This paper investigates the determinants of performance of the
investments of private equity and venture capital (PEVC) funds in Brazil. We
use two unique databases: the First Brazilian Private Equity and Venture
Capital Census and the Guia-GVcepe Endeavor, with information on this
industry for the period 1999 to 2007. As measures of performance we use the
percentage and number of exits through IPO, acquisition by a company or by
another investor. Our results indicate that the factors influencing the
performance of investments are: size of the fund, number of investments, the
practice of co-investment, experience and foreign origin of the managing
organization, focus on late stage, intensity of contact between managers and
portfolio companies and the number of seats on the boards of the invested
companies. The number of successes grows with the number of investments at a
declining rate. This can indicate 1) a limit to the ability of managers or
2) that a large number of investments allows for greater diversification of
risk, directing investments to companies of high risk but with a high
upside.
Purpose -We study how financial illiteracy affects the probability of a derogatory mark on the customer's credit history.Design/methodology/approach -We use the National Survey on Financial Inclusion in Brazil together with information on financial instrument usage and spending provided by the head of the household. To estimate the impact of financial illiteracy on the probability of a derogatory mark we use a logistic model to compute the odds ratio.Findings -Our main result is that financially illiterate individuals have between 57% and 143% more chance of having a derogatory mark. We find that age, income, expenses, and retirement positively affect the probability of a derogatory mark, while marriage has a negative impact. Gender does not have any impact over the probability of derogatory mark.Originality/value -The study uses a new proxy for financial illiteracy and correlates it to the investment and debt decisions of a family using a unique dataset.
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