The “risk matrix:”Predicting financial risk taking with FMRI

Brian KnutsonStanford University

Financial Education and Investor Behavior Conference, Rio de Janeiro, December 4, 2014

Overview

• Background• Findings

– Predictors of financial risk taking– Anomalies and aggregation

• Conclusions and implications

Questions

• Which brain mechanisms anticipate good and bad events (affective neuroscience)

• Does their activity influence choice? (neuroeconomics)

correlated activity

predictive activity

output input

Anticipatory Affect

Watson J, Clark LA, Tellegen A (1988). J Pers Soc Psychol, 54, 1063-1070.

Avoidance Arousal

calm

quiet

tired

unhappy

fearful

happy

excited

aroused

Approach

Valence

Positive Arousal

Negative Arousal

Anticipating Gains Versus Losses

Gain Anticipation > Loss Anticipation; n=21 studies

Knutson B, Greer SM (2008). Phil Trans Royal Soc B, 363, 3771-3786.

Anticipatory Affect Model

• Functional targets– Nucleus accumbens (NAcc): Gain anticipation– Anterior insula: Loss anticipation– Medial prefrontal cortex (MPFC): Value integration

Knutson B, Greer SM (2008). Phil Trans Royal Soc B, 363, 3771-3786.

Risk Taking Predictions

• Risks involve uncertain but large gains and losses• NAcc activation -> risk seeking• Anterior insula activation -> risk avoidance

Kuhnen CM, Knutson B (2005). Neuron, 47, 763-770.

AnteriorInsula

NucleusAccumbens

Behavioral Investment Allocation Strategy (BIAS) Task

• 20 blocks of 10 trials• At block outset, one stock is randomly assigned as “good,”

the other is “bad”• Subjects know one stock is better, but don’t know which:

Good stock: Bad stock: +$10 w/ prob. 50% +$10 w/ prob. 25% –$10 w/ prob. 25% –$10 w/ prob. 50% +$0 w/ prob. 25% +$0 w/ prob. 25%

Bond: +$1 w/ prob. 100%

Kuhnen CM, Knutson B (2005). Neuron, 47, 763-770.

BIAS Task Trial

BIAS Task Outcomes

• Choices:– Risk seeking: Choosing a stock– Risk averse: Choosing a bond

• Mistakes:– Violate risk neutrality, prior information– Risk seeking: Choosing a stock when the bond is optimal– Risk averse: Choosing a bond when the stock is optimal

Anticipatory activity predicts choice

• NAcc activity precedes risk seeking shift (bond to stock) • Anterior insula activity precedes risk averse shift (stock to bond)• Effects are stronger for “irrational” choices (mistakes)

...but...

Multiple systems or just one?

Tom S et al (2007). Science, 315, 515.

Anomalies

• Higher order moments (e.g., skewness)• Incidental affect

Skewed Gambles

Wu CC, Bossaerts P, Knutson B (2011) PLoS ONE, 6, e16838

Skewed Gambles

Wu CC, Bossaerts P, Knutson B (2011) PLoS ONE, 6, e16838

Skewed Gambles

Wu CC, Bossaerts P, Knutson B (2011) PLoS ONE, 6, e16838

< <

Skewed Gambles

Wu CC, Bossaerts P, Knutson B (2011) PLoS ONE, 6, e16838

= =

Skewed Gambles

Wu CC, Bossaerts P, Knutson B (2011) PLoS ONE, 6, e16838

Predicting choice with GraphNet

Grosenick L et al (2013). NeuroImage, 72, 304-321.

• Predictive• Interpretable• Generalizable

Neural predictors of gambling

Wu CC, et al (In Prep).

2 s

+$5.25

-$1.75

$0.00

+$5.25

-$1.75

$0.00

R

L

4 s

+ $5.25

Total: $15.25

2 s

2 s

+$5.25

-$1.75

$0.00

+$5.25

-$1.75

$0.00

R

L

4 s

+ $5.25

Total: $15.25

2 s

Neural predictors of gambling

Wu CC, et al (In Prep).

2 s

+$5.25

-$1.75

$0.00

+$5.25

-$1.75

$0.00

R

L

4 s

+ $5.25

Total: $15.25

2 s

LOSO CV = 65%

Neural predictors of gambling

Wu CC, et al (In Prep).

Neural predictors of gambling

Volumes of interest

Graphnet

Volumes of interest

Wu CC, Sacchet MD, Knutson B (2012). Frontiers in Neuroscience, 8, 159. see also Mohr PNC et al (2010) J Neurosci, 30, 6613-6619 .

n = 10 studiesn = 21 studies

Graphnet

Predictors vary by gamble type

DV: Risky Choice Coefficient (SEM) Z-scoreTrial number -0.008 (0.005) -1.73

Total earnings -0.079 (0.019) -4.24***Previous outcome -0.093 (0.033) -2.79**

Gamble 0.036 (0.259) 0.14NAcc (bilateral) 2.311 (0.316) 7.30***

Ant Ins (bilateral) -0.732 (0.239) -3.07*NAcc x Gamble 0.737 (0.233) 3.16*Ant Ins x Gamble -0.125 (0.293) -0.43

Constant 1.451 (0.468) 3.10*

# of observations: 2736Pseudo-R2: 0.194

*p < 0.05**p <0.01

***p < 0.001

Positive skew preference

Wu CC, et al (In Prep).

• Low and high magnitudes• Gain and loss frames• Hypothetical and actual

Incidental affect: Pictures

Knutson B et al (2008) NeuroReport, 19, 509-513.

• Incidental affective cues can influence financial risk taking

Incidental affect: Pictures

Knutson B et al (2008) NeuroReport, 19, 509-513.

• With partial mediation by brain activity

inhale

6 s

choose

choose

4 s

6 s+

4-8 s

Incidental affect: Odors

+ $2.65

- $2.65

+ $3.41

- $2.05

+ $4.58

- $1.53

+ $7.00

- $1.00

+ $16.52

- $0.42

- $2.65

+ $2.65

- $3.41

+ $2.05

- $4.58

+ $1.53

- $7.00

+ $1.00

- $16.52

+ $0.42

Positive Skew Negative Skew

Incidental affect: Odors

F(1, 27) = 4.27; p < .05

*

positive neutral negative

Pos

itive

-Ske

w P

refe

renc

e ±

SE

M

-0.3

-0.15

0

0.15

0.3

Smells

Incidental affect: Odors

positive neutral negative

Neg

ativ

e-S

kew

Pre

fere

nce

± S

EM

-0.3

-0.15

0

0.15

0.3

SmellsF(1, 27) = 1.571; p = .221

Incidental affect: Odors

Scaling to the aggregate?

Cohn A., Fehr E., Marechal MA (In Prep)

Scaling to the aggregate?

Andrade EA, Odean TA, Lin S (In Prep)

Challenge

• “Provide an example of a novel economic model derived originally from neuroeconomic research that improves our measurement of the causal relationship between a standard exogenous environmental condition -- one with which economists have been historically concerned -- and a standard economic choice.” (Bernheim)

Bernheim BD (2008). In Neuroeconomics: Decision Making and the Brain, 115-125.

Conclusions

• Two brain systems* predict risk taking– NAcc activity predicts financial risk seeking – Anterior insula activity predicts financial risk avoidance

• Neurally-informed models generate broader explanations and predictions than traditional models (e.g., mean-variance)

– Higher order moments– Incidental affect

*or more

Implications

• Do other circuits (e.g., habitual, symbolic, reflective) modulate or overshadow the influence of affective circuits (and if so, when)?

• Do individual predictions aggregate to the group level (and if so, when)?

• Can this information improve investor behavior?• Can the information help predict market events?

Thanks:

• Charlene Wu• Peter Bossaerts• Spanlab• McClure Lab• National Science Foundation• You