Why so Negative? Belief Formation and Risk Taking in Boom and Bust Markets - Pascal Kieren Jan Müller-Dethard

Why so Negative?
Belief Formation and Risk Taking in Boom and Bust Markets

 Pascal Kieren
 University of Mannheim

 Jan Müller-Dethard
 University of Mannheim

 Martin Weber
 University of Mannheim

 FIRM Forschungskonferenz 2021

Motivation

A major puzzle in financial economics is the fact that risk premiums vary strongly and
systematically with market cycles (Shiller, 1981; Campbell and Shiller, 1988; Cochrane, 2011)
 • Higher equity risk-premiums during recessions than during business cycle peaks
 • Returns are considerably more volatile than the underlying dividends
 • Significant predictability in the counter-cyclical nature of the equity risk-premium

What mechanism drives this pervasive finding?

Proposed Solution: Rational expectation models (Campbell and Cochrane, 1999; Barberis et
al., 2001)
 • Rest on two fundamental assumptions about individual behavior:
 • Introduce modifications into the representative agent’s utility function (“countercyclical
 risk-aversion”)
 • Assume that the representative agent knows objective probability distribution in
 equilibrium (“rational expectations”)
 • Assumptions imply that agents are fully aware of the countercyclical nature of the equity
 premium
 05.07.2021 Kieren, Müller-Dethard, Weber 2

Motivation

A major puzzle in financial economics is the fact that risk premiums vary strongly and
systematically with market cycles (Shiller 1981; Campbell and Shiller, 1988; Cochrane 2011)

Challenges with rational expectation models (Nagel & Xu, 2018):
 • How could an agent come to possess so much knowledge about parameters even
 econometricians struggle to estimate with precision?
 • Survey evidence shows that beliefs are – if anything – rather procyclical (Amromin & Sharpe,
 2014; Greenwood & Shleifer, 2014; Giglio et al., 2019)

If not rational expectation models, what else?
 • Risk-taking is a function of three parameters:
 = ( , , )
 • Can errors in investor beliefs explain the time-varying nature of the equity risk premium?

 05.07.2021 Kieren, Müller-Dethard, Weber 3

Research Goal

Asset prices are forward looking. Expectations about payoffs, probabilities, etc. play a
fundamental role in valuation of many assets.

Do business cycles systematically affect the way investors form expectations?
 • Adverse information much more common in bust markets (negative returns, earning
 announcements, unemployment rates, press coverage, etc…)
 • People learn differently from positive and negative outcomes (Kuhnen, 2015)

Objective of this study is to test:
 (1) How different market phases affect the formation of return expectations?
 (2) Whether and how systematic differences in beliefs translate to differences in risk-taking?
 (3) What are potential mechanisms that explain why investors behave this way?

 05.07.2021 Kieren, Müller-Dethard, Weber 4

Experimental Design – Overview

General idea: Combine a belief formation (forecasting) task in a boom/bust learning
environment with an incentive compatible investment task

Two stages (2x2 between-subject design):

 First Stage: Forecasting Task Second Stage: Investment Task
 • Boom Treatment: • Ambiguous lottery (unknown probabilities)
 − Learning environment that mirrors • Risky lottery (known probabilities)
 characteristics of Boom markets
 • Bust Treatment:
 − Learning environment that mirrors
 characteristics of Bust markets Ambiguous lottery

 Boom Treatment
 50 % Risky lottery

 Ambiguous lottery
 50 %
 Bust Treatment

 Risky lottery

 05.07.2021 Kieren, Müller-Dethard, Weber 5

Experimental Design – Stage 1: Forecasting

Task:
• Identify the state from which a risky asset is paying dividends from (good state vs. bad state)
• 2 Blocks each consisting of 8 rounds
• Each round: (1) Outcome announcement (2) Probability Forecast (3) Confidence in Forecast

 Domain-specific learning environment Expected value learning environment

 • Outcomes are exclusively positive • Lotteries with mixed outcomes but
 (boom) or negative (bust) with positive (boom) or negative (bust)
 • Motivated by prior research on belief expected value
 formation • Creates a more realistic market setting

 → Bayesian agent should make identical forecasts irrespective of treatment

 05.07.2021 Kieren, Müller-Dethard, Weber 6

Experimental Design – Stage 2: Investment

Estimating the influence of beliefs in the investment task

 Ambiguous lottery Risky lottery

 ?? % $ . ∙ 50 % $ . ∙ 

 : −$ : −$ 

 ?? % 50 % $ 
 $ 

 • Freedom to form beliefs about • Beliefs are fixed (known
 underlying true probability probabilities)
 • Investment decision affected by • Investment decision only affected
 both beliefs and preferences by preferences

 → Between-subject measure of belief- and preference-based risk taking

 05.07.2021 Kieren, Müller-Dethard, Weber 7

Hypotheses

Forecasting Task
 • H1: Pessimism Bias
 Subjects in the “bust”-treatment are significantly more pessimistic in their
 average probability forecast than subjects in the “boom”-treatment

Investment Task
 • H2a: Belief-induced Risk-Taking
 Subjects in the “bust”-treatment invest significantly less in the ambiguous lottery
 than subjects in the “boom”-treatment
 • H2b: Preference-based Risk-Taking
 Investments in the risky lottery should not significantly differ across treatments

 05.07.2021 Kieren, Müller-Dethard, Weber 8

Results I – Belief Formation and Risk-Taking

Main effect: Investment in risky versus ambiguous lottery

 50

 45
 Investment

 40

 35

 30
 Ambiguous Risky
 Bust Boom

 05.07.2021 Kieren, Müller-Dethard, Weber 9

Results I – Belief Formation and Risk-Taking

Main effect: Investment in risky versus ambiguous lottery
 
 = 0 + 1 + 2 + 3 + ෍ + 
 =1
 Dependent Variable Investment
 (1) (2) (3)
 Bust 3.184 2.271 5.505
 (0.99) (0.72) (1.22) Result 1:
 Ambiguous 5.438* 5.149* 5.661 • No effect on risky lottery
 (1.77) (1.71) (1.31) (H2b: preference-based risk-
 Bust x Ambiguous -11.69*** -11.23** -14.18** taking)
 (-2.60) (-2.54) (-2.32)
 • Strong effect on ambiguous
 Mixed 1.408 lottery (H2a: belief-induced
 (0.32)
 risk-taking)
 Bust x Ambiguous x Mixed 5.500
 (0.61)
 ➢ Different learning
 Constant 39.38*** 15.82* 14.99 environments affect beliefs,
 (17.83) (1.70) (1.51) but not risk aversion
 Observations 754 753 753
 Controls X X
 R2 0.011 0.060 0.061

 05.07.2021 Kieren, Müller-Dethard, Weber 10

Results II – Beliefs

Are participants’ expectations the driving factor behind observed differences in risk-taking?

 80
 Result 2:
 Success Probability Estimate (in %)

 70 • Beliefs about success
 probability of the
 60
 ambiguous strongly differ
 50
 across learning
 environments
 40
 • First indicator that
 30
 differences are driven by
 Pooled Domain-specific Mixed more pessimistic
 Bust Boom expectations

 05.07.2021 Kieren, Müller-Dethard, Weber 11

Results III – Beliefs and Risk-Taking

Do individuals act upon their beliefs?
Do beliefs about the success probability affect their investment decision?
 Dependent Variable Investment in Ambiguous Lottery
 (1) (2) (3) (4) (5)
 Probability 0.412*** 0.409*** 0.352*** 0.421*** 0.331***
 (6.45) (5.70) (3.85) (5.72) (3.41) Result 3:
 Bust -0.372 -2.968 -3.937 • Beliefs about the success
 (-0.11) (-0.73) (-0.95) probability are the
 Mixed -4.682 -1.205 -13.31 strongest predictor of
 (-0.66) (-0.28) (-1.30) individuals subsequent
 Probability x Mixed 0.105 0.179 risk-taking
 (0.88) (1.29)
 • Bust environment does
 Bust x Mixed 5.507 8.864 not affect anything else
 (0.90) (1.28)
 than beliefs
 Constant -3.304 -2.985 -0.0281 -3.616 3.638
 (-0.26) (-0.23) (-0.00) (-0.28) (0.28)
 Observations 377 377 377 377 377
 Controls X X X X X
 R2 0.146 0.146 0.148 0.148 0.152

 05.07.2021 Kieren, Müller-Dethard, Weber 12

Results III – Mechanism

How does the initial learning environment induce the observed pessimism?
1. How do expectations evolve across learning environments?
2. Are positive and negative signals treated equally for the formation of expectations?

 Result 4:
 • Bayesian beliefs should
 be equal across
 environments
 • For each possible
 objective posterior,
 individuals in bust
 environments form
 more pessimistic
 expectations

 05.07.2021 Kieren, Müller-Dethard, Weber 13

Results IV – Mechanism

Are beliefs indeed the driving mechanism behind our main effect?
1. How do expectations evolve across learning environments?
2. Are positive and negative signals treated equally for the formation of expectations?

 Probability Estimate in t+1 - Probability Estimate in t
 High Dividend High Dividend Low Dividend Low Dividend High Dividend Low Dividend
 High in Round t+1, in Round t+1, in Round t+1, in Round t+1, in Round t+1, in Round t+1,
 Low Dividend
 Dividend Probability Probability Probability Probability Probability Probability
 in Round t+1
 in Round t+1 Estimate Estimate Estimate Estimate Estimate Estimate
 in t < 50% in t > 50% in t < 50% in t > 50% in t < 30% in t > 70%
 Bust 8.77% -9.68% 18.14% 4.26% -1.34% -17.12% 21.04% -24.67%

Boom 9.16% -7.95% 23.58% 5.42% -1.21% -11.26% 28.37% -18.19%

Bust -
 -0.39% -1.73%*** -5.44%*** -1.16%** -0.13% -5.86%*** -7.33%*** -6.48%***
Boom

 Result 5:
 • Participants put significantly more weight on low outcomes when they are in the bust
 treatment compared to the boom treatment
 • Bust learners are significantly more pessimistic about outcomes which are inconsistent
 with prior expectations
 • …and take more time to recover from pessimistic beliefs
 05.07.2021 Kieren, Müller-Dethard, Weber 14

Results V – External Validity

Expectations outside the experimental setting: Return forecast for the Dow Jones

Result 6:
• Bust market learning environments even affect expectations in the real economy

 05.07.2021 Kieren, Müller-Dethard, Weber 15

Conclusion

Research has shown that risk premiums substantially vary across macroeconomic cycles
 • One explanation: countercyclical risk aversion
 • We test and propose an alternative channel motivated by economic theory

We show in a controlled environment that systematic distortions in investor’
expectations can explain differences in risk-taking across recessions and boom markets
 • Learning to form beliefs in recessions induces pessimism (and thus pro-cyclical
 expectations)
 • Individuals act upon this pessimism and reduce their risk-taking
 • As mechanism we identify that during recessions, investors …
 1) are more responsive to unfavorable outcomes
 2) take significantly more time to recover pessimistic expectations from
 temporary shocks

The proposed mechanism might be interesting for new theories featuring pro-cyclical
expectations.
 05.07.2021 Kieren, Müller-Dethard, Weber 16

Thank you
 for your attention!

05.07.2021 Kieren, Müller-Dethard, Weber 17

Appendix – Who is Most Affected?

Forecasting quality and the effect size
 Dependent Variable Investment
 Pooled Data Domain-specific Mixed
 Above Below Above Below Above Below
 Median Median Median Median Median Median
 Bust 6.126 -1.109 6.424 0.652 3.437 -2.713
 (1.38) (-0.25) (0.86) (0.11) (0.59) (-0.41)

 Ambiguous 10.94*** -1.448 11.48* -1.582 10.56* -2.073
 (2.65) (-0.33) (1.92) (-0.24) (1.75) (-0.34)

 Bust # Ambiguous -21.49*** -1.454 -22.15** -4.501 -19.14** 1.881
 (-3.54) (-0.23) (-2.44) (-0.52) (-2.25) (0.19)

 Constant 1.238 22.65 1.822 37.77** 5.365 4.365
 (0.10) (1.58) (0.11) (2.09) (0.29) (0.20)
 Observations 377 376 169 181 208 195
 R2 0.095 0.072 0.139 0.070 0.119 0.114

Result:
• Effect is driven by above-median forecasters

 05.07.2021 Kieren, Müller-Dethard, Weber 18

Appendix – External Validity

Expectations outside the experimental setting: Return forecast for the Dow Jones

Result 6:
• Adverse learning environments even affect expectations in the real economy

 05.07.2021 Kieren, Müller-Dethard, Weber 19