The target of the study is to find out if the direct methodology could provide same information about the parameters of the risk neutral probability density function (RND) than the reference RND methodologies. The direct methodology is based on for defining the parameters of the RND from underlying asset by using futures contracts and only few at-the-money (ATM) and/or close at-the-money (ATM) options on asset. Of course for enabling the analysis of the feasibility of the direct methodology the reference RNDs must be estimated from the option data. Finally the results of estimating the parameters by the direct methodology are compared to the results of estimating the parameters by the selected reference methodologies for understanding if the direct methodology can be used for understanding the key parameters of the RND. The study is based on S&P 500 index option data from year 2008 for estimating the reference RNDs and for defining the reference moments from the reference RNDs. The S&P 500 futures contract data is necessary for finding the expectation value estimation for the direct methodology. Only few ATM and/or close ATM options from the S&P 500 index option data are necessary for getting the standard deviation estimation for the direct methodology. Both parametric and non-parametric methods were implemented for defining reference RNDs. The reference RND estimation results are presented so that the reference RND estimation methodologies can be compared to each other. The moments of the reference RNDs were calculated from the RND estimation results so that the moments of the direct methodology can be compared to the moments of the reference methodologies. The futures contracts are used in the direct methodology for getting the expectation value estimation of the RND. Only few ATM and/or close ATM options are used in the direct methodology for getting the standard deviation estimation of the RND. The implied volatility is calculated from option prices using ATM and/or close ATM options only. Based on implied volatility the standard deviation can be calculated directly using time scaling equations. Skewness and kurtosis can be calculated from the estimated expectation value and the estimated standard deviation by using the assumption of the lognormal distribution. Based on the results the direct methodology is acceptable for getting the expectation value estimation using the futures contract value directly instead of the expectation value, which is calculated from the RND of full option data, if and only if the time to maturity is relative short. The standard deviation estimation can be calculated from few ATM and/or at close ATM options instead of calculating the RND from full option data only if the time to maturity is relative short. Skewness and kurtosis were calculated from the expectation value estimation and the standard deviation estimation by using the assumption of the lognormal distribution. Skewness and kurtosis could not be estimated by using the assumption of the lognormal distribution because the lognormal distribution is not correct generic assumption for the RND distributions.

Existing literature documents that a portfolio of value stocks outperforms a portfolio of glamour stocks and market portfolio. Researchers have different opinions regarding, “what derives premium returns from a long short value glamour strategy?” The central objective of this paper is to seek the source of value glamour return effect. We have mix results for various hypotheses tested. Our main findings are: European stocks have extremely negative performance for long short value glamour strategy during 1991 and 2011, second, Fscores effectively separate potential winners from potential losers, third, error in investor expectations partially affect the performance of value glamour strategy but central source of value glamour performance is the riskiness of value stocks in comparison to risk levels of overall market or glamour stocks and lastly, investor sentiments do amplify future returns providing partial evidence in favor of market mispricing. We conclude that successful value investing requires ability to pick quality stocks from within a broader portfolio and exposure to higher risk.

Empirical studies have analyzed how investors trade and perform in the financial markets. The studies show that rational trading needs do not explain the excessive manner of trading shown by the investors. Theoretical models offer overconfidence as one of the explanations for irrational trading behavior. Overconfidence is a psychological trait, argued to cause the investors to misinterpret useful information, which leads to an increase in trading activity and hurts their performance. In this study we analyze over 1.5 million Finnish trading records from the beginning of 1995 to the end of 2010. We evaluate the differences in trading behavior between males and females and with investors of diverse ages. We find that men trade securities more frequently and with higher turnover than females. Consistently with our reference studies we find that the level of turnover decreases as the investors age. We also analyze the profitability effects of trading by calculating raw returns and abnormal returns. The abnormal returns are adjusted with a passive benchmark portfolio. Earlier studies show that the more active trading of males reduces their abnormal returns. Our abnormal return ratios do not support this finding. However, we find consistently that the raw returns are higher for females than males. Females also hold portfolios with lower volatility than males. Finally, we find consistently with the models of overconfidence by Odean (1998b) and Gervais and Odean (2001) that the trading skill seems to get better with experience. Older investors receive higher raw returns and trade less, resulting in lower portfolio turnover. The transition in trading behavior may be an outcome of learning.

Individual investors quite often face a search problem of which stock they should select from many choices that are presented in the stock markets. Individual investors commonly face search costs, information cots, analyst cost and short-sale constraints that could increase the difficulty regarding an investment decision concerning which stock to buy from the market. Typically, when individual investors need to make a complex decision they tend to limit their problem by putting the stocks that have captured their attention into a consideration set. From this set, investors ease their decision whether to buy or sell a stock on the basis of their personal preferences. The scarce availability of information and the investor’s inability to retrieve relevant information causes investors to base their investment decisions on irrelevant factors such as stocks which grab their attention. These investment decisions are often regarded as irrational due to the fact that they are not on the basis of good analytical research but on factors which not necessarily provide good information whether stocks should be bought or sold. The paper uses the assumptions that are provided from the Barber and Odean (2008) study about the effect of attention to research the trading behavior on Finnish investors. In times when information is limited the few factors that remain visible to the individual investors are trading volume and stock returns. The paper uses abnormal trading volume and positive and negative stock returns as proxies for attention in order to analyze the effect of attention. The paper finds that individual investors tend to be affected by attention but the results although somewhat different are consistent with the Barber and Odean (2008) study. Passive or less experienced individual investors are found to have negative buy-sell imbalances which are mostly large in the extreme deciles, while active and experienced individual investors are found to have positive imbalances that are also large in the extreme deciles. In general, the trading performance is not affected by attention for the most active individual investors since they record a superior performance having to buy stocks which consequently deliver subsequent positive returns. On the other hand, passive individual investors tend to sell stocks that will provide them subsequent negative returns which in fact hurt their trading performance this finding is mostly documented in class 3 individual investors. Institutional investors have been found not to be influenced by attention, thus their imbalances vary across all deciles for sorts on the basis of abnormal volume and stock returns. Their trading performance usually is good since they buy/sell stocks that earn positive/negative returns although some institutional investors record the opposite performance. The importance of this study is that in general it confirms the assumptions of Barber and Odean (2008) that individual investors in Finland do base their choices on the basis of attention.

Mean-variance model of Markowitz is important milestone in the history of the quantitative finance but the model is problematic in real portfolio optimization implementations. The estimation error remains an insuperable problem to overcome despite of many improvements that enhance the performance of the mean-variance model. We derive an asset characteristic based portfolio solution based on the work of Brandt et al. (2009) and Hjalmarsson and Manchev (2012). The data include stock markets in 21 countries in the period of January 1986 to December 2011. Our objective is to show the performance of this kind of simple portfolio optimization method with a set of asset characteristics. We do not seek the best set of characteristics but choose five characteristics that are earnings-to-price ratio, dividend yield, price-to-book ratio, market value and momentum. In addition to asset characteristic portfolios we show performances of equally weighted portfolio and two simple risk parity strategies which we then combine with the asset characteristic portfolio. We also show the importance of the selected asset characteristics for portfolio performance. For portfolio performance metrics we compute Sharpe ratio, Jensen’s alpha ant turnover. Our results support the claim that equally weighted and simple risk parity portfolios are great alternatives to mean-variance model. By out-of-sample performance measures they beat the sample efficient mean-variance tangency portfolio easily. They also show better performance values than the benchmark index, MSCI WI, when measured by Sharpe ratio or Jensen’s alpha although we do not find these measures to be statistically significant. Moreover, our results suggest that performances of these alternative methods can be improved even further by combining them with an asset characteristic based portfolio. The presented portfolio selection methods provide stable and financially sensible results which are in-line with the previous literature.

Although stockholding rates have experienced slight but steady increment in the last two decades, stock market participation is still very far from universal among people. Finding the root-causes of lower-than-expected stock market participation among people has sparked off many ongoing debates in the area of behavioral finance. Irrespective of general economic prosperity and markets developments, in this thesis we tried to show what other fundamental drivers of stock market participation among individuals are. We took data on 6355 individuals who were born in 1966 in northern Finland and investigated to find most outstanding determinants of stock market participation. According to our analysis, level of education, level of net-wealth and degree of risk seeking are in direct proportion with stock market participation. Additionally, marriage increases the likelihood of stockholding in men whereas married women are less likely to hold stock. One interesting result of our study is that, social interactions do not increase the probability of stockholding. In fact, parameters such as high degree of sociability with spouse, surprisingly created negative effects on probability of stockholding. Even after adding reward dependence variables including sentimentality, attachment and dependence into our regression analysis, the negative effects of within-family social interactions and marriage on stock market participation are strengthened.

Some investors may not be able to use leverage at all or they face different margin requirements. Investing in securities with high-embedded leverage enables those investors to obtain desired level of market exposure without violating their margin constraints. Investors in hedge funds can gain access to this high-embedded leverage by investing in hedge fund share classes that have leverage multipliers higher than one. Hedge funds characteristically exploit different arbitrage and speculative investment strategies. These strategies typically entail illiquid assets, and to obtain the flexibility hedge funds often restrict investors’ ability for capital redemptions. This is done by applying different share restrictions. Illiquid investments can also yield to serially correlated returns. This study employs extensive hedge fund database, which is constructed by merging five individual databases. The use of this database contributes to previous academic studies, since such thorough database has not been employed in studies concerning hedge funds. Furthermore, this study contributes to the recent academic studies by investigating the return differences between unleveraged and leveraged hedge fund share classes. Additionally, this study considers, whether the return spread is larger for hedge funds that invest in illiquid assets. Finally, this study investigates whether some predefined macroeconomic and risk factors are able to explain those returns differences. This study finds two specific implications that contribute to the previous academic literature. First, there exists a return difference between unleveraged and leveraged share classes, and that difference is statistically and economically significant. Second, return difference can be partly explained by some macroeconomic and risk variables. Aggregate hedge fund flow has a positive relationship with the return difference and it acts as a key variable in explaining those return differences. Additionally, this study finds that overall movements in financial markets affect the returns of leveraged hedge fund share classes. Increases in different risk variables cause leveraged share classes to reduce their exposures and their leverage is not constantly at the promised level. This makes them to resemble more their unleveraged pairs and induces return difference to decrease. As a conclusion, returns of leveraged share classes, scaled with their respective leverage, are lower than the returns of unleveraged share classes. This finding brings important implications for hedge fund investors. Ability of some macro and risk variables to explain the return difference helps investors to understand the factors affecting the return spread and help them to time leveraged hedge fund investments properly.

This research presents an application of extreme value theory to estimate the value at risk of a market position particularly of the OMX Hex Index. There are many approaches to computing value at risk alongside the extreme value method. One fundamental problem the manager of risk face is “what is the optimal choice of value at risk estimator that will best predict the risk?” This implies that choosing an approach to best predict value at risk is challenging. This study proposed a method of estimating value at risk using the two-dimensional inhomogeneous Poisson model. An extreme value theory method that is based on the Peak Over Threshold (POT). The method takes into consideration time varying parameters through some explanatory variables. The study also shows how well theoretical model fit real financial data. The data used is the daily log return of the OMX Hex Index from the period 1990 to 2014. From the data we show empirically that the OMX Hex Index obeys a Fréchet distribution. The explanatory variables for the study are GARCH volatilities, annual trend and quarter dummies. The explanatory variables are all available at time t-1. With the help of the fitted models we adopt the two-dimensional inhomogeneous Poisson model approach to estimating value at risk over the two-dimensional homogeneous Poisson model and other classical or traditional methods, and find that this better predict value at risk estimates.

The aim of this thesis is to study risk factor based investing and test how well MSCI constructs their risk factor based indices. Risk factor based investing has gained a lot of media exposure in the recent years and “Smart Beta” products are becoming more popular. Blackrock estimated that there are more than 700 exchange traded products available and they have over $ 529 billion in assets under management. Risk factor investing aims to harvest the risk premia associated with factors like size, momentum and value. I tested whether MSCI is able to provide higher Sharpe ratios for higher risk exposure indices and how much they deviated from the parent index of MSCI World. I used the Ledoit & Wolf bootstrap inference test to find out whether the Sharpe ratios of high exposure and high capacity indices differ from each other. Furthermore, I tested how well the Fama & French Three Factor-model with the addition of Carhart momentum factor could explain the returns of MSCI’s risk factor indices. I also constructed different risk factor portfolios using risk-parity methods to see whether it is possible to enhance the returns of risk factor indices by combining them. The main results and conclusions of this thesis were that risk factor investing can provide excess returns. These excess returns are readily available by investing in MSCI’s risk factor indices. Another key finding was that by utilizing risk-parity methods an investor can achieve excess returns over an equally weighted risk factor portfolio and over the MSCI’s own Diversified Mix index. Furthermore, even though MSCI is the world leader in index creation, their way of creating indices doesn’t seem to be very efficient and it would be beneficial to analyse other index providers, too. The data used in this thesis were gathered from “MSCI’s end of day index data search”. The data consists of six risk factor indices from developed countries. The price data ranged from November 1998 to August 2015. For the Ledoit & Wolf test I gathered four high capacity indices and four high exposure indices from the same time period. The proxies for academic factors were provided by Kenneth French on his website.

The purpose of this thesis is to examine the determinants of interest rate in peer-to-peer business loans. The peer-to-peer lending is a novel business area and it is an alternative for either as an investment or as a way to raise funding. From both, the investor’s and the fund raiser’s perspective it is important to know how the interest rate is determined on the loans. Also the information offered on peer-to-peer business loans is evaluated from the perspective of screening the loans based on the fund raiser’s quality. The tested variables are the length of the loan period, the amount of capital raised, the credit score and the delays of the repayment. Additionally the effect of the maturity of the business is tested between four time periods. The sample data is from two online platforms offering peer-to-peer business loans at Finnish markets. These platforms are Fundu and Yrityslainat.fi. The length of the loan period and the credit score are statistically significant determinants of the interest rate. Both the length of the loan period and the credit score have a negative relation to interest rate. So, longer the loan period is and higher the credit score, the lower is the interest rate. Also the interest rates decreases when the business matures. The available information of fund raisers varies between platforms. This information has been used effectively to screen loans in case of consumer loans.