An empirical investigation of trading on asymmetric information and heterogeneous prior beliefs
ABSTRACT The purpose of this study is to identify and analyze inter-temporal trading patterns attributable to informed trading. Recent theoretical models posit that heterogeneous prior beliefs provide a source of trading volume in addition to the commonly accepted trading motives of liquidity and asymmetric information. After separating informed from uninformed trading using the estimation procedure of Easley et al. [Journal of Finance 51 (1996) 1405], we test for the presence of trading on heterogeneous beliefs as opposed to asymmetric information. The empirical findings confirm the existence of trading on heterogeneous prior beliefs and generally support the inter-temporal patterns proposed by Wang [Journal of Financial Markets 1 (1998) 321].
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ABSTRACT: An information flow over a period of time can affect the market in two ways: the value of the concerned assets and the degree of heterogeneity of investor anticipations. In this paper, we focus on the second aspect through the study of the information flow generated by European Commission interventions in the field of merger and acquisition monitoring. Our question is the following: does the information flow coming from the Directorate General of Competition create heterogeneity among investors on the market about the valuation of the concerned asset? We use a sample of 74 firms involved in 45 contested merger and acquisition operations during the years 1990 to 1999. Our methodology is based on the GARCH framework. The main result is that no systematic statistically significant increase in heterogeneity can be attributed to the activities of the European Commission. We conclude that the communication of information to the market by the Directorate General of Competition is done efficiently.SSRN Electronic Journal 04/2001; DOI:10.2139/ssrn.268970
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ABSTRACT: We have taken part in this study to analyse the intervening parties' behaviours, we have considered; that investors are unable to analyze rationally the information, supposing that their potentialities are limited. This was largely discussed by the behavioural finance which tries to explain the anomalies of the market in particular; the limited rationality, the mimetic and the opportunist behaviours; which set up the notion of privileged information. These latter largely worry the legal safety measures, which take care to penalise and to repress any user of confidential information having for aim to distort the normal market's functioning by disseminating false or misleading information, or while trying to handle the courses with various forms of trick or cheating. The behaviours of informed traders are not completely revealing their privileged information, these latter seek realizing profits which are not detected by the market; their operations constitute an anomaly which blocks the correct market functioning, thus putting back the question of efficiency.
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ABSTRACT: Chordia, Roll and Subrahmanyam (2005, CRS) estimate the speed of convergence to market efficiency based on short-horizon return predictability of the 150 largest NYSE firms. We extend CRS to a broad panel of NYSE stocks and are the first to examine the relation between electronic communication networks (ECNs) and the corresponding informational efficiency of prices. Overall, we confirm CRS's result that price adjustments to new information occur on average within 5–15 min for large NYSE stocks. We further show that it takes about 20 min longer for smaller firms to incorporate information into prices. Most importantly, we demonstrate that the speed of convergence to market efficiency is significantly related to the type of trading platform where orders are executed, even after controlling for relative order flows, trading costs, volatility, informational effects, trading conditions, market quality, institutional trading activity, and other firm-specific characteristics. Our findings provide direct answers and insights to issues raised in a recent SEC concept release document.Journal of Empirical Finance 09/2012; 19(5):702-720. DOI:10.1016/j.jempfin.2012.08.006 · 0.84 Impact Factor