Speculative Fever: Investor Contagion in the Housing Bubble
Patrick J. Bayer (Duke University), et al.
February 1, 2016
Historical anecdotes of new investors being drawn into a booming asset market, only to suffer when the market turns, abound. While the role of investor contagion in asset bubbles has been explored extensively in the theoretical literature, causal empirical evidence on the topic is virtually non-existent. This paper studies the recent boom and bust in the U.S. housing market, and establishes that many novice investors entered the market as a direct result of observing investing activity of multiple forms in their own neighborhoods, and that “infected” investors performed poorly relative to other investors along several dimensions.
Date Stamping Bubbles in Real Estate Investment Trusts
Diego Escobari and Mohammad Jafarinejad (University of Texas Rio Grande Valley)
October 19, 2015
We test for the existence of single and multiple bubble periods in four Real Estate Investment Trust (REIT) indices using the Supremum Augmented Dickey-Fuller (SADF) and the Generalized SADF. These methods allow us to estimate the beginning and the end of bubble periods. Our results provide statistically significant evidence of speculative bubbles in the REIT index and its three components: Equity, Mortgage and Hybrid REITs. These results may be valuable for real estate financial managers and for investors in REITs.
The Invisible Hand and the Rational Agent are Behind Bubbles and Crashes
Serge Galam (Sciences Po and CNRS)
The substantial turmoil created by both 2000 dot-com crash and 2008 subprime crisis has fueled the belief that the two classical paradigms of economics, which are the invisible hand and the rational agent, are not appropriate to describe market dynamics and should be abandoned at the benefit of alternative new theoretical concepts. At odd with such a view, using a simple model of choice dynamics from sociophysics, the invisible hand and the rational agent paradigms are given a new legitimacy. Indeed, it is sufficient to introduce the holding of a few intermediate mini market aggregations by agents sharing their own private information, to recenter the invisible hand and the rational agent at the heart of market self regulation including the making of bubbles and their subsequent crashes. In so doing, an elasticity is discovered in the market efficiency mechanism due to the existence of agents anticipation. This elasticity is found to create spontaneous bubbles, which are rationally founded, and at the same time, it provokes crashes when the limit of elasticity is reached. Although the findings disclose a path to put an end to the bubble-crash phenomena, it is argued to be rationality not feasible.
Bubbles in Hybrid Markets – How Expectations About Algorithmic Trading Affect Human Trading
Mike Farjam and Oliver Kirchkamp (University of Jena)
Bubbles are omnipresent in lab experiments with asset markets. Most of these experiments were conducted in environments with only human traders. Today markets are substantially determined by algorithmic traders. Here we use a laboratory experiment to measure changes of human trading behavior if these humans expect algorithmic traders. To disentangle the direct effect of algorithmic traders we use a design where we manipulate only the expectations of human traders. We find clearly smaller bubbles if human traders expect algorithmic traders to be present.
Measuring House Price Bubbles
Steven C. Bourassa (Florida Atlantic University), et al.
January 6, 2016
Using data for six metropolitan housing markets in three countries, this paper provides a comparison of methods used to measure house price bubbles. We use an asset pricing approach to identify bubble periods retrospectively and then compare those results with results produced by six other methods. We also apply the various methods recursively to assess their ability to identify bubbles as they form. In view of the complexity of the asset pricing approach, we conclude that a simple price-rent ratio measure is a reliable method both ex post and in real time. Our results have important policy implications because a reliable signal that a bubble is forming could be used to avoid further house price increases.
LPPLS Bubble Indicators over Two Centuries of the S&P 500 Index
Qunzhi Zhang (ETH Zurich), et al.
February 2, 2016
The aim of this paper is to present novel tests for the early causal diagnostic of positive and negative bubbles in the S&P 500 index and the detection of End-of-Bubble signals with their corresponding confidence levels. We use monthly S&P 500 data covering the period from August 1791 to August 2014. This study is the first work in the literature showing the possibility to develop reliable ex-ante diagnostics of the frequent regime shifts over two centuries of data. We show that the DS LPPLS (log-periodic power law singularity) approach successfully diagnoses positive and negative bubbles, constructs efficient End-of-Bubble signals for all of the well-documented bubbles, and obtains for the first time new statistical evidence of bubbles for some other events. We also compare the DS LPPLS method to the exponential curve fitting and the generalized sup ADF test approaches and find that DS LPPLS system is more accurate in identifying well-known bubble events, with significantly smaller numbers of false negatives and false positives.
Extrapolation and Bubbles
Nicholas Barberis (Yale School of Management), et al.
We present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals — an average of the asset’s past price changes and the asset’s degree of overvaluation. The two signals are in conflict, and investors “waver” over time in the relative weight they put on them. The model predicts that good news about fundamentals can trigger large price bubbles. We analyze the patterns of cash-flow news that generate the largest bubbles, the reasons why bubbles collapse, and the frequency with which they occur. The model also predicts that bubbles will be accompanied by high trading volume, and that volume increases with past asset returns. We present empirical evidence that bears on some of the model’s distinctive predictions.