We investigate the activity and performance of 64 Swedish registered mutual equity funds available in the Swedish Premium Pension System from October 2002 to December 2011. Fund activity is measured by applying the holdings based analysis Active Share combined with Tracking Error Volatility (TEV). Active Share is a relatively new measure that compares a fund’s holdings with its benchmark index constituents (Cremers & Petajisto, 2009; Petajisto, 2013). This is used as a proxy for the fund’s stock selection strategy. As a complement, TEV is used as a proxy for the factor timing strategy. Performance are measured by using Jensen’s (1968) model, Fama and French’s (1993) model and Carhart’s (1997) model. We document that Swedish funds in the Premium Pension System are relatively passive in term of Active Share compared to US funds. We attribute this finding to the relative number of stocks held by a fund compared to the market. Swedish equity funds hold a relatively larger share of the number of stocks in the Swedish market while US funds hold a relatively smaller share of the stocks in the US market. We run a panel regression analysis to test the relation between Active Share and various variables. We find that funds with higher TER fees and fewer stocks on average have higher Active Share. There are also indications that TEV is positively related to Active Share. However, the overall explanatory power of the variables is low. We attribute this as evidence that Active Share is an independent measure of fund activity. Overall, we find neutral performance for an equally weighted portfolio of all funds in the PPS. To examine the performance differences between different levels of activity, we sort funds into five portfolios based on Active Share and TEV. The results show that, given a medium-to-low TEV, funds with high Active Share significantly outperform funds with low Active Share. Furthermore, it appears that the fee rebate in the Premium Pension System is important especially for the passive funds. Without the rebate, the passive funds underperform significantly. We run a panel regression analysis on the future fund performance to test the predictive abilities of Active Share and TEV. The results indicate that Active Share does not explain future performance differences. Conversely, TEV is negatively related to future performance which can be explained by fund managers being overconfident

International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Automating Telemetry Tracking Systems Operational Readiness Tests is a concept that was introduced at White Sands Missile Range in the early 1980’s. The idea was to determine the condition of a Telemetry Tracking System in a reliable manner in a short time as possible. A series of RF and Servo Tests designed to determine the condition of a Telemetry Tracking System was implemented using HP BASIC. The latest personal computers are faster and have more storage capacity plus the capability to be programmed in higher level languages such as C/C++ and LabView. This technology makes it easier to automate system tests. Many of these tests need to be conducted just prior to supporting a mission. Some tests are required to be performed on mobile systems after moving the system from one location to another, especially if the move was over long distances and rugged terrain. Tests such as G/T are conducted before each mission because it yields accurate information on the Figure of Merit, or, System Sensitivity. Noise Figure Measurements are more difficult to perform to determine the System Sensitivity since modern RF Subsystems have pre-amplifiers with Noise Figures of less than 1.0 dB. The “down-sizing” of personnel increases the possibility of failure in mission support scenarios due to the many critical readiness tests needed to assess the Telemetry Tracking Systems. Also, conventional test methods can be time consuming and are subject to human error. This paper describes four critical tests that have been automated to improve reliability of the test data and decrease the amount of time required to conduct the tests. The “C/C++” language was used to write the automation programs. More tests will be automated later.

In two unrelated papers, we examine different aspects of mutual fund performance and other issues. In the first chapter, we look at exchange-traded funds (ETFs) and how they differ from index funds in performance and tracking error. Using daily data and a more comprehensive sample than past research, we find abnormal returns associated with the ETFs are higher than the alphas of the index funds in most cases. The results are much more prevalent in funds that follow the S&P 500 than funds that do not. When examining the tracking errors, we find index funds are able to track their indexes much better than ETFs and domestic ETFs are better than ETFs that track international indexes. In our most significant finding, we find that tracking error affects fund flow in the following period. While fund flows are generally increasing for both ETFs and index funds, funds that track their respective index better increase their net assets by a larger percentage than funds that track their index less well. In the second chapter, we look at the differences in performance and characteristics of mutual funds as they relate to the manager's gender. Using a larger sample and different techniques than have been used in the past, we find some differences in our matched comparison which suggest female managers have a lower risk tolerance than males. Females also tend to hold a higher number of assets (stocks) and fewer assets in their top 10 holdings than do male managers. In, pooled regressions, we find weak, but significant evidence that current female fund managers, when analyzed as a group, show slightly lower performance than male managers. We then analyze performance within funds over time. Our most consistent result is that when changing the composition of fund management, regardless of gender, the new management has significantly greater performance than prior management. We also find some evidence, although not conclusive, that the percentage of female managers managing a fund is negatively related to the fund's performance over time. Finally, we find the determinants of abnormal returns cannot be attributed to the fund manager's gender.

本文探討槓桿型/反向型ETF之追蹤誤差是否會因為追縱標的屬於不同產業而有差異。為此先將46檔具有顯著追蹤誤差之槓桿型/反向型ETF樣本分為七類不同的產業，並設定產業作虛擬變數放入多元迴歸模型中，結果為當追蹤指數屬於建築業、零售業及服務業之槓桿型/反向型ETF傾向得到較大的追蹤誤差，而追蹤指數屬於礦產業、製造業、水電業及金融業者，其追蹤誤差較小。追蹤誤差大之產業，其指數波動度也較大，推測指數波動度可能是造成特定產業追蹤績效不佳的原因。另外在本研究中也發現，追蹤礦產業、建築業及製造業指數之槓桿型/反向型ETF，其槓桿型績效優於反向型績效；而追蹤水電業、零售業、金融業及服務業指數之槓桿型/反向型ETF則是反向型績效優於槓桿型績效。其原因推測與ETF存在年限、ETF發行公司和指數成分股個數有關。 / This paper discusses whether the tracking error of leveraged/inverse ETF varies by industry of underlying indexes. We take 46 leveraged/inverse ETFs with significant tracking error into the samples, divide them into 7 different industries and set those industries as dummy variables in multiple regression models. The outcome shows that the tracking error tends to be larger if the underlying index of leveraged/inverse ETF belongs to construction, retails or service industries; otherwise, the tracking error tends to be smaller if it tracks the index from mining, manufacturing, utility or finance industries. The larger tracking errors may result from more volatile indexes of those industries. Besides, we also find that leveraged ETFs outperform the inverse ones if the index belongs to mining, construction or manufacturing industry; on the other hand, inverse ETFs outperform leveraged ones when the index comes from utility, retails, finance or service industry. The possible reasons may be the different characteristics of ETFs, such as the length of ETF’s existence, ETF’s issuer and the number of constituents in underlying index.

Diploma thesis deals with the evaluation and the comparison of the performance of mutual funds and investment funds with a focus on the real estate sector. The essence and principles of mutual funds, ETF and REIT are presented, and the resulting weaknesses and advantages. According to the selected indicators, the profitability, riskiness and expense of the investment opportunities are examined and investment recommendations for management of an investment company and potential retail investors are established.

This thesis deals with exchange traded funds (ETFs). The theoretical part is focused on familiarization with the issue from a broader perspective in the form of collective investment characteristics, leading through the current trends in this area. This part is followed by chapter about ETF, including specific areas. Selected classic and leveraged ETFs are analyzed in the practical part for the period between 2010 and 2015. Funds are analyzed on the basis of NAV in the terms of return and risk represented by selected indicators. Results are commented in a broader context in summary and discussion chapter as well as recommendations.

本研究針對臺灣摩根指數的成分股進行分析，研究樣本期間從2008年至2010年，合計三個年度，正好歷經景氣的一個多空循環週期。本研究利用技術指標作為判讀多空的工具，技術指標包含價與量的技術分析工具，價格的技術指標有趨勢指標MA、擺盪指標KD與MACD，量的技術指標則是OBV。並利用優化的方式挑選出合適的參數值。本研究的風險控管則是控管個股的偏離程度，當允許的偏離程度愈大，模型便愈能區別出強勢股與弱勢股，風險的衡量指標則是採用年化追蹤誤差值來衡量，本研究設定的限制條件為最大累積年化追蹤誤差值不得超越6%。 實證結果發現，當模組的模型年化追蹤誤差值設定愈大，個股的偏離程度就愈大，模組的報酬表現就愈佳，但同樣的風險也愈大，即年化追蹤誤差值愈大。當模型年化追蹤誤差值設定在24%，並搭配MA、MACD與OBV三個技術指標得到的績效最佳，同時亦能夠將風險控制在設定的6%水準之下。 / This study analyzed the component stocks in MSCI Taiwan Index. The analyzed data from 2008 to 2010 was exactly an economic cycle. The study was based on technical analysis, including price and volume to judge that the price was bullish or bearish. The price technical analysis included Moving Average (MA), Stochastic Line (KD) and Moving Average Convergence and Divergence (MACD). The volume technical analysis was On Balance Volume (OBV). The study used the method of optimization to choose the best parameter of each technical analysis. The risk control was to limit the bias of each stock. When the bias of each stock was larger, the model could easily distinguish the stock was bullish or bearish. The risk indicator was annual tracking error limited to 6% in the study. The empirical results showed that the larger the model annual tracking error set, the large bias the stock show, and the outperformance of the return. But with the performance of the return larger, the risk of tracking error was also getting larger. When the model annual tracking error set to 24%, and utilized MA, MACD and OBV would get the best performance and the risk of annual tracking error was under 6%.

Neste trabalho, deriva-se uma política de escolha ótima baseada na análise de média-variância para o Erro de Rastreamento no cenário Multi-período - ERM -. Referindo-se ao ERM como a diferença entre o capital acumulado pela carteira escolhida e o acumulado pela carteira de um benchmark. Assim, foi aplicada a metodologia abordada por Li-Ng em [24] para a solução analítica, obtendo-se dessa maneira uma generalização do caso uniperíodo introduzido por Roll em [38]. Em seguida, selecionou-se um portfólio do mercado de ações brasileiro baseado no fator de orrelação, e adotou-se como benchmark o índice da bolsa de valores do estado de São Paulo IBOVESPA, além da taxa básica de juros SELIC como ativo de renda fixa. Dois casos foram abordados: carteira composta somente de ativos de risco, caso I, e carteira com um ativo sem risco indexado à SELIC - e ativos do caso I (caso II). / In this work, an optimal policy for portfolio selection based on mean-varian e analysis for the multi-period tracking error - ERM - was derived. ERM is understood as the difference between the capital raised by the selected portfolio and benchmark portfolio. Thus, the methodology discussed by Li-Ng in [24] for analytical solution was applied, generalizing the single period case introduced by Roll in [38]. Then, it was selected a portfolio from the Brazilian stock trading based on the correlation factor, and adopted as benchmark the index of the stock trading of São Paulo State IBOVESPA, and the basic interest rate SELIC as fixed income asset. Two cases were dealt: portfolio composed of risky assets only, case I, and portfolio with a risk-free asset - indexed to SELIC - and assets of the case I (case II).

Made available in DSpace on 2016-04-25T16:45:31Z (GMT). No. of bitstreams: 1 Jose Augusto da Silva Rezende.pdf: 1815562 bytes, checksum: c40bcc71ae45d2b7fd4c4ec054a04ca9 (MD5) Previous issue date: 2010-07-28 / This dissertation brings a review on two management and risk assessment tools: VaR and Tracking Error and their applicability in pension funds. It describes the calculation methodologies and their employment in the management of a pension fund. It also reviews the literature on the employment of VaR and Tracking Error for risk assessment of pension funds. Consequently, make VaR and Tracking Error calculations for a theoretical pension funds portfolio, created from active principles and participations determined by the law. From this point on, it performs an exploratory study which seeks to analyze the tenure to a theoretical pension fund portfolio to a benchmark (inflation index composing the actuarial goal). For this purpose, six classes of financial assets were used such as stocks, interests and inflation risks. From these results, the work finds evidences that tenure assessment (Tracking Error), complemented by total risk Assessment, are useful tools for the management of pension funds and the findings on deviations of actuarial goals / Esta dissertação faz uma revisão de duas ferramentas de gestão de riscos: o VaR e o Tracking Error, e suas aplicabilidades aos fundos de pensão. Descreve as metodologias de cálculo e as suas utilizações na gestão de um fundo de pensão. Revisa a literatura acerca do uso do VaR e do Tracking Error como medidas de risco dos fundos de pensão. A seguir faz o cálculo do VaR e Tracking Error para uma carteira teórica de fundo de pensão, criada a partir dos principais ativos e participações determinados pela Legislação. A partir disso, realiza um estudo exploratório, que busca analisar a aderência da carteira teórica de um fundo de pensão a um benchmark (índice inflacionário componente da meta atuarial). Para isso, foram utilizados 6 classes de ativos financeiros com riscos de bolsa, juros e inflação. A partir dos resultados obtidos, o trabalho encontra evidências que medidas de aderência (Tracking Error), complementadas por medidas de risco absoluto (VaR), são ferramentas úteis para a gestão de fundos de pensão e, na apuração de desvios da meta atuarial

投資者面對龐大的股票市場，希望選取少量的股票使如指數基金般達到追蹤市場的效果，傳統的作法是使用指數追蹤的技術，建立一組投資組合使得報酬率與市場報酬率的績效相同。本論文除了最小化指數追蹤的下方追蹤誤差，還加入beta值的限制式，利用不同的beta值建立一組與市場成長趨勢相當或可能超越市場績效的投資組合。論文中使用提出之模型針對不同範圍的beta值進行研究，分析比較標的指數與建立的投資組合之績效表現。最後以台灣股票市場作為實證研究對象，實證結果顯示本論文模型所建立之投資組合在三個月內與標的指數表現相當，並在三個月後超越標的指數。 關鍵字：beta值、指數追蹤、下方追蹤風險、指數基金