ADAPTIVE EXPECTATIONS AND STOCK MARKET CRASHES*

A theory is developed that explains how stocks can crash without fundamental news and why crashes are more common than frenzies. A crash occurs via the interaction of rational and naive investors. Naive traders believe that prices follow a random walk with serially correlated volatility. Their expectations of future volatility are formed adaptively. When the market crashes, naive traders sell stock in response to the apparent increase in volatility. Since rational traders are risk averse as well, a lower price is needed to clear the market: The crash is a self‐fulfilling prophecy. Frenzies cannot occur in this model.     

Rational panics and stock market crashes

This paper offers an explanation for stock market crashes which focuses on the role of rational but uninformed traders. We show that uninformed traders can precipitate a price crash because as prices decline, they surmise that informed traders received negative information, which leads them to reduce their demand for assets and drive the price of stocks even lower. The model yields several implications, such as that crashes can occur even when the fundamentals are strong, and that the magnitude of the crash depends on the fraction of uninformed investors and the amount of unsophisticated passive investing present in the market.     

Asymmetric effect of margin-trading activities on price crashes: evidence from Chinese stock market

Prior literature on the impact of margin-trading activity on stock price crashes is mixed and does not reach consensus. Using data from a Chinese margin-trading pilot programme initiated in 2010, this article employs both margin-buying and margin-covering activities to investigate the asymmetric impact on stock price crashes. We find that margin-buying activities are beneficial reducing the price crash prone, especially in bad times. In contrast, margin-covering activities amplify price crashes in both good times and bad times.     

Stock-market crashes and depressions

Stock-market crashes are informative about the prospects for macroeconomic depressions. Long-term data for 30 countries reveal that, conditional on a crash, the probability of a minor depression is 31 percent and of a major depression is 10 percent. The largest depressions are particularly likely to be accompanied by crashes. We allow for flexible timing between crashes and depressions to compute the covariance between stock-returns and an asset-pricing factor, which depends on the decline of consumption during a depression. With a coefficient of relative risk aversion around 3.5, this covariance accounts for the observed average (levered) equity premium of 7 percent.     

Long-run equilibrium and short-run dynamics between risk exposure and highway safety

Based upon monthly California data, this exploratory analysis uses vector error correction methods and associated statistical tests to identify the long-run relationship and the short-run dynamics between highway exposure and crashes. The analysis finds that there is a cointegrating relationship between exposure and crashes, and for fatal, serious injury, and materials crashes, could not reject the hypothesis that crash exposure and frequency move proportionately. The analysis indicates that vector error correction models may be an important tool for improving our understanding of highway crashes and the near and longer term impacts of alternative safety policies.     

Psychological determinants of occurrence and magnitude of market crashes

We simulate the Dynamic Stochastic General Equilibrium model of Mehra-Prescott [14] to establish the link between the anticipation of endowment drops (for instance a recession) and sudden market crashes. Contrary to the commonly accepted view that those crashes are solely driven by large drops in endowments at the time they occur, the simulation shows that: 1—a large and subjective anticipation of an endowment drop amplifies the magnitude of the crash next period without permanent effects, and 2—there always exists an upper-bound on the maximal anticipation of the drop so that the crash magnitude next period remains constant regardless of the drop level. Those findings are independent of the risk aversion of agents, and of the formation process of the anticipation.     

SHAKEOUTS AND MARKET CRASHES*

This article provides a microfoundation for the rise in optimism that seems to precede market crashes. Small, young markets are more likely to experience stock‐price run‐ups and crashes. We use a Zeira–Rob type of model in which demand size is uncertain. Optimism then grows rationally if traders' prior distribution over market size has a decreasing hazard. Such prior beliefs are appropriate if most new markets are duds and only a few reach a large size. The crash occurs when capacity outstrips demand. As an illustration, for the period 1971–2001 we fit the model to the Telecom sector.     

The Effect of Protest Votes on the Estimates of WTP for Use Values of Recreational Sites

Contingent valuation studies are often characterized by a considerable number of protest responses, which may cause selectivity bias on the final estimates for WTP. Sample selection models can detect and – if necessary – correct selectivity bias. In economic applications where the relevant dependent variable is continuous, sample selection models are generally estimated using Heckman's 2-step method rather than the FIML estimator. Either method has its own drawback: computational complexity for the FIML method, susceptibility to collinearity problems for the 2-step method. Using data on valuation of forest resources for recreational use, we analyse the performance of the two estimators. In this application, given the presence of some collinearity, the FIML is preferred to the 2-step method. A procedure is outlined to deal with selectivity problems in similar settings.     

IMPACT OF PRICING STRUCTURE SELECTIVITY ON URBAN WATER DEMAND

Interest in demand management policies has intensified as residential water demand has grown in the United States. Using data from the 1984 American Water Works Association (AWWA) survey, the study here provides an empirical analysis of the differences in price elasticities of demand across water pricing block structures and examines these structures' "conservation-orientedness." However, a potential sample selection bias exists. That is, in cities where people are more interested in conservation, utility managers may be more likely to select a rate structure that they believe is conservation-oriented–an increasing block structure, for example. Managers' selectivity bias may cause research results either to understate or to overstate a particular block structure's impact on water conservation. The analysis here corrects for this selectivity bias in estimating water demand and tests whether consumers respond to average prices or to marginal prices. Correcting for selectivity bias involves an explicit analysis of the factors that influence utility managers' selection of rate structures. Estimating water demand under increasing and decreasing block structures suggests that sample selection bias remains a problem worthy of further investigation.     

Predictability of equity returns during a financial crisis

An occurrence of a market crash or a financial crisis has long been considered a cause of market inefficiency. An inefficient market commonly implies return predictability and the existence of profitable opportunities for traders and speculators. Technical analysis has been a popular tool to identify predictable patterns in asset prices. The usefulness of a large universe of technical trading rules popularized in the existing literature on technical analysis is tested when they are applied to a set of equity markets that are generally considered developed and efficient during the two most recent periods of major financial turmoil: the 1997 Asian financial crisis and the 2008 global financial crisis. Three major statistical deficiencies that existing studies on return predictability are commonly criticized for – data snooping bias, nonsynchronicity bias and transaction costs – have been incorporated in the analysis. Technical trading rules are largely unable to yield abnormal excess returns over the passive benchmark after data snooping bias, nonsynchronous pricing and transaction costs are accounted for. Chaotic price movements typical for a volatile market during a financial crisis are likely to have an adverse effect on the performance of active trend chasing trading strategies.     

Cognitive bubbles

Smith et al. (Econometrica 56(5):1119, 1988) reported large bubbles and crashes in experimental asset markets, a result that has been replicated many times. Here we test whether the occurrence of bubbles depends on the experimental subjects’ cognitive sophistication. In a two-part experiment, we first run a battery of tests to assess the subjects’ cognitive sophistication and classify them into low or high levels. We then invite them separately to two asset market experiments populated only by subjects with either low or high cognitive sophistication. We observe classic bubble and crash patterns in markets populated by subjects with low levels of cognitive sophistication. Yet, no bubbles or crashes are observed with our sophisticated subjects, indicating that cognitive sophistication of the experimental market participants has a strong impact on price efficiency.     

Slow boom, sudden crash

Many asset markets exhibit slow booms and sudden crashes. This pattern is explained by an endogenous flow of information. In the model, agents undertake more economic activity in good times than in bad. Economic activity generates public information about the state of the economy. If the economic state changes when times are good and information is abundant, asset prices adjust quickly and a sudden crash occurs. When times are bad, scarce information and high uncertainty slow agents’ reactions as the economy improves; a gradual boom ensues. Data from U.S. and emerging credit markets support the theory.     

Don’t let your robots grow up to be traders: Artificial intelligence, human intelligence, and asset-market bubbles

Researchers who have examined markets populated by “robot traders” have claimed that the high level of allocative efficiency observed in experimental markets is driven largely by the “intelligence” implicit in the rules of the market. Furthermore, they view the ability of agents (artificial or human) to process information and make rational decisions as unnecessary for the efficient operation of markets. This paper presents a new series of market experiments that show that markets populated with standard robot traders are no longer efficient if time is a meaningful element, as it is in all asset markets. While simple two-season markets with human subjects reliably converge to an efficient equilibrium, markets with minimally intelligent robot traders fail to attain this equilibrium. Instead, these markets overshoot the equilibrium and then crash below it. In addition to firmly establishing the role of trader intelligence in asset-market equilibrium, these experiments also provide insights into why bubbles and crashes are consistently observed in many asset-market laboratory experiments using human subjects.     

Earnings and assimilation of female immigrants

This paper investigates the earnings and assimilation of female immigrants to the US, using 1970 and 1980 census data. A correction for selectivity bias is employed. In addition, to correct for the lack of work history data in the census, an experience proxy derived from data in the Panel Survey on Income Dynamics is employed. Female immigrants, taken as a group, with similar characteristics to natives, are shown to have initiality lower earnings but to catch up within about ten years.     

Dealing with small sample bias in post-crisis samples

In this paper, we demonstrate that using finite sample correction bootstrapping techniques is advisable in samples that cover less than two complete business cycles, even when high-frequency data seemingly provide a sufficient number of observations to overcome the small sample bias. This is particularly relevant in the current research environment. Because the recent financial crisis is considered as a structural break, research on current problems is often conducted using post-crisis data. That is, the available samples cover only a few years of data, often spanning only one business cycle or even less. We provide ample simulation-based evidence that samples of daily or monthly dynamic data covering periods of this magnitude are prone to a fairly substantial bias. Moreover, we are able to show that standard bootstrap-based bias correction techniques still work in those cases.     

On bias correction in the multivariate sample-selection model

Heien and Wessells' two-step estimator for the multivariate sample-selection model has been used extensively during the past 15 years. A modified version of it, with slightly different selectivity regressors, has also appeared in the empirical literature. Both estimators are unfounded and generally do not correct for the sample selectivity bias as intended but have continued to gain popularity in empirical applications. The properties of the modified Heien–Wessells procedure are investigated in both the bivariate and multivariate contexts, and the conditions under which this estimator fails to correct for sample selectivity are examined. The theoretical properties are demonstrated with a simulated random sample.     

Human capital,reservation wages and job competition: Heckman's lambda re-interpreted

This study integrates insights from three theories into a single model explaining the simultaneous distribution of employment and wages. Human capital theory is taken as the general framework, whereas search theory and the more recent ‘crowding’ or ‘job competition’ hypothesis are used to explain selectivity in employment and the resulting bias in wage regressions. An empirical test on Belgian data, using a two-stage probit-OLS model, indicates that the crowding theory dominates the search hypothesis for men. For women, it seems to be outweighed by relatively higher reservation wages, probably due to women's different behaviour with respect to family responsibilities.     

保险资金持股与股价崩盘风险——市场“稳定器”还是崩盘“加速器”?

现阶段,有关保险资金持股与资本市场稳定相互关系的讨论多止于推测,少有学者以大样本数据进行实证考察。在理论上,保险资金持股上市公司,一方面可以抑制经理人负面信息隐藏,作为市场“稳定器”降低股价暴跌风险;另一方面,也能引发投资者跟风炒作,导致市场情绪高涨和股价高估,成为股价崩盘风险的“加速器”。本文基于2007—2016年中国沪深两市A股上市公司的数据,实证分析保险资金持股上市公司是否加剧市场暴跌及其作用机制。结果显示:保险资金持股加剧了股价崩盘风险,是公司股价崩盘的“加速器”。这一结论在经过DID模型、工具变量回归、PSM+OLS等内生性分析,以及稳健性检验之后依然成立。进一步研究表明,相比于民营、小型和财产保险公司,在股市投资规模更大的国有、大型和人寿保险公司更能引发市场跟风炒作,加剧股价崩盘风险;不过,当保险资金作为前十大股东参与公司治理时则能够降低股价崩盘风险。本文不仅为保险资金持股与资本市场稳定相互关系的讨论提供了经验证据,也为规范保险资金股权投资,维护资本市场稳定提供有益借鉴。     

普惠金融对不同群体就业的影响研究——基于世界银行全球普惠金融调查数据

普惠金融的发展对促发展、稳就业、保民生具有非常重要的意义.考虑到非正规金融服务是对普惠金融的一项重要补充,本文基于2014年与2017年世界银行全球普惠金融调查数据,实证分析了正规金融服务与非正规金融服务对不同群体就业的异质性影响.研究结果表明:①在青年群体中,正规账户服务、正规储蓄服务和正规信贷金融服务对就业具有显著的促进作用;②相比于城镇群体,正规信贷服务和数字金融服务对农村群体就业的促进作用并不显著;③非正规信贷服务对不同群体的就业具有显著的促进作用.在考虑了更换就业、普惠金融的代理指标,采用不同年份的样本及采用倾向得分匹配法来减少样本选择性偏误等内生性问题后,结果依然稳健.最后,指出要积极推进普惠金融服务的广度和深度,加大普惠金融服务对不同群体就业的拉动作用.     

Market responses to publicly-provided information: the case of automative safety

Over the last 25 years the federal government has become increasingly involved in the provision of information on automobile quality and safety. Its release of National Highway Traffic Safety Administration data on occupant crash survivablity is a prominent example. This paper examines the impact of the agency's crash test results on the sales growth of tested automobile lines. Finding no impact, the crash data is then compared to privately collected insurance industry data on actual bodily injury claims by car line. these data sets show little consistency. based on these findings, we conclude that consumers's apparent disregard of government crash test data may well be rational.