The aim of this master paper is to reveal the role of a judge. In the first part of the paper the ideas of social civil procedure school (that is essential in the Lithuanian Republic Civil procedure code) defining an active role in civil procedure and the model of civil procedure of Lithuanian Republic are discussed. The second part of the paper deals with analysis of stages of the civil procedure. By analyzing these stages the author of the paper tries to disclose by which means provided in law the judge realizes his (hers) performing role in the procedure. Also the intensity of the judge role depending on the stage of the civil procedure is shown. Attention is paid to certain categories of cases that influence the extension of the judge’s activity and the difference of performing intensity depending on contentious proceedings as well. In the third part of the paper the possibilities of making default judgment and judicial penalties assigning as means helping to control parties that overindulge processing right or do not care about the progress of proceedings are discussed.

This study analyzes 66 Swedish actively managed mutual funds investing in the Swedish stock market during the period 2005-2014. The purpose is through pooled data regressions analyze the relationship between both the mutual fund’s annual fee and risk-adjusted return to the fund’s characteristics. The characteristics of the study are the size of the fund's assets, age, if the fund is bank managed or not, Tracking Error, and standard deviation of return.By using the performance measures of CAPM, Fama and French 3-factor model, and Carhart’s 4-factor model monthly risk-adjusted returns are created for all funds over the period. Two pooled data regressions are performed with the Fixed Effect Model in which the annual fee and risk-adjusted return is set up as explanatory variables against the various characteristics.The results of the study show a clear correlation between annual fee and tracking error against the risk-adjusted return. A higher fee adds value to the investor through a higher risk-adjusted return, but will not fully compensate for the increased fee. The relationship between Tracking Error and risk-adjusted return is negative, which means that mutual funds that are distant from its benchmark perform worse than the mutual funds close to its benchmark. To explain annual fee this study finds low economic significance for the characteristics included. Although several variables show statistical significance, it is difficult to say anything about the characteristics that affect a mutual fund's annual fee due to the weak economic significance.

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We consider a model of active asset management in which mutual fund managers exert unobservable effort to earn excess returns. Investors allocate capital to actively managed funds and passively managed products. In equilibrium, investors are indifferent between investing an additional dollar with an active manager or with a passively managed product. As passively managed products become more attractive to investors, active managers’ revenues from portfolio-management services fall, reducing their effort incentives. More-severe decreasing-returns-to-scale are also associated with reduced incentives and increased moral hazard. Performance-based fees and holdings-based data are all unlikely to mitigate moral hazard.

Pension is a subject that soon or later affects all individuals in society. Within the premium pension system mutual fund fees are an important factor to consider since it can erode savings. This study investigates fees and performance of all mutual funds that existed in the premium pension system (PPS) between 2004 and 2016. Risk-adjusted performance and fees are adjusted to reflect the discount that is given for fees within the premium pension system to reflect actual investor experiences. The data needed to make these adjustments were obtained from the Swedish Pension Agency. The main purpose of this study is to investigate if there is a relationship between fee and risk-adjusted return within the Swedish premium pension system. Further on this study aims to explain what mutual fund characteristics can be used to predict performance and fees within the premium pension system.Theories used in this study are efficient market hypothesis, agency theory, behavioral finance, economies of scale and portfolio theory. The factors from the Carhart 4-factor model is used to construct the factor model utilized in this study to estimate risk-adjusted returns. This study adapts a quantitative research method and panel data regressions were conducted to determine how fee and risk-adjusted performance is related to various mutual fund characteristics. Hausman tests were conducted to evaluate if the fixed effects model or random effects model was the most appropriate to use. The result of the Hausman test proved that fixed effect model was the most appropriate model to use.This study will draw conclusions about whether the fee that the mutual fund companies charge can be justified given their risk-adjusted performance. The results for the sample of all mutual funds and the sample of equity funds imply that there exists a positive relationship between fee after discount and performance. Therefore, mutual funds in the premium pension system compensate for increasing fees with a higher risk-adjusted return. The sample of balanced funds differs since there is a negative relationship between risk-adjusted returns and fees. This study finds that size is a determinant of risk-adjusted performance, with larger mutual fund performing better than smaller funds. Because of this finding, it can be concluded that economies of scale do exist among the mutual funds in the premium pension system. Actively managed mutual funds charge higher mutual fund fees on average than passively managed funds. Further on, there is significant evidence that actively managed equity funds perform worse than passively managed equity funds. This suggests that investors are better off investing into cheaper equity index funds rather than expensive actively managed equity funds.

Against a backdrop of controversy surrounding market timing, this research assesses the merits of a tactical asset allocation strategy for the South African market. The purpose of this research is to assess whether a simple quantitative method - initially presented by Faber (2007) - can successfully reduce volatility and increase returns of selected indices within the Johannesburg Stock Exchange (JSE). The All Share (ALSI), Financial&Industrial (FINI), Resource (RESI), Africa Gold Mining (AGMI), Government Bond (GOVI) and Property Unit Trust (PUTI) indices were examined. A strategy based on a ten-month simple moving average was compared against a buy-and-hold strategy, with results presented for these strategies both excluding and including transaction costs. The strategies were tested over a 50-year period from 1961 to 2010. The results show that superior risk-adjusted returns are possible even in the presence of high transaction costs. Further insights suggest that tactical asset allocation strategies yield improved performances when used in specific sectors and/or asset classes, instead of in consolidated sectors represented by the market.Copyright / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

Modern portfolio theory commenced the ensuing debate regarding the benefits of active versus passive management in regards to mutual funds. The two opposing mutual fund management styles claim that they produce superior risk-adjusted performance. The dissertation explores the research question: During the full 1995 to 2008 market cycle, which investment management style, active or passive, produced the better risk-adjusted performance? The significance of this historic stock market period relates to the fact it contains the two greatest bull markets (1995-1999 & 2003-2007) followed by subsequent bear markets (2000-2002 & 2008). The study tested nine hypotheses, derived from the above research question, for the 5 different time periods (1995-1999, 2000-2002, 2003-2007, 2008, and 1995-2008). Based on previous research, one would expect the passive management styles to out-perform the active styles during expansion whereas the performance would reverse during market contraction. The Sharpe composite portfolio performance measure, that combines risk and return into a single value, was used to measure, analyze, and rank risk-adjusted performance. The study, comprised of 45 statistical tests, found that on a risk-adjusted basis that the active indices (proxies for active management) Sharpe ratios were significantly greater than those of the passive indices (proxies for passive management) Sharpe ratios for; 1) the midcap blend category for the periods 1995 to 2008 and 1995 to 1999; 2) the small blend category for the periods 1995 to 2008 and 1995 to 1999, and 3) the small value category for the periods 1995 to 2008, 1995 to 1999, and 2000 to 2002. Therefore, the active indices Sharpe ratio significantly exceeded the passive indices Sharpe ratio for 16% of the statistical tests conducted while the active indices Sharpe ratio did not significantly exceed the passive indices Sharpe ratio for 84% of the statistical tests conducted. The findings suggested that in the long run passive management produced better performance results than active management.

In active portfolio management, fund managers seek to follow an investment strategy with the objective of outperforming an investment benchmark index. Opportunities to outperform a benchmark in active fund management is made possible through crosssectional dispersion of returns in the market. It is cross-sectional volatility of returns that allows fund managers to identify changing trends in market relationships and to take advantage of market opportunities.Quarterly active share and active return data of Domestic General Equity funds was used to determine whether the level of active share and active return has a correlation with volatility measures such as cross-sectional variance of returns or the South African Volatility Index (SAVI). The actively-managed funds’ outperformance of the benchmark index during periods of differing cross-sectional variance was also looked at. Lastly, the possibility of whether market volatility can be used to inform fund investment decisions was also examined.The findings in this study are that there is no significant relationship between the crosssectional variance of returns, active share and active returns. In measuring fund performance in times of differing cross-sectional dispersion and breaking the analysis period into such intervals rather than as a continuous time series, active funds outperform the benchmark index during periods of low and moderate cross-sectional variance. The SAVI can be used as a fairly accurate and readily available approximation of cross-sectional variance. / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

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