Price discovery in dual-class shares across multiple markets

We extend the standard price discovery analysis to estimate the information share of dual-class shares across domestic and foreign markets. By examining both common and preferred shares, we aim to extract information not only about the fundamental value of the rm, but also about the dual-class premium. In particular, our interest lies on the price discovery mechanism regulating the prices of common and preferred shares in the BM&FBovespa as well as the prices of their ADR counterparts in the NYSE and in the Arca platform. However, in the presence of contemporaneous correlation between the innovations, the standard information share measure depends heavily on the ordering we attribute to prices in the system. To remain agnostic about which are the leading share class and market, one could for instance compute some weighted average information share across all possible orderings. This is extremely inconvenient given that we are dealing with 2 share prices in Brazil, 4 share prices in the US, plus the exchange rate (and hence over 5,000 permutations!). We thus develop a novel methodology to carry out price discovery analyses that does not impose any ex-ante assumption about which share class or trading platform conveys more information about shocks in the fundamental price. As such, our procedure yields a single measure of information share, which is invariant to the ordering of the variables in the system. Simulations of a simple market microstructure model show that our information share estimator works pretty well in practice. We then employ transactions data to study price discovery in two dual-class Brazilian stocks and their ADRs. We uncover two interesting ndings. First, the foreign market is at least as informative as the home market. Second, shocks in the dual-class premium entail a permanent e ect in normal times, but transitory in periods of nancial distress. We argue that the latter is consistent with the expropriation of preferred shareholders as a class.

The Brazilian class C women perception regarding premium mass products in the beauty industry

Recentemente, os mercados emergentes se tornaram um alvo potencial para a indústria da beleza e o Brasil está se tornando um mercado lucrativo para os produtos cosméticos Premium (Euromonitor International, 2013). A população de baixa renda no Brasil representa 70% de seus habitantes ao considerar as classes C, D e E (Barki e Parente, 2010), sendo a classe C representada por 56% da população (Neri, 2012). Este é um mercado potencial para as empresas multinacionais (MNCs), que enfrentam desafios de fazer negócios no país, visto que a classe C opta por gastar parte de sua renda com produtos relacionados a beleza (Silva e Parente, 2007) e ainda há um pouco conhecimento sobre o comportamento de consumo na base da pirâmide. Portanto, o objetivo deste estudo é investigar e descrever o comportamento de consumo das mulheres brasileiras da classe C no setor de beleza, em relação aos produtos Mass Premium, melhorando o conhecimento no que diz respeito a essa população e ao tema proposto. Para atingir esse objetivo, o autor utilizou uma metodologia baseada em uma análise descritivas qualitativa em que foram entrevistadas 20 mulheres de todas as faixas etárias, que pertencem à classe C brasileira e citações foram usadas para fornecer a confirmação dos resultados da análise. Os resultados sugerem que, ao longo dos anos, houve de fato um movimento de trade up no consumo de produtos de beleza. Além disso, foram identificados cinco aspectos principais, que conduzem decisão de compra das mulheres de classe C, no setor de beleza: confiabilidade, qualidade, status, autoestima e bem-estar. Apesar das limitações de um estudo exploratório, espera-se que a pesquisa aumente o conhecimento sobre o mercado da base da pirâmide, especialmente no que diz respeito à indústria da beleza.

Convergence analysis of sampling-based decomposition methods for risk-averse multistage stochastic convex programs

We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the recourse functions. This formula can be used to obtain an efficient implementation of Stochastic Dual Dynamic Programming applied to convex nonlinear problems. We prove the almost sure convergence of these decomposition methods when the relatively complete recourse assumption holds. We also prove the almost sure convergence of these algorithms when applied to risk-averse multistage stochastic linear programs that do not satisfy the relatively complete recourse assumption. The analysis is first done assuming the underlying stochastic process is interstage independent and discrete, with a finite set of possible realizations at each stage. We then indicate two ways of extending the methods and convergence analysis to the case when the process is interstage dependent.