GREAT EXPECTATIONS AND A TALE OF TWO CONFOUNDERSA BAYESIAN ANALYSIS OF INVESTOR OVERCONFIDENCE

2

To know what you

know and what you

don’t know, that is

true knowledge

Confucius

Gerlach

Content

• Introduction

• The Impact of Gender and Isolated Decision-Making on Investor Overconfidence

• Diagnostics and Results

• Sensitivity Analysis

• Discussion

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Importance of Overconfidence

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Note: 1-gram and bigram model (Michel et al. (2010), Lin et al.

(2012)) with third order smoothing on books published in English

(Oxford English Dictionaries (2017))

Prior Studies

Post-WWII

•Confidence independent of accuracy (e.g. Goldberg (1959))

•Oskamp (1965) connected both aspects in his seminal paper

1970s

•Lichtenstein, Fischhoff, and Slovic developed consistent measures

•Half-range and fixed interval are still very popular

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Common Measures

• Full-range Interval

• Half-range Interval

• Fixed 90% Confidence Interval

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0 100

50 100

Estimate Minimum Maximum

Prior Studies

2000s

• New measures and growing interest from other fields

• E.g. in economics with Shiller (2000), Barber and Odean (2001), Malmendier and Tate (2005)

In Consumer Research

• Alba and Hutchinson (2000), Bearden and Hardesty (2001), Billeter et al. (2010), etc.

Most Recently

• Kyle et al. (2017), Seo et al. (2017), Pikulina et al. (2017), etc.

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Why does it matter?

Behavior

• The 16-year-old who speeds without wearing a seatbelt (Semrud-Clikeman (2001))

Outcome

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NJ.com

Why does it matter?

Behavior

• The 16-year old who speeds without wearing a seatbelt (Semrud-Clikeman (2001))

• The student who willingly increases the probability to fail by answering a bonus question (Bengtsson et al. (2005))

Outcome

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Clip Art

Why does it matter?

Behavior

• The 16-year old who speeds without wearing a seatbelt (Semrud-Clikeman (2001))

• The student who willingly increases the probability to fail by answering a bonus question (Bengtsson et al. (2005))

• The GM worker who did not diversify and lost his job, health insurance, and employee stock-based retirement fund within a year (Shiller (2014))

Outcome

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MFI Miami

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Overconfidence

Unrealistic optimism Better-than-

average effect

Illusion of knowledgeIllusion of

control

Miscalibration

Self-serving

bias

Contribution

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Passive & DescriptiveResearch

Active &

Intervention

Research

Research Questions

What is the impact of

• gender (R1) and

• shared financial decision-making (R2)

on investor overconfidence?

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APS

Shared Decision-Making with Financial Planner, Broker, Professional

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Intrahouse-hold Decision-Making

Measure of overconfidence

Subjective assessment of investment knowledge > Actual level of

investment knowledge

On a scale from 1 to 7,

where 1 means very low

and 7 means very high,

how would you assess

your overall knowledge

about investing?

10 items worth 0.7 pts

each:

If a company files for

bankruptcy, which of the

following securities is

most at risk of becoming

virtually worthless?11.03.2018 16

Descriptive Statistics

79.50%

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Descriptive Statistics

Independent variables

Shared decision-making(binary indicators)

Lone Wolf 30.85%

Primary Decision-maker w/ advisor 35.65%

Intra-household only 10.80%

Intra-household w/advisor 22.25%

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Conceptual Model

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𝑜𝑐𝑖 = 𝛽0 + 𝛽1𝑠ℎ𝑎𝑟𝑒_𝑜𝑛𝑙𝑦_𝑎𝑑𝑣𝑖 + 𝛽2𝑠ℎ𝑎𝑟𝑒_𝑜𝑛𝑙𝑦_ℎℎ𝑖 + 𝛽3𝑠ℎ𝑎𝑟𝑒_𝑏𝑜𝑡ℎ𝑖 + 𝛽4𝑎𝑔𝑒𝑖 +

𝛽5𝑔𝑒𝑛𝑑𝑒𝑟𝑖 + 𝛽6𝑚𝑎𝑟𝑔𝑖𝑛𝑖 + 𝛽7𝑒𝑑𝑢𝑖 + 𝛽8𝑚𝑎𝑟𝑟𝑖𝑒𝑑𝑖 + 𝛽9𝑖𝑛𝑐𝑖 + 𝛽10𝑒𝑡ℎ𝑛𝑖𝑐𝑖𝑡𝑦𝑖 + 𝜏𝑖 (1)

Priors

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PRIOR FOR

REGRESSOR ON

OBJECTIVE MEAN OBJECTIVE

PRECISION

SUBJECTIVE MEAN SUBJECTIVE

PRECISION

SOURCE OF PRIOR

INTERCEPT 0 0.0001 1.6 0.5454 Piehlmaier (2014)

SHARED DM 0 0.0001 -0.157 8.7016 Li and Bao (2016)

AGE 0 0.0001 -0.29 61.3798 Piehlmaier (2014)

GENDER 0 0.0001 0.24 73.4376 Piehlmaier (2014)

ETHNICITY 0 0.0001 0.11 0.1849 Acker and Duck

(2008)

EDUCATION 0 0.0001 -0.93 12.2242 Piehlmaier (2014)

MARITAL STATUS 0 0.0001 0.014 27.9947 Xia et al. (2014)

INCOME 0 0.0001 -0.0188 2.7601 Bhandari and

Deaves (2006)

MARGIN

ACCOUNT

0 0.0001 -0.2215 0.28582 Kyrychenko and

Shum (2009)

RESIDUALS

(GAMMA)

OBJECTIVE

SHAPE

OBJECTIVE

SCALE

SUBJECTIVE

SHAPE

SUBJECTIVE

SCALE

SENSITIVITY ANALYSIS 0.01 0.01 N/A N/A

Informative Priors

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Gelman-Rubin

Diagnostic

Parameters Point Estimate Upper 95% CI

Intercept 1 1.01

Only Advisor 1 1.01

Only Household 1 1.00

Both 1 1.00

Age 1 1.00

Gender 1 1.00

Margin 1 1.00

Education 1 1.00

Married 1 1.01

Income 1 1.01

Ethnicity 1 1.00

Tau 1 1.00

Informative Priors

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Informative Priors

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Highest Posterior Density

Quantiles

Intercept

Only Advisor

Only HH

Both

Age

Gender

Margin

Education

Married

Income

Ethnicity

Tau

Sensitivity Analysis

How are these results affected by the selection of subjective priors?

The sensitivity analysis applies objective priors to Eq. 1

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Uninformative Priors

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Gelman-Rubin

Diagnostic

Parameters Point Estimate Upper 95% CI

Intercept 1 1.01

Only Advisor 1 1.00

Only Household 1 1.00

Both 1 1.00

Age 1 1.00

Gender 1 1.00

Margin 1 1.00

Education 1 1.00

Married 1 1.02

Income 1 1.00

Ethnicity 1 1.00

Tau 1 1.00

Uninformative Priors

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Uninformative Priors

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Quantiles

Intercept

Only Advisor

Only HH

Both

Age

Gender

Margin

Education

Married

Income

Ethnicity

Tau

Highest Posterior Density

Model Comparison

Subjective Priors

>Objective Priors

Mean Deviance: 7,694

Penalty: 14.15

Penalized Deviance: 7,708

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Mean Deviance: 7,694

Penalty: 12.64

Penalized Deviance: 7,707

Results

Impact of shared decision-making on overconfident investors

With spouse/ someone in household

With broker/ advisor

[9.51%; 1.91%]

4.38% to 0.69%

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Key Findings from the Cross-Section

• Sharing investment decision-making reduces overconfidence (R2)

• However, the effect can only be observed when investors share the financial decision-making with someone in their household (e.g., her/his spouse)

• Female investors are significantly more overconfident than male investors (R1)

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Limitations of the Cross-Section

• Causality

Endogeneity problem regarding the connection between overconfidence and shared decision-making

• Single measure for key construct

Overconfidence – single measure of subjective knowledge

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34

It ain’t what you don’t

know that gets you into

trouble. It’s what you know

for sure that just ain’t so.

Mark Twain

Boston University

DiscussionThank you for your attention

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