The recent academic literature has shown that some hedge funds are persistently able to provide superior risk-adjusted returns. Naturally such performance arises a question whether the performance could be predicted. This study proposes a predictive model to forecast future hedge fund returns using both macroeconomic and fund-specific characteristic predictive variables. With the proposed model I study in-sample, out-of-sample, and the economic value of predictability. The model I propose is based on a random coefficient model. It has appealing features to study return predictability. Contrary to time-series and cross-sectional models the random coefficient model is able to provide information at the individual hedge fund level and at the same time it takes into account all the information provided by the cross-section. To my best knowledge the random coefficient model has never been applied in hedge fund return predictability study before. In the proposed model I use a set of four economically motivated macroeconomic predictors: the default spread, the market return, the VIX, and the term spread. As fund-specific characteristic predictors I use the incentive fee, size, and age of an individual hedge fund. In this study I use a data sample provided by BarclayHedge database. My final data sample contains altogether over 6000 individual hedge funds from January 1994 to December 2010. I find that in the cross-section there are funds which are predictable in-sample with the used macroeconomic variables. The in-sample predictability varies clearly between distinctive strategy categories. It also has a very asymmetric nature; if there are positively predictable funds in a certain strategy category, it is unlikely that there are many negatively predictable funds. I study out-of-sample predictability of my model with portfolio sorting. I find that the decile my model predicts to perform the best also performs the best out-of-sample. This is actually true for the six highest decile portfolios; they all perform in the order predicted by my model. I study the economic value of predictability by constructing a hedge fund portfolio of 40 hedge funds selected by my model. I find that the mean annual excess return on the hedge fund portfolio selected by my model is 10%, clearly more than provided by any other strategy I consider except the VIX only strategy. In risk-adjusted basis my model performs much more poorly than the unconditional strategy which selects the best past performers. The results show that the random coefficient model can be used to predict future returns of hedge funds and possibly future returns of any asset class. The model I develop in this study could be used in a fund of hedge funds to select hedge funds to invest. However, it seems that the model has still room for improvements. In any case, the random coefficient model methodology looks promising for predicting future returns.

Risk parity portfolios are becoming more and more popular among investors due to its slogan of being a safe shield during the bad times, especially after the global financial crisis in 2008. As Qian (2006) stated, risk parity idea enables investors to quantify the loss contribution and ensures equal risk contribution from any assets in portfolio. Therefore, risk parity is well-balanced from risk perspective and possesses the unique diversification which investors so needed during the past global financial crisis. However, Lee (2011) expressed his doubt about risk parity portfolio effectiveness as he does not believe the underlying theory of risk parity where investors do not have to put an effort to forecast returns but are predicted to generate better results than those who actually estimate expected returns. The aim of this study is to test the effectiveness and benefit of risk parity portfolio if it had been adapted among hedge funds during the global financial crisis compared to the actual historical performances. Beside, alternative comparison portfolio methods include minimum variance portfolio and equally weighted portfolio due to its popularity and practical benefits in portfolio management industry. The data used in this research is aggregated hedge funds stock holdings database from Charle Cao, Jeremiah Green and Jiahan Li (2014). This database is based mainly on 13_F fillings from SEC and equity returns snapshot from CRSP (Center Research in Security Prices). The observation period of hedge funds fillings is fourteen years and 6 months from March 1999 to June 2013 with quarterly frequency. Meanwhile, US market stock returns were observed on monthly basic and with additional five years earlier starting from January 1994 to June 2013. The data consists of the 13_F section fillings and time series returns of the whole pool of stocks from CRSP with December 2013. The study emphasized that there is no single portfolio construction method could persistently deliver the best returns over time. However, its confirmed (Qian 2005) as well as (Ruban & Meles 2011) findings. Risk parity portfolios might not always be the best perfomer but there is a high possibility that these portfolios can surpass the performance of other traditional portfolios such as minimum variance, equally weighted portfolio during the bad times. This study agrees with (Chaves et al 2011), equal risk contribution portfolio is the one with the lowest volatility overall and able to deliver good and stable Sharpe Ratio during the whole observation periods. Additionally, following (Demiguel et al 2009), the study proved equally weighted portfolio is frequently among top performers over the observed period whereas the concentration issue of minimum variance portfolio was undeniable as (Clarke et al 2006). Taken into account the drawbacks from the database and portfolio rebalancing frequency, the study suggested future researches could be conducted with yearly or even quarterly portfolio rebalancing if the database allows. Another aspect to explore regarding to risk parity concept is about risk parity portfolio properties and its relation with leverage.

Työssäni esitellään elämänkaarisijoittamista yhtenä merkittävistä pitkän aikavälin sijoitusstrategioista sekä tutkitaan asiakasdatan avulla millaisiin sijoituskohteisiin Henki-Fennian olemassa olevat eläkevakuutusasiakkaat ovat säästönsä allokoineet. Elämänkaarisijoittamisen ajatuksen mukaan työssäkäyvän olisi järkevää säästää osa tuloistaan ja näin ollen varautua ennalta eläköitymisen aiheuttamaan tulotason alenemiseen. Elämänkaarisijoittamisen sijoitusfilosofiaa tullaan tarkastelemaan perinteisen taloustieteellisen lähestymistavan lisäksi myös sijoittajakäyttäytymisen perspektiivistä. Työn tavoitteena oli tutkia, miten Henki-Fennian eläkevakuutusasiakaskannan portfoliot ovat eri-ikäisillä rakentuneet, miten niitä voitaisiin jatkossa hoitaa tehokkaammin ja olisiko esim. elämänkaarirahastojen käyttöönotolle Henki-Fennialla tarvetta. Aineisto jaettiin viiteen ikäryhmään ja lisäksi kunkin portfolion riskisyys määriteltiin viiteen eri riskiluokkaan. Iän ja riskisijoitusten yhteyttä tarkastellaan lineaarisella regressioanalyysilla ja yksisuuntaisella anova-analyysilla. Riskinoton oletettiin laskevan lineaarisesti sijoittajan iän kasvaessa. Lineaarisella regressioanalyysilla tehty tarkastelu kuitenkin osoitti, että riskinotto ei laskenut lineaarisesti iän kasvaessa. Kun sijoittajien riskinottoa tarkasteltiin ANOVA-analyysilla ikäryhmittäin, huomattiin, että nuorimmat, eli 20–29-vuotiaat ottavat portfoliossaan kaikista ikäluokista vähiten riskiä (keskiarvo 3,30). Salkussaan merkittävimmin riskiä ottava luokka sen sijaan oli ikäluokka 40–49-vuotiaat (keskiarvo 3,82). Riskinottohalu taas kuitenkin laskee selvästi eläkeiän lähestyessä, sillä vanhin ikäluokka oli aineistossa riskinottohalultaan maltillisempi (keskiarvo 3,35). Näiden vanhempien ikäluokkien välillä ilmenevä ero riskiottohalussa siis kuitenkin puoltaa portfolioteoriaa. Kaiken kaikkiaan tulosten mukaan näyttää siltä, että sijoitusten riski on huipussaan 40–49-vuotiailla ja matalin nuorimmilla ja vanhimmilla ikäluokilla. Tutkimuksen valossa näyttäisi siltä, että Henki-Fennian kannattaisi ottaa käyttöön elämänkaarirahastot ja tarjota niitä vaivattomana ja turvallisena vaihtoehtona erityisesti nuorille asiakkaille.

This thesis studies systematic risk factors and return predictability in the Finnish stock market. The purpose is to test whether global Fama French factors and three interest rate spreads are risk factors that explain the cross sectional variation of excess returns in the Finnish stock market. The thesis also studies whether these factors are variables that forecast excess stock returns in the Finnish market. Research method is a linear factor pricing model, where excess returns are explained with these six risk factors. Main result of this study is that global Fama French factors, term spread and treasury spread are variables that can be used as systematic risk factors for explaining returns in the Finnish stock market. These variables explain about half of the cross sectional variation of excess returns in the Finnish market. Results regarding excess market return are unambiguous whereas results regarding SMB, HML, term spread and treasury spread vary along the estimated indices. Results of return predictability show that term spread and treasury spread are variables that forecast returns in the Finnish stock market. Limitation of this study is that these results are not supported with out of sample tests. Therefore these results cannot be generalized. Results of this study inspires to further research which would help to evaluate whether these variables can be used as systematic risk factors in other regional markets in addition to Finnish stock market.

Chan, Lakonishok and Sougiannis (2001) suggest an R&D intensive investment strategy, which results in excess return with high R&D intensive portfolios. We show how this investment strategy can be improved by taking into account the different industries and the competitive strategy. For this purpose we develop a simple proxy for Porter’s product differentiation and cost leadership strategies from DuPont identity components. Our strategy proxy seems to provide a good screen for stocks with negative excess returns. However, the improved excess returns are partly increased by loading unexplained risk. We partly replicate Chan et al. (2001) study for the period 1975–2011 with comparable results. We find the highest R&D intensity portfolios have excess returns after controlling for common risk factors. These stocks seem to be past losing stocks. In contrast to the reference study, we found slightly higher excess returns. The difference is explained by the different time period and lack of CRSP delisting return data in our study. We find support for our first (H1) hypothesis. H1: R&D intensity (measured by R&D expenses to market value of equity) has explanatory power over stock returns. To develop the investment strategy we explore the excess returns separately in ten different industries classified by SIC codes. After controlling for common risk factors, we show that R&D intensive portfolios’ excess returns are the phenomenon only in certain industries. These industries are hitec, healthcare and manufacturing. However, results for manufacturing excess returns were only slightly positive and lasted only for a year. We find support for our second (H2) hypothesis. H2: R&D intensity (measured by R&D expenses to market value of equity) has explanatory power over stock returns only among the high R&D intensity industries.

High technology sector in Finland is currently in the middle of structural change. This change is also creating a lot of new high tech entrepreneurs. Many of these new companies are seeking fast growth and position in global markets. Especially interesting area is how the fast growth and internationalization of small high tech firms can be financed. What are the financial needs and options? Are there financing gaps? Are these gaps constraining the growth and internationalization of the companies? What is the role of public institutions? Target is to bring more clarity to these questions, because the success in this ongoing transformation is important for many professionals working in high tech sector, but also for the economy of Finland and local communities. The change in the industry is influencing Oulu region very hard. There is a lot of expertise and competences in technology industry on a global scale, but quite little resources to finance this industry. This setting makes Oulu region an interesting area for studying financing challenges of high tech companies. Empirical research is done as qualitative research by using case study research method. Semi-structured interviews are used to collect data from companies. The case companies are five young technology firms, which have been established in Oulu, Finland. All of these companies are utilizing strong local technology expertise and competences and targeting to grow fast to global players in their area of business. This study compares their financing challenges and solutions to the theoretical knowledge and earlier empirical studies in high tech financing area. Target is to find if their financing challenges differ from theory or the findings in earlier studies. The study is also seeking if there are any new solutions used to meet the financing challenges. This study finds high tech SMEs as important contributors to economic growth. However, information opacity is limiting the financing options and causing financing gap. Empirical research in Oulu finds that financing has very critical role in the growth of high tech start-ups. In the early phases public support is critical for survival. In later phases, more local business angel resources and local venture capital funds would be needed. This is important for the growth but also for getting access to international funds. The results can be generalized to the other technology concentrations far from the world financing centers. The results can be used to enhance financing environment and services in those areas.

This paper attempts to analyse whether forward exchange arbitrage in currencies of managed rate regimes behave differently from currencies of free floating regimes in the forward exchange market. For this purpose, currencies of Great Britain, the European Union, and Japan are used as proxy currencies for free floating currencies. Proxy currencies for managed rate currencies are the Sri Lankan Rupee, the Indian Rupee, the Russian Rouble and the Brazilian Real. The US dollar is used as the anchor currency for both sets of currencies. The core of the paper revolves around the pricing difference between the fundamental forward price and the market forward price. Fundamental forward price is calculated based on the interest rate differentials of the two currencies; this is in concurrence with the interest rate parity condition. Market price is based on the forward pips and the spot rate. This analysis concentrates on the persistence of mispricing availability, the availability of forward pricing differences and the predictability of the mispricing in both sets of currencies. Finally, we also test whether the current forward price can predict the future spot price based on the interest rate parity theory. We present evidence that almost all the currencies have forward rate mispricing. However, we also present evidence to prove that mispricing in free floating currencies is extremely small, while managed rate currencies offer significant mispricing that could be exploited for arbitrage purposes. We also present evidence to establish that persistence of mispricing is specific to the currency pair and cannot be clearly attributed to the exchange rate regime. This paper also finds that it is not possible to statistically forecast the mispricing in both free floating and managed rate currencies. Further, failure of interest rate parity theory to accurately forecast the future spot rate is also documented.

The aim of the study is to investigate the factors affecting Euribor basis swap spreads. Variables are divided into three component; liquidity risk, credit risk, and macroeconomic and monetary policy. The Euribor basis swap was close to zero basis points, but during the early phases of the latest financial crises the spreads jumped. In empirical part of the study, the stationarity of the variables is tested. In the next step, Phillips-Ouliaris (P-O) co-integration test is tested to get 5 combinations that co-integrates with Euribor basis swap spread 3 month versus 12 month with 5 years to maturity. Thirdly, long-run equilibrium for the Models with Engle-Granger test is applied. Out of the five Models, picked in P-O, only three had long-run equilibrium. From the three long-run equilibrium Models the regression residuals are saved and estimated short-term equilibrium with Error Correction Model. At the end, Ordinary Least Square method with Newey-West corrections with the three co-integrated Models is tested. The variables for liquidity risk component are Open Market Operations, Aggregate Liquidity Factors, Deposit Facility, and Governing Council Meeting day -dummy. The variables for the credit risk component are Eurobond yield and Bank Credit Default Swap spread. The variables for the macroeconomic and monetary policy component are Euro Overnight-Index Average and exchange rate. The results show that the biggest determinants for the Euribor basis swap spread 3m vs 12m 5y are Open Market Operations, Meeting day, Eurobond yield 5y, Bank CDS, EONIA, and exchange rate of China.

It has been established in a vast number of financial and econometric literature that financial and macroeconomic variables such as dividend-price ratio and term spread forecast aggregate stock market returns at both short and long horizons in many developed and emerging economies. Using financial ratios and macroeconomic variables, Rapach and Wohar (2006) report in-sample and out-of-sample evidence of predictability of future excess stock returns. However, contrary results are reported in the literature such as Welch and Goyal (2008). It is argued that regressions analysis produced poorly predicted in-sample and out-of-sample results. Following the methodology of Hjalmarsson (2010), I apply the OLS regressions analysis, where aggregate stock market returns are predicted by financial ratios and macroeconomic variables. I conduct the analysis for each of the countries: Australia, Denmark, Finland, France, Germany, Japan, Norway, Sweden, United Kingdom, and the United States using data from Morgan Sterley Composite Index (MSCI). I find that financial ratios such as dividend-price and earnings-price ratios accept null hypothesis of no predictability of stock returns at short horizons for all the countries except Norway and United States where predictability is evident at 3 months horizons, but there exist predictability at the long horizons. This is a contrary view to what previous literature reported. The overall findings of this thesis suggests that dividend yield, earnings yield, term spread, and short interest rate predict stock returns at long horizons in all the countries with an exception of short interest rate and term spread that fail to forecast stock returns at all the horizons for Denmark. Book-to-market ratio predicts stock returns at short and long horizons in 80% of the countries that are covered in my sample.

This paper evaluates performance of the Black-Scholes option pricing model on European call options that are written on U.S. S&P 500 equity index in year 2014. Main purpose is to show empirical evidence about false assumptions contained in the model and complete it by relaxing unconditional restrictions. Analysis consists of investigating biasedness and heteroscedasticity properties by complementing the Black-Scholes model with GARCH(1,1) method based on maximum likelihood estimations. Varying volatility is studied also through implicit volatility surface. Depending on their characteristics, call options are categorized into specific groups according to their moneyness and maturity for further analysis. Using common econometrics and statistical methods, the paper shows that assumption about constant volatility is false, that the Black-Scholes model exhibits a bias which leads to mispricing of certain type of options and that assumption about normally distributed error term is false. Volatility is estimated through historical and implicit methods, of which the latter one uses GARCH(1,1) method to capture especially time-series characteristics of varying volatility. Findings regarding performance of the Black-Scholes option pricing model were expected and are in line with prior literature.