Examination Of The Fees And Performance Structure Of Distressed Securities Hedge Funds
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Examination of the Fees and Performance Structure of Distressed Securities Hedge Funds
Author | : Michel Guirguis |
Publisher | : |
Total Pages | : |
Release | : 2019 |
Genre | : |
ISBN | : |
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This article aims at testing empirically the major building blocks that affect the performance of distressed securities hedge funds: incentive fees, management fees, size, age, hurdle rate, high watermark provision and lockup period. Distressed securities are related to the corporate bonds of bankrupted companies that start to get out from the crisis and are trying to reduce their loan exposures in the market. It could be also trading in distressed companies that are going to bankrupt and their debt value are reduced. The corporate bonds and shares are traded in discounted values. Their price is low and the investors' anticipate a change in the management in terms of paying the debtors obligations. The risk that the hedge manager is exposed is that the company could increase its liabilities instead its assets and finally bankrupt. The stocks and bonds have no monetary value and the fund will display a negative NAV return. Therefore, the investors will face losses. Hedge funds or institutional investors buy distressed securities to better access the liquidity and the effort of the companies trying to get out from bankruptcy. Hedge fund mangers buy a high yield corporate bond from a company undergoing re-structuring. Hedge funds perform better when the economy is in recession and the companies are distressed. They examine the equity to debt ratio and accordingly they decide whether it is favourable to buy them at a discounted debt value and sell them at a higher price. The sample is provided from Data Feeder dataset. It is very comprehensive and includes distressed securities 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.
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