Bubbles are a topic of great importance and great controversy. This paper discusses alternative perspectives on the economic meaning and origin of bubbles. Drawing on historical approaches to bubbles, this article sets out a taxonomy of approaches used to explain the nature of bubbles. The paper also considers issues connected with the scientific thinking surrounding bubbles. The Author 2008. Published by Oxford University Press on behalf of the Society for Financial Studies. All rights reserved. For permissions, please e-mail: [email protected]., Oxford University Press.

This study uses laboratory experiments to determine the effects of trade and quote disclosure on market efficiency, bid-ask spreads, and trader welfare. We show that trade disclosure increases the informational efficiency of transaction prices, but also increases opening bid-ask spreads, apparently by reducing market-makers' incentives to compete for order flow. As a result, trade disclosure benefits market makers at the expense of liquidity traders and informed traders. We find that quote disclosure has no discernible effects on market performance. Overall our results demonstrate that the degree of market transparency has important effects of market equilibria and on trader and market-maker welfare. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

We develop a model of psychological-games-played-on-a-network to demonstrate a role for endogenously determined, rationally chosen ethics. Our analysis produces sharp results about contagion of nonethical or ethical behavior and the possible equilibrium configurations of each type of behavior. We find, and quantify, critical densities for clusters of each type of behavior that determine everything about contagion. We introduce society as a third player to investigate ethical failures as externalities. We use these results to show how regulations and network structure can affect whether clusters of ethical behavior can survive and how large they can be in a financial market setting.

We investigate the implications of ambiguity aversion for performance and regulation of markets. In our model, agents’ decision making may incorporate both risk and ambiguity, and we demonstrate that nonparticipation arises from the rational decision by some traders to avoid ambiguity. In equilibrium, these participation decisions affect the equilibrium risk premium, and distort market performance when viewed from the perspective of traditional asset pricing models. We demonstrate how regulation, particularly regulation of unlikely events, can moderate the effects of ambiguity, thereby increasing participation and generating welfare gains. Our analysis demonstrates how legal systems affect participation in financial markets through their influence on ambiguity.

Order flow is toxic when it adversely selects market makers, who may be unaware they are providing liquidity at a loss. We present a new procedure to estimate flow toxicity based on volume imbalance and trade intensity (the VPIN toxicity metric). VPIN is updated in volume time, making it applicable to the high-frequency world, and it does not require the intermediate estimation of non-observable parameters or the application of numerical methods. It does require trades classified as buys or sells, and we develop a new bulk volume classification procedure that we argue is more useful in high-frequency markets than standard classification procedures. We show that the VPIN metric is a useful indicator of short-term, toxicity-induced volatility.

We empirically examine the impact of industry exchange-traded funds (IETFs) on informed trading and market efficiency. We find that IETF short interest spikes simultaneously with hedge fund holdings on the member stock before positive earnings surprises, reflecting long-the-stock/short-the-ETF activity. This pattern is stronger among stocks with high industry risk exposure. A difference-in-difference analysis on the ETF inception event shows that IETFs reduce post-earnings-announcement drift more among stocks with high industry risk exposure, suggesting that IETFs improve market efficiency. We also find that the short interest ratio of IETFs positively predicts IETF returns, consistent with the hedging role of IETFs.

We use a laboratory market to investigate the behavior of traders who lack informational advantages and have no exogenous reason to trade. We find that these uninformed traders behave largely as irrational contrarian “noise traders,” trading against recent price movements to their own detriment. The uninformed traders provide some benefits to the market: increasing market volume and depth, while reducing bid-ask spreads and the temporary price impact of trades. However, their noise trading also diminishes the ability of market prices to adjust to new information. A securities transaction tax reduces uninformed trader activity, but it reduces informed trader activity by approximately the same amount; as a result, the tax does not alter the impact of noise trading on the informational efficiency of the market.

We show current market practices relating to odd-lot quotes create a large “inside” market where better prices routinely exist relative to the National Best Bid or Offer. We show that odd-lot quotes play a price discovery role, and these quotes provide valuable information to traders with access to proprietary data feeds. Using a XGBoost machine learning algorithm that uses odd-lot data to predict future prices, we demonstrate a simple and profitable trading strategy. We argue the SEC’s proposed round-lot redefinition reduces—but does not eliminate—the high incidence of superior odd-lot quotes within the NBBO.

Using the model structure of Easley and O'Hara (Journal of Finance, 47, 577–604), we demonstrate how the parameters of the market-maker's beliefs can be estimated from trade data. We show how to extract information from both trade and no-trade intervals, and how intraday and interday data provide information. We derive and evaluate tests of model specification and estimate the information content of differential trade sizes. Our work provides a framework for testing extant microstructure models, shows how to extract the information contained in the trading process, and demonstrates the empirical importance of symmetric information models for asset prices.

We examine the role of insurance companies in supporting resiliency in the corporate bond market. We show that during the COVID-19 liquidity crisis, insurers increased their corporate bond positions, particularly in bonds facing fire sales by mutual funds. Insurers with more stable funding were more likely to buy, and they bought more from dealers with whom they had prior trading relationships. Dealers improved their bond liquidity provision when they had trading relationships with insurers with more stable funding. Our work demonstrates that insurers can play an important role in supporting bond market resiliency during times of stress.