Previous research on the topic of immigrant entrepreneurship has found that one of the most important challenges immigrant entrepreneurs are facing is access to financial capital. Empirical evidence of prior scientific research has shown that immigrant entrepreneurs are facing array of challenges including discrimination when accessing financial capital. Motivated by the prior scientific research on the topic, aim of this study is to understand what challenges and restrictions Finnish immigrant entrepreneurs are facing in Kajaani region when accessing financial capital to finance their businesses. Aim of this study is also to explore whether they are utilizing already available financial capital and services offered by government organizations and financial institutions. This study was done with the help of qualitative research methods. Semi-structured interviews were conducted with 5 immigrant entrepreneurs and 1 government official from TE Services from Kajaani region. Findings show that immigrant entrepreneurs in Kajaani region are facing the same challenges as native entrepreneurs, with several important distinctions. First, language barriers, financial literacy, miscommunication, lack of communication and understanding between immigrant entrepreneurs and government and financial institutions represent major challenge when accessing financial capital from both government and financial institutions. Second, difficulties in proving financial responsibility, lack of credit history, lack of long-term bank-customer relationship and collateral are also found to be additional challenges when obtaining financial capital. No evidence on discrimination was found, but there were pointers toward trust issues between immigrant entrepreneurs and financial institutions and government organizations. Because of challenges immigrant entrepreneurs are facing when accessing financial capital, they follow certain patterns when it comes to overcoming financing gaps. As a source of financial capital they are using their own savings, followed by finances obtained from their friends and family, partners and fellow entrepreneurs. Findings show that more often than not, immigrant entrepreneurs are not utilizing available resources, which could potentially affect future of their businesses in a negative way. However, because of limited time and resources available for this master thesis, sample size and type of data available represent the limitation of this study. Hence, this study could serve as a call for a nationwide quantitative study of the topic.

In this study we examine the proportion of false discovery rate exists amongst the individual funds in Hedge Fund Research (HFR) database. Applying the Fung and Hsieh (2004) seven-factor model in a time series regression along with a statistical false discovery rate methodology construct the main framework of this study. False discovery rate helps to measure the proportion of lucky funds among hedge funds that have statistically significant alphas and explains how many percentages of funds with significant alphas would be achieved due to luck compared to skill. Even after adjusting for the backfill bias, the proportion of false discovery rate states that the hedge funds outperform due to skill compared to luck and underperforms due to be unlucky compared to be unskilled. Results for strategies demonstrate that the proportion of false discovery rate in Event Driven, Relative Value, and Multi Strategy is very low in the right tail respectively and the manager has skill compared to luck. In contrast, strategies such as CTA, Relative Value, and Short Bias have the lowest proportion of false discovery rate in the left tail respectively which implies the manager is more unskilled compared to be unlucky in his performance. The proportion of false discovery rate for small funds is greater than large funds in the right tail of the distribution and it implies that for small funds the manager outperforms mostly by luck compared to skill. Contrarily, the proportion of false discovery rate for large funds is greater than small funds in the left tail of the distribution and it implies that for large funds the manager is more unlucky compared to be unskilled to outperform in the market.

In this study, we aimed to test the performance of risk parity portfolios against classically optimized Markowitz portfolios and conclude which technique leads to better performing portfolios in well developed, informationally efficient markets. We constructed 5 risk parity portfolios, the Inverse Volatility portfolio, the Maximum Diversification portfolio, the Equal Risk Contribution portfolio, the Alpha Risk Parity portfolio and the Beta Risk Parity portfolio and 2 benchmark classical portfolios, the Equally Weighted (1/N) and the Minimum Variance portfolio to measure performance against. The data used is the 50 constituting stocks of the EuroSTOXX50 index. The index itself was used as the market benchmark that was included in the analysis. The study is designed in a horse race style measuring performance using mean returns, mean excess returns, maximum drawdowns, Sharpe ratios and information ratios. Our findings indicate that a risk parity portfolio will win the horse race against a classical portfolio, however risk parity portfolios are very sensitive to the asset universe and to make the most out of these techniques, a wide asset scope, managerial skill as well as other resources are needed which might not make them easily available to the average investor.

Background and objectives: CAPM implies that there should exists positive relation between the returns’ and betas’ of the stocks and this relation should be equal to size of market risk premium. However empirical research has found that this relationship is too flat or even completely flat. Low beta stocks perform better and high beta stocks perform worse, than expected according their beta. To measure performance difference there has been created betting against beta (BAB) factor, which goes long to low beta stocks and shorts high beta stocks and positions are levered to have neutral market position. It has been shown that this factor generates positive four factor risk adjusted returns in USA market and in numerous international markets. BAB factors risk adjusted returns have been one the highest in Belgium stock market. This study checks can unconditional or conditional asset pricing models with time varying betas explain returns related to BAB factor in Belgium stock market. It is also investigated how the factor exposures of BAB factor vary over time and different market conditions. Further it is checked can investor achieve statistically significant alpha in five year time interval by tilting his portfolio towards BAB factor in Belgium stock market. Data and methodology: It is used daily and monthly factor return data from Belgium stock market from July 1990 to December 2015. Data is achieved from AQR Capital Management’s database. BAB returns are investigated through basic static factor regression models. To capture time variance on the factor exposure there is generated conditional factor models for BAB returns using smoothed Kalman filter. Also BAB returns varying exposures and alphas are checked through rolling regression with 5 year time window. Results: Unconditional or conditional regression models can’t explain returns of BAB factor in Belgium stock market. In all broad sample regressions there exists statistically significant alpha. Adding momentum to three factor model cuts down alpha, but still statistically significant part of the returns are unexplained by the four factor model. In bear market times alpha related to BAB factor decreases substantially. BAB factor has negative market exposure most of the time. Overall BAB has positive exposure to momentum factor, which goes extremely strong in bear market times, but in bull markets this exposure vanishes. Overall BAB factor’s factor exposures get stronger in bear market times, except with size factor. Rolling regressions show that investor can rarely achieve statistically significant risk adjusted returns with 5 year investment horizon by tilting his portfolio towards BAB factor. Tilting portfolio towards BAB factor neither penalizes the investor in the unique way in the bad times.

Vaihtovelkakirja (convertible bond) on yrityslaina, joka voidaan vaihtaa ennalta päätettyyn määrään yrityksen osakkeita. Yleensä vaihtovelkakirjalainat ovat amerikkalaistyylisiä sijoitusinstrumentteja eli niiden muuntaminen osakkeiksi on mahdollista milloin tahansa ennen eräpäivää. Lisäksi vaihtovelkakirjalainoihin sisältyy myös muita ehtoja. Tällaisia ovat esimerkiksi liikkeellelaskijanyrityksen vaihtovelkakirjan ostomahdollisuus (callability), sijoittajan mahdollisuus myydä vaihtovelkakirjansa takaisin liikkeellelaskija yritykselle (putability), osinkosuojaus (dividend protection) ja osakkeiden muuntosuhteen uudelleenmäärityslause (refix clause). Nämä ehdot tekevät vaihtovelkakirjasopimusten käsittämisen ja niiden hinnoittelun hankalaksi. Vaikka vaihtovelkakirjoilla on pitkä historia, ne ovat useimmille sijoittajille tuntemattomia. Ensimmäisen vaihtovelkakirjalainan laski liikkeelle J. J Hillin yhdysvaltalainen rautatieyhtiö vuonna 1881. Suomessa vvk:t olivat pitkään täysin tuntemattomia useimmille ihmisille, mutta viime aikoina ne ovat nousseet esille sekä poliittisissa että taloudellisissa keskusteluissa. Esimerkiksi Juha Sipilä on maininnut vaihtovelkakirjat yhtenä keinona edistää kasvuyritysten rahoituksen saatavuutta. Vaihtovelkakirjojen hinnoittelemiseksi on olemassa kahdenlaisia malleja: pelkistetyn muodon malleja (reduced-form approach) ja rakenteellisia malleja (structural model). Näiden mallien keskeisenä erona on yrityksen maksukyvyttömyysriskin mallintaminen. Rakenteellisista malleissa tilamuuttujana on yrityksen arvo ja konkurssi määritetään endogeenisenä tapahtumana, joka tapahtuu yrityksen arvon laskiessa velkapääomaa pienemmäksi. Sen sijaan pelkistetyn muodon malleissa tilamuuttujana on yrityksen osakekurssi ja konkurssi mallinnetaan eksogeenisenä prosessina antamalla sille tietty todennäköisyys tietyllä aikavälillä. Näistä malleista rakenteelliset mallit ovat teoreettisesti parempia. Rakenteellisten mallien käyttäminen vaihtovelkakirjan arvonmäärityksessä on kuitenkin usein hankalaa, sillä dataa ei useimmiten ole riittävästi saatavilla. Tämän vuoksi tutkijat hyödyntävät vvk:en hinnan määrittämisessä yleensä pelkistetyn muodon malleja. Suosituin hinnoittelumalli akateemisten tutkijoiden keskuudessa on Tsioveriksen ja Fernandeksen (1998) kehittämä malli, jota hyödynnän myös omassa tutkimuksessani hinnoitteluvirheen määrittämisessä. Havaintoperiodilla 2005–2013 vvk:t ovat olleet keskimäärin 3,6 % ylihinnoiteltuja. Vaihtovelkakirjojen hinnoitteluvirhe oli suurimmillaan finanssikriisissä vuonna 2009, jolloin vvk:t olivat ylihinnoiteltuja keskimäärin noin 15 %. Finanssikriisin aikaan myös äärimmäiset hinnoittelupoikkeamat saavuttivat maksiminsa molempiin suuntiin. Hinnoitteluvirheiden kasvaminen on mahdollisesti osittain selitettävissä luottoriskipreemion ja volatiliteetin räjähdysmäisellä kasvulla finanssikriisissä. TF-mallin sisältämä oletus vakiovolatiliteetistä ja vakioluottoriskipreemiosta voi siten olla ongelmallinen poikkeuksellisessa tilanteessa. Tutkimuksessani tekemä havainto ylihinnoittelusta on poikkeuksellinen aikaisempiin tutkimuksiin verrattuna, sillä useimmat tutkimukset ovat todenneet vvk:en olleen alihinnoiteltuja. Tutkimuksessani selvitin vaihtovelkakirjojen hinnoitteluvirhettä kolmen tekijän suhteen. Näistä tekijöistä vvk:en juoksuajalla näytti olevan suurin vaikutus hinnoitteluvirheeseen. Pitkien yli 10 vuoden juoksuajan omaavien vvk:en ylihinnoittelu oli yli 12 %-yksikköä suurempaa kuin lyhyiden vvk:en. Muita vvk:en hinnoitteluvirheeseen vaikuttavia tekijöitä olivat kohdeyrityksen luottoluokitus ja vaihtovelkakirjan delta. Vaihtovelkakirjan luottoluokituksen vaikutusta tutkin jakamalla vvk:t investointiluokituksen omaaviin lainoihin ja muihin lainoihin ja deltan vaikutusta jakamalla vvk:t tavallisen yrityslainan kaltaisiin ja osakkeen kaltaisiin vaihtovelkakirjoihin estimoimani deltan perusteella.

Uncovered interest parity puzzle is one of the most prominent puzzles in international finance that has remained unsolved for over 30 years. This theory stipulates that the currencies of the countries where risk-free interest rates are high should depreciate relative to the currencies associated with low interest rates. However, empirical findings of last three decades report the opposite effect, and none of the proposed theoretical models have been yet able to explain why. In the meantime, a popular trading strategy that exploits the puzzle and is commonly referred to as carry trade has been gaining popularity among traders and speculators. It on average generates significant positive excess returns that cannot be explained by any existing asset pricing models, but it also is susceptible to sudden crashes. Growing popularity of this strategy, and the persistence of the uncovered interest parity puzzle motivates us to analyze possible causes of the failure and to propose a model that corrects for the bias. Most of the literature in the field rely on the use of forward rates, but we argue that futures data provides the means to analyze evolution of the foreign exchange risk premium over time. In this thesis we use currency futures rates to perform the tests of the uncovered interest parity condition. We then test the hypothesis of futures rates unbiasedness and we link the two concepts together with a risk premium component. We find that both deviations from rational expectations and a presence of the risk premium term are possible causes for the failure of the UIP tests. Using futures rates at daily and weekly frequencies we are able to provide evidence that the risk premium varies over time. Incorporating this nature, we propose a modified UIP model that corrects for the bias. We find that the inclusion of the risk-premium component mitigates the puzzle. We also find that carry trade portfolios have high abnormal returns due to higher exposure to volatility and funding liquidity risks. During periods of market turmoils, the uncovered interest parity condition holds better because the trades that normally exploit the puzzle lose money. Overall, we conclude that there should be some non-traditional state variables that have their innovations related to the risk-premiums in a cross-section of currency returns.

Factor investing has been one of the fundamental alternative investment since Lintner, (1965); Mossin, (1966) and Sharpe, (1964) defined the market risk factor as the systematic risk due to the market in the Capital asset pricing model (CAPM). The premium to these factors means investors are compensated for holding the respective risks. Liquidity factor is important to hedge fund industry given the illiquid nature of it investing. We use the innovation series of Pastor and Stambaugh (2003) to examine Hedge fund style indices from both investable (HFRX) and non-investable (HFRI) of the HFR database to establish differences in exposures as well as confirm the pricing of liquidity factor in investable and non-investable indices. In analysing premium to liquidity factor in individual indices, we estimate the beta coefficients for each style indices and further control for other factor effects by including the 7-factors of Fung and Hsieh (2004). The Fama-Macbeth (1973) two-stage approach is used to price liquidity factor in both investable and non-investable indices and autocorrelation is adequately corrected for using the Newey-West method which employs generalised Method of Moments (GMM) approach. Both investable and non-investable indices of the HFR database showed that exposures of this indices to liquidity factor are largely determined by their characteristics and formation methods. This is further explained by the effect of other factors in the 7-factors of Fung and Hsieh which showed that when specific characteristics are controlled for, the exposures of an index to liquidity factor can be insignificant. Liquidity factor is a priced factor in both investable and non-investable funds with significant liquidity premium even after controlling for autocorrelation. We further establish that the investable indices are poor estimator of the hedge fund universe by rejecting the null hypothesis of test of zero alphas.

This thesis investigates factor investing and risk parity methods by constructing seven risk parity portfolios. We find that both single-factor portfolios and multi-factor risk parity portfolios outperform the market and our benchmarks. The methods produce higher absolute returns and better risk-adjusted returns with lower volatilities and drawdowns. Therefore, the presented methodologies may benefit investors by providing more efficient portfolios and greater risk management. We use long-only factor tilt indices to construct the portfolios as this is one of the simplest ways to implement such strategies for both institutional and individual investors. Thus, our methods are both practical and realistic. In addition, we show that there are significant diversification benefits in combining factors into multi-factor portfolios. Mainly, volatilities and drawdowns are significantly decreased when six factor tilt indices are combined using the methods described in this thesis. Furthermore, traditional diversification methods can lead to skewed portfolio risk profiles where equity risk is significantly overweighted. Risk-based diversification methods offer an alternative approach where diversification is based on the risk contribution of each asset. This results in more balanced portfolio risk profiles. Therefore, we also analyze the compositions of the constructed portfolios and find that the majority overweight low volatility assets.

Tutkielman tavoitteena on selvittää liikearvon suhteellisen taseosuuden vaikutusta osakkeiden tuottoihin. Liikearvon osuus yritysten taseissa on kasvanut merkittävästi ja etenkin arvosijoittamisessa, jossa osakkeen hintaa verrataan yrityksen tasearvoon, on tärkeää, että tasearvo on luotettavasti laskettu. Korkea liikearvon määrä lisää riskiä yllätyksellisiin alaskirjauksiin ja sitä kautta tasearvon alenemiselle. Tutkielmassa selvitetään onko liikearvon olemassaololla ja määrällä vaikutusta osakkeiden tuottoihin ja voidaanko mahdollinen tuottoero selittää jollakin ennestään tiedetyllä tekijällä. Lisäksi selvitetään onko liikearvon olemassaololla ja määrällä vaikutusta koko- tai arvoanomaliaan. Tutkimusmenetelminä käytettiin osakkeiden jakoa portfolioihin eri ominaisuuksien mukaan ja keskimääräisten kuukausituottojen laskemista näin muodostetuille portfolioille. Regressioanalyysin avulla selvitettiin muiden kuin liikearvon osuutta selittävinä tekijöinä tuottoeroille. Tulosten luotettavuuden ja tilastollisen merkittävyyden arvioimiseksi käytettiin student-t ja R²-arvoja. Tutkielman tärkein tulos on se, että liikearvolla on vaikutusta osakkeiden tuottoon. Liikearvon olemassaolo heikentää osakkeiden tuottoa ja tuoton heikennys korostuu kasvuyritysten kohdalla. Vaikutus korostuu entisestään yrityksissä, joilla liikearvoa on yli 20 % taseen loppusummasta. Liikearvon puuttuminen vastaavasti parantaa etenkin arvoyritysten tuottoa. Liikearvoon liittyvä tuottoero voidaan markkinapainoisissa portfolioissa selittää lähes kokonaan Fama ja Frenchin kolmifaktorimallilla, mutta tasapainotetuissa portfolioissa erot jäivät vielä selittämättä. Saatuja tuloksia voidaan hyödyntää sen osalta, että liikearvon määrään yrityksen taseessa on syytä kiinnittää huomiota. Tulosten perusteella yritykset, joiden taseessa on liikearvoa yli 20 %, sisältävät muita enemmän riskiä heikomman tuottokehityksen suhteen. Sijoittajan kannattaa olla varovainen näihin yrityksiin sijoittaessa, etenkin jos yritys on kasvuyritys, eli sen B/M-luku on alhainen. Arvosijoittamisen kannalta on hyödyllistä tietää, että arvoyritykset, joilla ei ole liikearvoa taseessa tuottavat paremmin kuin arvoyritykset, joilla sitä on. Sen sijaan B/M-lukua laskettaessa ei ole väliä otetaanko liikearvo huomioon taseeseen, vai ei. Tutkimusaineistona käytettiin yhdysvaltalaisia osakkeita aikavälillä 1989–2009. Ajallisesti liikearvon tuottovaikutuksessa ei ollut eroa eri vuosien kesken. Maantieteellisesti tuloksia ei voida yleistää Yhdysvaltojen ulkopuolelle vielä tämän tutkimuksen perusteella.

This study aims to analyze the style and performance of actively managed Finnish equity mutual funds by applying returns-based style analysis of Sharpe (1988, 1992) as the main methodology. The main results support the findings of previous empirical studies, showing that the performance of actively managed equity mutual funds relative to passive market indices does not statistically differ from zero. Previous findings regarding the high correlation of mutual fund returns with standard asset classes and importance of asset allocation decisions are also strongly supported. The results of the study suggest that the styles obtained from returns-based style analysis are well in line with the stated investment objectives for most of the Finnish equity mutual funds, and that style analysis can effectively reveal additional information to support fund selection- and performance evaluation processes. However, the results indicate that investment styles of many Finnish equity mutual funds do not significantly differ from a broad market index. The finding appears to be strong especially for the group of largest funds, whose returns a single broad market index is able to explain with an average of over 95%. Thus, the results of the study indicate that returns-based style analysis based on more specific style indices seems to generally provide the greatest relative benefit in the case of smaller funds that seem to have a higher likelihood to apply active management and investment styles not completely captured by a broad market index. The study also documents differences in the consistency of investment styles between different funds during the evaluation period 2004–2013 by applying style analysis with rolling estimation windows. Main limitations of the study include the relatively small sample size and the general fact that the results obtained from style analysis and performance evaluation are very sensitive to the selection of benchmarks and the chosen sample period.