Practical Applications of Private Equity Valuations and Public Equity Performance
Bibliographic record
Abstract
Practical Applications Summary In Private Equity Valuations and Public Equity Performance from the Summer 2019 issue of The Journal of Alternative Investments, authors Megan Czasonis (of State Street Associates), Mark Kritzman (of Windham Capital Management and the MIT Sloan School of Management), and David Turkington (also of State Street Associates) demonstrate that private equity (PE) managers introduce positive bias into their quarterly investment valuations. Managers tend to overprice their shares by overstating how well their investments performed during the quarter. These optimistically high valuations are induced by public market gains that happen after quarter end; PE managers raise their share valuations when the public equity market goes up during the reporting delay after quarter end—but they do not lower valuations if the market declines. This uneven response to market gains and losses after quarter-end means that valuations are often unrealistically high. The underlying driver confirmation bias, the tendency of managers to only cite evidence that shows their investments did well. But since managers tend not to do this in the fourth quarter, when investment valuations are independently audited, PE funds appear to gain more in Q1 through Q3 than in Q4. This introduces artificial volatility in performance over the year and has serious implications for investors and advisors. TOPICS:Private equity, security analysis and valuation, performance measurement
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".