Examination Of The Fees And Performance Structure Of Event Driven Hedge Funds

This article aims at testing empirically the major building blocks that affect the performance of event driven hedge funds: incentive fees, management fees, size, age, hurdle rate, high watermark provision and lockup period. The fund manager primarily is targeting financial, micro and macro economic or political events, corporate events that will affect positively the net asset value, NAV, performance of the fund. The portfolio is exposed in long and short positions in bonds and shares according to business events, merger, acquisition, restructuring, legislative changes, and political turmoil. He /she is trying to increase the value of the portfolio by taking into consideration event driven scenario. This strategy is based in public information of forthcoming events that will affect positively or negatively the performance of the hedge fund. The fund manager seeks to take advantage of the price spread between current market prices of corporate securities and their value when the takeover is completed. Merger arbitrage involves buying the stock of the target companies after a merger announcement and shorting the acquiring company's stock. Hedge funds hedge this risk by holding a large portfolio of different probabilistic scenarios of merger prices to eliminate the discrepancy between the present value of the merger offer and the current stock price. The present value valuation model takes into consideration the possibility that the merger will fail. Hedge funds perform better when the economy is booming and there are positive news regarding the corporate business plan of the different companies. They could also take advantage of the political turmoil and instability and sell stocks or bonds by opening short positions. The investment vehicles that they use are equities, debt securities, options, futures and convertible bonds. The sample is provided from Data Feeder dataset. It is very comprehensive and includes event driven hedge funds for the period 1998 to 2003. There are other factors that could contribute to performance persistence such as lock-up periods, hurdle rate and high water mark. We are going to use a probit binary regression equation.