Valuation models and Simon's bounded rationality

This paper aims at reconciling the evidence that sophisticated valuation models are increasingly used by companies in their investment appraisal with the literature of bounded rationality, according to which objective optimization is impracticable in the real world because it would demand an immense level of sophistication of the analytical and computational processes of human beings. We show how normative valuation models should rather be viewed as forms of reality representation, frameworks according to which the real world is perceived, fragmented for a better understanding, and recomposed, providing an orderly method for undertaking a task as complex as the investment decision.

Fiscal policy and private investment: latin america in a comparative perspective

The main objective of the paper is to assess the impact of fiscal variables on private investment comparing some Latin-American economies to other advanced ones. For such purposes, the authors carry out an econometric analysis for the period 1990-2008. They make use of two dynamic panel models in which they group countries with similar characteristics and development levels. In one of them, they include Mexico, Brazil, Chile, Colombia and Uruguay; whereas in the second one the countries accounted for are the U.S., Canada, Spain, Korea, Ireland and Japan. They specify in both models an investment function using as arguments a wide range of variables, including those related with fiscal policy. From their results the authors infer that governments can, with higher spending, boost up the economy even when they finance spending with higher taxes. In Latin America, where income concentration is enormous, a proposal to boost up the economy through higher government expenditure financed with a progressive income tax, is even more justified.

Transaction costs: an empirical analysis of their relationship with investment and foreign direct investment

As stated by the New Institutional Economics theory, transaction costs play a relevant role in economics and, according to the extent of such costs, agents make investment decisions. Actually, transaction costs may represent a disincentive to entrepreneurship. This work aims to verify whether transaction costs are related to investment rate and foreign direct investment rate (FDI) in different business environments. The results suggest that foreign investors do not have precise information about other countries as domestic investors do; as it is observed, only the relation between transaction costs and investment rate is significant. Furthermore, there is evidence that the business environments of BRIC countries are less developed when compared to business environments of other countries in the study

Earnings management and economic crises in the brazilian capital market

The 2008 economic crisis challenged accounting, either demanding recognition and measurement criteria well adjusted to this scenario or even questioning its ability to inform appropriately entities' financial situation before the crisis occurred. So, our purpose was to verify if during economic crises listed companies in the Brazilian capital market tended to adopt earnings management (EM) practices. Our sample consisted in 3,772 firm-years observations, in 13 years - 1997 to 2009. We developed regression models considering discretionary accruals as EM proxy (dependent variable), crisis as a macroeconomic factor (dummy variable of interest), ROA, market-to-book, size, leverage, foreign direct investment (FDI) and sector as control variables. Different for previous EM studies two approaches were used in data panel regression models and multiple crises were observed simultaneously. Statistics tests revealed a significant relation between economic crisis and EM practices concerning listed companies in Brazil in both approaches used.

ABOUT PSYCHOLOGICAL VARIABLES IN APPLICATION SCORING MODELS

The purpose of this study is to investigate the contribution of psychological variables and scales suggested by Economic Psychology in predicting individuals’ default. Therefore, a sample of 555 individuals completed a self-completion questionnaire, which was composed of psychological variables and scales. By adopting the methodology of the logistic regression, the following psychological and behavioral characteristics were found associated with the group of individuals in default: a) negative dimensions related to money (suffering, inequality and conflict); b) high scores on the self-efficacy scale, probably indicating a greater degree of optimism and over-confidence; c) buyers classified as compulsive; d) individuals who consider it necessary to give gifts to children and friends on special dates, even though many people consider this a luxury; e) problems of self-control identified by individuals who drink an average of more than four glasses of alcoholic beverage a day.

INTERNATIONALIZATION OF STATE-OWNED ENTERPRISES THROUGH FOREIGN DIRECT INVESTMENT

ABSTRACT State-owned enterprises (SOEs) are created to focus on domestic needs, and yet recent evidence points to increasing outward foreign direct investment by SOEs. Existing International Business (IB) theories focus on efficiency-based motives for internationalization; therefore, they do not fully capture SOEs' internalization dynamics, which are driven largely by political factors and social welfare considerations. We integrate public management and IB theories to develop propositions that combine these questions: why SOEs internationalize; what are their motivations; and what are the main managerial outcomes of SOEs' internationalization. Our findings suggest that SOEs display little hesitancy in entering international markets, and that SOE international expansion is not contradictory with the goals of state-ownership if the purpose is to adjust the company to changing institutional environments both in the domestic and international markets. Our propositions about SOE internationalization are based on an in-depth case study of the outward foreign direct investment conducted by Brazil's Petrobras over the past three decades.

ANIMAL MODELS FOR THE STUDY OF LEISHMANIASIS IMMUNOLOGY

Leishmaniasis remains a major public health problem worldwide and is classified as Category I by the TDR/WHO, mainly due to the absence of control. Many experimental models like rodents, dogs and monkeys have been developed, each with specific features, in order to characterize the immune response to Leishmania species, but none reproduces the pathology observed in human disease. Conflicting data may arise in part because different parasite strains or species are being examined, different tissue targets (mice footpad, ear, or base of tail) are being infected, and different numbers (“low” 1×102 and “high” 1×106) of metacyclic promastigotes have been inoculated. Recently, new approaches have been proposed to provide more meaningful data regarding the host response and pathogenesis that parallels human disease. The use of sand fly saliva and low numbers of parasites in experimental infections has led to mimic natural transmission and find new molecules and immune mechanisms which should be considered when designing vaccines and control strategies. Moreover, the use of wild rodents as experimental models has been proposed as a good alternative for studying the host-pathogen relationships and for testing candidate vaccines. To date, using natural reservoirs to study Leishmania infection has been challenging because immunologic reagents for use in wild rodents are lacking. This review discusses the principal immunological findings against Leishmania infection in different animal models highlighting the importance of using experimental conditions similar to natural transmission and reservoir species as experimental models to study the immunopathology of the disease.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

Modelling Amazonian forest eddy covariance data: a comparison of big leaf versus sun/shade models for the C-14 tower at Manaus I. Canopy photosynthesis

In this study, we concentrate on modelling gross primary productivity using two simple approaches to simulate canopy photosynthesis: "big leaf" and "sun/shade" models. Two approaches for calibration are used: scaling up of canopy photosynthetic parameters from the leaf to the canopy level and fitting canopy biochemistry to eddy covariance fluxes. Validation of the models is achieved by using eddy covariance data from the LBA site C14. Comparing the performance of both models we conclude that numerically (in terms of goodness of fit) and qualitatively, (in terms of residual response to different environmental variables) sun/shade does a better job. Compared to the sun/shade model, the big leaf model shows a lower goodness of fit and fails to respond to variations in the diffuse fraction, also having skewed responses to temperature and VPD. The separate treatment of sun and shade leaves in combination with the separation of the incoming light into direct beam and diffuse make sun/shade a strong modelling tool that catches more of the observed variability in canopy fluxes as measured by eddy covariance. In conclusion, the sun/shade approach is a relatively simple and effective tool for modelling photosynthetic carbon uptake that could be easily included in many terrestrial carbon models.