Submitted by Rodolfo Zeidler (rodolfo.zeidler@gmail.com) on 2014-10-14T12:22:56Z No. of bitstreams: 1 Dissertação_RodolfoZeidler_MPFE_27.09.2014.pdf: 1031393 bytes, checksum: f0f0d7d38e2df5bf53a35f847db68555 (MD5) / Approved for entry into archive by JOANA MARTORINI (joana.martorini@fgv.br) on 2014-10-14T12:55:20Z (GMT) No. of bitstreams: 1 Dissertação_RodolfoZeidler_MPFE_27.09.2014.pdf: 1031393 bytes, checksum: f0f0d7d38e2df5bf53a35f847db68555 (MD5) / Made available in DSpace on 2014-10-14T13:09:41Z (GMT). No. of bitstreams: 1 Dissertação_RodolfoZeidler_MPFE_27.09.2014.pdf: 1031393 bytes, checksum: f0f0d7d38e2df5bf53a35f847db68555 (MD5) Previous issue date: 2014-10-13 / O objetivo deste trabalho é realizar procedimento de back-test da Magic Formula na Bovespa, reunindo evidências sobre violações da Hipótese do Mercado Eficiente no mercado brasileiro. Desenvolvida por Joel Greenblatt, a Magic Formula é uma metodologia de formação de carteiras que consiste em escolher ações com altos ROICs e Earnings Yields, seguindo a filosofia de Value Investing. Diversas carteiras foram montadas no período de dezembro de 2002 a maio de 2014 utilizando diferentes combinações de número de ativos por carteira e períodos de permanência. Todas as carteiras, independentemente do número de ativos ou período de permanência, apresentaram retornos superiores ao Ibovespa. As diferenças entre os CAGRs das carteiras e o do Ibovespa foram significativas, sendo que a carteira com pior desempenho apresentou CAGR de 27,7% contra 14,1% do Ibovespa. As carteiras também obtiveram resultados positivos após serem ajustadas pelo risco. A pior razão retorno-volatilidade foi de 1,2, comparado a 0,6 do Ibovespa. As carteiras com pior pontuação também apresentaram bons resultados na maioria dos cenários, contrariando as expectativas iniciais e os resultados observados em outros trabalhos. Adicionalmente foram realizadas simulações para diversos períodos de 5 anos com objetivo de analisar a robustez dos resultados. Todas as carteiras apresentaram CAGR maior que o do Ibovespa em todos os períodos simulados, independentemente do número de ativos incluídos ou dos períodos de permanência. Estes resultados indicam ser possível alcançar retornos acima do mercado no Brasil utilizando apenas dados públicos históricos. Esta é uma violação da forma fraca da Hipótese do Mercado Eficiente. / The main purpose of this work is to back-test the Magic Formula in the Bovespa Stock Exchange, gathering evidences of violations of the Efficient Market Hypothesis in the Brazilian market. The Magic Formula was developed by Joel Greenblatt and consists in a methodology for stock picking that creates portfolios of stocks with high ROICs and high Earnings Yield, following the Value Investing philosophy. Many portfolios were created in the period between December 2002 and May 2014 combining different number of assets per portfolio and different holding periods. All the portfolios, independently of their number of assets or holding periods, presented returns higher than Ibovespa. The differences between the CAGR from the portfolios and from the Ibovespa were significant, the worst performance portfolio presenting CAGR of 27,7%, as compared with 14,1% of Ibovespa. The portfolios also held positive results after being adjusted for risk. The worst return-volatility ratio was 1.2, as compared to 0.6 from Ibovespa. The portfolios containing the assets with the lowest scores also presented good results in the majority of the scenarios, contradicting the initial expectations and the results observed in other works. In addition, simulations were performed for various 5-year periods aiming to check if the results were robust. All the portfolios presented higher CAGR than Ibovespa in all the simulated periods, independently of the number of assets included in the portfolio or the holding period. These results indicate that it is possible to reach above-market returns using historical public data in Brazil. This is a violation of the Efficient Market Hypothesis in its weak form.

Submitted by Leonardo Milane (lemilane@santander.com.br) on 2016-02-15T21:54:56Z No. of bitstreams: 1 Tese - Leonardo Milane - Magic Formula.pdf: 348511 bytes, checksum: 91d8cd74adea7ad467c334e5d332aeb4 (MD5) / Approved for entry into archive by Renata de Souza Nascimento (renata.souza@fgv.br) on 2016-02-15T21:55:58Z (GMT) No. of bitstreams: 1 Tese - Leonardo Milane - Magic Formula.pdf: 348511 bytes, checksum: 91d8cd74adea7ad467c334e5d332aeb4 (MD5) / Made available in DSpace on 2016-02-16T11:16:35Z (GMT). No. of bitstreams: 1 Tese - Leonardo Milane - Magic Formula.pdf: 348511 bytes, checksum: 91d8cd74adea7ad467c334e5d332aeb4 (MD5) Previous issue date: 2016-02-04 / The main purpose of this work is to back-test the Magic Formula in the IBX- 100 index, in order to gather evidence of effectiveness of the respective methodology in the selection of the best stocks and portfolios that beat the IBX-100 in the long run. The Magic Formula was developed by Joel Greenblatt and consists in a methodology for stock picking that creates portfolios of stocks with high ROICs and high Earnings Yield, following the Value Investing philosophy. Many portfolios were created in the period between January 2000 and June 2015 combining different number of stocks per portfolio and different holding periods. Some portfolios did beat the market index, while some did not. Portfolios with a higher number of stocks and longer holding periods seem to perform better than portfolio with fewer stocks and shorter holding periods. The portfolio with 10 stocks, holding period of 1 year, showed the highest CAGR among all portfolios (17,77%), surpassing the IBX-100 CAGR of 13,17% in the same period, even risk-adjusting. Regardless the holding period and the number of stocks, all portfolios presented lower systematic risk than the IBX-100 index (all betas were significant and lower than 1). On the other hand, all alphas were low, rarely significant, suggesting that the active portfolio management that follows the Magic Formula criteria did not add substantial higher returns when compared to market returns. / O objetivo desse trabalho é realizar um procedimento de back-test da Magic Formula no IBX-100, a fim de reunir evidencias sobre a eficiência de tal metodologia no processo de seleção das melhores ações e formação de carteiras que superem o desempenho do IBX-100 no longo prazo. Desenvolvida por Joel Greenblatt, a Magic Formula é uma metodologia de formação de carteiras que consiste em escolher ações com altos ROICs e Earnings Yields, seguindo a filosofia de Value Investing. Diversas carteiras foram montadas no período de janeiro de 2000 a junho de 2015 utilizando diferentes combinações de número de ativos por carteira e períodos de permanência. Nem todas as carteiras apresentaram retornos superiores ao índice de mercado. Aparentemente, as carteiras com mais ações e períodos de permanência mais longos apresentam desempenho superior às carteiras menores e com rotatividade maior (períodos de permanência mais curtos). A carteira de 10 ações, com período de permanência de 1 ano, apresentou o maior CAGR dentre todas as outras (17,77%), superando o CAGR de 13,17% do IBX-100 no mesmo período. Esse resultado foi superior mesmo quando ajustado ao risco. Independentemente do período de permanência e número de ações, todas as carteiras apresentaram riscos sistemáticos menores do que o índice IBX-100 (todos os betas foram significativos e menores do que 1). Por outro lado, os alfas das carteiras foram muito baixos e, raramente, significativos, sugerindo que a gestão ativa de acordo com os critérios da Magic Formula não adiciona retornos substancialmente maiores do que o retorno relacionado à variações de mercado.

Investors and fund managers have, since the start of financial markets, always been on the lookout for new ways of beating the market. However, researchers of the Efficient Market Hypothesis have shown that markets are usually highly efficient, implying that there are few possibilities of earning returns that are higher than the market returns, on a risk adjusted basis. Prevailing theories, such as the Capital Asset Pricing Model, has shown that increased return must stem from taking on higher risk. Though, this model’s explanatory power has been challenged by numerous researchers who propose different factors, other than market risk, which could hold explanatory power when it comes to returns in the stock market. This area of research is called factor investing, and has shown that factors such as momentum, size, and value, all can lead to outperforming the market.This study examines how a model based on two common factors, quality and value, would have performed on the Swedish stock market. The study is based on five portfolios chosen by the quality and value factors, each one held for 5 years, examined over a 25-year time span and uses the capital asset pricing model as a tool to measure whether or not the selected factors outperform the market. The study has taken a quantitative approach to examining the research question, using a positivistic and objectivistic view.The results of the study show evidence that the quality and value factors can lead to significant outperformance relative to the market index. Both total returns and risk adjusted returns were higher than the market index for some of the portfolios created using the quality and value factors. Furthermore, statistical evidence was found of that CAPM not fully explains all returns, and thus, that the returns are in part explained by the quality and value factors. The findings led to the conclusion that the quality and value factors does, in fact, hold explanatory power beyond that of CAPM. Purchasing quality companies at a reasonable price is shown to be a sound investment strategy, and that a portfolio created using the quality and value factors has good chances of outperforming the market index.

The diploma thesis is focused on the determination of the business value of the company IMA - material for the furniture industry s. r. o. The theoretical part defines the procedure for determining the business value and valuation method. The analytical part is devoted to strategic analysis of the company based on the financial plan, which was created, and then final valuation of the analysed company is proposed.

Submitted by Catarino Lacerda e Silva (catarinolacerda@gmail.com) on 2018-09-18T12:35:32Z No. of bitstreams: 1 Dissertação - Catarino Lacerda e Silva - ESTRATÉGIAS DE INVESTIMENTOS EM AÇÕES .pdf: 1217319 bytes, checksum: 472a152e89a76ba49bfdb192b147d76f (MD5) / Rejected by Thais Oliveira (thais.oliveira@fgv.br), reason: Boa tarde, Catarino, Para que possamos aprovar sua Dissertação, serão necessárias as seguintes alterações: - A numeração de páginas começa na capa, porém deve aparecer A PARTIR da "Introdução" (pág 12) - A Ficha catalográfica deve conter o "texto" que existe fora do quadro, exatamente como foi enviado. Por gentileza, alterar e submeter novamente. Obrigada. on 2018-09-18T21:10:24Z (GMT) / Submitted by Catarino Lacerda e Silva (catarinolacerda@gmail.com) on 2018-09-19T10:54:33Z No. of bitstreams: 1 Dissertação - Catarino Lacerda e Silva - ESTRATÉGIAS DE INVESTIMENTOS EM AÇÕES .pdf: 1219830 bytes, checksum: a2aa93b6d34310ed33ec91af8f364412 (MD5) / Approved for entry into archive by Joana Martorini (joana.martorini@fgv.br) on 2018-09-19T14:33:48Z (GMT) No. of bitstreams: 1 Dissertação - Catarino Lacerda e Silva - ESTRATÉGIAS DE INVESTIMENTOS EM AÇÕES .pdf: 1219830 bytes, checksum: a2aa93b6d34310ed33ec91af8f364412 (MD5) / Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-09-19T17:39:21Z (GMT) No. of bitstreams: 1 Dissertação - Catarino Lacerda e Silva - ESTRATÉGIAS DE INVESTIMENTOS EM AÇÕES .pdf: 1219830 bytes, checksum: a2aa93b6d34310ed33ec91af8f364412 (MD5) / Made available in DSpace on 2018-09-19T17:39:21Z (GMT). No. of bitstreams: 1 Dissertação - Catarino Lacerda e Silva - ESTRATÉGIAS DE INVESTIMENTOS EM AÇÕES .pdf: 1219830 bytes, checksum: a2aa93b6d34310ed33ec91af8f364412 (MD5) Previous issue date: 2018-09-27 / O objetivo deste trabalho é examinar quais indicadores levaram a retornos excedentes no mercado brasileiro durante o período de 31 de março de 2000 a 31 de março de 2018, através das carteiras de ações construídas anualmente com base em um indicador ou dois indicadores quantitativos. Sendo os fatores testados: Retorno sobre Capital Investido (ROIC), Retorno sobre Ativos (ROA), Earnings Yield, Preço sobre Valor Patrimonial (PVPA), Preço sobre Vendas (PSR) e Índice de Força Relativa 120 dias úteis. Nas estratégias de um fator, o primeiro quartil do indicador Earnings Yield mostrou ser o melhor para seleção de ações no período, com maior índice de Sharpe 0,571, com maior média dos retornos anuais 41,03%, maior alfa 27,82%, superando Ibovespa 88,89% do tempo e com maior discrepância entre os retornos dos quartis, tanto que o pior resultado foi 4º quartil do Earnings Yield. Nas estratégias de dois fatores, a combinação dos indicadores de valor PSR e Earnings Yield, P/VPA e Earnings Yield obteve os maiores retornos médios anuais 42,51% e 39,1%, maiores alfas 29,40% e 26,19%, superando o Ibovespa em 88,89% e 83,33% do tempo, respectivamente. Porém foram as estratégias que combinaram um indicador de valor com um indicador de retorno, ROIC e Earnings Yield, ROA e Earnings Yield, que apresentaram os maiores índices de sharpe 0,623 e 0,619, respectivamente. / The objective of this study is examine which indicators led to excess returns in the Brazilian market during the period from March 31 2000 to March 31 2018, through stock portfolios constructed annually based on one indicator or two quantitative indicators. The following factors were test: Return on invested capital (ROIC), Return on Assets (ROA), Earnings Yield, Price-to-book (PVPA), Price Sales Ratio (PSR) and Relative Strength Index 120. In the one-factor strategies, the first quartile of the Earnings Yield indicator was the best for stock selection in the period, with highest Sharpe ratio 0.571, with the highest average annual returns 41.03%, the highest alpha 27.82% outperformed the Ibovespa 88.89% of the time and with greater discrepancy among quartile returns, so much that the worse result was fourth quartile of the Earnings Yield. In the two-factor strategies, the combination of the PSR and Earnings Yield, P/VPA and Earnings Yield value indicators obtained the highest average annual returns of 42.51% and 39.1%, higher alpha 29.40% and 26.19%, outperformed the Ibovespa in 88.89% and 83.33% of the time, respectively. However, it was the strategies that combined a value indicator with a return indicator, ROIC and Earnings Yield, ROA and Earnings Yield, which had the highest Sharpe ratio of 0.623 and 0.619, respectively.

已實現投資報酬率與無風險利率之差、被稱為超額報酬，而股票的預期報酬率超過無風險利率的部份則為股票風險溢酬，是許多資產評價模型的重要依據，例如資本資產定價模型。有不同的理論架構解釋說明風險溢酬值，例如；股票風險溢酬的迷思、短期損失的憎惡、生還存留因素和回歸與偏離平均值等等。 研究台灣股市的超額報酬與股票風險溢酬，有助投資大眾和企業理性面對股市的預期報酬和風險，對台股才有合理的期望報酬值。分析1967年迄2003年的台灣金融市場，計算過去37年長期的幾何平均年報酬率，以臺灣證券交易所發行量加權股價指數為台股市場報酬率，已實現台股實質年報酬率為6.71%。無風險報酬率使用第一銀行的一年期定期存款利率，實質台幣存款年利率為3.07%，消費者物價指數年增率則為4.80%。以年資料計算的台股實質超額報酬，算術和幾何值分別為12.48%和3.63%(年)，計算月資料算術平均和幾何平均值分別為0.77%和0.25%(月)。過去37年長期的台股超額報酬現象未較歐美市場的情況更加明顯，也比一般市場的預期報酬率低。 因資料取得的限制、台股的理論超額報酬方面，1991年迄2003年的近十三年來，經固定股利成長模式和盈餘成長模式的兩種計算方式，台股的實質超額報酬分別為 0.6%和-4.3%，此時期台股的投資報酬率比起台幣存款並不突出、且是低超額報酬。同期的已實現的實質超額報酬值；算術平均1.69%和幾何平均-3.35%。評估目前台股風險溢酬，將十分接近過去37年長期歷史資料得到的超額報酬數值，算術年均值為12.48%(年)和0.77%(月)，幾何平均分別為3.63%(年)和0.25%(月)，低風險溢酬是當前台灣股票市場的一般現象。 / The difference between the observed historical investment return and the risk-free interest rate is the excess return. The equity risk premium, ERP is the expected rate of return on the aggregate stock market in excess of the rate of risk-free security. ERP is one of important factor of many asset-pricing models, including Capital Asset Pricing Model, CAPM. There were many theories and factors to explain the equity risk premium; equity premium puzzle, myopic loss aversion, survivorship bias, mean reversion & aversion and etc. Studying the value of Taiwan equity excess return and risk premium is fundamental for investors and institutions evaluating the expected market investment return and risk. Analyzing the data from year 1967 to 2003 for thirty-seven years long holding period, Taiwan Stock Exchange Capitalization Weighted Stock Index as Taiwan stock market return, the realized real return was 6.71%. One-year bank time deposit rate as NT dollars risk-free asset rate and real interest rate was 3.07% and consumer price index, CPI annual growth rate was 4.80%. The historical real yearly excess return was 12.45% for arithmetic mean and 3.63% geometric mean; the historical real monthly excess return was 0.77% for arithmetic mean and 0.25% geometric mean. Taiwan realized equity excess returns were not higher than the returns in the developed countries and were also lower than the market's expectation. Due to the limits of available data, the theoretical equity excess returns that were calculated on two theoretical models; Constant Growth Dividend Discount Model (dividend yield model) and earnings yield model were 0.6% and -4.3% from year 1991 to year 2003. Comparing the same period of historical realized excess returns of 1.69% for arithmetic mean and -3.35% geometric mean, Taiwan stock market returns were not spectacular. The current equity risk premium of Taiwan stock market is low and should be near the level of the long historical realized equity excess return.