Título: RAE-Revista de Administração de Empresas (Journal of Business Management), 2019. V. 59, N. 4

1. ARTICLES Conditional pricing model with heteroscedasticity: Evaluation of Brazilian funds Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos Configurations of knowledge-intensive entrepreneurial ecosystems Andre Cherubini Alves | Bruno Fischer | Nicholas Spyridon Vonortas | Sérgio Robles Reis de Queiroz Business-to-business marketing: Brazilian scientific production from 2008 to 2018 Renato Calhau Coda | Gustavo Henrique Carvalho de Castro Voluntary disclosure: Empirical analysis of the tone used in conference calls Felipe Ramos Ferreira | Diego Campana Fiorot | Fabio Yoshio Suguri Motoki | Nadia Cardoso Moreira PERSPECTIVES Current challenges and promising avenues for entrepreneurship research Rose Mary Almeida Lopes | Edmilson Lima Evolution and trends of the international innovation research agenda Bibiana Volkmer Martins | Kadígia Faccin | Gustavo da Silva Motta | Roberto Bernardes | Alsones Balestrin BOOK REVIEW Paths, texts, and contexts of creative leadership Henrique Muzzio BOOK RECOMMENDATION Diversities, identities, and inclusion Eloisio Moulin de Souza RESEARCH AND KNOWLEDGE V. 59, N. 4, July–August 2019 fgv.br/rae

2. ISSN 0034-7590; eISSN 2178-938X© RAE | São Paulo | 59(4) | July-August 2019 RAE-Revista de Administração de Empresas (Journal of Business Management) CONTENTS EDITORIAL 224 SOMETHING TO CELEBRATE: RAE'S NEW IMPACT FACTOR Para comemorar: Novo fator de impacto da RAE Para celebrar: Nuevo factor de impacto de la RAE Maria José Tonelli ARTICLES | ARTIGOS | ARTÍCULOS 225 CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Modelo de precificação condicional com heteroscedasticidade: Avaliação de fundos brasileiros Modelo de valoración condicional con heteroscedasticidad: Evaluación de fondos brasileños Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 242 CONFIGURATIONS OF KNOWLEDGE-INTENSIVE ENTREPRENEURIAL ECOSYSTEMS Configurações de ecossistemas de empreendedorismo intensivo em conhecimento Configuraciones de ecosistemas de emprendimiento intensivo en conocimiento Andre Cherubini Alves | Bruno Fischer | Nicholas Spyridon Vonortas | Sérgio Robles Reis de Queiroz 258 BUSINESS-TO-BUSINESS MARKETING: BRAZILIAN SCIENTIFIC PRODUCTION FROM 2008 TO 2018 Marketing business-to-business: Análise da produção científica brasileira de 2008 a 2018 Marketing business-to-business: Análisis de la producción científica brasileña desde 2008 hasta 2018 Renato Calhau Coda | Gustavo Henrique Carvalho de Castro 271 VOLUNTARY DISCLOSURE: EMPIRICAL ANALYSIS OF THE TONE USED IN CONFERENCE CALLS Evidenciação voluntária: Análise empírica sobre o tom usado em audioconferências Divulgación voluntaria: Análisis empírico del tono usado en conferencias telefónicas Felipe Ramos Ferreira | Diego Campana Fiorot | Fabio Yoshio Suguri Motoki | Nadia Cardoso Moreira PERSPECTIVAS | PERSPECTIVES 284 CURRENT CHALLENGES AND PROMISING AVENUES FOR ENTREPRENEURSHIP RESEARCH Desafios atuais e caminhos promissores para a pesquisa em empreendedorismo Desafíos actuales y caminos prometedores para la investigación empresarial Rose Mary Almeida Lopes | Edmilson Lima 293 EVOLUTION AND TRENDS OF THE INTERNATIONAL INNOVATION RESEARCH AGENDA Evolução e tendências da agenda de pesquisa internacional em inovação Evolución y tendencias de la agenda internacional de investigación en innovación Bibiana Volkmer Martins | Kadígia Faccin | Gustavo da Silva Motta | Roberto Bernardes | Alsones Balestrin BOOK REVIEW | RESENHA | RESEÑA 308 PATHS, TEXTS, AND CONTEXTS OF CREATIVE LEADERSHIP Trilhas, textos e contextos da liderança criativa Pistas, textos y contextos de liderazgo creativo Henrique Muzzio BOOK RECOMMENDATION | INDICAÇÃO BIBIOGRÁFICA | RECOMMENDACIÓN BIBLIOGRÁFICA 310 DIVERSITIES, IDENTITIES, AND INCLUSION Diversidades, identidades e inclusão Diversidades, identidades e inclusión Eloisio Moulin de Souza

3. RAE-Revista de Administração de Empresas (Journal of Business Management) ISSN 0034-7590; eISSN 2178-938X224 © RAE | São Paulo | 59(4) | July-August 2019 | 224 EDITORIAL Maria José Tonelli Editora-chefe SOMETHINGTO CELEBRATE:RAE’S NEW IMPACT FACTOR The impact factor (IF) of the RAE-Revista de Administração de Empresas (Journal of Business Management) has increased in international indexes such as Journal Citation Reports (JCR), Scopus, SciELO, and Red Iberoamericana de Innovación y Conocimiento Científico (REDIB). According to the latest ranking published by Clarivate Analytics (June 2019), the increase in JCR was over 70% from 0.404 (2017) to 0.701 (2018). This result ranks RAE among the top 10 journals in Brazil indexed in the WoS/SSCI database, which tracks social science journals. These numbers are extremely significant when considering that they were obtained before 2018, when most of the articles were published in Portuguese. RAE became bilingual in 2018, publishing all its articles in Portuguese and English, or Spanish and English. Also indexed in Elsevier’sScopus, the CiteScore of RAE increased from 0.49 (2017) to 0.80 (2018). CiteScore is calculated as the total number of citations in 2018 divided by the number of documents published between 2015 and 2017. The IF in SciELO increased from 0.4286 (2017) to 0.6056 (2018), the highest among Brazilian business administration journals. The RAE’s Global Qualifier in REDIB is 26,637, ranking first in the general ranking of social sciences, which includes publications from Brazil, Mexico, Chile, Spain, and other countries. In addition to the invaluable collaboration of the Editorial Policy Committee, the Scientific Editorial Board (Associate Editors), and all the reviewers who contribute to the development of the articles, these advances would not be possible without the support of the authors who honor us with their valuable submissions and by citing our articles. Our deepest thanks to the entire scientific community, who make RAE’s visibility possible. We would like to thank Professor Eduardo Diniz, who, during his work as Editor-in-Chief, witnessed the inclusion of RAE in the WoS/SSCI database and raised RAE’s impact level to 0.408. We also thank the Assistant Editor, Felipe Zambaldi, and our editorial team, Ilda Fontes, Eldi Soares, Eduarda Pereira Anastácio, Denise Francisco Cândido, Andréa Cerqueira, Aline Santos, and Rute Almeida, who are behind the scenes, but without whom this would not be possible. A JCR of 0.701 allows us to participate in the international scene of academic publications in business administration, even as we continue to publish articles in Portuguese, which is in line with the mission of the Getulio Vargas Foundation of contributing to the development of Brazil. We thank FGV EAESP for its support in this work. Enjoy reading! Maria José Tonelli1 ORCID: 0000-0002-6585-1493 1 Fundação Getulio Vargas, Escola de Administração de Empresas de São Paulo, São Paulo, SP, Brazil Translated version DOI: http://dx.doi.org/10.1590/S0034-759020190401

4. RAE-Revista de Administração de Empresas (Journal of Business Management) 225 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X LEANDRO SANTOS DA COSTA1 leandrosdcosta@gmail.com ORCID: 0000-0003-2183-8796 FRANCES FISCHBERG BLANK1 francesblank@puc-rio.br ORCID: 0000-0002-2022-4226 FERNANDO LUIZ CYRINO OLIVEIRA1 cyrino@puc-rio.br ORCID: 0000-0003-1870-9440 CRISTIAN ENRIQUE MUÑOZ VILLALOBOS2 crisstrink@gmail.com ORCID: 0000-0002-7563-8469 1 Pontifícia Universidade Católica do Rio de Janeiro, Departamento de Engenharia Industrial, Rio de Janeiro, RJ, Brasil 2 Pontifícia Universidade Católica do Rio de Janeiro, Departamento de Engenharia Elétrica, Rio de Janeiro, RJ, Brasil ARTICLES Submitted 05.15.2018. Approved 05.07.2019 Evaluated through a double-blind review process. Scientific Editor: Fernanda Perobelli Original version DOI: http://dx.doi.org/10.1590/S0034-759020190402 CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Modelo de precificação condicional com heteroscedasticidade: Avaliação de fundos brasileiros Modelo de valoración condicional con heteroscedasticidad: Evaluación de fondos brasileños ABSTRACT Empirical studies have revealed that the conditional Capital Asset Pricing Model (CAPM) has a higher explanatory power than its unconditional version, particularly for the model in state-space form where the beta is estimated using Kalman filter. Most empirical analyses are based on stock portfolios to explain financial anomalies, but only a few studies proposed improving investment fund performance. The main contribution of this study is the assessment of Brazilian investment funds through traditional measures estimated from the CAPM model in state-space form with heteroscedastic and homoscedastic errors compared to alternative models, such as the unconditional CAPM and a four-factor model. Using a sample of stock funds from May 2005–April 2015, the results indicate that the conditional CAPM model produces better results than the alternative models, providing better performance evaluation practices for funds in both stock-picking and market-timing ability. KEYWORDS | Conditional Capital Asset Pricing Model, Kalman filter, time-varying betas, investment funds, performance analysis. RESUMO Os resultados empíricos na literatura demonstram que a versão condicional do Modelo de Precificação de Ativos Financeiros (CAPM), particularmente no que se refere ao modelo na forma em espaço de estado, no qual o beta é estimado pelo filtro de Kalman, possui maior poder explicativo do que a sua versão incondicional. A maioria das análises empíricas na literatura baseia-se em portfólios de ações para explicar anomalias financei- ras, porém poucos estudos propõem-se a melhorar a avaliação de desempenho de fundos de investimento. A principal contribuição deste artigo consiste em avaliar fundos de investimento brasileiros por meio de medidas tradicionais estimadas a partir do CAPM na forma em espaço de estado com erros heteroscedásticos e homos- cedásticos e comparar seus resultados com modelos alternativos, tais como CAPM incondicional, modelo de quatro fatores. Utilizando uma amostra de fundos de ações, os resultados indicam que o modelo CAPM condicional produz melhores resultados do que os modelos alternativos, proporcionando melhores práticas de avaliação de desempenho em relação às habilidades de stock-picking e market-timing. PALAVRAS-CHAVE | Modelo de precificação de ativos financeiros condicional, filtro de Kalman, betas variantes no tempo, fundos de investimento, análise de performance. RESUMEN Los resultados empíricos en la literatura revelan que la versión condicional del CAPM, particularmente con respecto al modelo en forma de espacio de estado, en el cual se estima beta mediante el filtro de Kalman, posee mayor poder explicativo que su versión incondicional. La mayoría de los análisis empíricos se basan en carteras de valores para explicar anomalías financieras, pero pocos estudios proponen mejorar el rendimiento de los fondos de inversión. La principal contribución de este estudio a la literatura es que lleva a cabo la evaluación de fondos de inversión a través de medidas condicionales generadas a partir del CAPM en forma espacio-estado con errores heteroscedásticos y homoscedásticos y que compara sus resultados con modelos alternativos, tales como CAPM incondicional, modelo de cuatro factores. Utilizando una muestra de fondos de acciones, los resultados indican que el modelo CAPM condicional produce mejores resultados que los modelos alternativos, proporcionando mejores prácticas de evaluación de desempeño en relación con las habilidades de stock-picking y market-timing. PALABRAS CLAVE | Modelo de valoración de activos de capital condicional, filtro de Kalman, betas variantes en el tiempo, fondos de inversión, análisis de rendimiento.

5. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 226 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X INTRODUCTION Empirical studies have shown the failures of the capital asset pricing model (CAPM) through consistently different returns from those predicted by the model. Since the development of the Fama and French (1993) and Carhart (1997) factor models, several studies have used these models in the performance analysis of investment funds to evaluate to what extent the returns could be attributed to two main managerial skills: stock-picking and market-timing. While the former concerns the manager’s ability to select the best assets for a given level of risk, the latter refers to managing the funds’ beta in anticipation of future market movements. In general, the evidence shows that after considering all fund expenses, managers do not have stock-picking ability (Carhart, 1997; Elton, Gruber, & Blake, 2012; Fama & French, 2009). The results are more controversial for market-timing, with some authors pointing to evidence of positive managerial timing (Bollen & Busse, 2000; Busse, 1999; Ferson & Schadt, 1996) while many others pointing to the lack of this ability (Elton et al., 2012; Treynor & Mazuy, 1966). In Brazil, the conclusions regarding managers' abilities vary greatly depending on the sample and the proposed model. In general, there are more positive results reported than in the international literature (Borges & Martelanc, 2015). Using the one-factor model and variations of CAPM, Eid and Rochman (2006) found evidence of superior performance of stock funds in relation to the market; Leusin and Brito (2008) observed positive and significant alphas, as well as weak evidence for few managers with market timing ability; and Matos and Nave (2012) verified the persistence among better performing funds. However, Casaccia, Galli, Macêdo, and Leitao (2011) did not identify any special abilities of managers in their sample. There are other studies on Brazilian funds using factor models where, in most cases, no superior managerial skills are evident. Castro and Minardi (2009) used the three-factor model (E. F. Fama & French, 1993) and four-factor model (Carhart, 1997), along with a fifth factor on market timing. The authors did not observe stock-picking ability of managers on comparing active and passive funds. Jordão and Moura (2011) analyzed an extensive sample from Carhart (1997) model for stock-picking ability and from Treynor and Mazuy (1966) for market-timing and found that less than 5% of the funds presented positive and significant results for such skills. Nerasti and Lucinda (2016) investigated the persistence in the performance of Brazilian stock funds with four models: traditional CAPM, the three-factor model proposed by Fama and French (1993), Carhart's four-factor model (1997), and an additional model, adding the risk factor associated with asset liquidity. They did not find persistence in the superior performance of Brazilian managers. Borges and Martelanc (2015) estimated the alphas in a sample comparing real funds and synthetic funds using the four-factor model of Carhart (1997) and found managers' positive ability to generate abnormal returns, albeit modest. Since managers assume different levels of risk depending on the kind of fund, empirical evidence shows that funds generally do not maintain constant levels of risk over time (Lee & Rahman, 1990; Mamaysky, Spiegel, & Zhang, 2008), which is different from the assumption in the unconditional CAPM and traditional factor models. Therefore, a more accurate modelling of the temporal variation in the fund’s risk should result in a more realistic assessment of its performance. The conditional version of CAPM was developed to address the limitations of the traditional static version. There are three main approaches to model the dynamic behavior of the beta: (i) modelling the conditional distribution function of returns as an explicit function of lagged conditioning variables (Jagannathan & Wang, 1996; Lettau & Ludvigson, 2001), (ii) describing the beta dynamics using conditional or stochastic volatility models (Bodurtha Jr & Mark, 1991; Bollerslev, Engle, & Wooldridge, 1988; Yu, 2002), (iii) using state-space models where the beta dynamics are directly modelled as a stochastic process (Adrian & Franzoni, 2009; Blank, Samanez, Baidya, & Aiube, 2014; Jostova & Philipov, 2005; Mergner & Bulla, 2008). Several studies have indicated that the conditional state-space CAPM specification provides more accurate estimates of beta than the others (Adrian & Franzoni, 2009; Faff, Hillier, & Hillier, 2000; Mergner & Bulla, 2008). Moreover, such differences are more pronounced in daily data than in monthly data (Bollen & Busse, 2000). One problem with financial returns is the temporal dependence on its conditional variance— the presence of heteroscedasticity. However, most studies assumed that the conditional CAPM residuals are homoscedastic, despite empirical tests finding high heteroscedasticity even after the conditional treatment of the model (Mergner & Bulla, 2008). Ortas, Salvador, and Moneva (2015) constructed the heteroscedastic version of the conditional CAPM model using the Kalman filter algorithm, where the errors are modelled as a generalized autoregressive conditional heteroscedasticity (GARCH) process. The results show that the heteroscedastic model surpasses the homoscedastic model in in-sample and out-of-sample analysis. For Brazil, Tambosi Filho, Garcia, Imoniana, and Moreiras (2010) test a conditional CAPM incorporating macroeconomic

6. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 227 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X and financial variables and verify a significant increase in the explanatory power of the model. Using a state-space form conditional CAPM, Mazzeu, Da Costa Júnior, and Santos (2013) observed a reduction in pricing errors using the time-varying beta model in a sample of 13 stocks in the Brazilian market. Blank et al. (2014) build portfolios of stocks based on book-to- market and market value characteristics and verify that when the beta is modeled as a random walk with conditioning variables, pricing errors are reduced. Caldeira, Moura, and Santos (2013 use a similar approach in combining a dynamic conditional covariance matrix based on a GARCH model and the risk factors proposed by Carhart (1997) with time-varying coefficients. The authors observe satisfactory results compared to benchmark models. Some international authors propose incorporating temporal dynamics in systematic risk. Ferson and Schadt (1996), based on a conditional model where the funds’ betas depend on lagged variables, analyze a sample of US funds and find that both static CAPM and a conditional factor model produce a negative alpha more often than positive ones. The results show that the conditional model eliminates evidence of negative timing of managers found by the unconditional model. Holmes and Faff (2008) compare conditional CAPM based on lagged variables and state-space conditional CAPM using an Australian funds sample. While the first model shows the presence of stock-picking ability, it is not observed in the state- space model. Similarly, Mamaysky et al. (2008) find the alpha and beta estimates of a large sample of US funds. Their predictions estimated through the Kalman filter are more accurate than those of ordinary models. The literature in Brazil does not use conditional models to analyze managers’ abilities and compare those models with traditional approaches, such as the unconditional CAPM and multifactor models. This paper aims to fill this gap, with two main objectives: (i) to evaluate conditional CAPM models in the space-state form applied to a sample of Brazilian stock funds; this is estimated from the Kalman filter with the errors of the regression equation in homoscedastic (SS-HOM) and heteroscedastic (SS-HET) forms, and (ii) to analyze how using traditional measures obtained from the conditional CAPM model can improve the current practice of evaluating the performance of investment funds and managers´ abilities of stock-picking and market timing, compared to alternative models, such as the unconditional CAPM and the Carhart’s (1997) four-factor model. Using selected Brazilian funds, the results show that the modelling of the heteroscedastic structure of the errors increases the CAPM conditional capacity to capture the alpha and beta temporal dynamics of investment funds. Given the superiority of the proposed conditional models, this study then evaluates the performance of stocks funds from 02/05/2005 to 30/04/2015. Evidence suggests that managers’ ability to select the best assets is directly related to significant appreciation in the stock market and that managers do not have the ability to anticipate periods of appreciation and fall in the market. The rest of this paper is organized as follows. The following section presents the econometric models of asset pricing. Then, the performance of the CAPM models in the state-space form with residuals of the homoscedastic and heteroscedastic observation equation is analysed. Next, the empirical application of the models is carried out, and, finally, the conclusions of the research are presented. MODEL AND ESTIMATION PROCEDURE The appeal of CAPM, developed independently by Sharpe (1964), Lintner (1965), and Mossin (1966), lies in its simplicity, where the expected return for a given asset is given as: E Ri ⎡⎣ ⎤⎦ = βi E Rm ⎡⎣ ⎤⎦( ) (1) βi = cov Ri ,Rm ⎡⎣ ⎤⎦ var Rm ⎡⎣ ⎤⎦ (2) where Ri and Rm are the excess returns on asset i and the market portfolio in relation to the risk-free asset, respectively, and βi is a risk measure that is not eliminated through diversification, also known as systematic risk or beta. However, one critical limitation of CAPM is its static nature. In such a hypothesis, the presence of anomalies could be due to beta time-varying dynamics that are not captured by CAPM in its original form. Preserving the structure of one model factor, different models are used to capture the time-varying systematic risk, with the conditional models in state-space usually giving the best results. The model described by equations (1) and (2) is built on expected values and is, therefore, non-observable. It is commonly tested on time series using observable measures of realized returns. Thus, considering a classic Gaussian univariate linear

7. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 228 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X regression model, the conditional version with time-varying alpha and beta can be written as the state-space model given by equations (3) to (5). Ri ,t =αi ,t + βi ,t Rm,t + ∈i ,t , ∈i ,t ~N 0,σ∈i 2 ( ),t =1,...n (3) αi ,t +1 =αi ,t +ϑi ,t , ϑi ,t ~N 0,σϑi 2 ( ) (4) βi ,t +1 = βi ,t +ηi ,t , ηi ,t ~N 0,σηi 2 ( ) (5) Equation (3) is known as observation one and (4) and (5) are the state equations. It is usually assumed that errors ϵi,t , ϑi,t , and ηi,t are serially independent and homoscedastic and αi,t is known as Jensen’s alpha. The intercept is statistically assumed to be zero in CAPM, which means that the market risk premium adjusted by the assets’ beta would be sufficient to explain the observed returns. However, if a portfolio manager can better forecast asset prices, higher returns than the ones implied in the model could be obtained, and Jensen’s alpha could represent an average incremental return rate of the portfolio by the unit of time exclusively due to the manager’s ability. In an unconditional form, equation (3) would be estimated by ordinary least squares (OLS) where alpha and beta are constant over time. A random walk process describes the alpha and beta time-varying dynamics. Pizzinga and Fernandes (2006) outlined three main reasons for justifying such a choice: (i) parsimony, (ii) simplicity, and (iii) the possibility of fundamental managerial changes over time due to the non-stationary property. The specification of the model in equations (3) to (5) allows the direct application of Kalman filter to estimate time-varying and constant parameters (Adrian & Franzoni, 2009; Faff et al., 2000; Mergner & Bulla, 2008). The constant parameters are estimated, in particular, through prediction error decomposition and maximization of the log-likelihood function given by: logL ψi( )= − n 2 log2π − 1 2 t =1 n log|Ft ψi( )|+ vt ψi( ) Ft ψi( ) ⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟ ,∑ (6) where ψi = σ∈,i 2 ,σϑ ,i 2 ,ση,i 2 ( ) is the hyper-parameter vector of the model; vt (ψi ) = Ri – E[Ri |Ωt-1, ψi ] and Ft (ψi ) = Var (vt (ψi )) are calculated through Kalman filter, known as one-step-ahead prediction error and its variance, respectively, and Ωt-1 ={R1 ,...,Rt-1 }. In this study, state vectors βt and αt follow random walk processes and the Kalman filtering used is in its diffuse exact form. Nonetheless, the volatility clusters, especially in the daily asset returns series, are responsible for creating a structure of time dependent conditional variance in this series, which are not captured by the model described in equations (3) to (5). In this context, Ortas et al. (2015) propose a heteroscedastic version of the conditional CAPM model estimated by Kalman filter algorithm, where the errors of the regression equation are modelled with conditional variance according to a GARCH (1,1) process: ∈i ,t |Ωt −1 ~N 0,σ∈i ,t 2 ( ), Ωt −1 = {R1 ,...,Rt −1 } (7)

8. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 229 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X σ∈i ,t 2 =ωi + ρ ∈i ,t −1 2 +γσ∈i ,t −1 2 , ωi >0,ρi ,γ i ≥0eρi +γ i <1 (8) The estimated model specified in equations (3)–(5) and (7)–(8) follows the iterative procedure proposed by Ortas et al. (2015). The process maximizes a partial log-likelihood function assuming constant mean parameters and then maximizes a partial log- likelihood function assuming constant variance parameters. The parameters are estimated based on the Kalman Filter algorithm. The procedure alternates between those two steps until it achieves convergence. Two well-established models in asset-pricing literature used in performance analysis of investment funds are also estimated for comparison purposes. The first is the unconditional CAPM, tested on the time series using observable measures of realized returns, estimated from equation (9). The estimation is performed using OLS with rolling windows of 120 days, which is a commonly used alternative in the ad hoc attempt to adjust the time-varying coefficients of the model. Rit =αi + βi Rmt( )+εit (9) where εit is the i.i.d. error term, with E(εit )=0 and Var εit( )=σεi 2 . The second approach is Carhart's (1997) four-factor model given in equation (10). Ri ,t =αi + βiM RM ,t + βiS SMBt + βiH HMLt + βiW WMLt +εit (10) where SMBt captures the anomaly in the size of the company; HMLt captures the anomaly in the book-to-market ratio; and WMLt captures the anomaly in the moment of stocks. Again, OLS is used with rolling windows of 120 days for the estimation. MODEL PERFORMANCE This section comparatively analyses the performance of conditional CAPM models in state-space form for homoscedastic and heteroscedastic observation equation errors and compares these results with well-established models. Data The data for this study were obtained from the Quantum Axis online database. We selected the funds classified in the category ‘Stocks’, such as ‘Free Stocks’, which had a complete sample from February 5, 2005 to April 30, 2015, totaling 2,474 daily observations for each series. This time span was used because it coincides with a period of accelerated development of the Brazilian funds industry. Itisworthmentioningthattheselectedsamplemightpresent survival bias since the sample comprises funds that have survived during the period of time analyzed. However, studies have shown (Brown, Goetzmann, Ibbotson & Ross, 1992; Carvalho, 2005) that the inclusion of bias in the end result of performance analysis measuresisminimal,withapositivebiasonmeanreturnsfrom0.2% to 0.8% per year. Still, according to Milan and Eid (2014), in the Brazilian market, the main reason for a majorityofinvestmentfunds to terminate istheir merger with other funds, notpoor performance. Both the fund quotas used as the market indexes were adjusted for the distribution of dividends and are net of the taxes charged by funds. We used the arithmetic return as a measure of daily return of the funds, calculated based on excess return in relation to the risk-free rate. Table 1 presents the main descriptive statistics for the daily return series. The funds are organized in descending order of the total accumulated returns in the analyzed period. A greater number of selected funds show a mean positive return for the period. Funds with the worst positions show average negative returns, which indicates that they did not show the capacity to overcome the risk-free assets, once the results shown are in terms of return excess. The Ljung–Box test applied to the squared returns confirms the heteroscedasticity of the daily returns financial series once the statistical value of the test for all funds rejects the null hypothesis that the autocorrelation of the series equals zero at the significance level of 1%.

9. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 230 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X Table 1. Descriptive statistics of excess return series of funds Fund Average Return Standard Deviation LB²(6) Net Worth Total Return Rm,t 0.018% 1.60% 1448.3¹ - 12.4% 1 0.042% 1.31% 1459.5¹ R$ 253,505.195 130.7% 2 0.040% 1.21% 932.0¹ R$ 223,226.073 123.2% 3 0.033% 1.17% 1735.4¹ R$ 96,507.324 89.5% 4 0.037% 1.53% 1789.2¹ R$ 48,425.162 86.0% 5 0.029% 1.17% 1827.4¹ R$ 141,638.916 74.9% 6 0.026% 0.89% 1709.2¹ R$ 12,462.034 74.2% 7 0.028% 1.19% 1072.0¹ R$ 213,115.019 68.7% 8 0.028% 1.27% 1651.3¹ R$ 47,754.743 62.5% 9 0.022% 0.95% 1458.1¹ R$ 16,210.743 54.7% 10 0.024% 1.16% 1503.6¹ R$ 167,376.501 54.8% 11 0.029% 1.57% 1570.5¹ R$ 113,865.041 51.2% 12 0.021% 1.25% 1509.7¹ R$ 43,797.267 38.6% 13 0.022% 1.41% 1748.6¹ R$ 15,557.122 35.6% 14 0.013% 0.86% 38.9¹ R$ 112,662.732 27.0% 15 0.013% 1.27% 1416.7¹ R$ 56,117.911 13.1% 16 0.014% 1.42% 1535.5¹ R$ 5,893.538 11.2% 17 0.013% 1.43% 1895.6¹ R$ 208,973.148 6.7% 18 0.015% 1.58% 2052.1¹ R$ 148,715.381 5.2% 19 0.014% 1.61% 1855.1¹ R$ 19,162.430 2.9% 20 0.009% 1.36% 1339.5¹ R$ 7,459.161 0.3% 21 0.011% 1.82% 1193.8¹ R$ 122,802.719 -13.3% 22 -0.006% 1.07% 515.9¹ R$ 20,835.687 -25.7% 23 0.003% 1.80% 1408.1¹ R$ 6,339.762 -28.8% 24 -0.006% 1.33% 793.2¹ R$ 29,408.209 -31.3% 25 -0.002% 1.68% 1687.2¹ R$ 24,322.706 -32.4% 26 -0.003% 1.80% 986.2¹ R$ 1,075.375 -37.8% 27 -0.006% 1.58% 1487.0¹ R$ 27,290.762 -37.6% 28 -0.005% 1.83% 1061.9¹ R$ 525,664.075 -41.3% 29 -0.012% 1.56% 1227.9¹ R$ 16,259.577 -45.1% 30 -0.013% 1.58% 1597.8¹ R$ 1,585.373 -46.7% 31 -0.013% 1.57% 1325.0¹ R$ 749.245 -47.0% 32 -0.036% 1.64% 1418.4¹ R$ 1,307.690 -70.9% 33 -0.042% 1.42% 907.9¹ R$ 2,354.023 -72.7% 34 -0.012% 2.89% 373.7¹ R$ 171,496.267 -73.9% 35 -0.040% 2.36% 102.4¹ R$ 2,532.896 -81.2% 36 -0.094% 2.24% 427.0¹ R$ 139.201 -94.7% Notes: 1, 2, 3 Significant at the 1%, 5%, and 10% level, respectively. This table shows the basic descriptive statistics of the daily fund returns from May 2, 2005 to April 30, 2015. JB is the Jarque–Bera normality test. ADF is the augmented Dickey–Fuller test for unit root. LB² is the Ljung–Box test applied to the squared returns. The number of ‘lags’ is determined according to Tsay (2010): Ln 2474( )= 7.81 8. The return in the period is calculated as the accumulated return for the entire sampling period.

10. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 231 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X Model Estimates Here, we analyze the constant parameter estimates of the conditional CAPM in state-space form when the observation equation residuals, ϵi,t , are homoscedastic (SS-HOM), in equations (3) to (5), and heteroscedastic (SS-HET), in equations (3) to (5) and (7) and (8). The results for the SS-HOM and SS-HET constant parameter estimation for each of the sample funds are shown in Table 2. Like in other studies, we observe that the funds’ betas seem to follow a time-varying dynamic process once the parameters ση,i 2 are statistically different from zero, at the 1% significance level, for 34 out of the 36 sample funds. Regarding investment funds, this result is expected since the forecasting strategies used by managers and/or the variation in stock beta that are part of their portfolios generate variations in funds’ betas. It is worth noting that the constant parameter estimates are lesser for the SS-HET model than for the SS-HOM model. Table 2. SS-HOM and SS-HET model estimates Fund ϵi,t σ∈,i ,t 2 ση,t 2 σv ,t 2 LogL LB²(6) JB 1 HOM 4.0E-05¹ 6.9E-04¹ 1.9E-10¹ 8941.26 281.53¹ 3323.95¹ HET 4.1E-05 2.1E-04¹ 1.9E-11* 9163.68 5.29 385.77¹ 2 HOM 7.2E-05¹ 2.2E-03¹ 6.6E-09¹ 8177.76 105.21¹ 1656.05¹ HET 6.8E-05 1.0E-03¹ 2.2E-10¹ 8388.27 15.09³ 412.54¹ 3 HOM 3.0E-05* 4.2E-04* 2.8E-22* 9269.42 227.23¹ 143.37¹ HET 3.2E-05 4.3E-04* 6.4E-23* 9364.25 6.33 31.79¹ 4 HOM 3.2E-05¹ 4.3E-04¹ 1.2E-10¹ 9201.67 226.55¹ 5276.06¹ HET 3.4E-05 2.2E-04¹ 9.6E-11¹ 9370.53 29.16¹ 222.78¹ 5 HOM 2.4E-05¹ 5.4E-04¹ 1.6E-10¹ 9557.61 346.30¹ 9142.01¹ HET 2.3E-05 1.8E-04¹ 5.7E-11¹ 10226.79 27.89¹ 678.44¹ 6 HOM 1.5E-05¹ 5.1E-04¹ 1.5E-09¹ 10093.85 391.11¹ 4828.62¹ HET 1.6E-05 2.5E-04¹ 1.4E-10¹ 10325.09 22.07¹ 413.71¹ 7 HOM 6.2E-05¹ 5.2E-04¹ 2.2E-09* 8390.70 42.20¹ 1955.76¹ HET 5.5E-05 4.6E-04¹ 1.6E-10¹ 8558.32 6.37 916.44¹ 8 HOM 5.0E-05¹ 9.8E-04¹ 2.7E-10¹ 8633.61 220.74¹ 1600.70¹ HET 5.4E-05 3.2E-04¹ 2.4E-1¹ 8828.70 7.23 548.61¹ 9 HOM 3.0E-05¹ 3.2E-04¹ 6.5E-09¹ 9295.25 389.24¹ 2672.03¹ HET 3.2E-05 5.7E-05¹ 1.3E-09¹ 9313.95 19.41² 252.10¹ 10 HOM 3.0E-05¹ 5.9E-04¹ 5.7E-09¹ 9269.83 155.45¹ 15029.31¹ HET 3.4E-05 3.5E-04¹ 1.5E-10¹ 9403.12 14.70³ 1566.99¹ 11 HOM 6.1E-05¹ 5.3E-04¹ 1.8E-10¹ 8425.89 243.99¹ 1565.72¹ HET 6.5E-05 2.4E-04¹ 1.2E-12* 8364.22 18.19² 66.04¹ 12 HOM 2.6E-05¹ 2.1E-04¹ 4.7E-11¹ 9491.40 364.40¹ 14732.96¹ HET 2.5E-05 1.1E-04¹ 2.9E-11¹ 10055.05 23.00¹ 792.44¹ 13 HOM 2.8E-05¹ 8.6E-04¹ 3.1E-11¹ 9360.39 468.20¹ 660.44¹ HET 3.1E-05 4.1E-04¹ 1.3E-11¹ 9289.92 21.69¹ 64.22¹ 14 HOM 6.7E-05* 2.1E-04¹ 9.5E-09¹ 8313.37 30.02¹ 5783.80¹ HET 7.1E-05 1.6E-04¹ 6.3E-09¹ 8362.87 11.29 10156.94¹ 15 HOM 4.5E-05¹ 5.8E-04¹ 1.5E-18* 8795.09 118.35¹ 2725.92¹ HET 4.9E-05 3.0E-04¹ 7.0E-19* 8832.44 6.45 263.13¹ 16 HOM 4.7E-05¹ 1.0E-03¹ 1.1E-09¹ 8711.18 71.46¹ 7999.75¹ HET 5.0E-05 9.8E-04¹ 5.1E-10¹ 8804.96 22.47¹ 993.42¹ 17 HOM 3.8E-05¹ 6.3E-04¹ 1.0E-18* 8991.46 239.25¹ 6668.44¹ (continue)

11. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 232 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X Fund ϵi,t σ∈,i ,t 2 ση,t 2 σv ,t 2 LogL LB²(6) JB HET 3.9E-05 3.1E-04* 1.7E-20* 9106.42 13.97³ 395.98¹ 18 HOM 5.1E-05¹ 4.8E-04¹ 5.1E-09¹ 8643.48 351.34¹ 1132.51¹ HET 5.4E-05 2.4E-04¹ 1.3E-09¹ 8865.84 22.65¹ 220.01¹ 19 HOM 3.2E-05¹ 2.5E-04¹ 4.7E-18 9216.70 780.17¹ 5222.83¹ HET 3.1E-05 2.4E-04¹ 1.9E-18¹ 9731.35 32.07¹ 203.54¹ 20 HOM 6.6E-05¹ 6.3E-04¹ 8.0E-09¹ 8313.00 239.70¹ 1040.05¹ HET 7.3E-05 6.3E-04¹ 3.4E-09¹ 8472.97 3.22 1038.71¹ 21 HOM 6.6E-05¹ 5.5E-04¹ 1.0E-18 8335.80 891.32¹ 1105.52¹ HET 6.8E-05 1.2E-04¹ 1.3E-18* 8836.12 120.66¹ 184.37¹ 22 HOM 6.4E-05¹ 8.8E-04¹ 9.1E-08¹ 8316.47 174.52¹ 1706.23¹ HET 7.4E-05 2.2E-04¹ 4.5E-08¹ 8171.99 10.3 502.16¹ 23 HOM 3.4E-05¹ 3.3E-04¹ 1.4E-10¹ 9140.05 700.00¹ 4847.10¹ HET 3.5E-05 3.4E-04¹ 7.1E-11¹ 9470.84 54.56¹ 136.66¹ 24 HOM 8.5E-05¹ 8.0E-03¹ 9.5E-09¹ 7904.87 408.83¹ 7857.83¹ HET 1.1E-04 3.7E-03¹ 1.9E-09¹ 7832.50 17.66² 1393.25¹ 25 HOM 3.4E-05¹ 4.5E-04¹ 7.1E-12* 9143.03 806.43¹ 3548.63¹ HET 3.6E-05 7.3E-04¹ 5.3E-11¹ 9729.34 21.05¹ 119.14¹ 26 HOM 7.2E-05¹ 4.0E-03¹ 7.4E-12¹ 8146.67 318.85¹ 427.95¹ HET 8.3E-05 2.4E-03¹ 1.8E-18¹ 8021.39 16.58² 94.87¹ 27 HOM 3.5E-05¹ 2.6E-04¹ 1.9E-11 9139.62 358.28¹ 6655.91¹ HET 3.5E-05 1.2E-04¹ 3.8E-19 9356.64 8.39 262.63¹ 28 HOM 7.9E-05¹ 4.9E-03¹ 4.8E-18* 8029.70 226.65¹ 186.25¹ HET 9.2E-05 3.1E-03¹ 1.2E-19* 7836.96 13.39³ 75.12¹ 29 HOM 3.8E-05¹ 6.9E-04¹ 1.8E-10¹ 9005.35 192.07¹ 15357.91¹ HET 3.8E-05 4.8E-04¹ 2.8E-10¹ 9278.35 33.24¹ 514.22¹ 30 HOM 3.7E-05¹ 4.4E-04¹ 4.1E-11 9045.65 702.41¹ 4383.64¹ HET 3.8E-05 3.7E-04¹ 1.1E-18* 9387.07 26.67¹ 117.59¹ 31 HOM 6.6E-05¹ 2.2E-04¹ 2.2E-09¹ 8340.41 149.95¹ 1229.98¹ HET 6.9E-05 3.3E-04¹ 2.1E-09¹ 8171.33 7.96 310.94¹ 32 HOM 5.8E-05¹ 7.2E-04¹ 1.2E-18² 8477.58 209.68¹ 7414.02¹ HET 5.9E-05 2.0E-04+ 6.5E-23* 8750.22 1.76 648.45¹ 33 HOM 6.0E-05¹ 5.3E-05¹ 1.3E-10¹ 8465.04 146.18¹ 19756.21¹ HET 6.1E-05 9.9E-05¹ 1.2E-10¹ 8573.00 3.86 35162.41¹ 34 HOM 8.0E-04 6.6E-05¹ 1.3E-16¹ 5293.45 372.73¹ 14620.96¹ HET 8.4E-04 2.2E-04¹ 2.7E-09¹ 5452.36 25.90¹ 5426.30¹ 35 HOM 4.2E-04¹ 2.0E-02¹ 2.0E-09¹ 5992.38 113.59¹ 1534487.60¹ HET 5.1E-04 2.7E-03¹ 3.9E-09¹ 6546.36 0.16 2104729.98¹ 36 HOM 4.5E-04* 6.4E-05¹ 6.5E-09¹ 5992.06 401.48¹ 13132.76¹ HET 4.8E-04 8.9E-06¹ 1.5E-09¹ 6243.57 9.43 8304.16¹ Notes: ¹, ², ³ Significant at the 1%, 5%, and 10% level, respectively; * Significance not measured by the program. The second column represents the assumption for residue ϵt of the observation equation (HOM: homoscedastic or HET: heteroscedastic). The next three columns represent the hyper-parameter variance maximum likelihood estimates, for observation equations and the state variables . The error variance of the observation equation refers to the unconditional variance of ϵt . LB² refers to the Ljung–Box test applied to the standardized squared residuals of the models. The number of lags is determined as in Tsay (2010): . JB refers to the Jarque–Bera normality tests. Table 2. SS-HOM and SS-HET model estimates (continuation)

12. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 233 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X With regard to the time-varying dynamics of alpha for the 26 sample funds, it is possible to reject the null hypothesis (σϑ ,i 2 to be statistically zero). However, it is observed that their estimates are close to zero. This suggests that the alpha parameter related to the managers’ stock-picking ability gradually varies over time. Goodness of fit Measure of Models The SS-HOM and SS-HET models discussed in the previous section are comparatively assessed in this subsection. We use the following measures to test the goodness of fit: Akaike’s information criterion (AIC), Bayesian information criterion (BIC), root mean square error (RMSE), and mean absolute error (MAE). For the residual diagnostic tests, we utilize the Jarque–Bera (JB) and Ljung–Box (LB) tests. Table 3 shows the AIC and BIC for the SS-HOM and SS-HET models for all the sample funds. The majority of funds, 26 out of the 36, show lower AIC and BIC values for the heteroscedastic model than for the homoscedastic model, thereby indicating that the approach of the heteroscedastic structure of errors ϵt increases the capacity of the conditional CAPM model to capture the time-varying dynamics of alpha and beta in the sample funds. Table 3. Goodness-of-fit measures Fund AIC BIC HOM HET HOM HET 1 -7.2241 -7.4048 -7.2124 -7.3954 2 -6.6069 -6.7779 -6.5952 -6.7685 3 -7.5046 -7.5822 -7.4928 -7.5728 4 -7.4347 -7.5720 -7.4229 -7.5626 5 -7.7224 -8.2642 -7.7107 -8.2548 6 -8.1559 -8.3436 -8.1442 -8.3342 7 -6.7928 -6.9294 -6.7810 -6.9200 8 -6.9896 -7.1484 -6.9778 -7.1390 9 -7.5103 -7.5262 -7.4986 -7.5168 10 -7.4898 -7.5983 -7.4780 -7.5889 11 -6.8075 -6.7585 -6.7958 -6.7491 12 -7.6689 -8.1253 -7.6571 -8.1159 13 -7.5630 -7.5068 -7.5512 -7.4974 14 -6.7301 -6.7711 -6.7184 -6.7616 15 -7.1060 -7.1370 -7.0942 -7.1276 16 -7.0381 -7.1148 -7.0264 -7.1054 17 -7.2647 -7.3585 -7.2530 -7.3491 18 -6.9834 -7.1640 -6.9717 -7.1546 19 -7.4468 -7.8637 -7.4351 -7.8543 20 -6.7299 -6.8602 -6.7181 -6.8508 21 -6.7347 -7.1400 -6.7229 -7.1306 22 -6.7191 -6.6031 -6.7073 -6.5937 23 -7.3848 -7.6531 -7.3731 -7.6437 (continue)

13. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 234 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X Fund AIC BIC HOM HET HOM HET 24 -6.3863 -6.3286 -6.3746 -6.3192 25 -7.3873 -7.8620 -7.3755 -7.8526 26 -6.5818 -6.4813 -6.5700 -6.4719 27 -7.3845 -7.5607 -7.3727 -7.5513 28 -6.4872 -6.3322 -6.4755 -6.3228 29 -7.2759 -7.4975 -7.2642 -7.4881 30 -7.3085 -7.5853 -7.2968 -7.5759 31 -6.7384 -6.6025 -6.7267 -6.5931 32 -6.8493 -7.0705 -6.8375 -7.0611 25 -7.3873 -7.8620 -7.3755 -7.8526 26 -6.5818 -6.4813 -6.5700 -6.4719 27 -7.3845 -7.5607 -7.3727 -7.5513 28 -6.4872 -6.3322 -6.4755 -6.3228 29 -7.2759 -7.4975 -7.2642 -7.4881 30 -7.3085 -7.5853 -7.2968 -7.5759 31 -6.7384 -6.6025 -6.7267 -6.5931 32 -6.8493 -7.0705 -6.8375 -7.0611 33 -6.8530 -6.9413 -6.8412 -6.9319 34 -4.2752 -4.4045 -4.2635 -4.3951 35 -4.8402 -5.2889 -4.8285 -5.2795 36 -4.8400 -5.0441 -4.8282 -5.0347 Notes: AIC is Akaike information criterion and BIC is Bayesian information criterion. The higher measures are in bold. Table 3. Goodness-of-fit measures Other measures used for the explanatory power analysis are RMSE and MAE. The in-sample forecast returns were estimated for each time t from January 2, 2006 to April 30, 2015 totaling 2,305 observations. Apart from the results shown for the SS-HOM and SS-HET models, we analyze the performance of the unconditional CAPM and the four-factor model of Carhart (1997) using OLS with rolling windows of 120 days. Considering the explanatory, not predictive, objective of this analysis, for conditional CAPM models in space-state form estimated by Kalman filter algorithm, the smoothed versions of the state variables are used. Table 4 shows the sample average RMSE and MAE for each model. The resultsinTable 4 show thatthe rolling windowsapproach could be better when the alpha and beta are time-varying and the RMSE and MAE tests for theSS-HOM andSS-HET models are inferior to the CAPM and the factors model. Hence, in the comparison between conditional models and the factors model, the results are favorable for conditional models, showing that considering in-sample explanatory power and time-varying alpha and beta could bring superior benefits to the introduction of more riskfactors. Conversely, the comparison between SS-HOM and SS-HET models suggests a preference for the homoscedastic model, showing a lower RMSE and MAE on average. From the viewpoint of the model’s explanatory power, the heteroscedastic model does not seem to obtain better results than the homoscedastic model, in general. (continuation)

14. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 235 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X Table 4. Consolidated result of the in-sample goodness of fit of the 36 sample funds Model RMSE MAE SS-HOM 8.00E-03 5.36E-03 SS-HET 8.15E-03 5.43E-03 CAPM 8.56E-03 5.77E-03 Factors model 8.37E-03 5.68E-03 Notes: Each cell shows the sample average RMSE and MAE for each model. Residual Diagnostic Tests The results of the residual diagnostic tests are shown in Table 2, in columns 7 and 8, for JB and LB tests, respectively. The normality hypothesis of standardized residual for both SS-HOM and SS-HET models is rejected for all the analyzed funds in the JB test. We observe a reduction in the latter compared to the former even though the t-statistic value for the SS-HET model is distant from the values of a normal standard distribution. The SS-HOM model is not able to capture the heteroscedasticity in the residuals, once the null hypothesis of non-autocorrelation of the squared standardized residuals is rejected for all funds in the LB test. Conversely, this hypothesis is not rejected for the squared standardized residuals of the SS-HET model for 14 out of the 36 sample funds. The finding that 22 out of the 36 analyzed sample funds still feature such dependence structure in standardized residuals of SS-HET model requires further investigation. Therefore, we analyze the correlograms of funds that persistently show heteroscedasticity signs in the residuals of the SS-HET model. Figure 1 shows two examples of this analysis. As with all the other funds not shown here, we observe that despite the values of residual autocorrelation being statistically different from zero, they are not relevant. In comparison, Figure 2 shows the correlograms of the standardized residuals of the SS-HOM model for the same funds. This comparison shows that the SS-HET model better captures the time-dependence structure of return series variance, given the substantial decrease in autocorrelation among the residuals. Figure 1. Standardized squared residuals correlograms for SS-HET model Note: This figure shows the correlograms of the standardized residuals of the SS-HET model for funds 05 (left) and 25 (right).

15. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 236 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X Figure 2. Standardized squared residuals correlograms for SS-HOM model Note: This figure shows the correlograms of the standardized residuals of the SS-HOM model for funds 05 (left) and 25 (right). Once the conditional models show a higher capacity to adjust to sample data, it is reasonable to suppose that the evaluation of performance measures obtained from this model might improve the actual performance evaluation. CONDITIONAL CAPM IN THE ANALYSIS OF BRAZILIAN EQUITY FUNDS This section uses the measures obtained from the conditional models to carry out the performance analysis of managers’ stock- picking and market-timing abilities. Fund Performance Measures: Conditional Alphas The metric used here to compare fund performance is intercept αi,t of equations (3), (9), and (10), also known as Jensen’s alpha. Since the objective of evaluation is to estimate the measures of performance evaluation from the impacts of different models, we built an equally weighted portfolio with all sample funds, in such a way that the portfolio returns in each period is given by mean returns of all funds in the same period. The portfolio’s alpha estimates for each of the models are shown in Graph 1. Unlike most of the previous studies, which usually deduce the existence of stock-picking ability by estimating the alpha in a given time span, our model allows us to obtain the alpha estimate at each instant of time, allowing the analysis of managers’ stock- picking ability over time. In this sense, observing Graph 1, we can conclude that managers' ability to select the best assets is directly related to periods of market upswings (2007–2008 and 2009–2010). During periods of market downturn (2008– 2009), managers, in general, deliver negative excess of return to their investors. Moreover, since the peak in 2010, managers’ stock-picking ability has reduced gradually, having shown overall negative values in the last years of the sample. A substantial difference can be noticed among the alpha estimates of each model, especially between the conditional models (SS-HOM and SS-HET) and the unconditional ones (unconditional CAPM and factors model), with the latter consistently higher than the former in absolute terms. One possible explanation for this phenomenon is that the lower

16. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 237 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X explanatory power of the unconditional models, confirmed by the RMSE and MAE measures, tends to overestimate the values of alpha intercept estimates. Hence, the time-varying beta of funds would not be captured satisfactorily with the rolling windows estimates, being confused with positive or negative abnormal returns in the CAPM and factors models. This means that a portion of the alpha values estimated by the commonly used unconditional models do not deal with the superior ability of managers but only inadequately capture the temporal variation in the fund's beta. Graph 1. Alpha point estimates for an equally weighted portfolio for all sample funds Note: This figure shows the portfolio’s alpha estimates for the SS-HOM, SS-HET, CAPM, and factors models. Thus, these results indicate four issues regarding managers’ stock-picking ability: (1) the managers’ ability to select the best assets might be directly related to periods of stock market upswings; (2) during periods of market downturns, the managers’ search for assets with potential appreciation greater than their risk level incurred abnormal or negative returns; (3) managers have consistently showed negative alphas in the last years of the sample; and (4) in general, a portion of the alpha values estimated by the unconditional models are not due to the managers’ stock picking ability, but merely the model’s inability to adequately capture the temporal variation in the beta. Fund Performance Measures: Conditional Betas Since managers’ market-timing ability is directly related to fund beta variation analysis over time, one must understand the evolution of estimated beta for the different models. Graph 2 shows the series of betas for some of the sample funds for SS-HOM and SS-HET models. A less noisy series of the SS-HET estimates than of the SS-HOM is observed in the first years of the sampling, especially between 2006 and 2008, a time of considerable market volatility caused by the global financial crisis of 2008. Further investigation shows that the daily standard deviation of market factor returns of 2006–2008 (2.04%) is higher than for 2009–2015 (1.29%). According to Ortas et al. (2015), a less noisy estimate from the heteroscedastic model outperformance occurs because the leptokurtosis of the unconditional distribution of ϵi,t reduces the influence of outliers during the beta estimation process. In other words, periods of greater volatility would be marked by greater differences in the beta estimates—as is observed.

17. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 238 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X Graph 2. Beta point estimates for sample funds Regarding conditional and unconditional models, it can be noted that periods of rising beta for the former, in general, are periods of falling beta for the latter, and vice versa. Since the estimate for conditional beta in a given period is carried out conditionally on the information for the following periods, the smoothed estimates usually anticipate the future changes in the estimated variable. To analyze managers’ market-timing ability, we used the approach of Holmes and Faff (2008), where a daily series of beta estimated by conditional and unconditional models is used as a dependent variable in market factor regression, as presented in the following equation: βi ,t k = constant +γ i k Rm,t +εi ,t (11) where subscript i is the analysed sample fund and superscript k the analyzed model; γ i k is the regression coefficient, estimated by OLS; and Rm,t represents the excess returns of the market factor. The manager shows market forecasting ability when γ i k >0.If γ i k <0 the manager increases beta during market downswing and lowers beta in market upswing. The results of the estimates of γ i k and their p-values are detailed in Table 5. The use of SS-HOM and SS-HET conditional models reveals more funds for which managers’ market-timing ability is negative than for the CAPM and factors models. Moreover, we verify a decrease in the mean coefficients of the sample funds γ i k compared to the CAPM and factors models, which begin to show substantially negative values for conditional models. These results show that the conditional models alter the market-timing ability analysis compared to the CAPM and factors models, thereby indicating that the managers of the analyzed sample cannot forecast periods of market downturns or upswings, even when doing the opposite by systematically increasing beta funds during market downswings and decreasing beta funds during upswings. However, with regard to the comparison between the results of SS-HOM and SS-HET models, no significant differences are observed.

18. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 239 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X Table 5. Comparison of market-timing coefficients γ i k for the conditional CAPM and factors model SS-HOM SS-HET CAPM Factors model Panel A: summary statistics No. of positive cases γ y k >0( ) 3 2 21 13 No. of negative cases γ y k <0( ) 33 (8) 34 (6) 15 23 Panel B: correlation coefficients between estimated beta series SS-HOM 0.9715 0.7560 0.6728 SS-HET 0.7812 0.6896 CAPM 0.8931 Notes: The numbers in brackets represent results that are statistically different from zero at the significance level of 10%. Panel B shows the means correlation coefficients of the beta series for the 36 sample funds. A negative coefficient related to market-timing ability warrants explanations. According to Ferson and Schadt (1996), a negative coefficient could arise when a manager has perverse ability to forecast market movement in the opposite direction. The negative correlation between the funds’ beta and the market factor returns could also be caused by the flow of investment funds: since significant investments in funds tend to decrease its beta, they tend to increase during periods of market upswings. The explanation for managers’ negative market-timing ability would be the significant flow of investments during market upswings. Further, Panel B of Table 5 shows that, in general, there is a strong correlation between the beta estimates of conditional models. That could be one explanation for the market-timing results not displaying substantial differences. This correlation decreases when the estimates of conditional models are analyzed against other models, the conditional and factors models. This could explain the market-timing results displaying more significant differences among the different models. CONCLUSIONS The results obtained in this study show that the measures of the heteroscedastic Kalman Filter model provide better performance evaluation for funds regarding managers’ ability in Brazilian stock funds, for stock-picking and market-timing, than the traditional models. Unlike many previous papers, especially in the Brazilian context, a heteroscedastic version of conditional CAPM is compared with the results of the homoscedastic version of the model, and alternative models, such as the four-factor model of Carhart (1997). Keeping the one-factor model structure, the results show that the modelling of the heteroscedastic structure of errors increases the capacity of the conditional CAPM model to capture the funds’ alpha and beta timing dynamics. The state-space models were also compared for goodness of fit to the unconditional CAPM and four-factor model of Carhart (1997), both estimated with rolling-windows. The results indicate the superiority of the conditional models for all pricing measures used. These results suggest that the alpha and beta timing variation bring superior benefits than the introduction of more risk factors. After the higher quality of goodness of fit to the conditional models is determined, the conditional measures of investment fund performance evaluation were estimated. We can draw four conclusions regarding managers’ stock- picking ability: (1) the managers’ ability to better select assets can be directly related to periods of market upswings; (2) during periods of market downturns, the managers’ search for better assets with an appreciation potential higher than their risk level, leads to abnormal negative returns; (3) managers have consistently shown negative alphas in the last years of the sample; and (4) in general, a portion of the alpha values estimated by the unconditional models do not show the presence of managers’ stockpicking ability but only an inability of the model

19. ARTICLES | CONDITIONAL PRICING MODEL WITH HETEROSCEDASTICITY: EVALUATION OF BRAZILIAN FUNDS Leandro Santos da Costa | Frances Fischberg Blank | Fernando Luiz Cyrino Oliveira | Cristian Enrique Muñoz Villalobos 240 © RAE | São Paulo | 59(4) | July-August 2019 | 225-241 ISSN 0034-7590; eISSN 2178-938X to adequately capture the temporal variation in the beta. As for the market-timing ability, the results indicate that managers of the analyzed sample not only do not have forecasting ability for market downturns or upswings, rather end up doing just the opposite— systematically increasing (decreasing) fund betas in market downturns (upswings). Further research in this area can be highlighted. Hybrid models combining the Kalman Filter approach and lagged macroeconomic variables as conditioning variables can bring more information on the managers’ performance based on their strategies. 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21. RAE-Revista de Administração de Empresas (Journal of Business Management) 242 © RAE | São Paulo | 59(4) | July-August 2019 | 242-257 ISSN 0034-7590; eISSN 2178-938X ANDRE CHERUBINI ALVES1 andre.alves82@gmail.com ORCID: 0000-0002-4222-5334 BRUNO FISCHER2 bfischer@unicamp.br ORCID: 0000-0003-3878-9097 NICHOLAS SPYRIDON VONORTAS3 vonortas@gwu.edu ORCID: 0000-0002-6745-4926 SÉRGIO ROBLES REIS DE QUEIROZ1 squeiroz@ige.unicamp.br ORCID: 0000-0002-6534-9022 1 Universidade Estadual de Campinas, Departamento de Política Científica e Tecnológica, Campinas, SP, Brazil 2 Universidade Estadual de Campinas, Faculdade de Ciências Aplicadas, Limeira, SP, Brazil ³The George Washington University, Institute for International Science and Technology Policy, Department of Economics, Washington, DC, United States of America ARTICLES Submitted 07.31.2018. Approved 06.04.2019 Evaluated through a double-blind review process. Scientific Editor: Yeda Swirski de Souza Original version DOI: http://dx.doi.org/10.1590/S0034-759020190403 CONFIGURATIONS OF KNOWLEDGE-INTENSIVE ENTREPRENEURIAL ECOSYSTEMS Configurações de ecossistemas de empreendedorismo intensivo em conhecimento Configuraciones de ecosistemas de emprendimiento intensivo en conocimiento ABSTRACT The dominant discourse on Entrepreneurial Ecosystems (EE) remains focused on the profile of a handful of successful locations. This has hindered a deeper comprehension of the economic mechanisms that shape evolutionary trends in entrepreneurial activity and how they operate in distinct places. We propose that EE have regularities, but they can also assume different configurations, i.e., varying combinations of influential dimensions. Through fuzzy set qualitative comparative analysis, we address this issue with data from the State of São Paulo, Brazil. This research focuses on five EE dimensions: Science & Technology, Human Capital, Market Dynamics, Business Dynamics, and Infrastructure. Findings point at the heterogeneous nature of EE distributed in three different paths. While configurations’ vary in terms of causal conditions, research universities, knowledge-intensive jobs and wider credit operations are core-causal conditions. Proximity to the main economic hub appears as a key differentiator among ecosystems. KEYWORDS | Entrepreneurial ecosystems, knowledge-intensive entrepreneurship, qualitative comparative analysis, configurations, geography of innovation. RESUMO O discurso dominante sobre os ecossistemas de empreendedorismo (EE) enfatiza o perfil de algumas localidades com reconhecido histórico de sucesso. Isso tem dificultado uma compreensão mais profunda dos mecanismos econômicos que moldam as tendências evolutivas na atividade empreendedora e como elas operam em lugares distintos. Nós propomos que esses ecossistemas possuem regularidades, mas elas também podem assumir diferentes configurações. Por meio de técnicas de fuzzy-set Qualitative Comparative Analysis (QCA), abordamos essa questão com dados do estado de São Paulo. Esta pesquisa concentra-se em cinco dimensões dos ecossistemas de empreendedorismo: ciência e tec- nologia, capital humano, dinâmica de mercado, dinâmica dos negócios e infraestrutura. Os resultados apontam para a natureza relativamente heterogênea dos ecossistemas. Não obstante, as universidades de pesquisa, a intensidade de empregos intensivos em conhecimento e a disponibilidade de crédito são condições fundamentais. A proximidade do principal centro econômico aparece como um diferencial importante entre os ecossistemas. PALAVRAS-CHAVE | Ecossistemas de empreendedorismo, empreendedorismo intensivo em conhecimento, qualitative comparative analysis, configurações, geografia da inovação. RESUMEN El discurso dominante respecto a los Ecosistemas de emprendimiento (EE) pone énfasis en el perfil de algunas localidades con reconocido historial de éxito. Esto ha dificultado una comprensión más profunda de los mecanismos económicos que moldean las tendencias evolutivas en la actividad emprendedora y cómo ellas operan en lugares distintos. Partimos de la proposición de que estos ecosistemas tienen regularidades, pero también pueden asumir diferentes configuraciones. A través de técnicas de fuzzy-set Qualitative Comparative Analysis, abordamos el caso del Estado de São Paulo, Brasil, enfocando cinco dimensiones de los Ecosistemas de emprendimiento: Ciencia y Tecnología, Capital Humano, Dinámica de Mercado, Dinámica de Negocios e Infraestructura. Los resultados apuntan a la naturaleza relativamente heterogénea de los ecosistemas. No obstante, las universidades de investigación, la intensidad de empleos intensivos en conocimiento y la disponibilidad de crédito son condiciones fundamentales. La proximidad del principal centro económico representa un aspecto diferencial importante entre los ecosistemas. PALABRAS CLAVE | Ecosistemas de emprendimiento, emprendimientos intensivos en conocimiento, qualitative comparative analysis, configuraciones, geografía de la innovación.

22. ARTICLES | CONFIGURATIONS OF KNOWLEDGE-INTENSIVE ENTREPRENEURIAL ECOSYSTEMS Andre Cherubini Alves | Bruno Fischer | Nicholas Spyridon Vonortas | Sérgio Robles Reis de Queiroz 243 © RAE | São Paulo | 59(4) | July-August 2019 | 242-257 ISSN 0034-7590; eISSN 2178-938X INTRODUCTION Knowledge-intensive entrepreneurship (KIE) refers to a phenomenon that drives economic competitiveness and innovation capabilities (Ács, Autio, & Szerb, 2014). It has, however, received relatively little attention in studies related to the approaches of innovation systems.The influence of context upon entrepreneurial activity is still often ignored in favor of a focus on individuals and firms (Borissenko & Boschma, 2016; Stam, 2015). As a result, we are still far from developing a thorough understanding of issues related to the emergence of new technology-based companies and its systemic determinants (Audretsch, 2012). It is clear that KIE is unevenly distributed across territories, a function of heterogeneous endowments in terms of knowledge, institutions, resources, and demand (Isaksen & Trippl, 2017). As a consequence, we observe a spiky geography of economic activities (Florida, 2005). In addition, evidence suggests that the impacts of entrepreneurial activity are mainly felt at the regional level (Ács & Armington, 2004), placing Entrepreneurial Ecosystems (EEs) as a key subject of interest for researchers and policymakers (Audretsch & Belitski, 2017; Borissenko & Boschma, 2016). The fact that KIE is deeply embedded in local contexts (Alvedalen & Boschma, 2017) poses challenges for analysts and policymakers, as one-size-fits-all initiatives and analytical models may be deemed inappropriate for most locations – although the dominant discourse remains focused on the profile of a handful of successful EEs (Nicotra, Romano, Giudice, & Schillaci, 2018; Stam, 2015). Accordingly, the economic mechanisms that shape evolutionary trends in entrepreneurship are not linear, and they operate differently in distinct locations (Ács, Stam, Audretsch & O’Connor, 2017; Brown & Mason, 2017; Boschma & Martin, 2010). Thus, a mechanistic approach to entrepreneurial ecosystems (EEs) – based on an input-output logic – is likely to ignore the context-specific traits of regions and their respective interactions (Feldman, 2001). In this article, we propose that entrepreneurial ecosystems can have regularities, but they can also assume different configurations, i.e., varying combinations of influential dimensions that may generate similar outcomes in terms of entrepreneurial intensity. This is a function of the distinct evolutionary path of each location. Our guiding research questions can be stated as follows: Are there different configurations of vectors of interest that shape successful entrepreneurial ecosystems? What are the key “ingredients” of these combinations? Drawing from different strands of literature addressing the dynamics of business locations, we examine a broad set of variables in order to identify the different foundational patterns behind ecosystems of entrepreneurship. As a concrete case, we assess the state of São Paulo, Brazil. We addressKIEthrough data from Pesquisa Inovativa em Pequenas Empresas(Innovative Research inSmallBusiness[PIPE]) projects, a program funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (São Paulo Research Foundation [FAPESP]) thatsupports innovative initiatives in small enterprises. Our goal is to develop further knowledge on the evolutionary geography of innovation and entrepreneurship in the context of a developing country, acknowledging the substantial discrepancies that these countries present relative to developed economies when it comes to the geographyofentrepreneurship (Crescenzi & Rodríguez-Pose, 2012). The empirical method applied is Qualitative Comparative Analysis (QCA) with fuzzy sets. We assessed data from a sample of 299 cities in the state ofSão Paulo. The analytical exercise focused on five dimensions as causal conditions for the emergence of entrepreneurialecosystems,namely:scienceandtechnology,human capital, market dynamics, business dynamics, and infrastructure. Our findings present distinct general paths across different configurationalsolutions,suggestingtheexistenceofheterogeneous patterns in entrepreneurial ecosystems. Nonetheless, a common coreisperceivedacrossthedifferentconfigurations,mainlyinvolving the science and technology environment, the availability of human capital, and local market dynamics. The article is structured as follows: we begin with the conceptual background of our assessment, as well as our proposal of a workable analytical model for the case of the state of São Paulo. After, we discuss the state of the literature and the need for a more flexible comprehension of the configurations of entrepreneurial ecosystems. We then outline the method and data used in our approach and empirical findings. Lastly we offer some concluding remarks, implications, and avenues for future research. Knowledge-intensive entrepreneurial ecosystems: Conceptual background Regions differ in their propensity to establish knowledge-intensive entrepreneurial activity (Florida, 2005). The resulting patterns reinforce themselves over time, as geographic proximity functions as a fundamental vector for knowledge exchange (Alvedalen & Boschma, 2017). These conditions put significant emphasis on local-level context as a determinant for KIE emergence, moving the analytical target beyond the mere understanding of firm-level capabilities (Mason & Brown, 2014; Ács et al., 2014; Audretsch & Belitski, 2017). It is by acknowledging these features of the socioeconomic environment that the Entrepreneurial Ecosystems concept has

23. ARTICLES | CONFIGURATIONS OF KNOWLEDGE-INTENSIVE ENTREPRENEURIAL ECOSYSTEMS Andre Cherubini Alves | Bruno Fischer | Nicholas Spyridon Vonortas | Sérgio Robles Reis de Queiroz 244 © RAE | São Paulo | 59(4) | July-August 2019 | 242-257 ISSN 0034-7590; eISSN 2178-938X gained ground in recent years. EEs are said to represent a “set of interconnected entrepreneurial actors, entrepreneurial organizations, institutions and entrepreneurial processes which formally and informally coalesce to connect, mediate and govern the performance within the local entrepreneurial environment” (Mason & Brown, 2014; p. 5). The underlying rationale of entrepreneurial ecosystems is focused on interactions between agents, tangi- ble and intangible factors of production, and how these vectors translate locally into entrepreneurship (Ács et al., 2017; Nicotra et al., 2018). Because of its (eco)systemic character, the productivity of these geographical units is affected by the performance of any of their components (Ács et al., 2014). By productivity we understand what Radosevic and Yoruk (2013) call “entrepreneurial propensity,” i.e., local capacity to generate and exploit innovation-oriented opportunities through the actions of entrepreneurs. Figure 1. Knowledge-Intensive Entrepreneurial Ecosystem SCIENCE AND TECHNOLOGY HUMANCAPITAL MARKET DYNAMICS BUSIN ESSDYNAMICSINFRASTRUCTURE PATENTS HUMAN DEVELOPMENT RESEARCH UNIVERSITIES KNOWLEDGE INTENSIVE LABORKNOWLEDGE INTENSIVE ENTREPRENEURSHIP BUSINESS CONCENTRATION MULTINATIONAL INVESTIMENT DISTANCE TO ECONOMIC HUB POPULATION ENERGY URBANIZATION GDP CREDIT Source: Adapted from Isenberg (2010), Mason and Brown (2014), and Stam (2015). Based on this literature, and with particular emphasis on the works of Isenberg (2010), Mason and Brown (2014), and Stam (2015), we offer a workable model of the entrepreneurial ecosystem concept (Figure 1). Our adaptation follows the basic principles contained in existing frameworks, thus contemplating issues associated to infrastructure, human capital, science and technology (comprising education, universities, and the technological support system), markets, and business dynamics. While the element (local-level) policy is not explicitly addressed in our model, it is intrinsically associated with features related to infrastructure, business dynamics, and human capital. The only dimension missing from our proposed model is entrepreneurial culture. However, as recent research underscores (Fritsch et al., 2019), such cultural traits are related to long-standing traditions and need adequate historical proxies, which are often unavailable for empirical exercises. Hence, our operational model offers a comprehensive perspective of EEs according to previous theoretical proposals, while remaining parsimonious as to address the entrepreneurial dynamics of ecosystems. Because of the local nature of EEs, the literature has recognized cities as the most adequate loci for empirical assessments (Audretsch & Belitski, 2017). Next, we address each of the five dimensions set out in the model in further detail. Science and technology Amongst the factors related to locational dynamics, access to a relevant knowledge base can be considered fundamental – more so for knowledge-intensive entrepreneurship (Nicotra et al., 2018; Boschma & Martin, 2010). Rich technological environments tend to facilitate entrepreneurial activity at the local level (Nicotra et al., 2018). In the state of São Paulo, Brazil, city-level patenting activity is strongly related to the emergence of KIE activity (Fischer, Queiroz, & Vonortas, 2018). Universities and research institutes are strategic agents in this respect (Spigel, 2017; Stam, 2015). These institutions not only contribute to knowledge generation, they also shape local conditions related to the population’s educational attainment (Isaksen & Trippl, 2017; Dorfman, 1983). Therefore, geographical proximity to research-oriented universities and research centers can be a valuable source of knowledge for high-tech entrepreneurial activity (Stam, 2015). Moreover, science-based entrepreneurship is significantly related to academic spin-offs (Di Gregorio & Shane, 2003), a situation that attributes key relevance to the local presence of preeminent universities. Guerrero et al. (2016) proposed that universities and academic researchers are fundamental agents of innovation systems through their involvement with knowledge transfer and entrepreneurial activities. This helps explain why high-tech clusters are often attached to university campus towns (Isaksen & Trippl, 2017). Fitjar and Rodríguez-Pose (2011) make such a case for regions that lack agglomeration economies while presenting high relative levels of innovative activity. However, universities’ impact on strong entrepreneurial ecosystems is

24. ARTICLES | CONFIGURATIONS OF KNOWLEDGE-INTENSIVE ENTREPRENEURIAL ECOSYSTEMS Andre Cherubini Alves | Bruno Fischer | Nicholas Spyridon Vonortas | Sérgio Robles Reis de Queiroz 245 © RAE | São Paulo | 59(4) | July-August 2019 | 242-257 ISSN 0034-7590; eISSN 2178-938X argued to be contingent upon the existence of a socioeconomic environment conducive to firm entry (Patton & Kenney, 2010). Human capital Chatterji, Glaeser, and Kerr (2013) state that the key pillars of entrepreneurship are essentially related to human capital. This is associated with the general education of the workforce and the local supply of individuals with entrepreneurial inclinations. This feature is widely recognized in the literature, be it under the concepts of human capital (Isenberg, 2011; WEF, 2014), worker talent (Spigel, 2017), human resources (Nicotra et al., 2018), or talent pool (Stam, 2015). Notably, the ratio of high-tech employment often predicts entrepreneurial activity in regions (Motoyama & Danley, 2012). The availability of highly skilled labor is a structural precondition for the creation of innovative entrepreneurial systems (Bresnahan, Gambardella, & Saxenian, 2001). Also, although labor is theoretically considered a mobile resource, entrepreneurial clusters are more likely to arise wherever professional talent is located or where it can be easily attracted (Dorfman, 1983). Accordingly, Audretsch and Feldman (1996) found that the propensity to cluster is stronger in industries that rely heavily on new economic knowledge, depending to a large degree upon skilled labor. This proximity allows KIE companies to more easily access available human capital (Storper, 1995). In addition, opportunity-driven entrepreneurial activity is strongly related to local income levels (Radosevic & Yoruk, 2013). Income approximates the level of education and capabilities within a pool of individuals and the quality of life and overall level of development of regions, key drivers of the location choices of knowledge-intensive entrepreneurs (Florida & Mellander, 2014). Market dynamics The third dimension of interest in our EE model concerns market dynamics. Despite the likelihood that knowledge-intensive new ventures will be oriented towards global markets, local- level conditions shape such ventures’ access to necessary complementary resources (Florida & Mellander, 2014). Larger markets offer increased levels of opportunities (Armington & Ács, 2002) and demand diversity (Bosma, Schott, Terjesen, & Penny, 2015). Isenberg (2010) follows this rationale, including local markets as significant vectors in the formation of entrepreneurial ecosystems. In addition, evidence from developed countries suggests that entrepreneurship tends to be more concentrated in large urban areas (Bosma & Sternberg, 2014). Market size also feeds entrepreneurial endeavors from a different direction: the size of the population is a representation of the pool of potential entrepreneurs in a given location (Stuart & Sorenson, 2003). Our category of market dynamics also includes the available funding for knowledge-intensive entrepreneurs (Isenberg, 2010). Geographic proximity is a critical feature in this discussion, as larger distances between capitalists and entrepreneurs increase the monitoring costs involved in financing operations (Dorfman, 1983). This reflects the strategic relevance attributed to credit as a platform for start-ups to operate (Lerner, 2002; Feldman, 2001). Besides private flows of capital, governments can engage in financing these incipient ventures (Lerner, 2002). Considering the Brazilian context, this vector is particularly critical, as small and medium-sized companies find it excessively difficult to access funding mechanisms due to scarce credit lines (Neto, Farias Filho, & Quelhas, 2014). Business dynamics Complementary to the idea of market dynamics, we introduce a perspective on the business environment of cities as an influential component of entrepreneurial ecosystems. Included here are the aspects of the level of development of regional economic structures, as well as its attractiveness for investments by incumbents. The literature recognizes the importance of adjacent firms as a driver of local competitiveness (Stuart & Sorenson, 2003). Positive externalities arise from the local clustering of firms, generating a critical mass of support for new companies (Isenberg, 2010; Isaksen & Trippl, 2017; Storper, 1995). Incumbent firms can also leverage the growth conditions for new firms (WEF, 2014) providing incentives for new business creation and contributing to start-up survival (Delgado et al., 2010). Established firms, and particularly multinational corporations, also play an important role in setting the stage for the emergence of new, knowledge- intensive ventures (Brown & Mason, 2017), as they can leverage overall capabilities in clusters (Bresnahan et al, 2001). An additional item of interest concerns the geographical reach of large (“core”) markets and how the proximity to these hubs can exert effects in neighboring areas.This happens because large cities do not just represent agglomeration of people, they are arguably also associated with the generation of innovative, science- based entrepreneurship (Duranton & Puga, 2002). In thisregard, an

25. ARTICLES | CONFIGURATIONS OF KNOWLEDGE-INTENSIVE ENTREPRENEURIAL ECOSYSTEMS Andre Cherubini Alves | Bruno Fischer | Nicholas Spyridon Vonortas | Sérgio Robles Reis de Queiroz 246 © RAE | São Paulo | 59(4) | July-August 2019 | 242-257 ISSN 0034-7590; eISSN 2178-938X “efficiency gap” exists when comparing peripheral regions to those located close to large markets (Crescenzi & Rodríguez-Pose, 2012). This is why high-tech clusters of entrepreneurship are frequently located in core regions (Isaksen & Trippl, 2017). This situation can be magnified in regions such as the state of São Paulo, which includes a megahub that functions as a center for business services, venture capital, and corporate demand (WEF, 2018). Infrastructure The fifth dimension in our model deals with the quality of infrastructure, a platform upon which economic activity strongly relies. Infrastructure facilitates urban linkages, labor mobility, and knowledge flows (Audretsch et al., 2015). Thus, its impacts as a determinant of the formation of entrepreneurial ecosystems must be acknowledged (Spigel, 2017; Nicotra et al., 2018; Stuart & Sorenson, 2003). The quality of physical infrastructure can also mitigate the detrimental effects associated with large market agglomeration diseconomies (Audretsch & Belitski, 2017). Knowledge-intensive entrepreneurial ecosystems: Making the case for heterogeneous configurations Notwithstanding agreement on the very basic definitions of entrepreneurial ecosystems, there are persistent controversies on causal links within its intrinsic dynamics (Borissenko & Boschma, 2016). What dimensions really matter for EEs? One of the main issues here concerns the poor generalizability of highly eminent cases to other contexts. These expositions – although informative – are developed around an idea of relative stability in the configuration of influential attributes in EEs. A first step in our approach deals with assessing the validity of such expectations. Hence, our first proposition can be stated as follows: Proposition 1. Successful Entrepreneurial Ecosys- tems rely on a set of critical dimensions that shape their respective capacity of sustaining the generation of knowledge-intensive entrepreneurship. On the other hand, researchers have increasingly criticized such formulations, as they lead to one-size-fits-all implications. Even if the dimensions included in these models are inclusive, some of its attributes may be more dominant in some cases than in others (Spigel, 2017). The central argument here is that economic mechanisms operate differently in distinct locations as a function of their historical backgrounds (Boschma & Martin, 2010). This is why top-down policies that aim to organize clusters of entrepreneurship are often deemed ineffective (Bresnahan et al, 2001; Chatterji et al, 2013; Feldman, 2001). This is the pillar of Evolutionary Economic Geography (EEG). In dealing with the dynamics of entrepreneurial ecosystems, the evolutionary view pinpoints the relevance of pre-existing conditions and assets for ecosystems to emerge (Isaksen, 2016). Accordingly, new science-based firms can be understood as functions of the prior existence of scientific research undertaken by universities and research institutes in a given location (Feldman & Lendel, 2011). In this debate, we hope to add the perspective that, because of distinct evolutionary paths, entrepreneurial ecosystems can achieve efficiency through different configurations. In other words, the relevance of EEs’ dimensions is bounded by location-specific trajectories, thus altering the dynamics of interconnection among actors, institutions, and organizations. Accordingly, our second proposition is presented: Proposition 2. Because of idiosyncratic evolutionary paths, Entrepreneurial Ecosystems can present heterogeneous configurations in terms of relevant drivers without compromising their respective level of entrepreneurial propensity. Recent literature has indicated some efforts in a similar direction. Brown and Mason (2017) develop a simplified taxonomy of entrepreneurial ecosystems based on illustrative cases of “embryonic” and “scale-up” locations. By assessing this argument, our goal is to empirically refine these introductory propositions and provide a more nuanced view on the variegated combinations of characteristics that can form a functional EE. METHODOLOGICAL APPROACH To address our research questions, we use Fuzzy-Set Qualitative Comparative Analysis (QCA). QCA is a method that is used to identify configurations or “recipes” of causal conditions associated with different outcomes, following the “equifinality principle,” meaning that multiple paths or solutions can lead to the same outcome (Ragin, 2008). Different from regression analysis, QCA also follows the principle of causal complexity, accounting for the combination of causal measures to a specific outcome within a property space. Analytical benefits associated with the QCA approach in comparison with standard econometric techniques applied to address the dynamics of entrepreneurial ecosystems concern its capacity for developing robust evaluations

26. ARTICLES | CONFIGURATIONS OF KNOWLEDGE-INTENSIVE ENTREPRENEURIAL ECOSYSTEMS Andre Cherubini Alves | Bruno Fischer | Nicholas Spyridon Vonortas | Sérgio Robles Reis de Queiroz 247 © RAE | São Paulo | 59(4) | July-August 2019 | 242-257 ISSN 0034-7590; eISSN 2178-938X of configurational issues (Fiss et al., 2013). While this is an aspect of interest in this field, traditional regression models fall short in offering the necessary knowledge for such research questions. Moreover, there is an increasing interest in the use of QCA in entrepreneurship studies (Kraus et al., 2018). The basic locus of empirical information in this study is knowledge-intensive entrepreneurship in the state of São Paulo, Brazil. The grants of the PIPE program are used as a proxy for KIE activity. This program is funded by the Fundação de Amparo à Pesquisa do Estado de São Paulo (Research Foundation of the state of São Paulo [FAPESP]) to support innovative initiatives in small enterprises. The program has a similar structure and objectives to the Small Business Innovation Research (SBIR) program in the United States. While SBIR data has been used in the context of entrepreneurial ecosystems by an extensive body of research (e.g. Wallsten, 2001; Qian & Haynes, 2014), information from PIPE has only recently been used for these purposes (e.g. Fischer et al., 2018). Such sources provide robust evidence on KIE activity, but also introduce sample bias in the analysis, since they deal with pre-selected and funded R&D projects that are often attached to academic spin-offs. Hence, conclusions drawn from this group should be taken cautiously, as they do not necessarily represent the broader context of overall KIE firms. The full dataset includes 1130 grants allocated across 114 cities in the state during the period 1998-2014. We collected data for a total of 299 cities in the state of São Paulo in order to account for differences in configurations between cities that have received project grants and those that have not. City-level analysis was chosen due to the local nature of entrepreneurial ecosystems, in which cities seem to be the most appropriate units of analysis (Audretsch & Belitski, 2017). While QCA has typically been used as a research methodology for Small-N, comparative work in the case-oriented tradition (Ragin, 1987), the method has increasingly been applied to examine large-N phenomena (Fiss, et al., 2013; Greckhamer et al., 2013; Emmenegger et al., 2014). Small-N samples in QCA range from 12-50 cases, whereas, Large-N samples involve 50+ cases (Greckhamer et al., 2013). Model and unit of analysis Following the literature review and the graphical representation of our analytical model (Figure 1), the socioeconomic dimensions included in our assessment are described in Exhibit 1. Most variables represent averages of city-level characteristics as proxies for economic conditions. This procedure avoids problems related to year-to-year variations, while also controlling for the time span during which projects have started (1998-2014). Calibration procedures Qualitative Comparative Analysis works using the principle of set-theoretic “membership.” We used quartiles to calibrate the fuzzy-sets. The only exception to this rule was the causal condition RESUNI, which is a binary variable indicating the presence (absence) of a major research university in the city. For the variable PROXCAP, quartiles were obtained by using only the cities that have displayed the outcome. This was done with the intent of creating a better visual geographical distribution and separation of cases. For the outcome variable, a rather flexible threshold was used to generate enough diversity in the sample and avoid skewness. We used the following set-membership threshold for our outcome variable: above 10 projects (full membership), between 4 to 10 projects (more-in-than-out), between 2 to 4 (more- out-than-in), and below 2 (full non-membership). Necessary conditions Conditions are considered necessary when they represent the superset of the outcome, that is, when the set membership values for the outcome Y are lower than that for a given causal condition (Ragin, 2006). Necessary conditions are verified using the following formulas for Consistency and Coverage. Consistency: ∑{min(Xi , Yi )]/∑(Yi ) (1) Covergage: (Yi ≤ Xi ): ∑{min(Xi , Yi )]/∑(Xi ) (2) According to Ragin (2008, p. 44), “consistency addresses the degree to which instances of the outcome agree in displaying the causal condition to be necessary, while coverage assesses the degree to which instances of the condition are paired with instances of the outcome.” Coverage indicates the relevance or importance of a set-theoretic connection. However, necessary conditions may be considered trivial if a condition is present in most cases whether the outcome is present or not (Schneider & Wagemann, 2012). Sufficient conditions The second test intends to identify the combinations of conditions that are in accordance with the presence of KIE activity, thus forming knowledge-intensive entrepreneurial ecosystems. A sufficient condition is one that, if satisfied, results in the achievement of the outcome (Schneider & Wagemann, 2012,