Empresas de controle familiar e informed trading: evidências de short selling no mercado brasileiro?

O objetivo desse trabalho é testar se no mercado brasileiro, empresas familiares são mais suscetíveis a insider trading.Testes feitos no mercado americano evidenciaram efeito do controle familiar no conteúdo informacional embutido em montagem de posições vendidas de companhias abertas. Lá, foram encontrados níveis acima do normal de posições short em companhias de controle familiar principalmente em momentos que antecipavam resultados negativos que iriam ser publicados. Não encontramos evidências claras de que o fato da companhia ter controle familiar poderia levá-la a apresentar ou não insider trading, já que por limitação do modelo não é possível comparar o nível de anormal short para empresas de controle familiar e outras pois essa variável é excluída do modelo. Entretanto, observamos nos modelos em painel fixo com interações que existe diferença do efeito de algumas variáveis de controle para empresas de controle familiar ou não sobre outras variáveis de controle o que poderia mostrar que alguma influência o controle familiar poderia ter sobre o insider trading. Testamos também se empresas de controle estatal apresentavam maior volume médio diário anormal de posições vendidas em momentos que antecediam surpresas de resultado, e também não encontramos evidências claras e diretas que isso acontecia.

Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The Örst reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modiÖed information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of Ötted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy ñreaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.

A Common-feature approach for testing present-value restrictions with financial data

It is well known that cointegration between the level of two variables (labeled Yt and yt in this paper) is a necessary condition to assess the empirical validity of a present-value model (PV and PVM, respectively, hereafter) linking them. The work on cointegration has been so prevalent that it is often overlooked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. The basis of this result is the use of rational expectations in forecasting future values of variables in the PVM. If this condition fails, the present-value equation will not be valid, since it will contain an additional term capturing the (non-zero) conditional expected value of future error terms. Our article has a few novel contributions, but two stand out. First, in testing for PVMs, we advise to split the restrictions implied by PV relationships into orthogonality conditions (or reduced rank restrictions) before additional tests on the value of parameters. We show that PV relationships entail a weak-form common feature relationship as in Hecq, Palm, and Urbain (2006) and in Athanasopoulos, Guillén, Issler and Vahid (2011) and also a polynomial serial-correlation common feature relationship as in Cubadda and Hecq (2001), which represent restrictions on dynamic models which allow several tests for the existence of PV relationships to be used. Because these relationships occur mostly with nancial data, we propose tests based on generalized method of moment (GMM) estimates, where it is straightforward to propose robust tests in the presence of heteroskedasticity. We also propose a robust Wald test developed to investigate the presence of reduced rank models. Their performance is evaluated in a Monte-Carlo exercise. Second, in the context of asset pricing, we propose applying a permanent-transitory (PT) decomposition based on Beveridge and Nelson (1981), which focus on extracting the long-run component of asset prices, a key concept in modern nancial theory as discussed in Alvarez and Jermann (2005), Hansen and Scheinkman (2009), and Nieuwerburgh, Lustig, Verdelhan (2010). Here again we can exploit the results developed in the common cycle literature to easily extract permament and transitory components under both long and also short-run restrictions. The techniques discussed herein are applied to long span annual data on long- and short-term interest rates and on price and dividend for the U.S. economy. In both applications we do not reject the existence of a common cyclical feature vector linking these two series. Extracting the long-run component shows the usefulness of our approach and highlights the presence of asset-pricing bubbles.

Empréstimo de ações no Brasil

Este trabalho investiga a atividade de empréstimo de ações no Brasil e sua conexão com operações de venda a descoberto em bolsa de valores. Descreve a organização do mercado, identifica fatores que determinam o nível de empréstimos e analisa os efeitos na eficiência da formação de preços das ações. Conclui que os vendedores atuam como contrários e que a atividade tem relação direta com o volume de negociação e inversa com a amplitude diária dos preços. Períodos de lock up e estabilização, esses após os IPOs, e o que antecede às ofertas subsequentes também influenciam o saldo de empréstimos, assim como a arbitragem tributária na distribuição, pelas empresas, de juros sobre capital próprio que tem efeito disruptivo sobre os preços das ações. Investidores a descoberto posicionam-se de modo a auferir excesso de retornos negativos e preferem ações com betas mais elevados. Os resultados também demonstram que o acréscimo de posições long-short a portfólio de investimento já existente aumentaria o retorno e reduziria o risco de mercado. Sobre a eficiência do mercado brasileiro, o estudo mostra que os preços das ações com saldos elevados de empréstimos ajustam-se com mais rapidez aos movimentos do mercado do que aquelas com menor saldo. O trabalho contribui, ainda, com recomendações pontuais para alterações da regulação e da organização operacional da atividade no País.

Títulos de dívida corporativa de empresas brasileiras: investir em emissões do mercado interno ou externo?

O objetivo deste trabalho é analisar a diferença de rentabilidade entre emissões de títulos de dívida corporativa de empresas brasileiras nos mercados local e externo. Sob a ótica do investidor interessado em comprar esses títulos, busca-se responder se, protegendo-se contra oscilações de fatores como câmbio e juros e controlando pelo prazo de vencimento, seria mais rentável em média adquirir um título local (debênture) ou externo (bond) de um mesmo emissor. Para isso, analisamos 177 emissões de debêntures e 119 emissões de bonds de 31 companhias não financeiras brasileiras no período entre janeiro de 2004 e abril de 2013. Regressões em painel com efeitos fixos para controlar pelas características de cada emissor verificam que, em média, o título do mercado externo paga de 164 a 197 bps a mais que o do mercado local, e que tal diferença é estatisticamente significante. A diferença deve-se à maior oferta e variedade de títulos de dívida corporativa no mercado externo, à existência de uma demanda cativa de investidores institucionais por debêntures no mercado local e à falta de integração entre os mercados. A participação de pessoas físicas no mercado externo também é identificada como fator relevante, assim como o possível maior custo de estruturação de dívida cobrado por bancos de investimento no mercado doméstico. Dentre as hipóteses formuladas para o motivo dessa diferença de rentabilidade não ser arbitrada, destacam-se o desconhecimento dos investidores e obstáculos operacionais, como custos de transação, impossibilidade de operação no mercado internacional e de venda a descoberto no mercado local. Tal fato causa ainda uma supervalorização das debêntures pela atuação dos investidores mais otimistas. A alta sensibilidade dos investidores institucionais locais à volatilidade dos retornos, também aparece como fator relevante.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Otimização de alavancagem e gestão de risco em estratégias long-short

Alavancagem em hedge funds tem preocupado investidores e estudiosos nos últimos anos. Exemplos recentes de estratégias desse tipo se mostraram vantajosos em períodos de pouca incerteza na economia, porém desastrosos em épocas de crise. No campo das finanças quantitativas, tem-se procurado encontrar o nível de alavancagem que otimize o retorno de um investimento dado o risco que se corre. Na literatura, os estudos têm se mostrado mais qualitativos do que quantitativos e pouco se tem usado de métodos computacionais para encontrar uma solução. Uma forma de avaliar se alguma estratégia de alavancagem aufere ganhos superiores do que outra é definir uma função objetivo que relacione risco e retorno para cada estratégia, encontrar as restrições do problema e resolvê-lo numericamente por meio de simulações de Monte Carlo. A presente dissertação adotou esta abordagem para tratar o investimento em uma estratégia long-short em um fundo de investimento de ações em diferentes cenários: diferentes formas de alavancagem, dinâmicas de preço das ações e níveis de correlação entre esses preços. Foram feitas simulações da dinâmica do capital investido em função das mudanças dos preços das ações ao longo do tempo. Considerou-se alguns critérios de garantia de crédito, assim como a possibilidade de compra e venda de ações durante o período de investimento e o perfil de risco do investidor. Finalmente, estudou-se a distribuição do retorno do investimento para diferentes níveis de alavancagem e foi possível quantificar qual desses níveis é mais vantajoso para a estratégia de investimento dadas as restrições de risco.

Asset float and speculative bubbles

We model the relationship between ftoat (the tradeable shares of an asset) and stock price bubbles. Investors trade a stock that initiaUy has a limited ftoat because of insider lock-up restrictions but the tradeable shares of which increase over time as these restrictions expire. A speculative bubble arises because investors, with heterogeneous beliefs due to overconfidence and facing short-sales constraints, anticipate the option to reseU the stock to buyers with even higher valuations. With limited risk absorption capacity, this resale option depends on ftoat as investors anticipate the change in asset supply over time and speculate over the degree of insider selling. Our model yields implications consistent with the behavior of internet stock prices during the late nineties, such as the bubble, share turnover and volatility decreasing with ftoat and stock prices tending to drop on the lock-up expiration date though it is known to aU in advance.