SLCG publica su Informe de Arbitraje de Valores de Puerto Rico actualizado que muestra más de $600 millones pagados hasta el momento en acuerdos y adjudicaciones con una cantidad similar que probablemente se pagará en los próximos años como resultado de las pérdidas de clientes de la firma de corretaje en Puerto Rico.

This paper studies a commonly used method of valuing companies, the comparable companies method, also known as the method of multiples. We use an intuitive graphical presentation to show why the comparable companies method is arbitrary and imprecise. We then show how valuations can be significantly improved using regression analysis. Regression analysis is superior to the comparable companies method because, by using more of the available data and imposing fewer unreasonable assumptions, it is more accurate and can value more firms.

We propose a robust portfolio optimization approach based on Value-at-Risk (VaR) adjusted Sharpe ratios. Traditional Sharpe ratio estimates using a limited series of historical returns are subject to estimation errors. Portfolio optimization based on traditional Sharpe ratios ignores this uncertainty and, as a result, is not robust. In this paper, we propose a robust portfolio optimization model that selects the portfolio with the largest worse-case-scenario Sharpe ratio within a given confidence interval. We show that this framework is equivalent to maximizing the Sharpe ratio reduced by a quantity proportional to the standard deviation in the Sharpe
ratio estimator. We highlight the relationship between the VaR-adjusted Sharpe ratios and other modified Sharpe ratios proposed in the literature. In addition, we present both numerical and empirical results comparing optimal portfolios generated by the approach advocated here with those generated by both the traditional and the alternative optimization approaches.