Den ständigt pågående debatten om aktiv respektive passiv förvaltning av fonder tycks aldrig upphöra. Det finns för- och nackdelar inom respektive kategori och vetenskapliga teorier kan argumentera för båda sätten. Men den senaste tiden har ett nytt förvaltningssätt introducerats, smart beta. Smart beta kan klassificeras som en hybrid mellan passiv och aktiv förvaltning. Tidigare studier inom området har uppvisat goda resultat för smart beta, dock i andra geografiska områden och med stora globala index. I denna studie introduceras en ny typ av smart beta strategi som har skapats efter ett lönsamhetsmått. Syftet med studien är att undersöka hur smart beta presterar på den svenska marknaden, med utgångspunkt i OMXS30. Studien har en positivistisk forskningsfilosofi tillsammans med en deduktiv ansats och en kvantitativ metod. Resultatet av studien visar att samtliga smart beta strategier genererar en högre avkastning än OMXS30. Tre effektivitetsmått har använts för att beräkna den riskjusterade avkastningen och även där påvisar samtliga smart beta strategier ett högre värde. Resultatet ligger i linje med tidigare studier inom området. Den strategi som genererade högst avkastning och högst riskjusterad avkastning var studiens nya smart beta strategi. Denna studie bidrar med att introducera en ny strategi, samt att undersöka effekten på den svenska börsen. Studien kan vara av värde för såväl etablerade aktörer inom finansbranschen, som för enskilda personer. För vidare forskning inom området bör tidsperioden utökas och fler strategier testas, framförallt med lönsamhetsmått. / The ongoing debate on active and passive fund management never seems to end. There are some pros and cons in each category and scientific theories can argue for both ways. But recently, a new strategy has been introduced, smart beta. Smart beta can be classified as a hybrid between a passive and active strategy. Previous studies have shown good results for smart beta, but in other geographic areas and with larger indexes. In this study, a new type of profitability smart beta is introduced.   The purpose of the study is to examine how smart beta performs on the Swedish market, with starting point in OMXS30. The study has a positivist research philosophy along with a deductive approach and a quantitative method.   The result of the study shows that all smart beta strategies generate a higher return than OMXS30. Three efficiency measures have been used to calculate the risk-adjusted return, and here too, all smart beta strategies demonstrate a higher value. The result is in line with previous studies in the field. The strategy that generated the highest risk-adjusted return was the study's new strategy.   The contribution of this study is to introduce a new strategy, as well as examine the effect of the previous strategies on the Swedish market. The study can be of value to both established actors in the finance industry, but also for individuals. For further research in the area, the time period should be extended and more strategies tested, especially with measures of profitability.

O objetivo dessa dissertação é avaliar o desempenho do Ibovespa a partir de uma nova ponderação dos ativos utilizando estratégias Smart Beta baseadas no risco. Para obter essas carteiras, restritas para vendas a descoberto, foram utilizadas três matrizes de covariância diferentes: matriz de covariância amostral, matriz RiskMetrics e o método de encolhimento conforme Ledoit & Wolf (2004). As medidas de desempenho fora da amostra utilizadas indicam que as estratégias Smart Beta utilizadas proporcionam melhores resultados em termos de retorno anualizado e volatilidade em relação ao Ibovespa no período analisado. / The goal of this dissertation is to evaluate the performance of the Ibovespa from a re-weighting of assets using risk-based Smart Beta strategies. For these portfolios, long-only, three differents covariance matrix were used: sample covariance matrix, RiskMetrics and the Shrinkage method as Ledoit & Wolf (2004). The performance measures used indicates that Smart Beta strategies provide better results in terms of annualized returns and volatility in relation to the Ibovespa in the period analyzed.

O objetivo dessa dissertação é avaliar o desempenho do Ibovespa a partir de uma nova ponderação dos ativos utilizando estratégias Smart Beta baseadas no risco. Para obter essas carteiras, restritas para vendas a descoberto, foram utilizadas três matrizes de covariância diferentes: matriz de covariância amostral, matriz RiskMetrics e o método de encolhimento conforme Ledoit & Wolf (2004). As medidas de desempenho fora da amostra utilizadas indicam que as estratégias Smart Beta utilizadas proporcionam melhores resultados em termos de retorno anualizado e volatilidade em relação ao Ibovespa no período analisado. / The goal of this dissertation is to evaluate the performance of the Ibovespa from a re-weighting of assets using risk-based Smart Beta strategies. For these portfolios, long-only, three differents covariance matrix were used: sample covariance matrix, RiskMetrics and the Shrinkage method as Ledoit & Wolf (2004). The performance measures used indicates that Smart Beta strategies provide better results in terms of annualized returns and volatility in relation to the Ibovespa in the period analyzed.

O objetivo dessa dissertação é avaliar o desempenho do Ibovespa a partir de uma nova ponderação dos ativos utilizando estratégias Smart Beta baseadas no risco. Para obter essas carteiras, restritas para vendas a descoberto, foram utilizadas três matrizes de covariância diferentes: matriz de covariância amostral, matriz RiskMetrics e o método de encolhimento conforme Ledoit & Wolf (2004). As medidas de desempenho fora da amostra utilizadas indicam que as estratégias Smart Beta utilizadas proporcionam melhores resultados em termos de retorno anualizado e volatilidade em relação ao Ibovespa no período analisado. / The goal of this dissertation is to evaluate the performance of the Ibovespa from a re-weighting of assets using risk-based Smart Beta strategies. For these portfolios, long-only, three differents covariance matrix were used: sample covariance matrix, RiskMetrics and the Shrinkage method as Ledoit & Wolf (2004). The performance measures used indicates that Smart Beta strategies provide better results in terms of annualized returns and volatility in relation to the Ibovespa in the period analyzed.

In recent decades, many investors have abandoned hopes of achieving above market returns through active management, and consigned themselves to passive investing in the form of market capitalization based portfolios. Using Swedish stock exchange data from 2002-2016, this thesis investigates if there is a way to harmonize the strengths of active management, yielding potential above market returns, and passive index investing, implying lower fees and transparency. Based on observations from 275 companies, analysed through market model regressions, the results suggest that fundamentally invested value and quality portfolios create an alpha of 1-2 percent quarterly relative the market capitalization benchmark portfolio. Moreover, the results constitute basis for performing real investments, as they take into consideration the transaction costs implied by portfolio turnover. Furthermore, the findings of greater risk-adjusted returns through fundamentally weighted portfolios stand in opposition to the efficient market hypothesis.

This paper analyzes U.S. factor ETF risk exposures and diversification benefits relative to the ETFs’ academic factor portfolios. The purpose of the paper is to observe whether the factor ETFs’ correlations and risk exposures reflect that of their academic factor portfolios, the long-short and long-only portfolios. The results exhibit the market factor as the fundamental agent of returns, although size, value, and momentum also provide exposure to the intended factors. When measuring the loadings of factor ETFs and their intended factor portfolios, the long-short investing approach provides the most optimal diversification strategy.

Recently, the debate on passive versus active fund management has been a major focus on the Swedish capital market. Passive management is gaining more and more market shares. However, theories and previous research show that Smart Beta strategies outperform their passive benchmark index. The Smart Beta strategy is described as a hybrid between active and passive fund management, where it takes advantage of the low management cost of passive fund management and active fund management’s ability to select. This study presents three new Smart Beta strategies based on the key ratios ROA, profit margin and gross margin. The purpose of the study is to investigate whether any of the three Smart Beta portfolios can perform better than the Swedish market based on OMXS30 from a risk-adjusted perspective. Previous studies have shown that Smart Beta portfolios outperform their benchmark index. However, this study's contributing key figures show no excess return for the investigated period on the Swedish stock market.

This study is a thesis ending a 120 credit masters program in Mathematics with specialization Financial Mathematics and Mathematical Statistics at the Royal Institute of Technology (KTH). The subject of Smart beta is defined and studied in an index fund context. The portfolio weighting schemes tested are: equally weighting, maximum Sharpe ratio, maximum diversification, and fundamental weighting using P/E-ratios. The outcome of the strategies is measured in performance (accumulated return), risk, and cost of trading, along with measures of the proportions of different assets in the portfolio. The thesis goes through the steps of collecting, ordering, and ”cleaning” the data used in the process. A brief explanation of historical simulation used in estimation of stochastic variables such as expected return and covariance matrices is included, as well as analysis on the data’s distribution. The process of optimization and how rules for being UCITS compliant forms optimization programs with constraints is described. The results indicate that all, but the most diversified, portfolios tested outperform the market cap weighted portfolio. In all cases, the trading volumes and the market impact is increased, in comparison with the cap weighted portfolio. The Sharpe ratio maximizer yields a high level of return, while keeping the risk low. The fundamentally weighted portfolio performs best, but with higher risk. A combination of the two finds the portfolio with highest return and lowest risk. / Denna studie är ett examensarbete som avslutar ett 120 poängs mastersprogram i Matematik med inriktning mot Finansiell Matematik och Matematisk Statistik på Kungliga Tekniska Högskolan (KTH). Ämnet Smart beta studeras i kontexten av en indexfond, där de olika testade principerna för viktning i portföljerna är: likaviktad, maximerad Sharpe-kvot, maximerad diversifiering, och fundamental viktning användandes av P/E-tal. Utfallet i testerna utvärderas i ackumulerad avkastning, portföljrisk, kostnad att handla i portföljen, och ett antal mått på fördelningen av tillgångarna. Studien går stegvis igenom processen för att samla in, ordna, och ”tvätta” data. En kort förklaring av historisk simulering, metoden för att estimera stokastiska variabler såsom kovariansmatriser, är inkluderad, såväl som en analys av distributionen av data. Processen för att optimera portföljerna och hur regler för att vara en UCITS-fond kan omformas till optimeringsvillkor beskrivs. Resultaten indikerar att alla utom den mest diversifierade portföljen har högre ackumulerad avkastning än den marknadsviktade portföljen under testperioden. I alla testade fall ökar handelsvolymen liksom marknadspåverkan när en annan strategi än marknadsviktad används. Portföljen med maximerad Sharpe-kvot ger en hög avkastning med bibehållen låg risk. Den fundamentalt viktade portföljen ger bäst avkastning, men med en litet förhöjd risk. Kombinationen av de båda metoderna ger den portföljen med högst ackumulerad avkastning och samtidigt lägst risk under testperioden.

本研究以近15年台灣股票市場所有上市、上櫃、下市、下櫃股票為樣本，利用每季公布之財務報表的資料，市值、現金流量與股價比率、本益比、資產報酬率、負債比率、報酬率之標準差等指標作為篩選股票依據。 首先，先用財務報表的資料建構出Smart Beta Factor，結合主成分分析將各股評分，作為股票篩選之指標。第一步驟先把市值較低、成交金額過低的股票刪除，並依照不同指標篩選出五倍符合投資組合之股票數，接著運用主成分分析評分後的指標將各公司排序，選出分數高的作為投資組合，以達到分散風險的目標。 本文所討論之Smart Beta Factors有Size、Quality、Value、Momentum、Volatility，並將各Smart beta factor結合主成分分析，計算分數以選出優良股票，並以等權重方式進行資產配置，希望能建構出最有利的投資組合，使得獲利穩定成長。 / In this study, using nearly 15 years quarterly financial statement of stock market in Taiwan as samples. Not only use the financial statement to construct the smart beta factor, also use the principle components analysis to calculate the scores of all the stocks, then choose the stock by the scores. First, delete the stocks of low market value and the stocks of low turnover rate. Second, selected five times the number of the investment portfolio by different indicators, then elect the number of investment portfolio stocks by the highest scores calculated by principal component analysis. To achieve the goal of risk diversification. The smart beta factors discussed in the paper are Size, Quality, Value, Momentum, Volatility, also the multiple factor. To combine the method of principal component analysis, calculate the score to select the stocks, in order to contract the portfolio which has the best performance, and can make stable growth of profits.

No description available.