Hedge fund survival analysis

Hedge funds’ (HF) assets under management (AUM) expanded at more than 20% per annum between 2000 and the mid-2008 peak of USD1.85 trillion. The 2008 Global Financial Crisis (GFC) led to a drastic industry contraction with an estimated 2,000 fund liquidations. Nonetheless, the industry’s AUM is expected to reach new highs by 2011. Clearly, this asset class continues to grow at a remarkable pace, despite being marked by both high attrition rates and — as the industry’s recent experience made clear — dramatic changes over time. Given the continued growth and the important role of hedge funds, tools that facilitate the understanding of their survival probabilities are of general interest and would specifically benefit investors, service providers, managers and regulators.We use data on individual hedge funds and a range of models to analyse the association between fund specific and market variables with two types of database attrition, liquidation and non-liquidation exits. First, we explore the drivers of not only HF liquidation but also non-liquidation exits. Understanding the latter exit type is of interest to institutional investors who prefer to regularly allocate capital across their HF portfolio. Secondly, we use flexible econometric techniques that can, and indeed do, identify inflection points and turning points between the two database exit types and both fund specific variables and market ones. Liquidation risk is shown to increase immediately and drastically as HF returns move to negative territory and surprisingly, liquidation risk increases at the right tail of the return distribution and decreases at the far left. Finally, we explore the dynamics of two time scales, the lifetime scale and the calendar time scale. Importantly, we find the lifetime analysis requires the flexibility of fractional polynomial models as the time interactions of many variables were non-monotonic. For example, while positive returns remain negatively related to liquidation risk, they have the least impact during the time period of peak liquidation risk, roughly at the 5.5 year age mark. Similarly, we show that the impact of variables are dynamic over the 14 year sample time period, and in particular during a number of the structural breaks, the relationship between specific market related variables and HF liquidation risk reverses