Capital Structure Decisions: Which Factors Are Reliably Important?

"This paper examines the relative importance of many factors in the capital structure decisions of publicly traded American firms from 1950 to 2003. The most reliable factors for explaining market leverage are: median industry leverage (+ effect on leverage), market-to-book assets ratio ( - ), tangibility (+), profits ( - ), log of assets (+), and expected inflation (+). In addition, we find that dividend-paying firms tend to have lower leverage. When considering book leverage, somewhat similar effects are found. However, for book leverage, the impact of firm size, the market-to-book ratio, and the effect of inflation are not reliable. The empirical evidence seems reasonably consistent with some versions of the trade-off theory of capital structure." Copyright (c) 2009 Financial Management Association International..

A test for the number of factors in an approximate factor model

SIGLEAvailable from British Library Document Supply Centre- DSC:5300.405(LSE-FMG-DP--137) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

The arbitrage pricing theory and multifactor models of asset returns

Available from British Library Document Supply Centre- DSC:5300.405(LSE-DP--149) / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Market Timing and Capital Structure: Evidence for Dutch Firms

capital structure, market timing, C23, G32,

The Effect of Market Regimes on Style Allocation

Regime switching, Style investing, Markov Chain Monte Carlo, Tactical asset allocation, G11, G12, G14,

Análise dos determinantes do endividamento das empresas de capital aberto do agronegócio brasileiro

Estudos sobre a estrutura de capital e a identificação de seus determinantes são temas relevantes nas pesquisas envolvendo a gestão financeira das empresas. Neste aspecto, o presente estudo teve como objetivo avaliar os determinantes da alavancagem das empresas do agronegócio brasileiro conforme o modelo de Rajan e Zingales (1995). Na definição da amostra, foram selecionadas 26 empresas que estavam enquadradas em alguma das três subdivisões do agronegócio brasileiro: a) o setor de produção agropecuária; b) setor fornecedor de insumos e fatores de produção e c) setor de processamento e distribuição, com base em classificação da CNA (Confederação da Agricultura e Pecuária do Brasil). O estudo foi feito com base no banco de dados da Economática®, tendo sido utilizado o modelo de regressão com dados em painel. Os resultados indicaram que as variáveis tangibilidade dos ativos, oportunidadede crescimento, tamanho e lucratividade foram estatisticamente significantes e podem ser interpretadas como fatores determinantes do endividamento das empresas do agronegócio brasileiro. Conclui-se, ainda, que o modelo estimado por meio da regressão com dados em painel gerou resultado compatível com aqueles preconizados pela pecking order theory.<br>Studies involving capital structure and the identification of its determinants are relevant issues in the field of corporate finance management research. In this regard, the present study intends to evaluate the determinants of corporate leverage in the Brazilian agribusiness sector using the model of Rajan and Zingales (1995). In the definition of the sample there were selected 26 companies that are classified in one of three subdivisions of the Brazilian agribusiness sector: a) the agriculture or cattle raising; b) inputs or production factors and c) processing and distribution sector, using as reference the CNA classification. The study used data from the Economatica® database, with the adoption of panel data methods. The results indicated that the variables tangibility of assets, growth opportunities, size and profitability were statiscally significant as determinant factors of the debt structure of Brazilian agribusiness companies. It is also possible to conclude that the model estimated by panel data generated results that are compatible with those suggested by the pecking order theory

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants