Second investment course november 2005
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Second Investment Course – November 2005. Topic Seven: Investment “Tournaments” & Manager Compensation. Some Background.

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Second Investment Course – November 2005

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Second investment course november 2005

Second Investment Course – November 2005

Topic Seven:

Investment “Tournaments” &

Manager Compensation


Some background

Some Background

  • Past studies (e.g., Goetzmann, Greenwald, and Huberman (1992)) have shown that mutual fund investors focus primarily on published rankings of relative performance when making their investment decisions.

  • Other studies (e.g., Sirri and Tufano (1992)) have shown that these allocation decisions are asymmetric in that funds with good relative performance experience net cash inflows while those with poor relative performance do not experience significant outflows.

  • From these facts we suggest that the mutual fund industry can be viewed as a tournament in which all funds with a similar objective compete with one another during the year.


The research premise

The Research Premise

  • From these facts we suggest that the mutual fund industry can be viewed as a tournament in which all funds with a similar objective compete with one another during the year.

  • This tournament structure, where cash flows into the funds and, ultimately, the manager’s compensation depends on relative performance, can provide incentives for managers to alter the investment characteristics of their portfolios.

  • Specifically, managers of those funds most likely to be “losers” at the end of the tournament will have the incentive to increase the risk of their portfolios more than those managing funds likely to be “winners”.

  • The study titled “Of Tournaments and Temptations: An Analysis of Managerial Incentives in the Mutual Fund Industry,” (by K. Brown, V. Harlow, and L. Starks) was published in Journal of Finance in 1996.


The economics of tournaments

The Economics of Tournaments

  • Many compensation and reward structures can be viewed as tournaments

  • Tournaments are most appropriate in situations where an agent’s effort it not observable and performance of all agents depend on a common economic “shock”

    - Relative performance measures help separate the agent’s contribution from that due to the state of nature

  • Tournament structures can outperform other reward schemes in mitigating moral hazards

    - Conditions for this include risk averse participants, a common shock component and a large number of agents

  • Little empirical evidence exists on how tournaments are organized and how they operate


The central hypothesis

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  • 0

  • 1

  • 0

The Central Hypothesis

  • Central Hypothesis:

    Interim Loser ( / ) > Interim Winner ( / )

    where is a risk measure for the first part of the tournament;

    , the last part of the tournament

  • Secondary Hypothesis:

    Fund characteristics will affect the incentives and the ability to increase risk.

    -- Size

    -- Age

    -- Marketing channel (load / no-load)

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  • 1


Data for the study

Data for the Study

  • Monthly returns for 334 growth-oriented equity mutual funds from data base maintained by Morningstar for the period 1976 to 1991

  • A fund is only included if it has return data for the entire year

  • We also updated the sample through 1996, which includes 478 funds


Growth oriented equity mutual funds 1976 1991

Growth-Oriented Equity Mutual Funds, 1976-1991

Number of Funds


Growth oriented equity mutual funds 1976 19911

Growth-Oriented Equity Mutual Funds, 1976-1991

Assets


Methodology

0

M

12

Post-Assessment

Period

Pre-Assessment

Period

RARjy = ( ) ( )

  • (12-M)

  • M

Methodology

  • For a particular year (i.e., tournament) consider the following assessment periods:

    where month “M” is the interim assessment month

  • Calculate the interim cumulative return (RTN) for the j-th fund as follows:

  • Calculate the Risk Adjustment Ratio (RAR) for each fund as follows:

    where and are the respective variances computed in the

    pre- and post-assessment periods

RTNjMy = [(1 + rjly)(1 + rj2y)] .... (1 + rjMy)] - 1

  • M

  • (12-M)


Methodology cont

Methodology (cont.)

  • For each year, rank fund sample from highest to lowest by RTN variable. Classify interim “winners” and “losers” by whether they are above or below median value, respectively.

  • For interim winner and loser funds, classify again according to whether RAR is above or below its median value.

  • These classifications lead to a 2 x 2 contingency table: (i) interim winners and losers; and (ii) high or low volatility ratios.


Methodology cont1

Methodology (cont.)

Advantages of Tournament Approach

  • No requirements to specify an appropriate benchmark portfolio

  • Market-timing assessment problems do not arise

  • Mean-variance efficiency of a benchmark is not an issue

  • Survivorship bias is not a problem (works against the central hypothesis)


Developing the risk change hypotheses

Developing the Risk Change Hypotheses

  • Null Hypothesis:

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

25.0 %

25.0 %

Interim

Winner

25.0 %

25.0 %


Developing the risk change hypotheses cont

Developing the Risk Change Hypotheses (cont.)

  • Predicted Alternative Hypothesis:

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

<25.0 %

>25.0 %

Interim

Winner

<25.0 %

>25.0 %


Risk change results

Risk Change Results

1980 - 1991 (2,484 observations)

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

27.7 %

22.2 %

Interim

Winner

22.4 %

27.7 %

(p-value 0.000)


Risk change results cont

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

29.7 %

20.2 %

Interim

Winner

20.4 %

29.7 %

(p-value 0.000)

Risk Change Results (cont.)

1986 - 1991 (1,633 observations)


Risk change results cont1

Risk Change Results (cont.)

1989 - 1991 (932 observations)

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

31.2 %

18.8 %

Interim

Winner

18.8 %

31.2 %

(p-value 0.000)


Developing the secondary hypotheses

Developing the Secondary Hypotheses

  • Extreme winners and losers

    - Classify by upper and lower quartiles of RTN

  • Window dressing effects

    - Analysis with and without December returns

  • “New” and “entrenched” funds

  • Small and large funds

  • Load and no-load funds

  • Influence of cumulative performance

    - Multi-period tournaments


Extreme winners and losers 1980 1991

High Risk

Ratio

Low Risk

Ratio

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

Interim

Loser

27.7 %

22.2 %

28.3 %

21.6 %

Interim

Winner

Interim

Winner

22.4 %

27.7 %

22.2 %

27.9 %

(p-value 0.000)

(p-value 0.000)

Extreme Winners and Losers (1980-1991)

Base Case (Median Ranks)

Extreme Upper and Lower Quartiles


Window dressing effects 1980 1991

Window Dressing Effects (1980-1991)

Without December Returns

With December Returns

High Risk

Ratio

Low Risk

Ratio

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

Interim

Loser

27.7 %

22.2 %

27.8 %

22.1 %

Interim

Winner

Interim

Winner

22.4 %

27.7 %

22.4 %

27.7 %

(p-value 0.000)

(p-value 0.000)


New and entrenched funds 1980 1991

“New” and “Entrenched” Funds (1980-1991)

New Funds

Entrenched Funds

High Risk

Ratio

Low Risk

Ratio

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

Interim

Loser

29.9 %

20.7 %

25.8 %

23.6 %

Interim

Winner

Interim

Winner

20.9 %

28.5 %

23.7 %

26.9 %

(p-value 0.000)

(p-value 0.000)


Small and large funds 1980 1991

Small and Large Funds (1980-1991)

Small Funds

Large Funds

High Risk

Ratio

Low Risk

Ratio

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

Interim

Loser

31.3 %

24.7 %

24.8 %

20.3 %

Interim

Winner

Interim

Winner

18.8 %

25.2 %

25.3 %

29.6 %

(p-value 0.000)

(p-value 0.000)


Load and no load funds

Load and No-Load Funds

  • We would expect no-load funds to be more sensitive to performance rankings

  • Simple tests indicate a significant tendency for no-load losers to increase portfolio risk in second part of year

  • However, no-load funds tend to be new funds

  • Controlling for other characteristics, no significant differences found between load and no-load funds


Influence of cumulative performance

Influence of Cumulative Performance

  • Current and past-year performance important in explaining new fund inflows (Sirri and Tufano (1992))

  • Viewed as a multi-period game, cumulative performance may be important in influencing portfolio risk changes

    - Three-year relative performance

    - Five-year relative performance


Influence of cumulative performance 1980 1991

Influence of Cumulative Performance (1980-1991)

Base Case (1 Year Ranking)

1 and 3 Year Rankings

High Risk

Ratio

Low Risk

Ratio

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

Interim

Loser

27.7%

22.2%

29.1%

22.7%

Interim

Winner

Interim

Winner

22.4%

27.7%

24.9%

23.4%


Influence of cumulative performance 1980 19911

Influence of Cumulative Performance (1980-1991)

Base Case (1 Year Ranking)

1,3 and 5 Year Rankings

High Risk

Ratio

Low Risk

Ratio

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

Interim

Loser

27.7%

22.2%

31.1%

22.5%

Interim

Winner

Interim

Winner

22.4%

27.7%

26.0%

20.6%


Influence of cumulative performance 1980 19912

Influence of Cumulative Performance (1980-1991)

  • The relationship between risk adjustments and past performance was investigated using logistic regressions

  • Results indicate that cumulative past performance is almost as important as performance in the current tournament


Important question 1

Important Question #1

  • Active decision

  • Passive -- loser portfolios are inherently riskier

How Do Managers Alter Portfolio Risk?


How do managers alter portfolio risk

How Do Managers Alter Portfolio Risk?

  • Through simulation experiments, we assess whether the results could be caused by the increase in risk occurring at the asset level rather than by the portfolio managers’ decisions.

  • Control portfolio samples

    - 250 simulated portfolios from CRSP database

    - 75-stock and 150-stock portfolios

    - Different periods within the 1980-1991 interval

  • Results strongly support active decision

    - All tests significant at the 0.001 probability level


Important question 2

Important Question #2

  • Traditional objective classification categories such as ‘growth” are not necessarily accurate indicators of fund style or future performance

  • All funds in the “growth” category may not be viewed by investors as being within the same performance tournament

Could Investment Objective Misclassification Cause Spurious Results?


Objective misclassification

Objective Misclassification

  • In order to assess the potential effects of objective misclassification, volatility-based subtournaments were investigated

  • Subgroups formed within the sample based on:

    - Systematic risk (beta)

    - Total risk (volatility)

  • Results suggest misclassification is not a source of the differences between winner and loser portfolios

    - All tests significant at the 0.021 level or better


Important question 3

Important Question #3

  • Risk change versus rank change

  • Strategic response by interim winners

Are Interim Losers Able to Change Their Ultimate Tournament Standing?


Terminal return hypotheses

Terminal Return Hypotheses

  • Null Hypothesis:

Terminal Loser

Terminal Winner

50.0 %

(less random error)

0.0 %

(plus random error)

Interim

Loser

Interim

Winner

0.0 %

(plus random error)

50.0 %

(less random error)


Terminal return hypotheses1

Terminal Loser

Terminal Winner

< 50.0 %

(less random error)

> 0.0 %

(plus random error)

Interim

Loser

Interim

Winner

> 0.0 %

(plus random error)

< 50.0 %

(less random error)

Terminal Return Hypotheses

  • Predicted Alternative Hypothesis:


Terminal return results

High Risk

Ratio

Low Risk

Ratio

Interim

Loser

41.0 %

9.0 %

Interim

Winner

9.0 %

41.0 %

Terminal Return Results

  • Average Spearman Rank Correlation coefficient between the interim and terminal rankings for the twelve annual tournaments was 0.81.

  • Chi-squared tests against the null hypothesis for all tournaments were statistically significant.

  • Logistic regression of the form (Rank Change) = f(RAR) had positive coefficient on RAR and significant at 0.001 level.

  • Typical contingency table:


Extensions

Extensions

  • Post - 1991 data

  • Other tests


Growth oriented equity mutual funds 1979 1996

1 Year Ranking

3 Year Ranking

5 Year Ranking

Growth - Oriented Equity Mutual Funds, 1979-1996

Interim Losers Which Increase Risk

Null

Hypothesis


Conclusions

Conclusions

  • Interim losers alter the volatility of their funds during the latter part of a year to a significantly greater extent than do interim winners.

  • This effect became significantly stronger during the last half of the 1980 - 1991 sample period when the number of new funds in the industry increased dramatically.

  • This tendency existed for all funds but was somewhat more pronounced for newer funds and for smaller funds.

  • Cumulative performance has almost as large an impact on the risk decision as does the interim return in the current tournament.

  • Analysis of a simulated set of unmanaged stock portfolios confirm that the observed risk changes were due to explicit managerial actions.

  • The difference in the interim, post-assessment period, and final annual rankings suggest that the mid-year volatility adjustments on the part of the interim losers did, in part, have the desired effect of increasing their rankings.


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