Negative bubbles: What happens after a crash

• Author: Dasol Kim,William N. Goetzmann
• DOI: http://doi.org/10.1111/eufm.12164
• Date: 01 March 2018
• Published date: 01 March 2018

DOI: 10.1111/eufm.12164

ORIGINAL ARTICLE

Negative bubbles: What happens after a crash

William N. Goetzmann

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Dasol Kim

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Finance Department, Yale School of

Management, Yale University, New

Haven, Connecticut

Email: william.goetzmann@yale.edu

2

Office of Financial Research, US

Department of Treasury, Washington,

District of Columbia

Email: dasol.kim@ofr.treasury.gov

Abstract

We study crashes using data from 101 global stock markets

from 1692 to 2015. Extremely large, annual stock market

declines are typically followed by positive returns. This is

not true for smaller declines. This pattern does not appear

to be driven by institutional frictions, financial crises,

macroeconomic shocks, political conflicts, or survivorship

issues.

KEYWORDS

global markets, negative bubbles, stock market crash

JEL CLASSIFICATION

G02, G11, G15, G17

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INTRODUCTION

Although stock market crashes are few and far between, they seem to constantly concern investors.

Analyzinginvestor surveys over the period1989–2016, Goetzmann, Kim, and Shiller(2017) find that the

average individualinvestor, responding to a survey, estimatedthat a catastrophic stock marketcrash had

a 20% chanceof happening within the next 6 months. Their findings suggesta high level of fear about the

consequences of a market crash. In this paper, we explore whether such fears are justified.

We use data from more than 100 global stock markets over three centuries to examine market

dynamics after major crashes. The large collection of markets and broad sweep of time afforded by the

data allow us to identify a larger number of related events than possible when focusing on a specific

market or more limited time period. We define a crash in various ways as a rapid and unusually large

The authors thank John Doukas (the Editor), Antti Ilmanen, Tyler Muir, Justin Murfin, Robert Shiller, and the conference

seminar participants at the Bank of England and participants in the European Financial Management Association Meeting

in Athens, 2017. We thank Elroy Dimson, Paul Marsh, and Mike Staunton for the use of their annual DMS data and

acknowledge Global Financial Data and the International Center for Finance at the Yale School of Management as the

source of monthly global market data. The opinions expressed are those of the authors and do not necessarily reflect those

of the Office of Financial Research or the US Department of the Treasury.

Eur Financ Manag. 2018;24:171–191. wileyonlinelibrary.com/journal/eufm © 2017 John Wiley & Sons, Ltd.

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decline in a national stock market index. We identify 1,032 events for which a market declined by more

than 50% over a 12-month period. Conditioning on these events, and controlling for a range of other

factors, we find that markets tend to rebound in the year following the crash. We refer to this pattern of

crash-and-rebound as a ‘negative bubble.’Interestingly, the pattern only holds for large crashes −

declines of lesser magnitude exhibit persistence, not reversal. This non-linearity presents a challenge to

standard econometric forecasting techniques and suggests that something more complex than mean

reversion is at work.

We consider some proposed macroeconomic, institutional, and sample selection explanations for

this pattern. Macroeconomic explanations include changing volatility, inflation risk, and disaster risk

as potential drivers of temporary asset price declines. Institutional explanations include financial sector

fragility and constraints on arbitrage. Selection bias theories posit that ex post data collection omits

markets that failed to rebound. We find negative bubbles are not well explained by macroeconomic or

institutional factors. Selection bias also does not seem to drive the results. Alternatively, there are

behavioral theories that are consistent with the findings. They include panics or other temporary shocks

to sentiment, and bubbles −temporary, irrational over-pricing leading to a crash. While promising,

they are difficult to test.

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BACKGROUND

There is a long literature of forecasting long-horizon stock market returns, much of it modeling a linear

relationship between a set of macroeconomic state variables and future returns. Although the

limitations of this approach have been studied and discussed, the consensus is that some combination of

past returns, financial ratios, and discount rate proxies have predictive power. This large body of work

seeks to identify some model or consistent rules that govern market behavior under a complete range of

conditions. Our approach in this paper is different in that we focus narrowly on extreme events in order

to document a comprehensive set of outcomes. We then explore varying conditions that may have

affected outcomes, and compare these to proposed theories. Documenting historical outcomes may not

provide a complete guide to predicting future consequences of a stock market crash, but it is better than

relying on personal experience, or selective anecdotes about prior crashes. Thus, our modest goal is to

report the frequency of market crashes and the distribution of subsequent returns.

Our second goal is to evaluate some of the theories proposed to explain the dynamics of markets

following crashes. Muir (2017) documents a V-shape in asset prices specifically associated with

financial crises over 140 years of history and 14 markets. Notably, this pattern is not associated with

more fundamental factors such as consumption shocks or other major macroeconomic events. Rather,

asset price dips may reflect distress caused by over-leveraged financial institutions (cf. Brunnermeier

& Sannikov, 2014). Financial constraints have been shown to cause fire sales in asset prices, which are

then followed by a rebound (cf. Brunnermeier & Pedersen, 2008; Geanakoplos, 2010; Gromb &

Vayanos, 2009; and Richardson, Saffi, & Sigurdsson, 2017). Coval and Stafford (2007) document this

pattern for stocks held by mutual funds experiencing large outflows. In short, both asset supply and

demand are affected by institutional distress. On the supply side, banks and arbitrageurs may be forced

to de-risk by selling assets. On the demand side, distressed banks may restrict credit to arbitrageurs in

periods when assets are priced cheap.

We test these hypotheses in two ways. First, we test whether crashes coincident with financial

crises are more or less likely to rebound.

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We find little evidence that financial crises matter when we

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We rely on the assumption that we have a comprehensive set of financial crises for the countries we study.

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