[Show abstract][Hide abstract] ABSTRACT: We test whether enforcement of insider trading laws deters illegal trading. Examining insider trading cases filed by the SEC over the last decade, we find that the price impact on insider trading days is much smaller than the effect documented by Meulbroek (1992) for the 1980s, consistent with increased fear of prosecution. Moreover, we find that insider volume is negatively related to resource-based enforcement measures, providing direct evidence that aggressive enforcement deters illegal activity. Finally, we confirm that higher levels of SEC enforcement intensity are associated with lower pre-announcement run-up in general samples of takeover bids and earnings announcements.
[Show abstract][Hide abstract] ABSTRACT: Models of adverse selection risk generally assume that market makers offset expected losses to informed traders with expected gains from the uninformed. We recognize that the expected loss captures a combination of two effects: (1) the probability that some traders have private information, and (2) the likely magnitude of that information. We use a maximum-likelihood approach to separately estimate the probability and magnitude of private information events for NYSE-listed stocks from 1993 through 2003. The results shed light on the price discovery process and have implications for many areas of finance.
[Show abstract][Hide abstract] ABSTRACT: We analyze contemporaneous and predictive relations between credit ratings and measures of equity market liquidity and find that common measures of adverse selection, which reflect a portion of the uncertainty about future firm value, are larger when credit ratings are poorer. We also show that future rating changes can be predicted using current levels of adverse selection. Collectively, our results validate widely used microstructure measures of adverse selection and offer new insights into the value of credit ratings and the specific nature of the information they contain. Copyright 2006, Oxford University Press.
Review of Financial Studies 02/2006; 19(1):119-157. · 4.75 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We examine the impact of preferencing on execution quality for NASDAQ and NYSE-listed stocks. Our theoretical model demonstrates that realized spreads are more reliable than effective spreads in the presence of preferencing, but even realized spreads are a poor measure of execution quality if the stocks being compared have different degrees of information asymmetry. We provide a new measure of the costs of preferencing that is independent of asymmetric information. Using data from the SEC 11Ac1-5 reports for marketable orders of up to 2000 shares, we find that both realized spreads and our preferencing measure are lower for NYSE-listed stocks.
[Show abstract][Hide abstract] ABSTRACT: The apparent conflict between the level of resources dedicated to technical analysis by practitioners and academic theories of market efficiency is a long-standing puzzle. We offer an alternative explanation for the value of technical analysis that is consistent with market efficiency -- specifically, that it reveals information about liquidity provision. We find evidence consistent with the hypotheses that support and resistance levels coincide with peaks in depth on the limit order book and that moving average forecasts reveal information about the relative position of depth on the book. These results demonstrate that technical analysis can have value even in an efficient market, and provide a practical method for estimating the level of liquidity on the book. 1.
[Show abstract][Hide abstract] ABSTRACT: In 1997, the Securities and Exchange Commission enacted significant reforms in U.S. markets. Several studies document that the new order handling rules increased competition for Nasdaq stocks, but the reforms were designed with an additional goal in mind—to increase quote competition for the trading of NYSE-listed securities on Nasdaq (i.e., third market trading). An evaluation of the reforms in the third market indicates that they did not achieve this objective. Instead, both quote quality and quoting frequency were diminished, due primarily to elimination of the excess spread rule. This suggests that more significant changes are needed to increase inter-exchange competition.
Journal of Financial and Quantitative Analysis 01/2004; 39(02):277-304. · 1.77 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This study examines volatility within three related intra-day series – transaction returns, quote midpoint returns, and limit order book midpoint returns – for a set of NYSE-listed stocks. We document statistically significant GARCH effects both overall and surrounding earnings announcements in all three series for the majority of stocks in the sample. We then compare the extent of volatility clustering among the series. In addition, the relation between volatility and market structure is examined via a set of cross-sectional regressions, and relations among the series over time are studied in a vector autoregressive framework.
Journal of Financial Markets 10/2001; · 1.12 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: New York Stock Exchange specialists disseminate information to market participants by displaying price schedules consisting of bid prices, ask prices, bid depths, and ask depths. We examine how specialists update these price schedules in a simultaneous equations model. We find that changes in the best prices and depths on the limit order book have a significant impact on the posted price schedule, while the effects of transactions and order activity are secondary. Furthermore, we show that specialists revise prices and depths differently, but find no evidence that they revise the price schedule in response to changes in inventory. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.
Review of Financial Studies 02/2001; 14(3):681-704. · 4.75 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The validity of many economic studies hinges on the ability to properly classify trades as buyer or seller-initiated. This study uses the TORQ data to investigate the performance of the Lee and Ready (1991, Journal of Finance 46, 733–746.) trade classification algorithm. I find that the algorithm correctly classifies 85% of the transactions in my sample, but systematically misclassifies transactions at the midpoint of the bid–ask spread, small transactions, and transactions in large or frequently traded stocks. I then provide evidence of the biases induced by inaccurate trade classification.
Journal of Financial Markets 08/2000; · 1.12 Impact Factor