The influence of the tag-along rights in the value of companies: an event study of the effects on the market and service companies

The aim of this work is to check the effect of granting tag-along rights to stockholders by analyzing the behavior of the return of the stock. To do so we carried out event studies for a group of 21 company stocks, divided into service provider companies and others, who granted this right to their stockholders after Law 10,303 was passed in October, 2001. In the test we used two models for estimating abnormal returns: adjusted to the market and adjusted to the risk and market. The results of the tests we carried out based on these models did not capture abnormal returns (surpluses), telling us that the tag-along rights did not affect the pattern of daily returns of the stocks of companies traded on BOVESPA (The Sao Paulo Stock Exchange). We did not expect this result because of the new corporate governance practices adopted by companies in Brazil.

Business Plan, financial and risk analysis from the start-up mathrix

Mathrix is an e-learning math website that will be launched in March 2016. This master thesis offered a unique chance to interact with experienced supervisors in venture capitalism and project investment. It could serve as guidelines for entrepreneurs who intend to raise funds. Starting with the company’s business plan, the thesis focuses on estimating the company’s value with its return on investment using three scenarios and taking into consideration the risks evolved.

Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures

We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the Stochastic Mirror Descent (SMD) algorithms. When the objective functions are uniformly convex, we also propose a multistep extension of the Stochastic Mirror Descent algorithm and obtain con dence intervals on both the optimal values and optimal solutions. Numerical simulations show that our con dence intervals are much less conservative and are quicker to compute than previously obtained con dence intervals for SMD and that the multistep Stochastic Mirror Descent algorithm can obtain a good approximate solution much quicker than its nonmultistep counterpart. Our con dence intervals are also more reliable than asymptotic con dence intervals when the sample size is not much larger than the problem size.