• 2018-07
  • 2018-10
  • 2018-11
  • Fig shows the Scree plot that indicates the


    Fig. 8 shows the Scree plot that indicates the contribution of each additional dimension to the explanation of the variance of the votes. The decay of the eigenvalues after the second dimension confirms that two dimensions describe well the behavior of the Court in the period. The first dimension, though, explains significantly more of the variance than does the second dimension. The third dimension, on the other hand, provides insufficient marginal explanatory power to warrant its consideration. Scree plots for other periods tell a similar story.
    Applications using estimated ideal points
    Introduction In the previous decade, the combination of several factors, such as an international scenario of high liquidity and the relative strengthening of capital market institutions in Brazil, created the conditions for a vigorous expansion of the Brazilian stock market. During this perk kinase period, mutual funds became a major conduit for private investment in the country: disregarding funds of funds, there were 6982 mutual funds in Brazil in August 2012, compared to 2483 in January 2002. In our sample, which considered only actively managed equity funds, the number of funds increased from 226 to 764 in the period, with a 510% increase in total assets under management, from R$ 10 to R$ 61 billion – which represents approximately the value of all free float stocks in Bovespa. The backdrop for the popularity of mutual funds is their presumed ability to provide professional management to the “uninformed” investors, generating higher returns. The natural question that arises is whether this professionalism really adds value to the investors. Since the seminal works of Treynor (1965), Sharpe (1966), and Jensen (1968), a large number of studies have focused on assessing whether portfolio managers actually obtain superior returns, with the majority of these studies (especially before the 2000s) centering their analysis in the significance of alphas from regressions of the CAPM or Fama-French models. The results are not uniform, but most of them show no clear evidence that mutual funds systematically achieve superior performance. In Brazil, on the contrary, the results are more favorable to the existence of superior performance. Leusin and Brito (2008), using the CAPM plus the market timing term from Treynor and Mazuy as a benchmark, found a fairly high number of funds with positive and significant alphas (15 funds in a sample of 243) during the period 1998–2003. Gomes and Cresto (2010) analyze the performance of funds that employ the long-short strategy, using the same approach of Leusin and Brito, and find strong evidence of superior performance: 8 of 45 funds using the CAPM, and 17 of 45 when using the CAPM plus the market timing. Castro and Minardi (2009), using the Carhart model plus the market timing term, found evidence of superior performance in 4.8% of their sample, which comprised 626 equity funds in the 1996–2006 period. In our study, we found a very different scenario. The approach adopted was similar to that employed by Kosowski et al. (2006) and Fama and French (2010), in which the alphas and the t-statistics from the “traditional” regressions (using the Carhart model as benchmark) are compared to their respective simulated cross-sectional distributions, obtained via bootstrap techniques, in which all funds are assumed to have zero alpha. This procedure allowed us to differentiate between funds with real superior performance from funds whose apparent superior performance had been achieved by mere luck. Analyzing the performance of the equity funds industry in Brazil, considering a universe of 1111 funds during the period 2002–2012, we found evidence of superior performance on only 19 funds and, more wearisome, there were strong signs of inferior performance in more than half of the funds. Besides this introduction, the paper is divided into the following sections: Section 2 – Data, where we describe the funds that are part of our analysis; Section 3 – Model, where we deal with the methodology adopted and its advantages compared to more traditional analysis; Section 4 – Results, with a summary of our major findings; and Section 5 – Conclusion.