Homo infidelis : preferências, assimetria de informação e signaling

No presente trabalho analisamos a eficácia de estratégias de signaling no contexto do relacionamento extra-diádico. Adicionalmente, mensuramos tanto as preferências declaradas quanto as reveladas pelos indivíduos em suas interações no ambiente virtual, comparando gêneros e países. Pesquisas anteriores sobre a temática extra-diádica apoiaram-se excessivamente sobre metodologias com substanciais lacunas: baixo número de observações, universo amostral restrito a estudantes universitários, viéis auto-declaratório comprometendo a correta interpretação da realidade dos fatos, e ausência de comparativos internacionais. Todas essas ponderações foram superadas no presente estudo, no qual analisamos 100 mil indivíduos, sendo 6 mil homens e 4 mil mulheres para cada um dos 10 países da amostra: Argentina, Austrália, Brasil, Canadá, Chile, Itália, México, Espanha, Reino Unido e Estados Unidos. Assim, temos um universo amostral plúrimo em termos de nacionalidade e número de observações. As evidências empíricas aqui reveladas mostraram que indivíduos do sexo masculino são mais ativos na conquista extra-conjugal, refutando a sugestão de uma convergência comportamental. Homens contactaram em média per capita 30 mulheres diferentes no horizonte temporal de 3 meses, contra 13 na direção oposta. Em termos de preferências físicas, constatamos que indivíduos de âmbos os gêneros procuram consumar a relação com uma contraparte idealizada. Mulheres mostram interesse por homens altos e experientes. Os homens querem mulheres mais novas e em forma. Homens baixos ou novos, e mulheres altas ou velhas, foram os mais rechaçados da amostra. Com relação às preferências declaradas e reveladas sobre o tipo de encontro extra-diádico almejado, tanto homens quanto mulheres mostraram maior interesse por um “caso rápido”, indicando o intuito de estabelecer um contato pouco aprofundado, casual, de cunho físico-sexual e com baixa vinculação afetiva. A opção menos escolhida foi “um caso virtual”, sugerindo a efetiva intenção de concretizar a infidelidade, e não mantê-la restrita ao ambiente virtual. Por fim, verificamos que as estratégias de signaling analisadas (por meio de foto, feedback e presente virtual) foram fortemente eficazes. Em todos os casos indivíduos que adotaram tais sinalizações obtiveram substancial vantagem competitiva em relação aos demais e passaram a exercer maior atratividade, recebendo elevada parcela de mensagens e contatos.

Diversificação internacional no contexto brasileiro

Este trabalho contribui a discussão de diversificação internacional no contexto de investidores brasileiros com referência na moeda local (Reais). O trabalho testa as seguintes hipóteses: (1) se a adição de ativos internacionais não aumenta a eficiência (melhora na relação retorno/risco) de carteiras somente com ativos brasileiros, (2) se carteiras de menor risco exigem mais alocações internacionais e, (3) se alocação de ativos é parecida para investidores com referências em dólar ou em reais. Esse trabalho utiliza modelos já conhecidos de fronteiras eficientes com aplicação de técnicas que utilizam rotinas de Monte Carlo para suavizar possíveis erros na estimação dos retornos das classes de ativos, que incorporam ainda incertezas sobre o câmbio. Nas simulações são utilizadas uma cesta de ativos locais e uma cesta de ativos que melhor representa o mercado internacional. Apesar da grande maioria dos investidores brasileiros utilizarem muito pouco ativos internacionais em suas carteiras, seja por motivos de Home Bias, fatores históricos macroeconômicos evidenciados pelas altas taxas de juros ou limitações regulatórias, os resultados empíricos demonstram que existem ganhos de eficiência para as carteiras de investidores brasileiros ao se incluir ativos internacionais na alocação de ativos. Esses ganhos de eficiência são evidenciados para todos os perfis de risco, desde os mais conservadores até os perfis mais agressivos. Os resultados mostram que quanto maior o perfil de risco da carteira, maior é a alocação internacional que maximiza a eficiência da carteira. E por último, a referência da moeda muda significativamente a alocação eficiente de carteiras, carteiras com referência em dólar exigem menos diversificação com ativos em reais do que carteiras com referência em Reais exigem diversificação com ativos internacionais.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual frequencies. Data consists of metal-commodity prices at a monthly and quarterly frequencies from 1957 to 2012, extracted from the IFS, and annual data, provided from 1900-2010 by the U.S. Geological Survey (USGS). We also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009). We investigate short- and long-run comovement by applying the techniques and the tests proposed in the common-feature literature. One of the main contributions of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding out-of-sample forecasts, our main contribution is to show the benefits of forecast-combination techniques, which outperform individual-model forecasts - including the random-walk model. We use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates and functional forms to forecast the returns and prices of metal commodities. Using a large number of models (N large) and a large number of time periods (T large), we apply the techniques put forth by the common-feature literature on forecast combinations. Empirically, we show that models incorporating (short-run) common-cycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation.

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian Consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data over twelve months ahead. The disaggregated models were estimated by SARIMA and will have different levels of disaggregation. Aggregated models will be estimated by time series techniques such as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy comparison will be made by the selection model procedure known as Model Confidence Set and by Diebold-Mariano procedure. We were able to find evidence of forecast accuracy gains in models using more disaggregated data

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.

Um estudo sobre os impactos da surpresa dos indicadores macroeconômicos de atividade e inflação no mercado futuro brasileiro de juros

As diretrizes de política monetária são definidas com base em resultados dos indicadores macroeconômicos divulgados ao mercado periodicamente. Os agentes deste mercado respondem rapidamente às alterações de cenário, com o objetivo de obter lucro ou evitar perdas financeiras expressivas. Com este motivacional, a proposta deste trabalho é avaliar como reage o mercado futuro de juros diante da divulgação de surpresas em determinados indicadores macroeconômicos, propondo um indicador de surpresa agregado para prever os impactos causados. Através dos dados extraídos da Bloomberg e da BM&F Bovespa, foi construída uma base de dados simplificada pela adoção de premissas para mensuração do impacto das surpresas divulgadas no preço do DI Futuro. A padronização dos parâmetros, a realização dos testes de média e as regressões otimizadas pelo método OLS possibilitaram ponderar os indicadores econômicos de acordo com a oscilação que os mesmos causam a este mercado. Por fim, o teste de comparação mostrou que o indicador de surpresa proposto foi mais eficiente nas previsões da reação do mercado do que um indicador que pondere de forma igualitária todos os indicadores macroeconômicos.

The distribution of average and marginal effective tax rates in European Union member states

Macro-based summary indicators of effective tax burdens do not capture differences in effective tax rates facing different sub-groups of the population. They also cannot provide information on the level or distribution of the marginal effective tax rates thought to influence household behaviour. I use EUROMOD, an EU-wide tax-benefit microsimulation model, to compute distributions of average and marginal effective tax rates across the household population in fourteen European Union Member States. Using different definitions of ‘net taxes’, the tax base and the unit of analysis I present a range of measures showing the contribution of the tax-benefit system to household incomes, the average effective tax rates applicable to income from labour and marginal effective tax rates faced by working men and women. In a second step, effective tax rates are broken down to separately show the influence of each type of tax-benefit instrument. The results show that measures of effective tax rates vary considerably depending on incomes, labour market situations and family circumstances. Using single averages or macro-based indicators will therefore provide an inappropriate picture of tax burdens faced by large parts of the population.

Bias and size effects of price-comparison search engines : theory and experimental evidence

We analyze the impact on consumer prices of the size and bias of price comparison search engines. In the context of a model related to Burdett and Judd (1983) and Varian (1980), we develop and test experimentally several theoretical predictions. The experimental results confirm the model’s predictions regarding the impact of the number of firms, and the type of bias of the search engine, but reject the model’s predictions regarding changes in the size of the index. The explanatory power of an econometric model for the price distributions is significantly improved when variables accounting for risk attitudes are introduced.

Improvement on the sales forecast accuracy for a fast growing company by the best combination of historical data usage and clients segmentation

Industrial companies in developing countries are facing rapid growths, and this requires having in place the best organizational processes to cope with the market demand. Sales forecasting, as a tool aligned with the general strategy of the company, needs to be as much accurate as possible, in order to achieve the sales targets by making available the right information for purchasing, planning and control of production areas, and finally attending in time and form the demand generated. The present dissertation uses a single case study from the subsidiary of an international explosives company based in Brazil, Maxam, experiencing high growth in sales, and therefore facing the challenge to adequate its structure and processes properly for the rapid growth expected. Diverse sales forecast techniques have been analyzed to compare the actual monthly sales forecast, based on the sales force representatives’ market knowledge, with forecasts based on the analysis of historical sales data. The dissertation findings show how the combination of both qualitative and quantitative forecasts, by the creation of a combined forecast that considers both client´s demand knowledge from the sales workforce with time series analysis, leads to the improvement on the accuracy of the company´s sales forecast.

Overconfidence and confirmation bias: are future managers vulnerable?

Decision makers often use ‘rules of thumb’, or heuristics, to help them handling decision situations (Kahneman and Tversky, 1979b). Those cognitive shortcuts are taken by the brain to cope with complexity and time limitation of decisions, by reducing the burden of information processing (Hodgkinson et al, 1999; Newell and Simon, 1972). Although crucial for decision-making, heuristics come at the cost of occasionally sending us off course, that is, make us fall into judgment traps (Tversky and Kahneman, 1974). Over fifty years of psychological research has shown that heuristics can lead to systematic errors, or biases, in decision-making. This study focuses on two particularly impactful biases to decision-making – the overconfidence and confirmation biases. A specific group – top management school students and recent graduates - were subject to classic experiments to measure their level of susceptibility to those biases. This population is bound to take decision positions at companies, and eventually make decisions that will impact not only their companies but society at large. The results show that this population is strongly biased by overconfidence, but less so to the confirmation bias. No significant relationship between the level of susceptibility to the overconfidence and to the confirmation bias was found.

Forecast comparison with nonlinear methods for brazilian industrial production

Este trabalho avalia as previsões de três métodos não lineares — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model e Autometrics com Dummy Saturation — para a produção industrial mensal brasileira e testa se elas são mais precisas que aquelas de preditores naive, como o modelo autorregressivo de ordem p e o mecanismo de double differencing. Os resultados mostram que a saturação com dummies de degrau e o Logistic Smooth Transition Autoregressive Model podem ser superiores ao mecanismo de double differencing, mas o modelo linear autoregressivo é mais preciso que todos os outros métodos analisados.

Forecast comparison with nonlinear methods for Brazilian industrial production

This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.

Home bias no mercado de capitais brasileiro : um estudo sobre a custódia de investidores em ações

A presente dissertação apresenta uma análise da concentração do portfólio de a-ções de investidores brasileiros nas próprias empresas onde trabalham com o intuito de observar se o Home Bias se aplica à amostra analisada. Nosso estudo foi reali-zado com uma amostra extraída da custódia de 86 clientes de uma corretora de va-lores mobiliários, sendo estes dados reais de mercado. Restringimos a seleção da amostra de forma que metade fosse de clientes que trabalham em empresas de ca-pital aberto e a outra metade não. Foi feita análise cross section de quanto os inves-tidores alocam em ações das empresas para a qual trabalham e verificou-se qual o percentual desta participação em seus portfólios, em comparação a uma amostra de controle de investidores que não trabalham nesta mesma empresa. Além destas a-nálises, separamos a amostra pelo valor total do portfólio e realizamos os mesmos estudos com estes dois grupos diferentes da amostra. Como uma análise de robus-tez, identificamos empresas listadas que não adotam a remuneração com ações como uma sub-amostra do estudo. Como resultado, encontramos evidências de que os funcionários investem significativamente mais (seja em proporção do portfólio ou em valores) em ações das empresas onde trabalham em relação aos demais inves-tidores, evidenciando um viés de familiaridade na tomada de decisão de investimen-tos.

Cherry picking with synthetic controls

The synthetic control (SC) method has been recently proposed as an alternative method to estimate treatment e ects in comparative case studies. Abadie et al. [2010] and Abadie et al. [2015] argue that one of the advantages of the SC method is that it imposes a data-driven process to select the comparison units, providing more transparency and less discretionary power to the researcher. However, an important limitation of the SC method is that it does not provide clear guidance on the choice of predictor variables used to estimate the SC weights. We show that such lack of speci c guidances provides signi cant opportunities for the researcher to search for speci cations with statistically signi cant results, undermining one of the main advantages of the method. Considering six alternative speci cations commonly used in SC applications, we calculate in Monte Carlo simulations the probability of nding a statistically signi cant result at 5% in at least one speci cation. We nd that this probability can be as high as 13% (23% for a 10% signi cance test) when there are 12 pre-intervention periods and decay slowly with the number of pre-intervention periods. With 230 pre-intervention periods, this probability is still around 10% (18% for a 10% signi cance test). We show that the speci cation that uses the average pre-treatment outcome values to estimate the weights performed particularly bad in our simulations. However, the speci cation-searching problem remains relevant even when we do not consider this speci cation. We also show that this speci cation-searching problem is relevant in simulations with real datasets looking at placebo interventions in the Current Population Survey (CPS). In order to mitigate this problem, we propose a criterion to select among SC di erent speci cations based on the prediction error of each speci cations in placebo estimations