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Market-Timing and Agency Costs: Evidence from Private Equity
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The 57 checked references that resolve
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no DOI — not checkedLeveraged buyouts and private equity
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no DOI — not checkedand Robustness In this section I provide additional details about the simulations-based estimation method that I deploy in Section 5.5.2 to refine the estimates of the key coefficient of interest that measures the sensitivity of subsequent Industry Returns to Informed Rush. The method involves three steps. First, I estimate models of expected ln(stopping ? time) and ? ?1 (Rush) for all funds in our sample as linear functions of: (i) Vintage-Industry fixed effects; (ii) Fund size, PME-to-date, IRR-rank-to-date; (iii) GPs follow-on fund start dates and investments activity where available. 77 We treat the two equations as Seemingly Unrelated Regressions as per Zellner
no DOI — not checkedAlthough consistency of the third step will not depend on whether the distribution of actual stopping-times and Rush are close to the simulated ones, it is useful to examine this question as it may affect inference. Figure B.1 reports comparisons of univariate distributions and bivariate relations of actual stopping-times and Rush (Actual Funds) vis-a-vis those of placebo exits (Simulated Funds) for a simulated sample. It appears that simulated bivariate distributions tend to have more weight in tails which is unlikely to bias-down the parameter variance 77 The sample industry-vintage universe is rather sparse before 1990 (relatively few funds to begin with) and post 2003 (as relatively few funds reach the stopping-time threshold)
no DOI — not checkedIn this section, I will focus on the funds that as of the actual stopping-time had positive timing track record (T T R > 1, Section 3) and IRR > HurdleRate. Specifically, I will examine statistical properties of the main coefficient of interest, ?. To insure that ? estimates are robust to the simulation starting point (seed value) and yet to keep the procedure computationally attractive, I repeat the second and third steps 1,000 times. Each time I randomly choose simulation seeds for shocks and the covariance matrix draws which also alleviates the autocorrelation problem in pseudo-random number generators. Hence, I obtain independent estimates of Model (2) over 1,000 samples of identical data for actual funds augmented with different simulated pseudo exits (henceforth independent simulation). The estimates (confidence intervals) for ? that I report in Tables 6 and 7 in the main text and in Figures B.2 and B.3A are (based on) equally weighted means of ? s (avar(?) s ) over these 1,000 independent simulation. 82 In essence, I run Fama-Macbeth (1973) procedure which is asymptotically equivalent and typically as efficient as panel LeastSquares methods
no DOI — not checkedThe estimates are virtually unchanged across all cases in both panels. Hence, the results are not driven by a few calendar clusters. Next, in Figure B.3 Panel A I examine how the predictability changes when I assign non-native Industry returns. For each month I compute 5-year rolling pairwise correlations for 10 GICS sectors portfolios so that for each fundmonth the Industry portfolios are ranked by correlation proximity to the native-industry (Case 1). Clearly, if the effect I estimate has to do with GPs' expertise, the strongest predictability should be with respect to the native industry
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