Are the ratings useful tools selecting mutual funds?

Resumo

Abstract This thesis explores three topics that contribute to understanding how ratings can help investors selecting mutual funds. Specifically, this thesis pretends understand the ability to detect funds which outperform their peers based on Morningstar Star Rating, Morningstar Analyst Rating and Morningstar Sustainability Scores. The chapters are independent of each other in terms of theoretical grounding and methodology but complement each other by investigating the three different angles related. In Chapter 2 is analysed if the Morningstar Star Rating can have the predictive power of future performance, as well how another’s variables mixed with these ratings can help investors making better choices for increase their wealth and minimize their risk. In the Chapter 3 the roll of qualitative ratings is studied, more specifically the Morningstar Analyst Rating. In particular, we analyse if the ratings based on analyst opinions can help identifying mutual funds that outperform their peers in the short and long time in terms of risk adjusted performance, and if the addition of other variables can improve the selecction. Of particular interest is the combination of quantitative ratings (Star Rating) and qualitative (Analyst) to improve the selection. In Chapter 4 Morningstar Sustainability Scores and ESG (environmental, social and governance) scores are used to evaluate the effect of portfolio management decisions based on sustainable criteria. The objective of the Chapter 2 is to analyse if ratings are reliable tools in selecting mutual funds. Our sample contains all European equity funds rated by Morningstar from 2004 to 2014. Our conclusions support the ability of ratings to identify outperformers in the short and medium term. In this sense, we have found that on average, funds lower rated have a worst out of sample performance in terms of risk adjusted measures and annual returns. The strongest predictability is observed for one year ahead, but is also good for the three years’ period. The inclusion of costs and other variables like turnover, age and size, reflect the importance to consider other factors to explain future performance. Finally, the best ratings have a better behaviour in terms of VaR (value at risk) for the 99% confidence levels, showing that the investment in good rated funds can help to preserve better the investors´ wealth under unfavourable market conditions. The objective of Chapter 3 is to evaluate the usefulness of analyst assesments to select investments, using the Morningstar Analyst. Morningstar Analyst Ratings are forward-looking qualitative and quantitative analyses of mutual fund about five pillars: Process, Performance, People, Parent and Price, that includes factors like the cost, past performance, quality of management, interest alignment, etc. In this chapter, we assess to what extent selecting mutual funds based on Morningstar Analyst and Star ratings criteria has an impact on the performance of investors. In particular, we try to answer if good analyst ratings outperform non-recommended ones in the short (12 month) and long term (36 month) and if it is useful to combine both ratings in the screening process to identify good future performers. Our sample contains US equity funds covering the period August 2012 to August 2016. Our conclusions support the ability of Gold ratings to select funds that will behave better in terms of future performance. We have found little evidence that, on average, funds with a better Analyst Rating (Gold) have a better performance in terms of risk adjusted measures (alpha and sharpe). The predictability is observed in several analyses done in one year ahead but not for three-years. This evidence is more relevant in the case of the analysis made by investment style´s category. The combination of Stars and Analyst ratings does have medium-term differentiation results, with a higher performance in terms of Sharpe's ratio for bronze (3, 4 or 5 stars) or three-star gold funds. The objective of Chapter 4 is to evaluate the effect of sustainability scores and the different dimensions in which ESG score is subdivided (Environmental, Social and Governance) on performance and cash flow. On the other hand, the conventional dichotomous variable has been added to the models to evaluate to what extent the results may be different. Our result shows that there are a large number of funds that are not declared sustainable but their portfolio is comparable to sustainable mutual funds. Furthermore, Morningstar Sustainability Score is significant explaining the level of performance for all the metrics analysed (alpha, Sharpe and annual return), with negative sign in most models. In terms of downside risk, the level of sustainability is negatively and significantly related with the VaR of the fund, supporting that higher scored mutual funds protect better against extreme losses.