finding fortune: how do institutional investors pick...

Finding Fortune: How Do Institutional Investors Pick AssetManagers?

Gregory Brown1, Oleg Gredil2, and Preetesh Kantak1

1UNC Kenan-Flagler Business School2Tulane A.B. Freeman Business School

November 19, 2016

Motivation and Literature Univariate Analysis Main Results Agenda and Conclusion

Research Question

How do asset allocators combine “limited” public and “expensive” privateinformation on delegated managers when choosing with whom to invest?

Asset allocators: endowment, SwF,foundation, insurance and pensions.Managers: explosive AUM growth of“specialist” vs. traditional funds.Limited info.: “bespoke” - not welldefined - managers.Costly info.: manager choice aided byin-house, external consultants.


Research Question


26.640.3

10.7

23.6

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

2004 2013

Delegated AUM

Trillions of U

SD

Asset allocators: endowment, SwF,foundation, insurance and pensions.

Managers: explosive AUM growth of“specialist” vs. traditional funds.Limited info.: “bespoke” - not welldefined - managers.Costly info.: manager choice aided byin-house, external consultants.


Research Question


26.640.3

10.7

23.6

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

2004 2013

Delegated AUM

Trillions of U

SD

Asset allocators: endowment, SwF,foundation, insurance and pensions.Managers: explosive AUM growth of“specialist” vs. traditional funds.

Limited info.: “bespoke” - not welldefined - managers.Costly info.: manager choice aided byin-house, external consultants.


Research Question


26.640.3

10.7

23.6

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

2004 2013

Delegated AUM

Trillions of U

SD

Asset allocators: endowment, SwF,foundation, insurance and pensions.Managers: explosive AUM growth of“specialist” vs. traditional funds.Limited info.: “bespoke” - not welldefined - managers.

Costly info.: manager choice aided byin-house, external consultants.


Research Question


26.640.3

10.7

23.6

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

2004 2013

Delegated AUM

Trillions of U

SD

Asset allocators: endowment, SwF,foundation, insurance and pensions.Managers: explosive AUM growth of“specialist” vs. traditional funds.Limited info.: “bespoke” - not welldefined - managers.Costly info.: manager choice aided byin-house, external consultants.


Data Source

Large institutional investor in long-short hedge funds who maintains...

1 timeline of meetings with funds,

2 depository of pitch-books and notes from meetings,3 dataset of AUM and returns from funds met, and4 flag of funds “admitted” for investment.

Research Findings:

1σ ↑ private information proxy doubles probability of fund admissionand reduces due-diligence time by 20%.“Subjective” judgements not wasteful - 12m peer-adjusted averagereturns is 1.5% higher for admitted funds.


Data Source


1 timeline of meetings with funds,2 depository of pitch-books and notes from meetings,

3 dataset of AUM and returns from funds met, and4 flag of funds “admitted” for investment.

Research Findings:



Data Source


1 timeline of meetings with funds,2 depository of pitch-books and notes from meetings,3 dataset of AUM and returns from funds met, and

4 flag of funds “admitted” for investment.

Research Findings:



Data Source


1 timeline of meetings with funds,2 depository of pitch-books and notes from meetings,3 dataset of AUM and returns from funds met, and4 flag of funds “admitted” for investment.

Research Findings:



Data Source



Research Findings:1σ ↑ private information proxy doubles probability of fund admissionand reduces due-diligence time by 20%.

“Subjective” judgements not wasteful - 12m peer-adjusted averagereturns is 1.5% higher for admitted funds.


Data Source



Research Findings:1σ ↑ private information proxy doubles probability of fund admissionand reduces due-diligence time by 20%.“Subjective” judgements not wasteful - 12m peer-adjusted averagereturns is 1.5% higher for admitted funds.


Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Et(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted

Allocator has a prior of funds’ abilities, α0 ∼ N(µ, 1

τ0

).

Bayesian-normal updated - now with info from meetings, mt, to t.

Etαt+1 =mtSs+tHtht+µτ0

mts+th+τ0

Etτt+1 = mts+ th+ τ0,

Threshold: tradeoff benefits of greater precision given current α andτ estimate vs. cost of additional due-diligence.


Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Et(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted


τ0

).

E.g. µ is universe’s average excess returns, rext+1 = rt+1 − rpeert+1 .



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Et(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted


τ0

).


Assume Bayesian-normal updating - Ht and ht are realized returnsand precision, inception (t=0) to t.



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Et(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted


τ0

).



Etαt+1 =tHtht+µτ0th+τ0



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Et(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted


τ0

).



Etαt+1 =tHtht+µτ0th+τ0

Etτt+1 = th+ τ0,



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Et(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted

Monthly decision thresholds.



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Research Zone

Reject Zone

Admit ZoneE

t(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted

Monthly decision thresholds.Average annual rex of admitted funds is 6.6%.



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Research Zone

Reject Zone

Admit ZoneE

t(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted

Monthly decision thresholds.Average annual rex of admitted funds is 6.6%.Later we show why thresholds are actually defined endogenously.



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Research Zone

Reject Zone

Admit ZoneE

t(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted

Allocator has option to have costly meetings with fund.Practitioners estimate costs between $50k-$100k per manager.



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Research Zone

Reject Zone

Admit ZoneE

t(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted

Allocator has option to have costly meetings with fund.Practitioners estimate costs between $50k-$100k per manager.At each meeting allocator receives a signal on ability ∼ N

(S, 1

s).



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Research Zone

Reject Zone

Admit ZoneE

t(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted

Allocator has option to have costly meetings with fund.Practitioners estimate costs between $50k-$100k per manager.At each meeting allocator receives a signal on ability ∼ N

(S, 1

s).

Signal ∼ as entire sample of returns - i.e. inception to June 2012.



mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Research Zone

Reject Zone

Admit ZoneE

t(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted

Bayesian-normal updated - now with info from meetings, mt, to t.Etαt+1 =

mtSs+tHtht+µτ0mts+th+τ0




Simple Framework

03/09 07/09 11/09 03/10 07/10 11/10

Date

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Ann

ualiz

ed A

lpha

Admitted

Research Zone

Reject Zone

Admit ZoneE

t(, | R

t, I

meet)

Et(, | R

t)

11/06 11/07 11/08 11/09 11/10 11/11

Date

Not Admitted

Research Zone

Reject Zone

Admit Zone

Bayesian-normal updated - now with info from meetings, mt, to t.Etαt+1 =

mtSs+tHtht+µτ0mts+th+τ0




Literature

Mainly regarding mutual funds, little outside...Choosing managers: Evans & Fahlenbrach (2012); Jenkinson, Jones &Martinez (2015); etc.

Skill vs. luck: Korteweg & Sorensen (2014); Berk & Van Binsbergen(2015); etc.Pooling of funds: Berk & Green (2004); Gervais & Strobl (2015); etc.

Contribution to literature:

1 Quantify effects of “private” information on timing and performanceof investment decisions.

2 Develop rigorous, tractable framework for the institutional portfoliomanagement problem.


Literature

Mainly regarding mutual funds, little outside...Choosing managers: Evans & Fahlenbrach (2012); Jenkinson, Jones &Martinez (2015); etc.Skill vs. luck: Korteweg & Sorensen (2014); Berk & Van Binsbergen(2015); etc.

Pooling of funds: Berk & Green (2004); Gervais & Strobl (2015); etc.





Literature

Mainly regarding mutual funds, little outside...Choosing managers: Evans & Fahlenbrach (2012); Jenkinson, Jones &Martinez (2015); etc.Skill vs. luck: Korteweg & Sorensen (2014); Berk & Van Binsbergen(2015); etc.Pooling of funds: Berk & Green (2004); Gervais & Strobl (2015); etc.





Literature


Contribution to literature:1 Quantify effects of “private” information on timing and performance

of investment decisions.



Literature


Contribution to literature:1 Quantify effects of “private” information on timing and performance

of investment decisions.2 Develop rigorous, tractable framework for the institutional portfolio

management problem.


Historical Returns Are Not Enough0

2040

60D

ensi

ty

-.04 -.02 0 .02 .04

1-4 Months

-.04 -.02 0 .02 .04

5-12 Months

Admitted Not Admitted

Closest matched by observables - AUM, age, past info ratio.

Matches limited to same month and strategy - e.g. EM, sector.Ranksum and KS tests rejected at 5%.



2040

60D

ensi

ty

-.04 -.02 0 .02 .04

1-4 Months

-.04 -.02 0 .02 .04

5-12 Months


Closest matched by observables - AUM, age, past info ratio.Matches limited to same month and strategy - e.g. EM, sector.

Ranksum and KS tests rejected at 5%.



2040

60D

ensi

ty

-.04 -.02 0 .02 .04

1-4 Months

-.04 -.02 0 .02 .04

5-12 Months


Closest matched by observables - AUM, age, past info ratio.Matches limited to same month and strategy - e.g. EM, sector.Ranksum and KS tests rejected at 5%.


Quantity of Soft Information0

.1.2

.3.4

.5M

eetin

gs p

er m

onth

3 6 91=First Due-Diligence Month

-9 -6 -3-1=Last Due-diligence Month

Not Admitted Admitted

First 9 months of due-diligence period.Last 9 months of due-diligence period.Occurrence of meeting - good proxy for quantity of information.



.1.2

.3.4

.5M

eetin

gs p

er m

onth




First 9 months of due-diligence period.

Last 9 months of due-diligence period.Occurrence of meeting - good proxy for quantity of information.



.1.2

.3.4

.5M

eetin

gs p

er m

onth




First 9 months of due-diligence period.Last 9 months of due-diligence period.

Occurrence of meeting - good proxy for quantity of information.



.1.2

.3.4

Mee

tings

per

mon

th




First 9 months of due-diligence period.Last 9 months of due-diligence period.Occurrence of meeting - good proxy for quantity of information.


Sentiment in Notes

[Company]: long idea FAILED twice in Phase 3. Why diff this time? Manytreatments are heavy on side effects and this one is not and has twoapplications. The efficacy data has been GOOD and should get a GOODpop from regulator approval; then will re-evaluate as he likes it for theM&A POSSIBILITIES. [IO] thinks that there is 80-85% chance of approvaland if MISS then $16 goes to $6 and if MAKE, upside is $30. The companyis financially STABLE and runs a lean but GOOD business model.

Word lists from Laughran and McDonald (2011).

Positive and negative words tend to involve discussions about valueof positions, under-

or overvalued.

Uncertain words tend to focus on concerns about fund strategy,inconsistency and viability.


Sentiment in Notes

[Company]: long idea FAILED twice in Phase 3. Why diff this time? Manytreatments are heavy on side effects and this one is not and has twoapplications. The efficacy data has been GOOD and should get a GOODpop from regulator approval; then will re-evaluate as he likes it for theM&A POSSIBILITIES. [IO] thinks that there is 80-85% chance of approvaland if MISS then $16 goes to $6 and if MAKE, upside is $30. The companyis financially STABLE and runs a lean but GOOD business model.

Word lists from Laughran and McDonald (2011).Positive and negative words tend to involve discussions about valueof positions, under-

or overvalued.Uncertain words tend to focus on concerns about fund strategy,inconsistency and viability.


Sentiment in Notes

[Company]: short idea where [IO] looked through the case studies andfound that the vaccine didn’t produce the proper anitbodies; onceproven INEFFECTIVE, the stock tanked.

Word lists from Laughran and McDonald (2011).Positive and negative words tend to involve discussions about valueof positions, under- or overvalued.

Uncertain words tend to focus on concerns about fund strategy,inconsistency and viability.


Sentiment in Notes

RISK management is done through sizing of positions and he has a will-ingness to go to CASH if need be. Why? many times these events have 80%type moves so you don’t need full EXPOSURE to move the needle for theoverall portfolio. [Manager] SEEMS to be a solid “sandbox” player in HCand has enough insights to play events.

Word lists from Laughran and McDonald (2011).Positive and negative words tend to involve discussions about valueof positions, under- or overvalued.Uncertain words tend to focus on concerns about fund strategy,inconsistency and viability.


Quality of Soft Information-.1

-.05

0.0

5.1

Not

e in

dex

1 2 31=First Meeting

-3 -2 -1-1=Last Meeting


Measure of quality - note j at time t: nij,t = posj,t + negj,t − uncj,t.

First 3 meetings of due-diligence period.Last 3 meetings of due-diligence period.



-.05

0.0

5.1

Not

e in

dex




Measure of quality - note j at time t: nij,t = posj,t + negj,t − uncj,t.First 3 meetings of due-diligence period.

Last 3 meetings of due-diligence period.



-.05

0.0

5.1

Not

e in

dex




Measure of quality - note j at time t: nij,t = posj,t + negj,t − uncj,t.First 3 meetings of due-diligence period.Last 3 meetings of due-diligence period.


Stock of Information1

23

4S

oft i

ndex




Define variable that captures stock of soft information, SIi,t:

SIi,t =

Imeeti,t + B (nii,t) at t = 1SIi,t−1 if no meeting at t,SIi,t−1 + Imeeti,t + B (nii,t) if meeting occurs at t.



23

4S

oft i

ndex





SIi,t =

Imeeti,t + B (nii,t) at t = 1

SIi,t−1 if no meeting at t,SIi,t−1 + Imeeti,t + B (nii,t) if meeting occurs at t.



23

4S

oft i

ndex





SIi,t =

Imeeti,t + B (nii,t) at t = 1SIi,t−1 if no meeting at t,

SIi,t−1 + Imeeti,t + B (nii,t) if meeting occurs at t.



23

4S

oft i

ndex





SIi,t =

Imeeti,t + B (nii,t) at t = 1SIi,t−1 if no meeting at t,SIi,t−1 + Imeeti,t + B (nii,t) if meeting occurs at t.


Multi-variate Hazard

Logistic discrete time hazard of sequential Admission process -

λ (t|Xi,t) = P (Ai = t|Ai ≥ t)

= log(

pi1− pi

)= µi,0 + µ0,t + βXi,t + f (θ; t)

Xi,t includes hard (observables) and soft (private) information.Simple parametrization of baseline hazard, f (θ; t).Can add time, strategy FE and firm time-invariant attributes.

Variables of interest -

Hard information - returns and AUM.Soft information - SIi,t, affiliated school and fund dummies.Baseline parametrization: f (θ; t) = θdurti + θcon ln ti




λ (t|Xi,t) = P (Ai = t|Ai ≥ t) = log(

pi1− pi

)= µi,0 + µ0,t + βXi,t + f (θ; t)








pi1− pi

)= µi,0 + µ0,t + βXi,t + f (θ; t)

Xi,t includes hard (observables) and soft (private) information.

Simple parametrization of baseline hazard, f (θ; t).Can add time, strategy FE and firm time-invariant attributes.







pi1− pi

)= µi,0 + µ0,t + βXi,t + f (θ; t)

Xi,t includes hard (observables) and soft (private) information.Simple parametrization of baseline hazard, f (θ; t).

Can add time, strategy FE and firm time-invariant attributes.







pi1− pi

)= µi,0 + µ0,t + βXi,t + f (θ; t)








pi1− pi

)= µi,0 + µ0,t + βXi,t + f (θ; t)


Variables of interest -Hard information - returns and AUM.

Soft information - SIi,t, affiliated school and fund dummies.Baseline parametrization: f (θ; t) = θdurti + θcon ln ti





pi1− pi

)= µi,0 + µ0,t + βXi,t + f (θ; t)


Variables of interest -Hard information - returns and AUM.Soft information - SIi,t, affiliated school and fund dummies.

Baseline parametrization: f (θ; t) = θdurti + θcon ln ti





pi1− pi

)= µi,0 + µ0,t + βXi,t + f (θ; t)


Variables of interest -Hard information - returns and AUM.Soft information - SIi,t, affiliated school and fund dummies.Baseline parametrization: f (θ; t) = θdurti + θcon ln ti


Main ResultsHard information:

Information ratio 0.00103 0.00094 0.00089 0.00063 0.00061(6.68) (6.60) (6.86) (6.02) (6.35)

Log(AUM) 0.00095 0.00082 0.00091 0.00060 0.00067(4.63) (4.39) (5.13) (3.82) (4.57)

Soft information:

Affiliated fund (D) 0.00071 0.00053(2.83) (2.26)

Affiliated college (D) 0.00079 0.00082(3.46) (3.81)

Soft index (lagged) 0.00096 0.00088(9.88) (10.67)

Due-diligence spell:

Log(Duration) 0.00073 0.00116 0.00106 0.00055 0.00050(2.93) (3.77) (3.73) (2.38) (2.32)

Duration − 0.00007 − 0.00008 − 0.00008 − 0.00007 − 0.00006(−3.90) (−4.36) (−4.26) (−4.96) (−4.92)

Market variables + + + + +Year FE + + + +Strategy FE + +

Observations 45,714 45,714 45,714 45,714 45,714Pseudo R2 0.1029 0.1223 0.1342 0.1714 0.1819LR-test p-value 0.0000 0.0000 0.0000 0.0000

Univariate Hazard



Information ratio 0.00103

0.00094 0.00089 0.00063 0.00061

(6.68)

(6.60) (6.86) (6.02) (6.35)

Log(AUM) 0.00095

0.00082 0.00091 0.00060 0.00067

(4.63)

(4.39) (5.13) (3.82) (4.57)

Soft information:




Due-diligence spell:

Log(Duration) 0.00073 0.00116 0.00106 0.00055 0.00050(2.93) (3.77) (3.73) (2.38) (2.32)

Duration − 0.00007 − 0.00008 − 0.00008 − 0.00007 − 0.00006(−3.90) (−4.36) (−4.26) (−4.96) (−4.92)



Univariate Hazard




0.00094 0.00089 0.00063 0.00061

(6.68)

(6.60) (6.86) (6.02) (6.35)

Log(AUM) 0.00095

0.00082 0.00091 0.00060 0.00067

(4.63)

(4.39) (5.13) (3.82) (4.57)

Soft information:




Due-diligence spell:Log(Duration) 0.00073

0.00116 0.00106 0.00055 0.00050

(2.93)

(3.77) (3.73) (2.38) (2.32)

Duration − 0.00007

− 0.00008 − 0.00008 − 0.00007 − 0.00006

(−3.90)

(−4.36) (−4.26) (−4.96) (−4.92)



Univariate Hazard




0.00094 0.00089 0.00063 0.00061

(6.68)

(6.60) (6.86) (6.02) (6.35)

Log(AUM) 0.00095

0.00082 0.00091 0.00060 0.00067

(4.63)

(4.39) (5.13) (3.82) (4.57)

Soft information:





0.00116 0.00106 0.00055 0.00050

(2.93)

(3.77) (3.73) (2.38) (2.32)


− 0.00008 − 0.00008 − 0.00007 − 0.00006

(−3.90)

(−4.36) (−4.26) (−4.96) (−4.92)

Market variables +

+ + + +Year FE + + + +Strategy FE + +


Univariate Hazard




0.00094 0.00089 0.00063 0.00061

(6.68)

(6.60) (6.86) (6.02) (6.35)

Log(AUM) 0.00095

0.00082 0.00091 0.00060 0.00067

(4.63)

(4.39) (5.13) (3.82) (4.57)

Soft information:





0.00116 0.00106 0.00055 0.00050

(2.93)

(3.77) (3.73) (2.38) (2.32)


− 0.00008 − 0.00008 − 0.00007 − 0.00006

(−3.90)

(−4.36) (−4.26) (−4.96) (−4.92)

Market variables +

+ + + +

Year FE

+ + + +Strategy FE + +


Univariate Hazard




0.00094 0.00089 0.00063 0.00061

(6.68)

(6.60) (6.86) (6.02) (6.35)

Log(AUM) 0.00095

0.00082 0.00091 0.00060 0.00067

(4.63)

(4.39) (5.13) (3.82) (4.57)

Soft information:





0.00116 0.00106 0.00055 0.00050

(2.93)

(3.77) (3.73) (2.38) (2.32)


− 0.00008 − 0.00008 − 0.00007 − 0.00006

(−3.90)

(−4.36) (−4.26) (−4.96) (−4.92)

Market variables +

+ + + +

Year FE

+ + + +

Strategy FE

+ +


Univariate Hazard




0.00094 0.00089 0.00063 0.00061

(6.68)

(6.60) (6.86) (6.02) (6.35)

Log(AUM) 0.00095

0.00082 0.00091 0.00060 0.00067

(4.63)

(4.39) (5.13) (3.82) (4.57)

Soft information:





0.00116 0.00106 0.00055 0.00050

(2.93)

(3.77) (3.73) (2.38) (2.32)


− 0.00008 − 0.00008 − 0.00007 − 0.00006

(−3.90)

(−4.36) (−4.26) (−4.96) (−4.92)

Market variables +

+ + + +

Year FE

+ + + +

Strategy FE

+ +

Observations 45,714

45,714 45,714 45,714 45,714

Pseudo R2 0.1029

0.1223 0.1342 0.1714 0.1819

LR-test p-value

0.0000 0.0000 0.0000 0.0000

Univariate Hazard



Information ratio 0.00103 0.00094

0.00089 0.00063 0.00061

(6.68) (6.60)

(6.86) (6.02) (6.35)

Log(AUM) 0.00095 0.00082

0.00091 0.00060 0.00067

(4.63) (4.39)

(5.13) (3.82) (4.57)

Soft information:




Due-diligence spell:Log(Duration) 0.00073 0.00116

0.00106 0.00055 0.00050

(2.93) (3.77)

(3.73) (2.38) (2.32)

Duration − 0.00007 − 0.00008

− 0.00008 − 0.00007 − 0.00006

(−3.90) (−4.36)

(−4.26) (−4.96) (−4.92)

Market variables + +

+ + +

Year FE +

+ + +

Strategy FE

+ +

Observations 45,714 45,714

45,714 45,714 45,714

Pseudo R2 0.1029 0.1223

0.1342 0.1714 0.1819

LR-test p-value 0.0000

0.0000 0.0000 0.0000

Univariate Hazard



Information ratio 0.00103 0.00094 0.00089

0.00063 0.00061

(6.68) (6.60) (6.86)

(6.02) (6.35)

Log(AUM) 0.00095 0.00082 0.00091

0.00060 0.00067

(4.63) (4.39) (5.13)

(3.82) (4.57)

Soft information:




Due-diligence spell:Log(Duration) 0.00073 0.00116 0.00106

0.00055 0.00050

(2.93) (3.77) (3.73)

(2.38) (2.32)

Duration − 0.00007 − 0.00008 − 0.00008

− 0.00007 − 0.00006

(−3.90) (−4.36) (−4.26)

(−4.96) (−4.92)

Market variables + + +

+ +

Year FE + +

+ +

Strategy FE +

+

Observations 45,714 45,714 45,714

45,714 45,714

Pseudo R2 0.1029 0.1223 0.1342

0.1714 0.1819

LR-test p-value 0.0000 0.0000

0.0000 0.0000

Univariate Hazard



Information ratio 0.00103 0.00094 0.00089 0.00063

0.00061

(6.68) (6.60) (6.86) (6.02)

(6.35)

Log(AUM) 0.00095 0.00082 0.00091 0.00060

0.00067

(4.63) (4.39) (5.13) (3.82)

(4.57)

Soft information:Affiliated fund (D) 0.00071

0.00053

(2.83)

(2.26)

Affiliated college (D) 0.00079

0.00082

(3.46)

(3.81)

Soft index (lagged) 0.00096

0.00088

(9.88)

(10.67)

Due-diligence spell:Log(Duration) 0.00073 0.00116 0.00106 0.00055

0.00050

(2.93) (3.77) (3.73) (2.38)

(2.32)

Duration − 0.00007 − 0.00008 − 0.00008 − 0.00007

− 0.00006

(−3.90) (−4.36) (−4.26) (−4.96)

(−4.92)

Market variables + + + +

+

Year FE + + +

+

Strategy FE +

+

Observations 45,714 45,714 45,714 45,714

45,714

Pseudo R2 0.1029 0.1223 0.1342 0.1714

0.1819

LR-test p-value 0.0000 0.0000 0.0000

0.0000

Univariate Hazard



Information ratio 0.00103 0.00094 0.00089 0.00063 0.00061(6.68) (6.60) (6.86) (6.02) (6.35)

Log(AUM) 0.00095 0.00082 0.00091 0.00060 0.00067(4.63) (4.39) (5.13) (3.82) (4.57)

Soft information:Affiliated fund (D) 0.00071 0.00053

(2.83) (2.26)Affiliated college (D) 0.00079 0.00082

(3.46) (3.81)Soft index (lagged) 0.00096 0.00088

(9.88) (10.67)Due-diligence spell:

Log(Duration) 0.00073 0.00116 0.00106 0.00055 0.00050(2.93) (3.77) (3.73) (2.38) (2.32)

Duration − 0.00007 − 0.00008 − 0.00008 − 0.00007 − 0.00006(−3.90) (−4.36) (−4.26) (−4.96) (−4.92)



Univariate Hazard


Marginals

+1σ SI

-1σ SI

0.0

05.0

1.0

15P

rob

of A

dmit

0 20 40 60 80Month

Baseline Hazard

0.0

05.0

1.0

15-2 -1 0 1 2

Information Ratio

Hard vs. Soft Info Tradeoff

1 Highest and steepest hazard peak occurs when SI is high.

2 “Compounding ascent” - suggests HI and SI complementarity.


Marginals

+1σ SI

-1σ SI

0.0

05.0

1.0

15P

rob

of A

dmit

0 20 40 60 80Month

Baseline Hazard

+1σ SI

-1σ SI

0.0

05.0

1.0

15-2 -1 0 1 2

Information Ratio

Hard vs. Soft Info Tradeoff

1 Highest and steepest hazard peak occurs when SI is high.2 “Compounding ascent” - suggests HI and SI complementarity.


Future Returns and SI0

1020

3040

Den

sity

-.06 -.04 -.02 0 .02 .04

1-12 Months

-.06 -.04 -.02 0 .02 .04

13-24 Months


Closest matched by AUM, age and pre-admission returns.

Matches limited to month of admission and strategy.Ranksum rejected at 5% in 1-12m, not 13-24m.



1020

3040

Den

sity

-.06 -.04 -.02 0 .02 .04

1-12 Months

-.06 -.04 -.02 0 .02 .04

13-24 Months


Closest matched by AUM, age and pre-admission returns.Matches limited to month of admission and strategy.

Ranksum rejected at 5% in 1-12m, not 13-24m.



1020

3040

Den

sity

-.06 -.04 -.02 0 .02 .04

1-12 Months

-.06 -.04 -.02 0 .02 .04

13-24 Months


Closest matched by AUM, age and pre-admission returns.Matches limited to month of admission and strategy.Ranksum rejected at 5% in 1-12m, not 13-24m.


Model

V (α) = max{0, π (α), βEVt+1 (α)− c}

Time

Estim

ated

Alpha

Estimated Alpha

Estim

ated

Profits


Model

V (α) = max{0, π (α), βEVt+1 (α)− c}

Time

Estim

ated

Alpha

Estimated Alpha

Estim

ated

Profits

0


Model

V (α) = max{0, π (α), βEVt+1 (α)− c}

Time

Estim

ated

Alpha

Estimated Alpha

Estim

ated

Profits

‐c

0


Model

V (α) = max{0, π (α), βEVt+1 (α)− c}

Time

Estim

ated

Alpha

Estimated Alpha

Estim

ated

Profits

‐c

0

1 The value of collecting information is non-increasing in time.


Model

V (α) = max{0, π (α), βEVt+1 (α)− c}

Time

Estim

ated

Alpha

Estimated Alpha

Estim

ated

Profits

‐c

0

N

1 The value of collecting information is non-increasing in time.2 There is an optimal N ≤ ∞ where research stops.


Model

V (α) = max{0, π (α), βEVt+1 (α)− c}

Research Zone

Acceptance Zone

Rejection Zone

Time

Estim

ated

Alpha

Estimated Alpha

Estim

ated

Profits

‐c

0

N

1 The value of collecting information is non-increasing in time.2 There is an optimal N ≤ ∞ where research stops.3 For t ≤ N, there is a lower (rejection) and upper (accept) boundary.


Conclusion

Empirical results and intuition line up on key dimensions:Both HI and SI are consistent with model.Baseline - i.e. passage of due-diligence time - effect is concave.


Importance of the “time-to-decision” in spirit of Berk & Green (2004).Pecuniary benefits of SI in contrast to Jenkinson et al (2015).

Going forward, better link model to richness of data:

1 Different sources - HI cheap/low quality, SI expensive/high quality.2 Varying intensity - precision of meetings is choice variable.3 Competing Hazards - to meet or admit? HI vs. SI complementarity.4 Importance of SI may vary over time as more HI collected.


Conclusion


Contribution to literature:Importance of the “time-to-decision” in spirit of Berk & Green (2004).Pecuniary benefits of SI in contrast to Jenkinson et al (2015).

Going forward, better link model to richness of data:

1 Different sources - HI cheap/low quality, SI expensive/high quality.2 Varying intensity - precision of meetings is choice variable.3 Competing Hazards - to meet or admit? HI vs. SI complementarity.4 Importance of SI may vary over time as more HI collected.


Conclusion



Going forward, better link model to richness of data:1 Different sources - HI cheap/low quality, SI expensive/high quality.

2 Varying intensity - precision of meetings is choice variable.3 Competing Hazards - to meet or admit? HI vs. SI complementarity.4 Importance of SI may vary over time as more HI collected.


Conclusion



Going forward, better link model to richness of data:1 Different sources - HI cheap/low quality, SI expensive/high quality.2 Varying intensity - precision of meetings is choice variable.

3 Competing Hazards - to meet or admit? HI vs. SI complementarity.4 Importance of SI may vary over time as more HI collected.


Conclusion



Going forward, better link model to richness of data:1 Different sources - HI cheap/low quality, SI expensive/high quality.2 Varying intensity - precision of meetings is choice variable.3 Competing Hazards - to meet or admit? HI vs. SI complementarity.

4 Importance of SI may vary over time as more HI collected.


Conclusion



Going forward, better link model to richness of data:1 Different sources - HI cheap/low quality, SI expensive/high quality.2 Varying intensity - precision of meetings is choice variable.3 Competing Hazards - to meet or admit? HI vs. SI complementarity.4 Importance of SI may vary over time as more HI collected.

Additional Material

Hazard Models

Hazard test time and covariates effects on prob of event given event notyet occurred.

Soft vs. hard information on decision to admit.

Right censoring - append public (HFI, CISDM, HFR, TASS) sources ofreturn data.

A simple time-invariant hazard model...

Define due-diligence for fund, Ai, as beginning on first meeting andending on admission or censor date.OLS specification -

ln Ai = µ+ βhiHard Infoi + βsiSoft Infoi + εi.

Assumes a proportional time to hazard, Ai = exp (x′iβ) A0.

Additional Material

Hazard Models


Soft vs. hard information on decision to admit.Right censoring - append public (HFI, CISDM, HFR, TASS) sources ofreturn data.

A simple time-invariant hazard model...

Define due-diligence for fund, Ai, as beginning on first meeting andending on admission or censor date.OLS specification -



Additional Material

Hazard Models



A simple time-invariant hazard model...Define due-diligence for fund, Ai, as beginning on first meeting andending on admission or censor date.

OLS specification -



Additional Material

Hazard Models



A simple time-invariant hazard model...Define due-diligence for fund, Ai, as beginning on first meeting andending on admission or censor date.OLS specification -



Additional Material

Time-invariant Hazard

Full-sample Matched-sample

β(t-stat)

β(t-stat)

β(t-stat)

β(t-stat)

β(t-stat)

β(t-stat)

Hard information:Et(R− peers) −0.081∗∗∗ −0.079∗∗∗ −0.041 −0.034

(−3.01) (−2.86) (−1.41) (−1.13)σt(R− peers) 0.244∗∗∗ 0.219∗∗∗ 0.284∗∗∗ 0.242∗∗∗

(7.99) (7.74) (9.92) (8.63)Soft information:Meeting number (All) 0.130∗∗∗ 0.126∗∗∗ 0.182∗∗∗ 0.146∗∗∗

(8.20) (7.54) (7.72) (6.54)Words per Document −0.254∗∗∗ −0.179∗∗∗ −0.177∗∗∗ −0.131∗∗∗

(−11.80) (−7.47) (−5.69) (−4.28)

Observations 747 979 744 556 556 548R2 0.1527 0.1439 0.2372 0.1620 0.1050 0.2254

Multivariate Hazard

finding fortune: how do institutional investors pick...

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