Hedge fund risk transparency : unravelling the complex and controversial debate
Bibliographic record
Abstract
CONTENTS Section I: Introduction What is Risk? Types of Transparency/Translucency Measurement Reporting and Visualization Hedge Fund Systems Section II: Institutional Overview of institutional investing in hedge funds Pension plan sponsors hedge fund investing including perspectives on transparency from the following distinguished practitioners: William Cook, Aegon USA Investment Management Mark Anson, CalPERS Pierre Jette, CDP Capital Paul Platkin, General Motors Pension Plan Ron Mock, Ontario Teachers Pension Plan Endowments and foundations hedge fund investing including perspectives on transparency from the following distinguished practitioners: Jay Yoder, Smith College Matthew Stone, The University of Chicago Mark Yusko, The University of North Carolina Chapel Hill Director of Investment Strategies at the endowment of a large university Section III: Funds of Funds Section III covers the role of funds of funds and the unique risk issues of fund of fund investing. It also includes the perspectives of following eminent fund of funds managers on transparency by their underlying hedge funds and in turn by funds of funds to their institutional investors: Bruce Lipnick, Asset Alliance Barry Seeman, AXA Richard Bookbinder, Bookbinder Capital Jack Heidt, Heidt Capital John Trammell, Investor Select Advisors Kelsey Biggers, K2 Advisors Sean McGould, Lighthouse Partners Jean Karoubi, LongChamp Group Jeff Chicoine, Mesirow Alternatives Tom Strauss, Ramius Capital Pierre-Yves Moix and Stefan Scholz, RMF Investment Products Section IV: Hedge Funds The overview includes: an overview of hedge funds, a comparison of hedge fund chapter sources, a comparison of hedge fund indices and a historical performance of hedge funds by style. It also includes perspectives on transparency from the following distinguished practitioners: Andrew Pernambuco, Alexandra Investment Management Michael Rulle, Graham Capital Management Bill McCauley, III Lee Ainslie, Maverick Capital Myron Scholes, Oak Hill Capital Mike Linn, Omega Partners Andrew Weisman, Strativarious Convertible Arbitrage Emerging Markets Equity Long/Short Event-Driven including distressed securities and merger arbitrage Fixed Income Global Macro Managed Futures Market Neutral Short Biased Chapters two through ten in this section each include: A description of the strategy A list of largest players A comparison of coverage of funds within strategy by hedge fund data sources An analysis of indices and their components Historical performance by return graphics, aum quartiles, source, leverage, sharpe ratio, etc. An identification of the key risks of the strategy A discussion of the applicably of VAR to the strategy Key due diligence questions for funds in the strategy Publicly disclosed problems that have impacted the strategy. Section V: Appendices Appendix 1: The findings of the Investor Committee (IRC) on hedge fund risk transparency. Appendix 2: Sound Practices for Hedge Fund released by Caxton Corporation, Kingdon Capital Management, LLC, Moore Capital Management, Inc., Soros Fund Management, LLC and Tudor Investment Corporation. Appendix 3: Due Diligence by Jon Lukomnik of Capital Market Advisors, Inc. (CMRA) from A Guide to Fund of Hedge Funds Management and Investment published by the Alternative Investment Management Association Limited (AIMA), October 2002. This article puts forward due diligence suggestions that expand the work of the Alternative Investment Managers Association (AIMA) to include with a risk focus. Appendix 4: A CMRA-enhanced version of the AIMA due diligence review guidelines for hedge fund managers. Appendix 5: Risk Standards for Institutional Investment Managers and Institutional Investors prepared in 1996 by The Standards Working Group (comprised of eleven plan sponsors) with technical assistance from CMRA. Appendix 6: A detailed comparison of proprietary buy-side risk systems. Appendix 7: Glossary. Appendix 8: Bibliography.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".