CAIA L2 - 8.2 - Selection of a Fund Manager Flashcards
Describe
the three phases of the
GP-LP life cycle
(aspects of the GP-LP relationship)
8.2 - Selection of a Fund Manager
- Entry and establish. GP raises the initial funds. (no track record, high failure rate,
- Build and harvest. The growth and profitability phase.
- Decline, exit, or transition. When competition ends, exit occurs, or the fund is sold to other GPs (either the fund was successful or not). (no feel to work hard, loss of key namagement)
Table:
* Fund Characteristic
Entry and Establish => Build and Harvest => Decline, Exit, or Transition
* Management team
Forming => Succeeding => Succession plans and potential spinouts
* Investment strategy
Differentiation => Star brand => Unexciting
* Size
Too small => Optimal size => Too large/too many funds
* Fundraising
Challenging => Easier due to loyal LP base => LPs depart; substituted with other investors (new entrants, secondary plays)
* Performance
Uncertain => Probably a top performer => Consistent (but not top) performer
Economies of scale
Lacks size to be successful => Ideal alignment of interests => GPs were successful
8.2 - Selection of a Fund Manager
Define
Gatekeepers
(in a relation GP-market)
8.2 - Selection of a Fund Manager
skilled intermediaries
such as
* consultants,
* placement agents, and
* fund of funds,
who represent their investing clients.
They offer
* benchmarking services,
* reviews of GP performance, and
* potential access to the best GPs
8.2 - Selection of a Fund Manager
Explain
Transition Matrix
(in Private Equity
performance persistence)
8.2 - Selection of a Fund Manager
Matrix 4x4
* lines: 4 quartiles of past funds
* rows: 4 quartiles of follow on fund
Used to verify performance persistence of funds of same manager
Venture Capital:
Performance persistence was consistent (48.5% chance of repeating top-quartile performance with the next fund / bottom-quartile fund had a 38.3% chance of being followed by another bottom-quartile fund) => way above 25% “fair”
‘–
Buyout 1984-2000:
Performance persistence was consistent (37.5% chance of repeating top-quartile performance with the next fund)=> way above 25% “fair”
‘–
Buyout 2001-2011:
Performance persistence was consistent (22% chance of repeating top-quartile performance with the next fund) => near 25% “fair”
8.2 - Selection of a Fund Manager
Describe
6 significant challenges
of performance persistence hypothesis
(as used in transition matrices
8.2 - Selection of a Fund Manager
The performance persistence hypothesis has six significant challenges:
- Unclear definition of top performance. GPs have a natural incentive to quantify past performance in whatever way will allow them to present themselves to LPs as top-quartile performers. Although top quartile means top 25%, the issue is how to define the population. Does the top quartile measure multiples, internal rates of return, interim or final returns, gross returns, or net returns? Is the population all of PE, or a specific strategy only (e.g., buyout funds)? Presentation could be misleading if a fund is in the top quartile of gross returns and in the second quartile of net returns, but markets itself as a top-quartile performer.
- Comparing dissimilar funds. Ideally, funds that are measured against each other should face identical market conditions and be part of the same vintage-year cohort for the entire peer group. In addition to the peer group changing whenever the vintage year changes, different fundraising timelines also make it difficult to truly compare funds. Comparing performance through assessing GP skills implies that the peer group has the same beginning point and the same group of funds, which is often not the case.
- Skill versus luck. A new fund begins approximately every six years. Therefore, over an entire career, a GP may only manage four or five funds all the way through to liquidation with complete performance data. That relatively small sample size makes it difficult to reliably ascertain performance. Using that example, a potential investor would have a maximum of three or four previous funds with which to assess a GP, which is a very small sample (and it assumes that the GP did not launch any follow-on funds).
- Fund size growth. Up to a point, studies imply that larger funds outperform smaller funds. Because a manager’s initial fund tends to be small, the improved performance associated with follow-on funds may be the result of increased fund size rather than improved skill.
- Market trends. When all fund managers generate very strong returns driven by exceptional sector returns, a positive trend in that sector will play a larger role in fund performance than individual skill.
- Variable performance dispersions. Performance dispersion between managers could vary. For example, a manager subject to high dispersion could have an equal chance of being in the top or bottom quartiles, whereas a manager subject to low dispersion could have an equal chance of being in the middle two quartiles.
Performance Presentation Implementation Issues
“by the time investors see evidence that a fund is doing well, it is too often late to take advantage of the opportunity”
8.2 - Selection of a Fund Manager
Define
Adverse selection
within funds
(LP-GP relationship)
8.2 - Selection of a Fund Manager
Occurs before LP invested in GP
deceit example
‘–
when unproven GPs or with a bad track record seek out inexperienced LPs,
or conversely
when inexperienced LPs are forced to form relationships with unproven GPs
—thereby increasing the likelihood of underperformance and subsequent departure from the market.
—
Example:
when an LP looks for a low-fee fund
and ends up finding a poor-performing GP
who claims to be a strong performer
8.2 - Selection of a Fund Manager
Define
Moral hazard
(LP-GP relationship)
8.2 - Selection of a Fund Manager
Occurs after LP invested in GP
unethical behaviour
when incentives cause a change in behavior
—
Example:
GP risks too much to earn higher incentive payment
Or
GP risks too little to earn a (high) fixed management fee
8.2 - Selection of a Fund Manager
Define
Holdup problem
(LP-GP relationship)
8.2 - Selection of a Fund Manager
when two parties will not cooperate with each other
out of fear that by doing so, it will provide more bargaining power to the other party.
The result would be lower profits for the cooperating party
opportunism example
8.2 - Selection of a Fund Manager
Identify
How to screen
Fund Management (fund screening process)
regarding:
* nature of a fund’s investment program
* investment objective of PE funds
* investment process of PE funds
* value added by the fund manager of PE Funds
8.2 - Selection of a Fund Manager
nature of a fund’s investment program = objective + process + value added
the fund’s investment objective
1. the markets and instruments in which the manager invests,
2. the general investment strategy, and
3. the benchmark
the manager’s investment process
1. creating,
2. implementing, and
3. monitoring investment decisions
the nature and source of value added by the manager
1. identifying mispriced assets,
2. generating higher risk premiums (e.g., illiquidity), and
3. employing tax minimization/deferral strategies.
2 forms of value added:
* Information gathering refers to a manager’s skill in obtaining greater depth and/or breadth of reliable information or formulating outstanding information sets (that gives the fund a competitive advantage) as opposed to skill in data analysis.
* Information filtering refers to a manager’s ability to use data that is widely available publicly and use it to obtain valuable and actionable insights that others cannot. Those insights can then help generate more value for the fund. An example of information filtering would be the development of superior algorithms.
8.2 - Selection of a Fund Manager
List
5 issues
to consider when assessing the
reliance on past data
to predict future returns
8.2 - Selection of a Fund Manager
4 for past data: Accuracy, Representativeness, Gaming, Appropriateness (Performance in line with the fund’s strategy?)
1 for Future: Stationarity
-
Accuracy.
When measuring accuracy, it is important to determine if any misrepresentation of performance is deliberate or accidental. The expectation bias may cause managers to accept and give more emphasis on conclusions that are congruent with their previous beliefs. This could lead to incorrect reporting of data, especially if it improves the reported performance. -
Representativeness.
Data provided represents the whole fund?
Investors should assess whether reported fund data makes sense and gives a complete sense of the overall fund data. Alternatively, has the manager cherry picked specific reporting periods or exhibited other selection bias to present more favorable results? -
Stationarity.
Will the manager be able to replicate past returns in the future?
Increased competition could significantly diminish future profitability in efficient markets. -
Gaming.
Is there shifts in performance (that increases sharpe, for ex?)
Gaming can significantly distort performance figures. Gaming refers to fund investment activity that prioritizes improving reported performance over benefitting investors. For example, a manager could shift profits from a profitable period to an unprofitable period to reduce return volatility. The reduced volatility increases the fund’s Sharpe ratio, even though actual return has not changed. Studies have revealed that a significant percentage of respondents believe hedge fund managers engage in misleading performance presentation by exercising through highly arbitrary security valuations to smooth returns. -
Appropriateness.
Performance in line with the fund’s strategy?
Performance measures should make sense given the fund’s investment strategy. Sharpe ratios, for example, may not be appropriate for nonlinear or nonnormal options strategies. In addition, autocorrelations and correlations of prior periods may not be the same for the future. Stressed markets tend to have higher correlations, so the omission of stressed markets in an analysis will understate risk estimates for the future.
8.2 - Selection of a Fund Manager
Calculate
Annualized volatility
f(daily vol); f(monthly vol)
considering
* no autocorrelation
* autocorrelation is 100% (perfect positive autocorrelation)
8.2 - Selection of a Fund Manager
if daily vol = 1%
* no autocorrelation: annual vol = 1% x sqrt(252) = 15.9%
* perfect positive autocorrelation = 1% x (252) = 252%
if monthly vol = 10%
* no autocorrelation: annual vol = 10% x sqrt(12) = 34.6%
* perfect positive autocorrelation = 10% x (12) = 120%
8.2 - Selection of a Fund Manager
List
4 classifications of
Management Teams
8.2 - Selection of a Fund Manager
The ranking results in a series of management classifications:
- Blue-chip management team. To qualify, all of the funds (4+) (four or more sequential funds) must have performed in the top quartile over two or more business cycles.
- Established management team. To qualify, most of the funds (4+) (four or more funds) must have performed in the top quartile over two or more business cycles.
- Emerging management team. The classification is reserved for newer teams that have the potential to become classified as established.
- Reemerging management team. The team was formerly classified as blue chip or established but has been restructured following underperformance or a significant operation issue and has the potential to become blue chip or established again.
8.2 - Selection of a Fund Manager
List
Steps
in the PE Fund Manager
Selection Process
8.2 - Selection of a Fund Manager
- Wish List
- Classify Management Teams
- Deal Sourcing
- Due Diligence (Screening, Meet manager, Evaluation, Final Due Diligente)
- Decision
- Commitment
8.2 - Selection of a Fund Manager
