Prediktion av beta för fonder

Sammanfattning: SEB Merchant Banking provides to its institutional customers a true market neutral product called Dynamic Manager Alpha (DMA). The DMA is constructed by a long position in an exceptionally well performing mutual fund and a beta adjusted short position in an appropriate index. The key to making the product market neutral is adjusting with the correct beta, since the beta changes, it is very important to have a good model for predicting beta in the future. This master thesis begins with describing what beta is in a CAPM sense. It then continues with recognizing the so called “Two Beta Trap”, which separates two kinds of beta. The first is in CAPM sense, with a market portfolio represented by the whole market. The second is a “best fit” beta where the market portfolio is the index which explains as much as possible of the fund returns. It is this second way of calculating beta that is used in this thesis and therefore beta can be viewed upon like a hedgeratio. The purpose of this thesis is to predict the future beta for mutual funds with as high accuracy as possible. The starting point has been historic OLS (Ordinary Least Squares) estimation of beta. From earlier studies and own studies in this thesis a lot of different techniques for predicting beta has been tested. For example the eriodicity in the returns, the interval length, different regression methods as LAD (Least Absolute Deviation) and IRLS (Iteratively Reweighted Least Squares). Also different adjustments to beta have been tested for better catching the momentum in beta and general mean reverting tendencies. The results of the studies show that when possible, returns calculated with daily compounding is not favorable. For daily but especially weekly returns, LAD and IRLS are superior to OLS in predicting beta. Adjusting techniques have a positive effect in predicting beta, especially for weekly returns. Monthly returns seem to be most stable and have the smallest prediction errors, but with the right model and adjustment, betas with weekly returns have almost as good characteristics. Since the prediction model needs to have a fast response to market changes, returns calculated with short compounding is favorable. It is therefore very encouraging that the results from this thesis have showed great improvements in prediction of beta for returns calculated with weekly compounding.