proposed, time-series cross-section regressions should decrease bias.

In 1972 Black, Jensen and Sholes have tested the stocks traded on the New York Stock

Exchange in the period January 1926- March 1966 (Black et al. 1972). The authors defined monthly returns on the market portfolio as returns from the portfolio of all securities traded on NYSE at the beginning of the month. Each asset would have equal weight in the portfolio.

They selected the risk-free rate to be 30-day Treasury Bills or dealer commercial paper rate for the period that the latter was not available. The average asset's return was above the intercept of the returns predicted by CAPM for low beta and below for higher beta. Friend and Blume (1970) and Stambaugh (1982) have concluded similar results. The time-series analysis showed a positive relationship between the portfolio's beta and its mean return; however, it was too flat. Black et al. (1972) suggested that the company's beta can only maintain stability if it continues operating at the same level without increasing risk. To acquire better judgment, one must use more extended time-series data and use sector beta rather than company beta because it eliminates statistical noise over the more extended period

(Roll, 1974). The statement could suggest why researches on Baltic Stock Exchange firms yield different results. The market lacks companies that would operate in the same segment and have a lasting history.

Moreover, Black, Jensen, and Sholes stated that the assumption of unlimited risk-free borrowing and lending is highly unrealistic. Furthermore, the interception point with the y- axis was not at a point Rf (Figure 5), as theoretically argued by Sharpe and Lintner. The

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 30

paper's finding was that the expected return of an asset was not proportional to its beta; therefore, using CAPM is flawed. The authors reject the traditional form of the model. Black

(1972) argued that the asset pricing model does not need to assume that all investors hold the same market portfolio. Instead, the market portfolio is the weighted sum of all individual portfolios and should be an efficient frontier. The author also excludes the possibility of unlimited lending and borrowing and states that you only need the zero-beta portfolio and beta for expected return calculations. Thus, the asset's risk is relative to the risk of the market portfolio of risky investments. Therefore, the estimation of expected returns is the same as provided in the derivation of CAPM, except for one additional constraint:

퐸(푅푖) = 퐸(푅푍푀) + [퐸(푅푀) − 퐸(푅푍푀)]훽푖푀, 푟푙 < 퐸(푅푍푀) < 푟푏,

In the constraint, investors can borrow at a rate of rb and lend at a rate rl . The alternative expression of the model is:

퐸(푧푗) − 훾0 = 훽푗[퐸(푧푚) − 훾0],

where zj is the return to asset j in excess of the risk-free rate zm the return of the market portfolio in excess of the risk-free rate, and 0 the return of the zero-beta portfolio in excess of the risk-free rate. Therefore, the difference between CAPM and Black CAPM models is how they explain E(RZM) and how to approach the proper beta estimation. Using

Black CAPM can explain the flat market beta and asset return tradeoff; however, it does not incorporate any other factor in expected return calculation (Fama, French, 2004).

Overviewed theories have one thing in common; they signify the market portfolio's importance as the main factor for estimations. However, Richard Roll, in the paper A

Critique of the Asset Pricing Theory's Tests (1977), also known as "Roll's critique" challenges the feasibility of market portfolio attainability. First of all, the author introduces the Mean-variance tautology that implies CAPM satisfaction of any mean-variance efficient

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 31

portfolio. In other words, testing CAPM is the same as trying the efficiency of the portfolio.

The second and more critical critique statement is that it is impossible to observe the market portfolio (Roll, 1977). A real market portfolio should include every possible investment in any market that has value. Without such observation, it is impossible to implement an asset pricing theory test to assess whether the portfolio is mean-variance efficient.

Consequently, it is impossible to apply the CAPM to its fullest extent. In their empirical testing, many researchers select market indexes, such as S&P 500, FTSE 100,

Nikkei 225, or Baltic Stock market OMXB10 as a proxy for market return input in CAPM calculations. Roll states that even a small change in the market's return rate yields significantly different results (ibid.). Therefore, academics have started to investigate other models and introduce additional factors to represent more accurate market return leverage in expected return calculations.

2.8. Fama and French Three-Factor model

In 1977 S.Basu, in the research Investment Performance of Common Stocks in

Relation to their Price-Earnings Ratios: A Test of The Efficient Market Hypothesis, tried to empirically determine the relationship between the Price-to-earnings ratio of equity investments and their performance. The author tested data from April 1957 to March 1971 and found out that low P/E ratio portfolios have earned higher absolute and risk-adjusted returns than portfolios with higher P/E (Basu, 1977). Basu concluded that the findings did not follow efficient market hypothesis rules and asset pricing models lacked validity. During the period, the asset prices did not fully incorporate the price-to-earnings information, contrary to the view that security prices momentarily include all publicly available information.

Moreover, Rolf W. Banz (1981), in the paper the Relationship Between Return and

Market Value of Common Stock, investigated the company size effect on risk-adjusted

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 32

returns. He found out that small NYSE firms over forty years on average had significantly higher returns than large companies. However, the coefficient of the size factor had a high fluctuation throughout the investigated period. The author argues that there is no strong theoretical reason why such a relationship holds. Reinganum (1980) denied the conclusion of

Basu that the P/E ratio has an impact on the anomaly. The price-to-earnings is a proxy for the size effect and not the other way around. Klein and Bawa (1977) argued that investors do not want to hold securities with limited information availability. Given that the amount of information depends on the company's size, only a subset of investors will invest in smaller companies due to the lack of information being riskier.

Lastly, Rosenberg et al. (1985), in the paper Persuasive evidence of market inefficiency, tested 1400 stock traded on the New York Stock Exchange from 1980 to 1984.

They found out that the stocks' mean returns positively correlate with the company's book value of common equity and market value. In other words, firms with high book-to-market equity ratios (value stocks) historically have outperformed companies with low book-to- market equity ratios (growth stocks). Capaul et al. (1993) investigated Japan and four

European markets and found out that the price ratio anomalies exist.

Fama and French (1992), in their research The Cross-Section of Expected Stock

Return, tested NYSE, AMEX, and NASDAQ data from 1962 to 1990. They managed to confirm the assumptions of Rosenberg et al. (1985) and Banz (1981) that size and book-to- market ratios influence stock returns. The authors state that CAPM beta does not explain the average return on the analyzed stocks. When there is no relation of beta to size, beta becomes unreliable concerning mean returns. Moreover, the book-to-market equity ratio captures the effect of market and book leverage in market returns. Fama and French assert Reinganum's

(1980) conclusion that the size factor absorbs the price-to-earnings ratio impact on returns.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 33

Based on the evidence mentioned above and assumptions Fama and French composed a model that includes three factors for expected return calculation:

퐸(푅푖푡) − 푅푓푡 = 훽푖푀[퐸(푅푀푡) − 푅푓푡] + 훽푖푠퐸(푆푀퐵푡) + 훽푖ℎ퐸(퐻푀퐿푡),

where SMB represents the difference between the historical returns of portfolios consisting of small and big stocks and HML is the difference between portfolios of high and low book-to-market equity ratios stocks. Betas are the slopes in multiples regression of Rit –

Rft on RMt – Rft, SMBt, and HMLt (Fama, French, 2004).

2.9. Research methodology. CAPM

Risk-free rate. According to Sharpe and Lintner CAPM, the risk-free rate represents riskless lending. Therefore, the expected return calculations will include the interest rate on

Baltic countries' government bonds weighted by companies' total capitalization traded in the respective market. To test Black's CAPM theory and check whether it is more applicable in the baltic stock market, the model will substitute the risk-rate with the average monthly returns of stocks most uncorrelated with the market index.

Market return. The model will include the average three-year monthly return of OMX

Baltic All-Share for E(RM) variable. The index represents all stocks traded on the Main and

Secondary lists of the Baltic exchanges. Therefore, it is the best depiction of the overall market in terms of other indexes.

Beta. The market beta estimation will include the division of the 36-months covariance of asset's and the market's return and variance of the market return. The process is repeated for each month of the observed period. Moreover, the company beta will be substituted with sector beta to check Roll's (1974) assumption that it better explains the market return.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 34

푐표푣(푅푖,푅푀) 훽푖푀= 2 휎 (푅푀)

The estimated variables for all currently traded stocks with at least a 36-month listing history on the stock exchange will be put in the formula below.

퐸(푅푖) − 퐸(푅푍푀) = [퐸(푅푀) − 퐸(푅푍푀)]훽푖푀

The CAPM estimation part's primary goal is to find each stock's expected returns in the data set and compare them to the actual returns. The goodness of fit will help to estimate the level of applicability of the CAPM.

2.10. Research methodology. Fama-French Three factor model

The Fama-French Three-factor model encompasses the same risk-free rate, market risk premium, and market beta parameters in the equation. Therefore, the risk-free rate, market return, and market beta calculations are the same as in the CAPM model. However, it includes two additional size and value factors.

Small minus Big. The E(SMBt) part of the model represents the excess returns of small-capitalization stocks portfolios over large-capitalization stocks portfolios. The calculation of excess returns at time t comprises 36-months data collected from the Nasdaq

Baltic Stock market.

푆푀퐵푡 = 퐸(푆푡) − 퐸(퐵푡)

High minus Low. Calculations are similar for high minus low excess return calculation. The criteria for each type of stock is the book-to-market equity ratio. Value stock has a high ratio, and growth stock have a small ratio relative to other companies.

퐻푀퐿푡 = 퐸(퐻푡) − 퐸(퐿푡)

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 35

SMB and HML betas. The beta coefficients will include the division of the 36-months covariance of asset and the respective (SMBt or HMLt) return and variance of the respective

(SMBt or HMLt) return.

푐표푣(푅푖푡,푅푆푀퐵푡) 훽푖푠= 2 휎 (푅푆푀퐵푡)

푐표푣(푅푖푡,푅퐻푀퐿푡) 훽푖ℎ= 2 휎 (푅퐻푀퐿푡)

The last step to find the expected return using the Fama-French model is to plug the three factors and the risk-free rate into the formula below.

퐸(푅푖푡) − 푅푓푡 = 훽푖푀[퐸(푅푀푡) − 푅푓푡] + 훽푖푠퐸(푆푀퐵푡) + 훽푖ℎ퐸(퐻푀퐿푡),

The Fama-French model's expected returns, the same as in the CAPM model, will be compared to individual stocks' actual returns. The research aims to determine which model provides better goodness of fit and is more applicable in the Baltic Stock market environment. In multiple regression, the determination coefficient R2 will direct the goodness of fit for each model. The adjusted R2 measure closer to 1 should imply that the model explains a higher market variability percentage.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 36

3. Empirical Research

3.1. Empirical Research description

The last part of the paper analyses the data, tests CAPM and Fama-French models, and presents the discussion of key findings. The research begins with a description of the data. The observation of the visual representation of data points helps form the assumptions and make modifications that would better reflect asset pricing models' proper application.

The research will use Microsoft Excel and specialized statistical software GRETL for statistical tests.

The primary purpose of this part is to find out which of the two models, CAPM or

Fama-French, has more predictive power in the calculation of the expected return. Moreover, testing will consider the additional expansions and critiques of the models to improve the regression results.

3.2. Data and descriptive statistics of monthly returns

The monthly historical data gathered included all 68 companies currently listed on

Baltic regulated and First North markets for the research. The data ranges from January 2010 to October 2020. However, calculations of expected returns incorporated only companies with at least three years of history on the stock market. The basis of such a selection of approaches is to guarantee that company's betas are feasible for application. Therefore, out of

68 companies, 60 firms have met the criteria.

In terms of monthly stock returns, companies' medians are close to zero. The scores below -1% or above 0.6% in the data set are outliers. Median monthly returns' in the market range from -2.41% to 1.62%; however, the midpoint of more than 50% of scores is zero.

Although from the medians, it seems that the data does not vary much, the standard deviation's mean and median of monthly returns are 22% and 8% accordingly and range from

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 37

3.87% up to 495.94% (21.49% excluding the extreme scores). Therefore, one could expect high return deviations from month to month even though they should approach the mean over a more extended period. The variability implies that it might be challenging to explain the monthly stock returns only in terms of market return unless the benchmark index fluctuates in a similar magnitude. Therefore, the average returns of more extended periods will provide a better picture.

Figure 6. Median stock returns and standard deviations

Source: author's estimations

Factoring the median returns and standard deviations by the lists allows a better comparison among stock groups. The factorized boxplots depict them below. The number 1 represents the Baltic Main list, 2 – Baltic Secondary list, and 3 – First North list. The Baltic

Main list, demanding the highest listing requirements and supervision, seems to have the most normal returns. The mean and the median is near zero, and there are no outliers in returns. The standard deviation is also the lowest among the three lists, guaranteeing slighter variability of returns for most stocks listed.

On the other hand, the Secondary list has many outliers and a high variation of standard deviation. Any stock that has a median of returns not equal to zero is an oddity. On an ordinary day, the trading volumes are minuscule, and shift in prices usually occurs during quarterly reports, important market news, and position closure or opening of large investors.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 38

The data of First North Baltic consists only of six companies. Therefore, it is inadvisable to draw any conclusions from such a small sample. Further empirical testing of First North

Baltic stock and Secondary list stocks will provide more information on asset pricing methodology applicability.

Figure 7. Distribution of median returns and standard deviations by lists

Distribution of MedianReturn by List

0,015

0,01

0,005

0

MedianReturn -0,005

-0,01

-0,015

-0,02

-0,025 1 2 3 List

Distribution of Stdev by List

0,2

0,15

Stdev

0,1

0,05

1 2 3 List

Source: author's estimations

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 39

3.3. OMX Baltic All-Share Descriptive statistics and normality

In terms of the OMX Baltic All-Share index, there is no normal distribution of monthly changes. Out of 129 valid observations, the distribution of returns is leptokurtic. It means that distribution has heavier tails than normal, which might result in extreme two-side returns. Therefore, the portfolio has a higher risk, which is usually valid for underdeveloped markets. However, the median and the mean are close to zero. Combined with a low number of transactions, it suggests relevant illiquidity in the market. Implementing the first difference, logarithmizing, squaring, or in any way transforming the data does not result in the normal distribution of the index's returns. Therefore, beta and market risk premium calculations will use the original values.

Figure 8. Summary statistics and normality of OMX Baltic All-Share monthly returns

Summary Statistics, using the observations 2010:01 - 2020:10 for the variable MonthlyReturn (129 valid observations) Mean Median Minimum Maximum 0,0072048 0,0097402 -0,16138 0,12706 Std. Dev. C.V. Skewness Ex. kurtosis 0,035555 4,9348 -0,94444 4,4538 5% Perc. 95% Perc. I.Q. range Missing obs. -0,056169 0,059026 0,035379 1

Source: Nasdaq Baltic, author's estimations

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 40

The volatility and growth of OMX Tallinn GI, OMX Riga GI, and OMX Vilnius GI have remained similar throughout the observed period. The average monthly returns of respective indexes were 0.72%, 1.12%, and 0.84%. The Standard deviations of these three market portfolio returns were also at almost the same level (4.29%, 4.71%, 4.22%). The markets' similar qualities allow us to combine them into one portfolio (OMX Baltic All-Share

Index) and use it as a proxy for systematic risk in beta calculations.

Figure 9. Historical returns of OMX Tallinn, OMX Riga, and OMX Vilnius 2010-2020

Source: Nasdaq Baltic

3.4. Risk-free rate

One of the inputs in the CAPM and Fama-French calculations is the risk-free rate.

Estimating the risk-free rate involved taking each Baltic country's rate and weighting it by the respective market capitalization. On average, Tallinn's, Riga's, and Vilnius's weights were

32%, 14%, and 54% throughout the observed period. The EMU Convergence criterion bond yield for long-term central government bonds served as a proxy for Lithuania's and Latvia's interest rates. Since Estonia only issued a long-term government bond in 2020 and has

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 41

adopted the euro in 2011, the substitute in the calculation was ten-year Euro Yield curves of

AAA-rated euro area central government bonds.

Figure 10. The weighted Baltic risk-free rate 2010-2020

Sources: Eurostat, Nasdaq Baltics, author's estimations

3.5. Explanatory power of CAPM

The Capital Asset Pricing Model's main implication is that investors should receive fair compensation for taken risks. Moreover, the only systematic risk that matters is the variation of the market portfolio returns. Therefore, estimating an asset's return relationship with the market is a crucial part of the model. Calculation of betas involved taking the results of the individual security's latest 36-month return and the 36-month average market index return. The scatter plot indicates the relationship only between the stocks' returns and the market index returns in the same month without the effect of beta.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 42

Figure 11. Stock vs. market returns 2010-2020.

Stockreturn versus market (with least squares fit) 6 Y = 0,00726 + 0,804X

5

4

3

Stockreturn

2

1

0

-1 -0,15 -0,1 -0,05 0 0,05 0,1 market Sources: Eurostat, Nasdaq Baltics, author's estimations

Even though most returns do fall around the zero return plot, the variability is too significant, and there is no relationship to observe. The p-value in the OLS regression equation of the market risk factor is below zero; however, such a model has an Adjusted R-squared score of only 0.02. Therefore, more extended period time-series data can be a better measurement.

Due to the high volatility of stock returns, it is impossible to predict next month's returns accurately. Consequently, the regression equation involved comparing average 36- month tracing returns and the average 36-month market risk variable estimated by CAPM.

The median and the mean of the constant term in the OLS regression of all companies, without two extreme scores, approaches zero. However, the CAPM coefficient fluctuates from -8.785 to 7.500. The 38% of coefficients (23 companies) fall in the range from 0.5 to

1.5, which is acceptable considering that market risk would entirely explain returns if the adjusted R-squared measure and coefficients were equal to 1. Out of the 23 companies, 15

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 43

had adjusted R-squared of more than 0.500 (0.744 on average), meaning that the independent variable explained more than half of these dependent variables' variance.

Figure 12. Frequency of CAPM market risk premium coefficients

Source: author's estimations

The overall average and median of the CAPM coefficients received through the dataset's regression analysis are 0.923 and 1.123, respectively. Moreover, the mean and the median Adjusted R-squared are 0.540 and 0.640. Even though there are opportunities for portfolio diversification, the results' variability is too great to draw any common conclusion for the sample.

3.6 Statistical issues of CAPM model

The data has many statistical issues related to normality, heteroskedasticity, and misspecification. Half of the tested residuals do not fall on the normal distribution curve, meaning that it can produce bias as an error is not consistent throughout the whole data.

Likewise, only 18 companies had a p-value greater than the 0.05 confidence level while performing White's test for heteroscedasticity. The variances of predicted variables were not constant during the observed period. The shorter-period irregular changes of market value

(especially during February and March of 2020 due to coronavirus pandemic) may have

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 44

triggered such variation. However, market returns are not autocorrelated; therefore, it is more likely that unique company events are the reason for heteroscedasticity and should be analyzed on an individual basis.

Figure 13. Autocorrelation and Partial Autocorrelation functions for market returns

ACF for Change

0,2 +- 1,96/T^0,5

0,1

0

-0,1

-0,2

0 5 10 15 20 lag

PACF for Change

0,2 +- 1,96/T^0,5

0,1

0

-0,1

-0,2

0 5 10 15 20 lag Source: author's estimations

Moreover, the p-values of the Ramsey Regression Equation Specification Error Test for

47 companies tested did not fail to reject the null hypothesis of the correct specification. In other words, the CAPM linear model regression fails to explain the dependent variable to its fullest extent. There may be quadratic, cubic, logarithmic, or other data relationships that would better explain excess returns. However, such an assumption would violate the theory of CAPM. Consequently, due to the issues mentioned above, the Capital Asset Pricing Model in its simplest form may not be the best method to explain the variability of returns, even though the market risk coefficients and adjusted R-squared measures do not contradict with

Sharpe-Lintner theory.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 45

3.7. Sector Beta CAPM

The Capital Asset Pricing model has received a lot of criticism for questionable assumptions. As discussed in the Literature review chapter, Black et al. (1972) suggested that unlimited lending and borrowing are impossible. Therefore the risk-free rate is an inadequate proxy for the intercept. However, the interest rate has been decreasing significantly since

2010 and has a lesser effect on the CAPM model. The regression analysis of excess returns and market risk premium identified that the mean and the median constant term without extreme values in the model approaches zero, meaning that the linear equation crosses near the (0,0) point on the coordinate plane. Due to this reason, the following empirical analysis does not include a test for Black Zero Beta CAPM.

Roll (1974) argued that individual company beta has too much statistical noise.

Therefore, combining stocks into portfolios of related industries and calculating mutual beta should increase the expected returns estimation precision over a more extended period. The following regression analysis included the portfolios of nine different sectors in the Baltic stock market that had at least three companies except for telecommunications, where Telia contributed on average 98% towards capitalization. Other companies were removed due to a lack of historical data.

Table 11. Companies by sectors Basic Consumer Consumer Financials Health Industrials RE Telecomm- Utilities Mat. Disc. Staples care unications Average Beta 0.74 1.10 0.77 0.94 1.35 1.16 1.02 0.57 0.40 Companies in 5 15 10 8 3 9 7 3 4 sector Companies in 5 14 10 4 3 9 7 0 4 regression Source: Nasdaq Baltic, author's estimations

The average and median of the CAPM coefficients received through the regression analysis with sector betas are 1.525 and 1.16, respectively. The standard deviation of coefficient returns is 1.92, where the standard deviation of the coefficient of individual beta

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 46

CAPM is 2.26. Approximately 49% of results (27 companies) fall in the range from 0.5 to 1.5 compared to 38% in previous calculations. Moreover, sector beta results in a slightly higher mean and median adjusted R-squared of the model. The mean and the median of the first regression are 0.540 and 0.634, whereas in the second – 0.555 and 0.659.

Figure 14. Frequency of CAPM market risk premium coefficients with sector beta

Source: author's estimations

Even though normality and heteroskedasticity problems exist in both versions of the

CAPM model, they are lesser when using sector betas. 60% of regression residuals fall on the normal distribution curve compared to 48% in the first model. Moreover, marginally fewer data suffer from heteroskedasticity (65% compared to 70%). However, the results of

Ramsay's reset test are similar in both cases. Using sector beta does not protect the model from misspecification. Even though sector betas approximate the means of market risk premium coefficients closer to one for most sectors, that is not the case when using medians.

Therefore, there are probably other non-linear relationships in the model, making CAPM invalid to the theory.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 47

In terms of results by sectors, using sector beta has increased the Adjusted R-squared median for the companies in six sectors and slightly decreased in one (Financials). Using sector beta proved to be ineffective in the Real Estate sector. However, the results might contain bias due to limited company sample sizes and the issues presented above.

Table 12. Sector beta regression results (median) Coefficient Coefficient (sector) Adjusted-R2 Adjusted-R2 (sector) Consumer Discretionary 0.91 0.96 0.44 0.49 Real Estate 1.53 0.57 0.20 0.07 Consumer Staples 1.27 1.93 0.73 0.75 Basic Materials 0.78 0.91 0.52 0.58 Industrials 1.26 1.15 0.76 0.83 Health care 0.18 1.68 0.71 0.84 Financials 1.17 1.15 0.69 0.66 Utilities 3.46 1.95 0.77 0.83 Source: author's estimation

Table 13. Sector beta regression results (mean) Coefficient Coefficient (sector) Adjusted-R2 Adjusted-R2 (sector) Consumer 0.65 0.77 0.44 0.44 Discretionary Real Estate 1.12 1.77 0.35 0.31 Consumer Staples 1.46 1.74 0.68 0.66 Basic Materials 0.87 0.82 0.44 0.52 Industrials 1.45 1.96 0.69 0.76 Health care -0.14 1.44 0.58 0.62 Financials 1.20 2.46 0.53 0.54 Utilities 3.77 2.34 0.69 0.73 Source: author's estimation

The CAPM model application does become slightly more reliable when incorporating sector betas. Roll's (1974) statement that industry betas should yield more precise results and eliminate statistical noise over more extended periods seems to be correct. More companies' market risk premiums have approached the range from 0.5 to 1.5 and overall adjusted R- squared measures increased while decreasing the problem of residuals not being normally distributed. However, both models still suffer from misspecification, and there are potentially other factors that impact returns.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 48

3.8. Fama-French regression

Like the Capital Asset Pricing Model, the Fama-French three-factor model theory states that investors should receive fair compensation for the market risk. However, the

CAPM's risk premium is not sufficient to explain the whole variability in expected returns.

Therefore, the Fama-French model includes two additional factors: size and value that historically impacted security performance. Small companies have outperformed big companies, and high-value companies have outperformed growth companies.

The market capitalizations of all companies acted as a proxy for size. The "big"

companies had a higher capitalization than the sum of the sample mean and one standard deviation. Companies with a capitalization lower than the median fell into a "small" category due to the high skewness of data. The estimation of two separate portfolios' returns consisting of only big and small companies contained the capitalization-weighted returns. The size factor (SMB) was calculated by subtracting the portfolio containing big companies' returns from small portfolio returns in each respective month. During the observed period, the excess returns averaged at 1.6%, confirming Fama and French's assumption that small companies' stocks historically tend to outperform large companies' stocks.

The second additional factor (HML) estimation involved the calculation of the book- to-market equity ratio. The average annual equity values were extracted from the annual reports of each company. A ratio exceeding 1 means that the company is trading below its book value and is relatively undervalued compared to the companies with lower ratios. Thus, two portfolios in terms of value (high and low) included companies with ratios higher and lower than one. The calculation of value company premium followed the same subtraction of two portfolios' returns as in SMB. On average, from 2010 to 2020, value stocks have

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 49

outperformed growth stocks by 0.7%, meaning that the book-to-market ratio does affect the return.

The regression equation involved comparing average 36-month tracing returns and the average 36-month market risk variable as in the CAPM model. In addition, the Fama-French ordinary least squares regression determined the relationship of the same 36-month returns with SMB and HML factors. To calculate the coefficients of Rm-Rf, SMB, and HML, the

OLS regression used more extended 36-month averaged returns and factors' values. Such a method prevents the high variability of results and provides higher precision.

3.9. Fama-French regression results

The multiple linear regression of three factors yielded much better coefficient of determination results than the CAPM. The individual beta CAPM explanatory power was higher only for three companies: Apranga, Hansa Matrix, and Kauno Energija out of the sample of 60, and the difference was comparably insignificant (0.008, 0.09, and 0.004 respectively). Moreover, the CAPM with sector betas managed to achieve higher adjusted R- squared measures only for seven companies, with the results higher by 0.086 on average.

These companies were from different sectors. Therefore, one can not conclude that sector beta CAPM has more explanatory power in a particular industry.

The mean and the median adjusted R-squared in the Fama-French OLS regression equations were 0.733 and 0.810 accordingly. Keeping all other things constant, such a result implies that the three-factor model can explain more than 73% of the dependent variable variance. Moreover, the standard deviation of the adjusted R-squared return is 0.214, lower than in both CAPM variations by 0.08 and 0.106, respectively. Therefore, one can expect more consistent coefficients of determination throughout the sample of Baltic stocks.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 50

Approximately 83% of companies had the adjusted R-squared measure higher than 0.5, and

60% of results higher than 0.75.

Figure 15. Frequency of adjusted R-squared in the Fama-French regression

Source: author's estimations

3.10. Statistical issues of the Fama-French model

Compared to the CAPM, the Fama-French model has mixed goodness of data. In terms of normality, 65% of residuals fall on the normal distribution curve compared to 48% and 60% in individual and sector CAPM, respectively, meaning that the residuals are more consistent throughout the sample and should result in lower bias at 95% confidence level.

However, White's test for heteroskedasticity produced similar results throughout the three tests. Data of 18 companies in Fama-French and single beta CAPM and 19 companies in sector beta CAPM had even variances. The error term's size differed across the values of independent variables and might have led to incorrect conclusions about the significance of regression coefficients. Adding two additional factors into regression did not solve the

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 51

problem of misspecification. Even though additional variables did increase the coefficients of determination, Ramsay's RESET test failed to deny the model's non-linear relationships.

Since the Fama-French model includes three factors, the regression analysis had a multicollinearity test. Collinearity is the relationship between two variables in the multiple regression when the independent variable can linearly predict the other independent variable.

Although it does not impact the adjusted R-squared, the factors' coefficient estimates may vary due to small data changes. Only 48% of the companies did not have multicollinearity issues with all variance inflation factors (VIF) below 10. The Fama-French regression equation results for other firms should be used with caution since they contain the same information about the dependent variable. Even the small data adjustments may lead to large model changes.

3.11. Applicability, limitations, and recommendations for future research

The regression analysis of the CAPM and Fama-French models offers practical applicability to some extent. The combined estimations of all companies seem to have strong explanatory power. The Fama-French model appears to have better application potential based on the data than the CAPM. However, the basis for analysis was only the stocks traded on the Baltic stock exchanges, which creates a bias due to the small research sample. Low trading market volumes and illiquidity may prevent investors from fully utilizing the asset pricing models in their trading decisions. Even though the medians and means of adjusted R- squared measures for the whole dataset seem to have strong explanatory power, the variability among the different companies is too significant. Not a single model could provide an equation for a company with high adjusted R-squared and no statistical data problems. To apply the CAPM or the Fama-French model, it is necessary to analyze stocks on an individual basis and make data transformations that would result in the least bias possible.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 52

The empirical analysis of the CAPM and Fama-French models focused on the adjusted R-squared coefficient of determination. However, such an approach contains many limitations due to insufficient data. The regressions included the selected time of ten years; however, that does not mean that the conclusions' consistency was the same throughout the period. The tests on heteroskedasticity implied that the variance of error term sizes differed in most companies' data. Therefore, the application of the model might produce benefits during only a specific timespan. Moreover, the beta calculations were made on 36-month average returns, and risk premium estimations were averaged based on the same period. Investors who would want to apply the paper's regressions results could not modify the data to meet other time periods' demand. Likewise, the selected benchmark in the analysis was the OMX

Baltic All-Share index. The Capital Asset Pricing Model and the Fama-French three-factor model assume that the stock market is perfectly efficient. However, the Baltic stock market is illiquid, and the selected benchmark returns do not fall on the normal distribution curve.

Due to the lack of companies in various sectors, the model could not test the full explanatory power of sector beta CAPM. Furthermore, the few companies that do belong to the same sector differ in terms of subsector and are different in their economic nature.

Therefore, the possibility of bias while performing the regression exists. In terms of the

Fama-French model, there are no universal categories of size and value. The criteria involved in the analysis are subjective and might produce bias. The Fama-French model is sensitive to the separation of data into size groups or the book-to-market ratio. Thus, other benchmark selection might entirely change the results of the regression.

In terms of future research based on the CAPM and the Fama-French model application, it would be beneficial to explore other markets that are similar in economic nature to the Baltic stock markets. The combination of a larger sample could help to reduce the bias and create more extensive portfolios of different sectors. Moreover, additional model

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 53

expansion methods such as Value-at-Risk, Intertemporal capital asset pricing model, or

Fama-French five-factor could help understand the nature behind stock price variability and find mutual connections to explains the effect of various factors. Additionally, the usage of generalized linear models and robust standard errors in regression analysis could reduce statistical issues and provide a more definitive answer to which of the analyzed models is better for application.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 54

Conclusions

1. The Baltic stock market could be a significant measure of raising capital through equity;

however, since the peak of market capitalization in 2010, the recovery process has

remained slow. Even though the Baltic states' economic development has been rapid in

the last decade and the disposable income with employment levels increased, households'

stock market participation has remained minor. Therefore, limited activity prevents

financial markets from fulfilling capital allocation's role to its fullest extent. Below par

financial literacy, relatively low savings rates, and little research on investment subject

prevent the Baltic Stock market from becoming an efficient and robust financial system

player.

2. The Baltic stock market's inefficiency and limited participation present one of the biggest

challenges in estimating company stocks' expected returns. Both the analyzed Capital

Asset Pricing Model and the Fama-French three-factor model assume that markets are

efficient. Therefore, the market risk premium should explain the stock returns'

variability. However, due to market inefficiencies, unrealistic assumptions (such as

unlimited borrowing), and biased beta values, it is improbable to accurately estimate

company stocks' expected returns. Moreover, it is impossible to observe the market

portfolio as it would have to contain any investment in any market that holds value.

3. The analyzed Capital Asset Pricing Model and the Fama-French model are similar in

their nature. The practitioners and the academic community have appraised the CAPM as

an elegant and straightforward way of estimating the expected returns. However, it

contains many real-world application disadvantages. Therefore, the model assumes

market efficiency and does not account for the anomalies that could significantly change

estimation results. The model proposed by Fama and French includes additional size and

value factors in terms of market capitalization and book-to-market equity ratio. However,

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 55

the quantitative criteria for creating categories remain subjective and may vary from

market to market.

4. The calculation of expected returns in the empirical analysis contained the estimation of

beta coefficients representing the asset returns' relationship with the variables. The

Capital Asset Pricing model included only one factor – market premium, whereas the

Fama-French model made the expansion of size and value factor. The ordinary least

squares regression was the primary tool used to estimate the adjusted R-squared

coefficient of determination. The measure provided information on how well the

equation variables explained stock returns variability in both analyzed asset pricing

models.

5. The empirical analysis concluded that the coefficient of determination is higher in the

Fama-French model than in the CAPM and therefore has more explanatory power.

According to the adjusted R-squared, the OLS regression results can explain more than

73% of dependent variable variance compared to the maximum 66% sector beta CAPM.

Moreover, the Fama-French model contained fewer statistical issues, with 60% of

companies' residuals falling on the normal distribution curve. However, the model's

factors are highly correlated; thus, small changes in data might change coefficients

significantly. Even though the results imply that the three-factor model is better in

explaining the variability of expected returns, it is sensitive to the subjective value of size

and book-to-market equity ratio criteria. Selecting other values could significantly

impact the Fama-French model's applicability, whereas the one factor of the Capital

Asset Pricing model has fewer ways to be misapprehended.

6. The research findings imply that the Fama-French model with selected assumptions and

data sample can better explain stock return variability than the CAPM model. However,

effectiveness varies under individual companies. The data has many statistical issues that

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 56

are not consistent throughout the tested period. Therefore, investors should extend the

analysis by adjusting the analyzed period. Moreover, to fully utilize the asset pricing

models and avoid bias, one must make data modifications and not rely only on the results

estimated by one method.

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 57

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ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 61

Appendices

Appendix 1. OMX Baltic All-Share monthly returns

Date Value Change Date Value Change Date Value Change

2020.10.30 783.97 -2.2% 2017.02.28 653.84 -0.1% 2013.07.31 465.76 3.5%

2020.09.30 801.29 -1.0% 2017.01.31 654.59 1.9% 2013.06.28 450.11 1.2%

2020.08.31 809.62 -0.9% 2016.12.30 642.65 1.4% 2013.05.31 444.72 -0.2%

2020.07.31 816.66 2.2% 2016.11.30 633.72 1.0% 2013.04.30 445.53 -0.5%

2020.06.30 799.42 2.3% 2016.10.31 627.46 2.6% 2013.03.28 447.61 7.6%

2020.05.29 781.21 4.6% 2016.09.30 611.4 2.2% 2013.02.28 416.08 -1.0%

2020.04.30 746.69 12.7% 2016.08.31 598.44 -1.0% 2013.01.31 420.1 4.5%

2020.03.31 662.51 -16.1% 2016.07.29 604.22 4.0% 2012.12.28 401.82 3.5%

2020.02.28 790 -5.5% 2016.06.30 580.95 -1.3% 2012.11.30 388.1 1.5%

2020.01.31 835.87 3.7% 2016.05.31 588.58 1.2% 2012.10.31 382.43 0.5%

2019.12.30 806.27 0.7% 2016.04.29 581.8 0.9% 2012.09.28 380.46 -1.1%

2019.11.29 801 1.1% 2016.03.31 576.63 3.7% 2012.08.31 384.54 1.8%

2019.10.31 792.36 1.4% 2016.02.29 555.9 2.4% 2012.07.31 377.92 2.8%

2019.09.30 781.71 -0.3% 2016.01.29 542.86 -0.2% 2012.06.29 367.48 1.7%

2019.08.30 783.83 -1.4% 2015.12.30 543.96 0.9% 2012.05.31 361.39 -1.3%

2019.07.31 794.62 2.4% 2015.11.30 539.2 1.2% 2012.04.30 366.31 4.2%

2019.06.28 776.31 -0.4% 2015.10.30 532.82 0.9% 2012.03.30 351.53 0.2%

2019.05.31 779.28 -0.7% 2015.09.30 528.18 3.5% 2012.02.29 350.78 4.0%

2019.04.30 784.97 2.9% 2015.08.31 510.32 -0.5% 2012.01.31 337.28 3.6%

2019.03.29 762.75 0.8% 2015.07.31 513.05 1.6% 2011.12.30 325.54 -2.1%

2019.02.28 756.75 -0.6% 2015.06.30 505.04 -1.4% 2011.11.30 332.68 -9.4%

2019.01.31 761.49 6.1% 2015.05.29 512.16 -0.1% 2011.10.31 367.02 5.8%

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 62

2018.12.28 717.75 -4.5% 2015.04.30 512.74 1.6% 2011.09.30 346.81 -10.1%

2018.11.30 751.7 1.6% 2015.03.31 504.56 1.3% 2011.08.31 385.62 -7.7%

2018.10.31 740.1 -3.9% 2015.02.27 498.02 3.9% 2011.07.29 417.93 1.4%

2018.09.28 769.83 -1.0% 2015.01.30 479.51 4.7% 2011.06.30 412.09 -0.6%

2018.08.31 777.26 -2.8% 2014.12.29 458.12 -2.2% 2011.05.31 414.59 0.6%

2018.07.31 799.86 0.0% 2014.11.28 468.54 1.8% 2011.04.29 412.21 -3.8%

2018.06.29 800.19 1.0% 2014.10.31 460.28 -1.1% 2011.03.31 428.7 -3.4%

2018.05.31 791.91 -0.2% 2014.09.30 465.6 -0.7% 2011.02.28 444 1.9%

2018.04.30 793.42 -0.5% 2014.08.29 469.1 -2.5% 2011.01.31 435.7 3.4%

2018.03.29 797.74 -0.7% 2014.07.31 481.06 -0.9% 2010.12.30 421.36 0.8%

2018.02.28 803.42 1.0% 2014.06.30 485.32 1.7% 2010.11.30 418.12 5.5%

2018.01.31 795.67 4.3% 2014.05.30 477.32 2.1% 2010.10.29 396.5 5.6%

2017.12.29 763.02 -1.0% 2014.04.30 467.54 -0.2% 2010.09.30 375.6 7.5%

2017.11.30 770.82 1.3% 2014.03.31 468.31 -3.9% 2010.08.31 349.54 5.0%

2017.10.31 761.23 1.0% 2014.02.28 487.07 -0.2% 2010.07.30 332.75 3.4%

2017.09.29 753.4 -2.6% 2014.01.31 487.82 5.1% 2010.06.30 321.83 -1.7%

2017.08.31 773.72 3.6% 2013.12.30 464.36 0.1% 2010.05.31 327.41 -5.7%

2017.07.31 747.1 6.1% 2013.11.29 463.93 -0.1% 2010.04.30 347.37 4.3%

2017.06.30 704.46 3.2% 2013.10.31 464.43 -0.7% 2010.03.31 332.94 6.0%

2017.05.31 682.61 1.1% 2013.09.30 467.62 0.7% 2010.02.26 314.16 -6.8%

2017.04.28 675.42 1.8% 2013.08.30 464.41 -0.3% 2010.01.29 337.04

2017.03.31 663.49 1.5%

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 63

Appendix 2. Weighted Risk-free rate

TIME Estonia Latvia Lithuania Total TLN RIG Vilnius Risk-free cap weight weight weight rate 2020-10 -0.54% -0.23% 0.16% 7394 33% 12% 55% -0.12% 2020-09 -0.46% -0.20% 0.16% 5913 41% 15% 44% -0.15% 2020-08 -0.45% -0.19% 0.16% 5981 41% 15% 44% -0.14% 2020-07 -0.43% -0.17% 0.16% 6017 42% 14% 43% -0.14% 2020-06 -0.35% 0.01% 0.17% 7162 36% 12% 52% -0.04% 2020-05 -0.40% 0.26% 0.31% 7058 36% 12% 52% 0.05% 2020-04 -0.35% 0.30% 0.31% 6966 35% 11% 54% 0.08% 2020-03 -0.46% -0.06% 0.31% 6379 34% 11% 54% 0.00% 2020-02 -0.39% -0.04% 0.31% 7210 38% 11% 51% 0.00% 2020-01 -0.24% 0.11% 0.31% 7521 39% 11% 51% 0.08% 2019-12 -0.22% 0.16% 0.31% 7252 38% 11% 50% 0.09% 2019-11 -0.28% 0.10% 0.31% 7104 37% 11% 51% 0.07% 2019-10 -0.40% 0.00% 0.31% 6979 38% 11% 51% 0.01% 2019-09 -0.53% -0.11% 0.31% 6856 37% 11% 51% -0.05% 2019-08 -0.59% -0.07% 0.31% 6883 38% 12% 51% -0.07% 2019-07 -0.30% 0.15% 0.31% 6968 38% 11% 51% 0.06% 2019-06 -0.21% 0.33% 0.31% 6784 38% 12% 50% 0.11% 2019-05 -0.03% 0.51% 0.31% 6902 38% 12% 50% 0.20% 2019-04 0.06% 0.58% 0.31% 7083 39% 11% 50% 0.24% 2019-03 0.11% 0.70% 0.31% 7058 39% 11% 50% 0.28% 2019-02 0.17% 0.81% 0.31% 7032 38% 11% 51% 0.31% 2019-01 0.25% 0.95% 0.31% 7105 39% 11% 50% 0.36% 2018-12 0.32% 1.05% 0.31% 6639 39% 11% 50% 0.40% 2018-11 0.44% 1.05% 0.31% 6946 39% 11% 50% 0.44% 2018-10 0.51% 1.01% 0.31% 7217 39% 11% 50% 0.46% 2018-09 0.48% 0.94% 0.31% 7529 39% 10% 51% 0.44% 2018-08 0.39% 0.95% 0.31% 7595 39% 10% 51% 0.41% 2018-07 0.39% 1.06% 0.31% 7802 39% 11% 51% 0.42% 2018-06 0.47% 0.93% 0.31% 7833 39% 11% 50% 0.44% 2018-05 0.58% 0.86% 0.31% 7314 35% 12% 54% 0.47% 2018-04 0.60% 0.80% 0.31% 7411 35% 11% 54% 0.47% 2018-03 0.64% 0.83% 0.31% 7487 35% 11% 53% 0.49% 2018-02 0.77% 0.75% 0.31% 7482 37% 11% 52% 0.53% 2018-01 0.61% 0.60% 0.31% 7853 34% 16% 50% 0.46% 2017-12 0.42% 0.59% 0.31% 7588 34% 16% 50% 0.39% 2017-11 0.43% 0.69% 0.31% 7318 35% 14% 52% 0.40% 2017-10 0.48% 0.71% 0.31% 7303 34% 14% 52% 0.42% 2017-09 0.45% 0.72% 0.31% 7261 34% 13% 52% 0.41% 2017-08 0.45% 0.85% 0.31% 7350 35% 13% 51% 0.43% 2017-07 0.58% 0.98% 0.31% 7157 35% 13% 51% 0.50% 2017-06 0.37% 0.85% 0.31% 6839 34% 14% 52% 0.41% 2017-05 0.44% 0.88% 0.31% 6755 34% 13% 53% 0.43% 2017-04 0.32% 0.92% 0.31% 6802 35% 12% 53% 0.39% 2017-03 0.43% 0.94% 0.31% 6785 35% 12% 53% 0.43%

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 64

2017-02 0.38% 0.99% 0.31% 6624 36% 12% 53% 0.41% 2017-01 0.38% 0.89% 0.31% 6624 35% 12% 53% 0.40% 2016-12 0.35% 0.90% 0.31% 6586 35% 12% 53% 0.40% 2016-11 0.25% 0.56% 0.31% 6513 34% 12% 53% 0.32% 2016-10 0.01% 0.19% 0.31% 6465 34% 12% 54% 0.19% 2016-09 -0.07% 0.10% 0.79% 6382 34% 12% 54% 0.42% 2016-08 -0.12% 0.12% 0.86% 6405 33% 13% 54% 0.44% 2016-07 -0.10% 0.30% 0.86% 6432 34% 13% 53% 0.46% 2016-06 0.05% 0.48% 0.86% 6284 34% 13% 53% 0.53% 2016-05 0.22% 0.51% 0.86% 6460 35% 13% 52% 0.59% 2016-04 0.24% 0.61% 1.31% 6342 32% 13% 54% 0.87% 2016-03 0.32% 0.71% 1.42% 6335 32% 13% 55% 0.97% 2016-02 0.34% 0.88% 1.42% 6145 32% 13% 55% 1.01% 2016-01 0.62% 1.05% 1.47% 6052 30% 14% 56% 1.15% 2015-12 0.71% 1.08% 1.49% 6533 29% 19% 52% 1.19% 2015-11 0.66% 1.19% 1.57% 6454 29% 19% 52% 1.23% 2015-10 0.65% 1.07% 1.64% 6394 28% 20% 52% 1.25% 2015-09 0.78% 1.03% 1.64% 6313 29% 17% 54% 1.29% 2015-08 0.74% 0.96% 1.64% 6223 29% 14% 56% 1.28% 2015-07 0.88% 1.25% 1.64% 6225 29% 14% 56% 1.36% 2015-06 0.96% 1.28% 1.41% 6177 29% 15% 56% 1.26% 2015-05 0.67% 0.84% 0.99% 6381 29% 15% 56% 0.88% 2015-04 0.23% 0.42% 0.58% 6533 29% 14% 57% 0.46% 2015-03 0.31% 0.56% 1.11% 6477 29% 14% 57% 0.80% 2015-02 0.41% 0.78% 1.20% 6264 30% 14% 56% 0.90% 2015-01 0.51% 1.10% 1.66% 6069 30% 14% 56% 1.24% 2014-12 0.76% 1.63% 1.90% 5853 28% 15% 57% 1.54% 2014-11 0.91% 1.77% 2.17% 6015 29% 15% 57% 1.75% 2014-10 1.00% 2.18% 2.27% 5875 29% 15% 56% 1.89% 2014-09 1.13% 2.28% 2.42% 5918 29% 15% 56% 2.03% 2014-08 1.17% 2.35% 2.61% 5913 29% 15% 56% 2.15% 2014-07 1.36% 2.40% 2.90% 6082 29% 15% 56% 2.38% 2014-06 1.54% 2.53% 2.92% 6043 30% 16% 55% 2.45% 2014-05 1.62% 2.74% 2.98% 5864 31% 16% 53% 2.52% 2014-04 1.77% 2.80% 3.26% 5908 31% 15% 54% 2.73% 2014-03 1.84% 2.87% 3.33% 6084 30% 16% 54% 2.81% 2014-02 1.90% 3.07% 3.33% 6110 31% 16% 53% 2.85% 2014-01 2.09% 3.48% 3.42% 6064 31% 16% 52% 3.01% 2013-12 2.13% 3.62% 3.69% 5732 33% 17% 51% 3.17% 2013-11 2.00% 3.71% 3.99% 5738 34% 16% 50% 3.27% 2013-10 2.08% 3.78% 4.01% 5730 34% 16% 50% 3.32% 2013-09 2.21% 3.45% 3.89% 5778 33% 16% 50% 3.26% 2013-08 2.08% 3.25% 3.65% 5727 34% 17% 50% 3.05% 2013-07 1.95% 3.25% 3.54% 5926 33% 16% 51% 2.98% 2013-06 1.98% 3.17% 3.54% 5888 32% 16% 52% 2.99% 2013-05 1.70% 3.10% 3.54% 5920 31% 15% 53% 2.89% 2013-04 1.64% 3.15% 3.95% 6125 32% 14% 54% 3.09% 2013-03 1.83% 3.17% 4.15% 6195 33% 14% 53% 3.25%

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 65

2013-02 1.98% 3.22% 4.06% 5828 32% 15% 54% 3.28% 2013-01 1.93% 3.21% 3.97% 5837 32% 15% 54% 3.21% 2012-12 1.77% 3.24% 4.00% 5603 32% 15% 53% 3.18% 2012-11 1.86% 3.32% 4.11% 5449 30% 15% 54% 3.31% 2012-10 1.98% 3.52% 4.32% 5281 29% 15% 56% 3.53% 2012-09 2.03% 3.92% 4.53% 5245 29% 16% 56% 3.72% 2012-08 1.92% 4.45% 4.84% 5337 28% 15% 56% 3.95% 2012-07 1.96% 4.67% 4.82% 5266 28% 15% 57% 4.01% 2012-06 2.13% 5.07% 4.96% 5168 27% 16% 57% 4.21% 2012-05 2.23% 5.15% 5.30% 5086 27% 17% 56% 4.44% 2012-04 2.56% 5.10% 5.30% 5364 27% 16% 57% 4.52% 2012-03 2.63% 5.15% 5.29% 5237 27% 17% 56% 4.55% 2012-02 2.65% 5.45% 5.15% 5212 27% 17% 56% 4.53% 2012-01 2.70% 5.74% 5.35% 4996 26% 17% 57% 4.74% 2011-12 2.76% 5.93% 5.75% 5206 24% 16% 60% 5.07% 2011-11 2.89% 5.73% 5.25% 5306 25% 16% 60% 4.74% 2011-10 2.72% 5.62% 5.06% 5527 24% 16% 61% 4.59% 2011-09 2.50% 5.60% 5.09% 5302 24% 16% 60% 4.56% 2011-08 2.83% 5.60% 5.05% 5559 25% 16% 59% 4.58% 2011-07 3.24% 5.67% 5.05% 6422 25% 15% 61% 4.70% 2011-06 3.35% 5.87% 5.05% 6370 24% 16% 60% 4.78% 2011-05 3.43% 6.36% 5.05% 6435 25% 15% 60% 4.84% 2011-04 3.65% 6.47% 5.12% 6535 26% 14% 60% 4.94% 2011-03 3.55% 6.49% 5.15% 6789 26% 15% 59% 4.93% 2011-02 3.55% 6.17% 5.15% 7033 26% 15% 59% 4.89% 2011-01 3.41% 5.38% 5.15% 6961 26% 14% 60% 4.73% 2010-12 3.32% 7.55% 5.15% 6847 25% 14% 62% 5.03% 2010-11 2.92% 8.99% 5.15% 6505 25% 14% 60% 5.13% 2010-10 2.73% 9.24% 5.15% 6605 24% 14% 62% 5.15% 2010-09 2.69% 9.97% 5.15% 6307 23% 15% 62% 5.32% 2010-08 2.71% 10.00% 5.15% 5738 24% 17% 59% 5.38% 2010-07 3.00% 10.00% 5.15% 5512 24% 16% 60% 5.44% 2010-06 3.08% 10.12% 5.15% 5428 24% 16% 59% 5.45% 2010-05 3.20% 10.13% 5.15% 5592 25% 15% 60% 5.41% 2010-04 3.47% 10.13% 5.15% 5990 26% 15% 59% 5.47% 2010-03 3.51% 10.54% 5.15% 5753 25% 14% 61% 5.49% 2010-02 3.57% 13.62% 7.15% 5513 24% 14% 62% 7.20% 2010-01 3.68% 13.76% 8.15% 5958 25% 13% 62% 7.78%

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 66

Appendix 3. Betas by sector.

Basic Consumer Consumer Fin. Health Industrials RE Telecom Utilities Materials Disc. Staples care 2013.02.28 1.19 1.28 0.67 1.28 0.95 1.14 1.53 0.45 0.32 2013.03.28 1.30 1.24 0.77 1.35 1.07 1.14 1.50 0.45 0.35 2013.04.30 1.26 1.21 0.84 1.32 1.20 1.10 1.33 0.44 0.43 2013.05.31 1.27 1.21 0.85 1.34 1.16 1.10 1.34 0.46 0.41 2013.06.28 1.26 1.18 0.83 1.38 1.14 1.12 1.40 0.37 0.35 2013.07.31 1.27 1.17 0.83 1.38 1.14 1.13 1.36 0.38 0.36 2013.08.30 1.23 1.14 0.83 1.36 1.13 1.14 1.34 0.38 0.35 2013.09.30 1.15 1.11 0.80 1.38 1.15 1.16 1.22 0.37 0.43 2013.10.31 1.17 1.11 0.81 1.37 1.18 1.06 1.33 0.30 0.47 2013.11.29 0.99 1.09 0.80 1.12 1.24 1.04 1.24 0.29 0.49 2013.12.30 1.06 1.05 0.82 0.99 1.26 1.03 1.20 0.28 0.54 2014.01.31 1.06 1.06 0.82 0.99 1.26 1.03 1.20 0.28 0.54 2014.02.28 1.07 1.02 0.86 1.01 1.32 1.03 1.13 0.31 0.55 2014.03.31 1.06 1.02 0.86 1.00 1.32 1.04 1.14 0.31 0.54 2014.04.30 1.09 1.04 0.88 0.98 1.38 1.04 0.98 0.27 0.50 2014.05.30 1.13 1.03 0.91 1.00 1.46 1.00 0.99 0.27 0.50 2014.06.30 1.17 1.02 0.91 1.00 1.46 0.99 1.00 0.23 0.49 2014.07.31 1.18 1.01 0.91 1.00 1.48 0.99 0.96 0.26 0.50 2014.08.29 1.17 1.00 0.92 0.98 1.48 0.98 0.94 0.28 0.51 2014.09.30 1.05 1.01 0.89 1.02 1.56 0.84 0.78 0.29 0.43 2014.10.31 0.90 0.97 1.04 0.91 1.52 0.79 0.52 0.34 0.37 2014.11.28 0.97 0.91 0.99 0.86 1.60 0.72 0.54 0.37 0.40 2014.12.29 1.02 1.35 0.99 0.73 1.93 0.89 0.33 0.42 0.63 2015.01.30 0.84 1.32 1.06 0.72 1.97 0.80 0.08 0.47 0.67 2015.02.27 0.39 1.39 0.96 0.70 1.94 0.87 -0.09 0.47 0.64 2015.03.31 0.49 1.39 0.93 0.58 1.81 1.01 0.01 0.43 0.52 2015.04.30 0.48 1.41 0.93 0.62 1.79 1.01 0.02 0.43 0.53 2015.05.29 0.60 1.41 0.98 0.38 1.90 1.02 0.04 0.44 0.59 2015.06.30 0.57 1.39 0.98 0.27 1.82 1.01 0.05 0.43 0.54 2015.07.31 0.63 1.26 0.96 0.23 1.84 1.01 -0.10 0.45 0.58 2015.08.31 0.62 1.27 0.95 0.24 1.87 1.02 -0.12 0.42 0.62 2015.09.30 0.68 1.23 0.99 0.26 1.89 1.05 -0.06 0.42 0.60 2015.10.30 0.50 1.16 0.94 0.18 1.78 0.96 0.00 0.37 0.53 2015.11.30 0.50 1.16 0.94 0.15 1.78 0.96 -0.04 0.38 0.53 2015.12.30 0.49 1.16 0.94 0.15 1.76 0.96 -0.04 0.36 0.54 2016.01.29 0.48 1.08 1.02 0.20 1.76 1.01 -0.04 0.30 0.55 2016.02.29 0.32 1.04 0.94 0.09 1.85 1.02 -0.16 0.22 0.49 2016.03.31 0.33 1.07 0.93 0.17 1.86 1.03 -0.13 0.21 0.49 2016.04.29 0.48 0.92 0.77 0.33 1.61 1.40 -0.10 0.31 0.45 2016.05.31 0.54 0.91 0.75 0.34 1.59 1.40 -0.13 0.35 0.47 2016.06.30 0.57 0.87 0.76 0.34 1.68 1.37 -0.15 0.32 0.45 2016.07.29 0.62 0.91 0.77 0.41 1.68 1.31 -0.13 0.37 0.49 2016.08.31 0.54 0.96 0.69 0.55 1.70 1.47 -0.38 0.48 0.43

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 67

2016.09.30 0.54 0.99 0.68 0.56 1.59 1.49 -0.37 0.47 0.46 2016.10.31 0.51 0.99 0.72 0.55 1.56 1.50 -0.39 0.47 0.40 2016.11.30 0.40 1.00 0.75 0.70 1.51 1.47 -0.40 0.45 0.44 2016.12.30 0.41 1.01 0.73 0.66 1.53 1.45 -0.39 0.45 0.44 2017.01.31 0.41 0.99 0.72 0.75 1.53 1.43 -0.41 0.47 0.45 2017.02.28 0.26 1.08 0.65 0.73 1.41 1.50 -0.40 0.43 0.30 2017.03.31 0.28 1.05 0.65 0.72 1.40 1.53 -0.37 0.43 0.33 2017.04.28 0.06 0.99 0.59 0.59 1.13 1.64 0.06 0.69 0.34 2017.05.31 0.15 0.95 0.58 0.55 1.16 1.70 0.09 0.69 0.33 2017.06.30 0.02 0.98 0.57 0.53 1.15 1.75 0.08 0.80 0.32 2017.07.31 -0.03 0.91 0.61 0.53 1.27 1.69 0.29 0.71 0.24 2017.08.31 0.47 1.05 0.47 0.78 1.16 1.47 0.80 0.76 0.20 2017.09.29 0.47 1.08 0.40 0.66 0.95 1.53 0.67 0.85 0.12 2017.10.31 0.47 1.07 0.47 0.69 0.78 1.45 0.84 0.90 0.08 2017.11.30 0.40 1.14 0.42 0.75 0.72 1.46 0.87 0.92 0.14 2017.12.29 0.41 1.14 0.43 0.77 0.72 1.47 0.89 0.90 0.13 2018.01.31 0.62 0.99 0.38 0.87 0.92 1.63 1.07 0.85 0.13 2018.02.28 0.84 0.82 0.45 1.02 0.94 1.66 2.80 1.00 0.10 2018.03.29 0.73 0.76 0.53 1.17 1.04 1.45 3.07 1.04 0.09 2018.04.30 0.68 0.86 0.54 1.14 1.00 1.34 3.07 0.90 0.09 2018.05.31 0.64 0.93 0.55 1.14 1.00 1.28 3.14 0.87 0.10 2018.06.29 0.67 0.90 0.58 1.17 1.08 1.27 2.96 0.90 0.10 2018.07.31 0.67 1.12 0.56 1.29 1.05 1.25 3.28 0.90 -0.02 2018.08.31 0.61 1.14 0.60 1.29 1.12 1.24 3.21 0.88 0.02 2018.09.28 0.73 1.12 0.53 1.21 1.08 1.15 2.78 0.90 0.12 2018.10.31 0.85 1.23 0.67 1.30 1.27 1.26 2.87 0.92 0.13 2018.11.30 0.81 1.18 0.71 1.40 1.29 1.25 2.54 0.86 0.21 2018.12.28 0.79 1.21 0.70 1.39 1.30 1.24 2.53 0.86 0.20 2019.01.31 0.78 1.24 0.69 1.42 1.13 1.21 2.12 0.76 0.29 2019.02.28 0.84 1.22 0.75 1.46 1.28 1.11 1.85 0.73 0.41 2019.03.29 0.84 1.22 0.77 1.46 1.27 1.11 1.87 0.73 0.41 2019.04.30 0.85 1.22 0.78 1.46 1.31 0.92 1.88 0.72 0.40 2019.05.31 0.81 1.20 0.75 1.43 1.52 0.89 1.84 0.76 0.40 2019.06.28 0.83 1.22 0.75 1.42 1.46 0.94 1.87 0.81 0.39 2019.07.31 0.89 1.21 0.75 1.37 1.40 1.02 1.88 0.78 0.38 2019.08.30 0.96 1.20 0.76 1.38 1.36 0.88 2.05 0.80 0.46 2019.09.30 0.98 1.18 0.78 1.35 1.45 0.89 2.12 0.80 0.47 2019.10.31 1.02 1.18 0.76 1.37 1.47 0.86 2.17 0.81 0.50 2019.11.29 1.11 1.18 0.72 1.28 1.54 0.88 2.17 0.85 0.49 2019.12.30 1.10 1.18 0.72 1.26 1.56 0.88 2.18 0.86 0.48 2020.01.31 1.11 1.18 0.72 1.26 1.58 0.88 2.21 0.85 0.48 2020.02.28 0.91 1.14 0.76 1.29 1.44 0.93 2.03 0.87 0.53 2020.03.31 0.83 1.11 0.82 1.22 1.28 0.95 1.86 0.87 0.53 2020.04.30 0.57 1.19 0.84 1.31 0.87 1.08 1.14 0.55 0.46 2020.05.29 0.63 1.06 0.92 1.31 0.75 1.05 0.92 0.65 0.52 2020.06.30 0.61 1.10 0.90 1.28 0.74 1.13 0.89 0.64 0.53 2020.07.31 0.62 1.11 0.89 1.29 0.70 1.14 0.86 0.65 0.54 2020.08.31 0.54 1.06 0.92 1.30 0.71 1.16 0.78 0.68 0.56

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 68

2020.09.30 0.53 1.06 0.89 1.29 0.72 1.16 0.82 0.67 0.56 2020.10.30 0.54 1.05 0.89 1.29 0.75 1.17 0.80 0.66 0.57

Appendix 4. SMB and HML estimates

Company Small Big portfolio SMB High Low HML portfolio portfolio portfolio 2020.10.30 3.8% -0.8% 4.6% -1.6% -0.3% -1.4% 2020.09.30 2.7% -1.6% 4.2% -0.5% -0.4% -0.1% 2020.08.31 3.6% -2.5% 6.1% 0.1% -1.6% 1.7% 2020.07.31 3.4% -0.7% 4.1% 4.7% -2.0% 6.7% 2020.06.30 0.2% 1.5% -1.2% 3.2% 1.1% 2.1% 2020.05.29 11.1% 1.2% 9.9% 1.8% 3.1% -1.3% 2020.04.30 4.4% 10.9% -6.5% 13.0% 10.2% 2.8% 2020.03.31 -7.5% -16.4% 8.9% -17.0% -13.9% -3.1% 2020.02.28 -1.8% -5.4% 3.6% -2.1% -5.2% 3.0% 2020.01.31 4.5% 5.6% -1.2% -0.6% 5.7% -6.3% 2019.12.30 13.3% 0.4% 12.9% 1.2% 0.1% 1.1% 2019.11.29 0.3% 1.1% -0.8% 0.3% 1.9% -1.6% 2019.10.31 1.4% 1.1% 0.4% 1.0% 0.9% 0.1% 2019.09.30 -3.6% 1.0% -4.6% 0.5% 0.0% 0.5% 2019.08.30 -0.3% -1.2% 0.9% -2.0% -1.4% -0.7% 2019.07.31 1.2% 3.0% -1.8% 3.9% 2.4% 1.5% 2019.06.28 3.2% -1.6% 4.8% 1.7% -2.7% 4.5% 2019.05.31 -2.9% -2.3% -0.6% -2.0% -2.6% 0.6% 2019.04.30 3.2% 0.0% 3.2% 3.6% 0.8% 2.8% 2019.03.29 -2.7% 2.8% -5.5% -1.3% 2.5% -3.8% 2019.02.28 14.0% -0.9% 14.9% 2.3% -0.4% 2.8% 2019.01.31 3.3% 6.3% -3.1% 6.4% 7.0% -0.6% 2018.12.28 -1.7% -4.6% 2.9% -4.6% -3.6% -1.0% 2018.11.30 -2.3% 2.4% -4.7% -0.2% 0.8% -1.0% 2018.10.31 -1.2% -3.8% 2.7% -6.3% -3.3% -3.0% 2018.09.28 -2.6% -3.1% 0.5% -0.7% -4.0% 3.3% 2018.08.31 -5.3% 0.0% -5.3% -0.9% -0.8% -0.1% 2018.07.31 0.4% 0.5% -0.1% -1.7% 0.8% -2.6% 2018.06.29 -1.4% -1.5% 0.1% 0.2% -2.5% 2.7% 2018.05.31 -2.7% -1.5% -1.2% -0.4% -3.3% 2.8% 2018.04.30 -1.8% 1.2% -3.0% -0.7% 1.6% -2.3% 2018.03.29 0.8% 0.4% 0.4% 0.6% -0.4% 0.9% 2018.02.28 -3.0% -1.4% -1.5% -1.2% -1.5% 0.3% 2018.01.31 26.2% 4.3% 21.9% 7.0% 4.7% 2.3% 2017.12.29 -3.5% -4.8% 1.2% 1.5% -3.5% 5.1% 2017.11.30 0.7% 1.7% -1.0% 0.3% 1.5% -1.2% 2017.10.31 0.8% 0.2% 0.6% 2.7% 0.1% 2.7% 2017.09.29 3.5% -1.9% 5.4% -1.8% -1.3% -0.5% 2017.08.31 33.6% 4.3% 29.3% 4.1% 8.5% -4.4%

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 69

2017.07.31 -3.0% 6.9% -9.9% 6.5% 4.1% 2.4% 2017.06.30 0.5% 2.0% -1.5% 3.2% 1.5% 1.7% 2017.05.31 -1.1% 1.2% -2.2% 4.0% -2.3% 6.3% 2017.04.28 2.3% 2.1% 0.2% 4.8% 3.0% 1.8% 2017.03.31 1.4% 3.4% -2.1% 4.3% 1.8% 2.5% 2017.02.28 -0.3% -2.1% 1.8% 0.6% -1.8% 2.4% 2017.01.31 0.3% 0.4% -0.1% -0.1% 0.5% -0.6% 2016.12.30 -0.1% 0.3% -0.4% -0.5% 0.6% -1.1% 2016.11.30 0.0% 2.4% -2.4% 6.3% 0.5% 5.8% 2016.10.31 -0.2% 6.5% -6.8% 8.3% 1.4% 6.9% 2016.09.30 3.7% 0.0% 3.7% 3.2% 0.6% 2.6% 2016.08.31 1.1% -1.3% 2.4% 1.0% -0.2% 1.1% 2016.07.29 4.9% 6.4% -1.5% 3.9% 5.0% -1.0% 2016.06.30 -3.4% -4.9% 1.5% 0.6% -3.3% 3.8% 2016.05.31 5.6% 2.6% 3.0% 5.0% -0.4% 5.4% 2016.04.29 1.8% 4.7% -2.9% 4.9% 0.6% 4.3% 2016.03.31 -6.5% -0.9% -5.6% -2.0% 1.4% -3.4% 2016.02.29 9.5% 2.1% 7.4% 4.9% 2.9% 2.0% 2016.01.29 0.3% 9.8% -9.5% 8.9% 3.5% 5.3% 2015.12.30 1.1% 12.1% -11.0% 18.8% -2.2% 21.0% 2015.11.30 1.4% 2.4% -1.0% -0.1% 3.3% -3.4% 2015.10.30 7.2% 0.0% 7.2% 1.3% -2.5% 3.8% 2015.09.30 -4.6% -1.1% -3.6% -1.1% -2.0% 0.9% 2015.08.31 -12.1% -0.3% -11.8% -2.3% -3.1% 0.7% 2015.07.31 1.1% 1.5% -0.4% 1.9% 2.5% -0.6% 2015.06.30 23.5% -3.8% 27.4% 1.6% -4.3% 5.8% 2015.05.29 -0.1% -1.2% 1.0% -2.5% 0.7% -3.2% 2015.04.30 1.8% 2.5% -0.6% 4.4% 2.1% 2.3% 2015.03.31 4.2% 0.3% 3.9% -0.2% 1.1% -1.4% 2015.02.27 -4.8% 2.7% -7.5% 1.9% 1.1% 0.8% 2015.01.30 -0.8% 2.8% -3.6% 0.2% 3.4% -3.2% 2014.12.29 0.1% -3.1% 3.2% -2.7% -2.6% -0.1% 2014.11.28 1.1% 6.7% -5.6% 5.4% 2.4% 3.0% 2014.10.31 -0.8% 1.1% -1.9% 0.2% 11.0% -10.7% 2014.09.30 2.1% -0.3% 2.4% 1.7% -1.3% 3.0% 2014.08.29 -5.0% -4.0% -0.9% -5.5% -3.0% -2.5% 2014.07.31 1.0% 1.3% -0.3% 1.7% 0.2% 1.5% 2014.06.30 3.6% 4.9% -1.3% 5.6% 0.2% 5.5% 2014.05.30 -1.2% -1.6% 0.5% -1.9% 1.3% -3.2% 2014.04.30 -1.8% 2.0% -3.8% -0.1% 0.5% -0.6% 2014.03.31 -4.1% -2.7% -1.3% -2.6% -5.1% 2.5% 2014.02.28 2.7% 0.5% 2.2% 1.7% 0.6% 1.1% 2014.01.31 9.4% 10.4% -1.0% 12.2% 3.9% 8.3% 2013.12.30 1.3% 1.2% 0.1% 0.6% 1.4% -0.8% 2013.11.29 -3.0% -0.7% -2.3% -0.3% -0.3% -0.1% 2013.10.31 -6.9% 3.6% -10.5% 0.6% 3.0% -2.5% 2013.09.30 0.8% 0.8% 0.0% 0.0% -0.7% 0.7% 2013.08.30 2.2% -1.2% 3.3% -1.2% -0.9% -0.3%

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 70

2013.07.31 3.9% 0.6% 3.3% 1.5% 1.2% 0.2% 2013.06.28 1.9% -1.2% 3.1% -0.6% -0.8% 0.1% 2013.05.31 3.2% -2.1% 5.3% 2.7% -2.1% 4.9% 2013.04.30 3.0% 2.8% 0.1% 0.6% 1.1% -0.5% 2013.03.28 4.7% 3.5% 1.2% 2.2% 5.7% -3.6% 2013.02.28 -2.6% 3.7% -6.3% 4.6% -1.6% 6.2% 2013.01.31 3.3% 6.3% -3.0% 4.5% 9.0% -4.5% 2012.12.28 4.5% -3.5% 8.0% -4.6% 2.0% -6.5% 2012.11.30 2.8% 1.2% 1.6% -0.1% 1.1% -1.1% 2012.10.31 9.2% 3.2% 6.0% 1.5% 0.6% 0.8% 2012.09.28 -2.6% -1.7% -0.9% -1.9% 2.5% -4.5% 2012.08.31 6.7% 6.4% 0.3% 5.4% 7.3% -1.9% 2012.07.31 5.5% -1.2% 6.7% -1.2% 3.5% -4.7% 2012.06.29 -3.9% -2.2% -1.8% -0.9% -1.2% 0.4% 2012.05.31 -3.6% -4.2% 0.6% -5.0% -4.1% -1.0% 2012.04.30 2.4% -0.1% 2.5% -1.5% 2.5% -4.0% 2012.03.30 8.6% 4.4% 4.1% 2.9% 3.1% -0.3% 2012.02.29 3.1% 7.5% -4.3% 4.3% 6.0% -1.7% 2012.01.31 2.1% 2.0% 0.1% 5.3% 2.9% 2.4% 2011.12.30 -2.8% -2.2% -0.6% -1.6% -3.3% 1.8% 2011.11.30 -7.2% -4.4% -2.8% -7.5% -3.1% -4.4% 2011.10.31 8.5% 0.4% 8.1% 6.7% 0.8% 5.9% 2011.09.30 -1.4% -11.6% 10.2% -13.1% -6.1% -7.0% 2011.08.31 -7.4% -6.9% -0.5% -7.6% -8.7% 1.1% 2011.07.29 4.3% 5.5% -1.2% 5.1% 2.3% 2.9% 2011.06.30 -1.5% -0.9% -0.5% 4.0% -4.8% 8.8% 2011.05.31 -1.5% 2.9% -4.4% 5.5% -2.4% 7.9% 2011.04.29 -3.8% -1.2% -2.6% -0.6% -2.6% 2.0% 2011.03.31 -0.2% -3.5% 3.3% -3.5% -1.1% -2.5% 2011.02.28 3.2% 2.0% 1.1% 3.0% -1.3% 4.3% 2011.01.31 0.9% 2.2% -1.3% -1.7% 4.7% -6.4% 2010.12.30 -3.3% 1.3% -4.6% -2.0% 2.4% -4.3% 2010.11.30 4.8% 0.6% 4.2% 1.0% 4.9% -3.9% 2010.10.29 11.5% 5.9% 5.6% 7.2% 4.3% 2.9% 2010.09.30 7.1% 0.0% 7.1% 4.4% 2.5% 2.0% 2010.08.31 95.8% 0.9% 94.9% 9.2% 17.2% -8.0% 2010.07.30 5.0% 1.0% 4.0% 5.1% 1.9% 3.2% 2010.06.30 -4.5% -5.3% 0.8% -0.8% -4.5% 3.6% 2010.05.31 -9.6% -10.5% 1.0% -3.6% -10.0% 6.4% 2010.04.30 8.1% 1.4% 6.7% 1.2% 4.3% -3.1% 2010.03.31 8.4% 6.1% 2.3% 4.9% 6.9% -2.0% 2010.02.26 -8.1% -3.1% -4.9% -0.8% -4.9% 4.1%

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 71

Appendix 5. Simple CAPM OLS regression results

Heterosk Normality edasticity Ramsey's Const CAPM const CAPM Adjusted of residuals. White's RESET p- coef coef p p R-sq P-value test p- value value Apranga -0.01 0.30 <0.01 <0.01 0.49 <0.05 <0.05 <0.05 Arco Vara -0.01 1.60 <0.01 <0.01 0.01 >0.05 >0.05 <0.05 Auga Group 0.00 1.33 0.09 <0.01 0.44 >0.05 <0.05 <0.05 Baltika -0.01 0.95 <0.01 <0.01 0.01 >0.05 >0.05 <0.05 Ditton pievadķēžu 0.01 2.11 <0.01 <0.01 0.58 >0.05 >0.05 <0.05 rūpnīca Insufficient Insufficie East West Agro -0.05 5.42 <0.01 <0.01 0.99 >0.05 Data nt Data Ekspress Grupp -0.01 0.77 <0.01 <0.01 0.55 <0.05 >0.05 <0.05 Grigeo 0.00 0.91 0.20 <0.01 0.68 >0.05 <0.05 <0.05 Grindeks 0.00 1.68 <0.01 <0.01 0.87 >0.05 <0.05 <0.05 Harju Elekter 0.00 1.47 0.61 <0.01 0.94 >0.05 <0.05 <0.05 Invalda INVL 0.00 0.77 <0.01 <0.01 0.82 <0.05 <0.05 <0.05 INVL Baltic 0.00 -0.04 0.02 0.74 -0.02 >0.05 >0.05 <0.05 Farmland INVL Baltic -0.05 9.56 <0.01 <0.01 0.80 <0.05 <0.05 <0.05 Real Estate INVL -0.06 6.88 <0.01 <0.01 0.52 >0.05 >0.05 >0.05 Technology K2 LT 0.02 -4.87 0.20 <0.01 0.12 >0.05 <0.05 <0.05 Kauno Energija 0.01 4.28 <0.01 <0.01 0.82 >0.05 <0.05 <0.05 Klaipėdos -0.01 1.21 <0.01 <0.01 0.66 <0.05 <0.05 <0.05 Nafta Kurzemes 0.06 2.08 <0.01 <0.01 0.59 >0.05 <0.05 <0.05 atslēga 1 Latvijas 0.02 2.15 <0.01 <0.01 0.49 <0.05 <0.05 <0.05 Balzams Latvijas Gāze 0.00 2.72 <0.01 <0.01 0.93 <0.05 <0.05 >0.05 Latvijas Jūras medicīnas 0.04 2.46 <0.01 <0.01 0.84 <0.05 <0.05 <0.05 centrs LHV Group 0.00 0.68 0.94 0.34 0.00 >0.05 >0.05 >0.05 Linas 0.00 0.92 <0.01 <0.01 0.75 <0.05 <0.05 <0.05 Linas Agro -0.01 0.94 <0.01 <0.01 0.83 <0.05 <0.05 >0.05 Group Linda Nektar -0.03 2.77 0.23 0.55 -0.03 >0.05 >0.05 >0.05 LITGRID -0.01 1.19 <0.01 <0.01 0.84 >0.05 <0.05 <0.05 Merko Ehitus -0.01 1.05 <0.01 <0.01 0.83 >0.05 <0.05 <0.05 Nordecon -0.01 1.09 <0.01 <0.01 0.83 >0.05 >0.05 <0.05 Nordic -0.02 1.06 <0.01 <0.01 0.86 >0.05 >0.05 <0.05 Fibreboard Olainfarm -0.01 0.17 <0.01 <0.01 0.16 <0.05 >0.05 <0.05 Panevėžio -0.02 0.98 <0.01 <0.01 0.85 <0.05 >0.05 <0.05 statybos trestas PATA Saldus 0.01 0.54 <0.01 <0.01 0.40 >0.05 <0.05 <0.05

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 72

Pieno -0.02 0.87 <0.01 <0.01 0.84 >0.05 >0.05 <0.05 žvaigždės PRFoods -0.01 2.05 <0.01 <0.01 0.87 >0.05 >0.05 <0.05 Pro Kapital -0.02 -0.22 <0.01 0.02 0.07 >0.05 <0.05 <0.05 Grupp Rīgas autoelektroapar 0.62 -0.72 <0.01 0.21 0.01 <0.05 <0.05 <0.05 ātu rūpnīca Rīgas elektromašīnbū 0.11 3.66 <0.01 <0.01 0.58 >0.05 <0.05 <0.05 ves rūpnīca Rīgas juvelierizstrādā 0.01 0.97 <0.01 <0.01 0.47 >0.05 >0.05 <0.05 jumu rūpnīca Rīgas kuģu -0.02 0.84 <0.01 <0.01 0.44 <0.05 <0.05 <0.05 būvētava Rokiškio sūris 0.00 1.98 <0.01 <0.01 0.92 <0.05 <0.05 <0.05 Siguldas ciltslietu un mākslīgās 0.02 1.88 <0.01 <0.01 0.68 >0.05 >0.05 <0.05 apsēklošanas stacija Silvano fashion -0.01 1.49 <0.01 <0.01 0.69 >0.05 <0.05 <0.05 group Snaigė -0.02 0.56 <0.01 <0.01 0.48 >0.05 <0.05 <0.05 Šiaulių Bankas 0.01 1.52 <0.01 <0.01 0.80 <0.05 <0.05 <0.05 Tallink Grupp -0.01 0.94 <0.01 <0.01 0.82 >0.05 <0.05 <0.05 Tallinna Kaubamaja 0.00 1.21 0.01 <0.01 0.83 >0.05 <0.05 <0.05 Grupp Tallinna Vesi -0.01 1.16 <0.01 <0.01 0.35 <0.05 <0.05 <0.05 Trigon Property -0.01 0.57 <0.01 <0.01 0.42 >0.05 <0.05 <0.05 Development Utenos 0.03 1.11 <0.01 <0.01 0.18 <0.05 <0.05 <0.05 Trikotažas Valmieras -0.01 -0.38 <0.01 <0.01 0.17 >0.05 <0.05 <0.05 stikla šķiedra VEF 0.02 1.68 <0.01 <0.01 0.88 >0.05 >0.05 >0.05 VEF Radiotehnika 0.65 4.14 <0.01 0.47 -0.01 <0.05 <0.05 <0.05 RRR Vilkyškių 0.00 1.35 0.45 <0.01 0.68 <0.05 <0.05 <0.05 pieninė Vilniaus Baldai -0.02 0.08 <0.01 0.04 0.04 >0.05 >0.05 <0.05 Žemaitijos 0.01 2.08 <0.01 <0.01 0.86 <0.05 <0.05 <0.05 pienas

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 73

Appendix 6. Sector Beta CAPM regression results

Normality Heterosked Ramsey's Const CAPM const CAPM Adjusted of asticity RESET p- coef coef p p R-sq residuals. White's test value P-value p-value Apranga -0.01 0.30 <0.01 <0.01 0.49 <0.05 <0.05 <0.05 Arco Vara -0.01 1.60 <0.01 <0.01 0.01 >0.05 >0.05 <0.05 Auga Group 0.00 1.33 0.09 <0.01 0.44 >0.05 <0.05 <0.05 Baltika -0.01 0.95 <0.01 <0.01 0.01 >0.05 >0.05 <0.05 Ditton pievadķēžu 0.01 2.11 <0.01 <0.01 0.58 >0.05 >0.05 <0.05 rūpnīca East West Insufficient Insufficient -0.05 5.42 <0.01 <0.01 0.99 >0.05 Agro data data Ekspress -0.01 0.77 <0.01 <0.01 0.55 <0.05 >0.05 <0.05 Grupp Grigeo 0.00 0.91 0.20 <0.01 0.68 >0.05 <0.05 <0.05 Grindeks 0.00 1.68 <0.01 <0.01 0.87 >0.05 <0.05 <0.05 Harju Elekter 0.00 1.47 0.61 <0.01 0.94 >0.05 <0.05 <0.05 Invalda 0.00 0.77 <0.01 <0.01 0.82 <0.05 <0.05 <0.05 INVL INVL Baltic 0.00 -0.04 0.02 0.74 -0.02 >0.05 >0.05 <0.05 Farmland INVL Baltic -0.05 9.56 <0.01 <0.01 0.80 <0.05 <0.05 <0.05 Real Estate INVL -0.06 6.88 <0.01 <0.01 0.52 >0.05 >0.05 >0.05 Technology K2 LT 0.02 -4.87 0.20 <0.01 0.12 >0.05 <0.05 <0.05 Kauno 0.01 4.28 <0.01 <0.01 0.82 >0.05 <0.05 <0.05 Energija Klaipėdos -0.01 1.21 <0.01 <0.01 0.66 <0.05 <0.05 <0.05 Nafta Kurzemes 0.06 2.08 <0.01 <0.01 0.59 >0.05 <0.05 <0.05 atslēga 1 Latvijas 0.02 2.15 <0.01 <0.01 0.49 <0.05 <0.05 <0.05 Balzams Latvijas 0.00 2.72 <0.01 <0.01 0.93 <0.05 <0.05 >0.05 Gāze Latvijas Jūras 0.04 2.46 <0.01 <0.01 0.84 <0.05 <0.05 <0.05 medicīnas centrs LHV Group 0.00 0.68 0.94 0.34 0.00 >0.05 >0.05 >0.05 Linas 0.00 0.92 <0.01 <0.01 0.75 <0.05 <0.05 <0.05 Linas Agro -0.01 0.94 <0.01 <0.01 0.83 <0.05 <0.05 >0.05 Group Linda Nektar -0.03 2.77 0.23 0.55 -0.03 >0.05 >0.05 >0.05 LITGRID -0.01 1.19 <0.01 <0.01 0.84 >0.05 <0.05 <0.05 Merko -0.01 1.05 <0.01 <0.01 0.83 >0.05 <0.05 <0.05 Ehitus Nordecon -0.01 1.09 <0.01 <0.01 0.83 >0.05 >0.05 <0.05 Nordic -0.02 1.06 <0.01 <0.01 0.86 >0.05 >0.05 <0.05 Fibreboard Olainfarm -0.01 0.17 <0.01 <0.01 0.16 <0.05 >0.05 <0.05

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 74

Panevėžio statybos -0.02 0.98 <0.01 <0.01 0.85 <0.05 >0.05 <0.05 trestas PATA 0.01 0.54 <0.01 <0.01 0.40 >0.05 <0.05 <0.05 Saldus Pieno -0.02 0.87 <0.01 <0.01 0.84 >0.05 >0.05 <0.05 žvaigždės PRFoods -0.01 2.05 <0.01 <0.01 0.87 >0.05 >0.05 <0.05 Pro Kapital -0.02 -0.22 <0.01 0.02 0.07 >0.05 <0.05 <0.05 Grupp Rīgas autoelektroa 0.62 -0.72 <0.01 0.21 0.01 <0.05 <0.05 <0.05 parātu rūpnīca Rīgas elektromašīn 0.11 3.66 <0.01 <0.01 0.58 >0.05 <0.05 <0.05 būves rūpnīca Rīgas juvelierizstrā 0.01 0.97 <0.01 <0.01 0.47 >0.05 >0.05 <0.05 dājumu rūpnīca Rīgas kuģu -0.02 0.84 <0.01 <0.01 0.44 <0.05 <0.05 <0.05 būvētava Rokiškio 0.00 1.98 <0.01 <0.01 0.92 <0.05 <0.05 <0.05 sūris Siguldas ciltslietu un mākslīgās 0.02 1.88 <0.01 <0.01 0.68 >0.05 >0.05 <0.05 apsēklošanas stacija Silvano fashion -0.01 1.49 <0.01 <0.01 0.69 >0.05 <0.05 <0.05 group Snaigė -0.02 0.56 <0.01 <0.01 0.48 >0.05 <0.05 <0.05 Šiaulių 0.01 1.52 <0.01 <0.01 0.80 <0.05 <0.05 <0.05 Bankas Tallink -0.01 0.94 <0.01 <0.01 0.82 >0.05 <0.05 <0.05 Grupp Tallinna Kaubamaja 0.00 1.21 0.01 <0.01 0.83 >0.05 <0.05 <0.05 Grupp Tallinna Vesi -0.01 1.16 <0.01 <0.01 0.35 <0.05 <0.05 <0.05 Trigon Property -0.01 0.57 <0.01 <0.01 0.42 >0.05 <0.05 <0.05 Development Utenos 0.03 1.11 <0.01 <0.01 0.18 <0.05 <0.05 <0.05 Trikotažas Valmieras -0.01 -0.38 <0.01 <0.01 0.17 >0.05 <0.05 <0.05 stikla šķiedra VEF 0.02 1.68 <0.01 <0.01 0.88 >0.05 >0.05 >0.05 VEF Radiotehnika 0.65 4.14 <0.01 0.47 -0.01 <0.05 <0.05 <0.05 RRR Vilkyškių 0.00 1.35 0.45 <0.01 0.68 <0.05 <0.05 <0.05 pieninė Vilniaus -0.02 0.08 <0.01 0.04 0.04 >0.05 >0.05 <0.05 Baldai

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 75

Žemaitijos 0.01 2.08 <0.01 <0.01 0.86 <0.05 <0.05 <0.05 pienas

Appendix 7. Fama-French three-factor model regression results

Const Capm SMB HML Contant CAPM SMB HML Adjusted coef Coef Coef Coef p-value p-value p-value p-value R-squared

Amber Grid 0.01 -2.06 -0.26 -0.03 0.35 0.02 <0.01 <0.01 0.91 Apranga -0.01 0.26 0.00 0.00 <0.01 <0.01 0.74 0.52 0.50 Arco Vara -0.01 2.23 0.10 0.01 <0.01 <0.01 <0.01 <0.01 0.98 Auga -0.01 0.24 -0.07 0.05 <0.01 0.29 <0.01 <0.01 0.86 Group Baltic -0.09 -8.77 0.25 0.19 0.68 0.03 0.09 0.11 0.44 Technology Ventures Baltika -0.02 0.09 -0.02 0.02 <0.01 0.43 0.04 <0.01 0.57 Dittopievad 0.01 3.08 0.00 -0.02 0.01 <0.01 0.94 0.01 0.66 kezurupnia ca East West 0.04 4.53 -0.09 -0.15 0.14 0.16 0.65 0.19 0.98 Agro Ekspress -0.02 1.15 0.11 -0.03 <0.01 <0.01 <0.01 <0.01 0.94 Grupp Grigeo 0.01 0.02 -0.26 0.01 <0.01 0.88 <0.01 0.26 0.86 Grindeks 0.01 1.75 0.48 0.00 <0.01 <0.01 <0.01 0.47 0.89 Hansa 0.00 -2.34 -0.02 0.02 0.90 0.42 0.81 0.69 0.33 matrix Harju 0.01 2.95 -0.23 -0.02 <0.01 <0.01 <0.01 0.02 0.94 Elekter Invalda 0.00 0.37 0.02 -0.01 0.40 <0.01 0.02 0.11 0.78 INVL INVL 0.00 -2.58 0.06 0.00 0.56 <0.01 0.21 0.81 0.25 Baltic Farmland INVL 0.01 2.85 -0.04 0.08 0.55 <0.01 0.07 0.09 0.80 Baltic Real Estate INVL 0.00 4.35 0.02 -0.07 0.12 <0.01 0.29 0.13 0.69 Technology K2 LT -0.06 7.97 0.09 -0.25 <0.01 <0.01 <0.01 <0.01 0.82 Kauno 0.01 5.30 0.01 0.02 0.19 <0.01 0.48 0.42 0.70 Energija Klaipėdos -0.02 -0.68 -0.16 -0.06 <0.01 0.15 <0.01 <0.01 0.88 nafta KurzemesA 0.05 1.21 -0.01 0.02 <0.01 0.13 0.23 <0.01 0.43 tslega1 Latvijas 0.01 1.85 -0.07 -0.02 0.03 <0.01 <0.01 <0.01 0.91 Balzams Latvijas 0.00 6.26 0.04 0.01 0.54 <0.01 0.42 0.03 0.84 Gaze LatvijasJAr -0.02 -1.64 0.06 0.07 <0.01 0.15 <0.01 0.05 0.82 asmedicina scentr

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 76

LHV Group 0.05 -3.74 -0.10 0.02 <0.01 <0.01 <0.01 0.55 0.61 Linas 0.00 1.06 -0.07 0.00 <0.01 <0.01 <0.01 <0.01 0.86 Linas Agro 0.00 0.03 -0.05 0.03 0.33 0.87 <0.01 <0.01 0.89 Group Linda -0.03 1.30 -0.18 -0.01 <0.01 0.59 0.10 0.07 0.12 Nektar LITGRID -0.01 0.58 -0.05 0.00 <0.01 <0.01 <0.01 0.83 0.86 Merko 0.00 0.96 0.02 0.04 0.97 <0.01 0.52 <0.01 0.96 Ehitus Nordecon -0.01 1.24 0.05 0.02 <0.01 <0.01 <0.01 <0.01 0.88 Nordic -0.04 0.12 -0.18 -0.06 <0.01 0.47 <0.01 <0.01 0.92 Fibreboard Olainfarm 0.00 -0.17 0.11 0.00 <0.01 0.26 <0.01 0.61 0.69 Panevėžio -0.01 0.74 0.02 0.02 <0.01 <0.01 0.83 <0.01 0.88 statybos trestas PATA 0.02 1.75 -0.03 0.00 <0.01 <0.01 <0.01 0.92 0.31 Saldus Pieno -0.01 2.85 0.14 -0.03 <0.01 <0.01 <0.01 <0.01 0.90 Žvaigždės PRFoods 0.00 3.35 0.03 0.11 0.64 <0.01 0.13 <0.01 0.93 Pro Kapital -0.01 -2.12 -0.05 -0.05 0.09 <0.01 <0.01 <0.01 0.41 Grupp Rigakugub -0.01 -0.88 -0.01 -0.05 0.07 <0.01 0.14 <0.01 0.66 uvetava Rigasautoel 0.08 -2.90 -0.01 0.01 <0.01 <0.01 0.17 0.14 0.56 ektroaparat urupnic Rigaselektr 0.11 -4.08 0.09 -0.16 <0.01 <0.01 0.07 <0.01 0.50 omaAinbuv esrupnic Rigasjuveli 0.00 1.62 -0.01 -0.02 0.65 <0.01 0.35 0.06 0.77 erizstradaju murupn Rokiškio 0.00 2.62 -0.06 -0.05 0.76 <0.01 0.03 <0.01 0.96 sūris SAF 0.01 -0.23 -0.06 -0.03 0.04 0.41 <0.01 <0.01 0.60 Tehnika Siguldascilt 0.00 3.60 0.24 0.01 0.81 <0.01 <0.01 <0.01 0.79 slietuunmA kslA Silvano -0.01 4.45 0.09 -0.05 <0.01 <0.01 <0.01 <0.01 0.95 Fashion Group Snaigė -0.02 1.28 0.04 -0.03 <0.01 0.01 <0.01 <0.01 0.49 Šiaulių 0.03 1.39 -0.20 -0.13 <0.01 <0.01 <0.01 <0.01 0.93 bankas Tallink -0.02 0.88 -0.05 -0.03 <0.01 <0.01 <0.01 <0.01 0.87 Grupp TallinnaKa 0.00 1.50 -0.11 -0.02 0.73 <0.01 0.10 0.52 0.89 ubamajaGr upp Tallinna -0.08 5.50 -0.09 -0.12 <0.01 <0.01 <0.01 <0.01 0.85 Vesi Telia -0.01 2.78 0.05 0.01 <0.01 <0.01 <0.01 0.06 0.98 Lietuva

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 77

TrigonProp 0.02 -3.67 -0.11 0.08 <0.01 <0.01 <0.01 <0.01 0.69 ertyDevelo pment Utenos -0.02 0.34 0.10 -0.04 <0.01 0.07 <0.01 <0.01 0.76 Trikotažas Valmierasst -0.01 -1.13 -0.12 0.11 <0.01 <0.01 <0.01 <0.01 0.59 iklaAkiedra VEF 0.00 -2.60 0.08 -0.01 0.36 <0.01 <0.01 0.34 0.55 VEF 0.12 1.76 -0.04 0.16 0.04 <0.01 <0.01 <0.01 0.78 Radiotehni ka RRR Vilkyškių -0.01 0.70 0.07 0.01 <0.01 <0.01 <0.01 0.39 0.71 pieninė Vilniaus -0.02 0.35 -0.01 0.01 <0.01 0.05 0.04 0.10 0.22 Baldai Žemaitijos 0.00 1.23 -0.02 0.01 0.85 <0.01 0.62 0.12 0.85 pienas

Appendix 8. Statistical issues of the Fama-French model

Normality Heterosked Ramsey's CAPM SMB HML of asticity. RESET p- VIF VIF VIF residuals. White's test value P-value p-value Amber Grid >0.05 <0.05 <0.05 8.82 3.10 10.92 Apranga <0.05 >0.05 <0.05 3.45 3.24 5.01 Arco Vara >0.05 >0.05 <0.05 6.64 7.00 1.25 Auga Group <0.05 >0.05 <0.05 15.52 16.88 1.46 Baltic Technology Ventures >0.05 <0.05 >0.05 2.03 1112.37 1140.70 Baltika <0.05 >0.05 <0.05 2.51 1.33 2.06 Dittopievadkezurupniaca >0.05 <0.05 <0.05 4.18 6.34 11.99 East West Agro >0.05 >0.05 >0.05 21.73 60.04 16.80 Ekspress Grupp >0.05 <0.05 >0.05 1.69 3.08 2.47 Grigeo >0.05 <0.05 <0.05 12.49 7.25 3.77 Grindeks >0.05 <0.05 <0.05 2.01 2.15 1.10 Hansa matrix >0.05 >0.05 >0.05 23.75 7.36 15.53 Harju Elekter >0.05 >0.05 <0.05 17.90 18.39 4.41 Invalda INVL >0.05 <0.05 >0.05 9.45 3.19 12.19 INVL Baltic Farmland >0.05 >0.05 >0.05 1.36 12.76 12.01 INVL Baltic Real Estate <0.05 >0.05 <0.05 3.99 33.29 22.98 INVL Technology <0.05 >0.05 >0.05 151.60 43.84 47.73 K2 LT >0.05 >0.05 <0.05 3.02 8.89 5.80 Kauno Energija <0.05 <0.05 <0.05 3.91 1.50 4.28 Klaipėdos nafta >0.05 >0.05 <0.05 60.41 4.04 46.92 KurzemesAtslega1 <0.05 <0.05 <0.05 8.62 2.70 5.55 Latvijas Balzams >0.05 <0.05 <0.05 3.75 2.79 4.85 Latvijas Gaze <0.05 <0.05 >0.05 14.09 13.74 1.11 LatvijasJArasmedicinascentr <0.05 <0.05 <0.05 100.88 26.64 35.21 LHV Group >0.05 >0.05 >0.05 4.48 4.74 1.44 Linas >0.05 <0.05 <0.05 2.76 2.75 1.94

ANALYSIS OF CAPM AND FAMA-FRENCH MODEL APPLICATION 78

Linas Agro Group >0.05 >0.05 <0.05 33.08 15.95 11.54 Linda Nektar >0.05 <0.05 <0.05 3.34 5.55 3.46 LITGRID <0.05 <0.05 <0.05 1.82 2.15 2.26 Merko Ehitus >0.05 <0.05 <0.05 1.44 42.02 39.53 Nordecon >0.05 <0.05 <0.05 5.65 1.34 6.14 Nordic Fibreboard <0.05 <0.05 >0.05 12.19 13.19 2.13 Olainfarm <0.05 <0.05 <0.05 38.70 3.30 49.35 Panevėžio statybos trestas >0.05 <0.05 <0.05 8.34 12.17 3.29 PATA Saldus <0.05 >0.05 >0.05 2.69 6.37 9.11 Pieno Žvaigždės >0.05 <0.05 <0.05 15.69 10.52 5.46 PRFoods >0.05 <0.05 <0.05 11.29 19.35 19.37 Pro Kapital Grupp >0.05 >0.05 <0.05 30.62 35.91 2.96 Rigakugubuvetava >0.05 >0.05 <0.05 10.78 5.25 13.63 Rigasautoelektroaparaturupnic <0.05 <0.05 <0.05 38.46 68.01 174.38 RigaselektromaAinbuvesrupnic >0.05 <0.05 <0.05 50.84 13.23 20.46 Rigasjuvelierizstradajumurupn >0.05 >0.05 <0.05 25.43 13.89 21.00 Rokiškio sūris >0.05 >0.05 <0.05 9.95 8.84 3.26 SAF Tehnika >0.05 <0.05 <0.05 6.22 4.39 3.85 SiguldasciltslietuunmAkslA >0.05 <0.05 >0.05 1.48 1.12 1.63 Silvano Fashion Group >0.05 <0.05 <0.05 6.79 5.99 2.68 Snaigė <0.05 <0.05 <0.05 1.18 1.61 1.84 Šiaulių bankas >0.05 <0.05 <0.05 1.14 2.06 2.19 Tallink Grupp >0.05 <0.05 <0.05 6.31 5.84 7.02 TallinnaKaubamajaGrupp <0.05 <0.05 <0.05 18.55 36.76 85.18 Tallinna Vesi <0.05 <0.05 <0.05 19.67 2.77 19.52 Telia Lietuva >0.05 <0.05 <0.05 1.10 1.90 1.97 TrigonPropertyDevelopment <0.05 <0.05 <0.05 3.59 6.24 3.27 Utenos Trikotažas >0.05 <0.05 <0.05 2.32 5.78 5.42 ValmierasstiklaAkiedra >0.05 <0.05 <0.05 4.17 5.27 4.61 VEF >0.05 <0.05 <0.05 5.56 4.04 1.84 VEF Radiotehnika RRR <0.05 <0.05 <0.05 3.03 80.76 68.49 Vilkyškių pieninė >0.05 <0.05 <0.05 2.24 3.05 2.39 Vilniaus Baldai <0.05 <0.05 <0.05 20.22 1.85 18.84 Žemaitijos pienas >0.05 <0.05 <0.05 1.31 4.11 3.82