Download or read book Essays on Stock Trading Volume, Volatility and Information written by Hanfeng Wang and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Essays on Stock Trading Volume, Volatility and Information" by Hanfeng, Wang, 王漢鋒, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of the thesis entitled Essays on Stock Trading Volume, Volatility and Information Submitted By Hanfeng WANG For the Degree of Doctor of Philosophy at the University of Hong Kong in June 2007 We focus on three topics that relate to trading volume in stock market in this thesis. In the first essay we find that trading volume not only contributes positively to the contemporaneous volatility, as indicated in previous literature, but also contributes negatively to the subsequent volatility. This pattern between trading volume and volatility is consistently held among individual stocks, volume-based portfolios, size-based portfolios, and market index, and among daily data and weekly data. These empirical findings tend to support that the Information-Driven-Trade (IDT) hypothesis is more pervasive and powerful in explaining trading activities in the stock market than the Liquidity-Driven-Trade (LDT) hypothesis. Our additional tests obtain three interesting findings, 1) liquidity and the degree of information asymmetry influence the relation between volume and subsequent volatility, 2) the effect of volume on subsequent volatility and volume size have a non-linear relationship, indicating that at least empirically there exists a most information-intensive volume for each stock, which is consistent with Barclay and Warner (1993, JFE)'s finding, 3) the effect of volume on subsequent volatility is asymmetric when the stock price moves up and down, and we attribute this asymmetry to the short-selling constraints. 2 In the second essay we examine the price and trading volume reaction around annual earnings announcements in the Chinese A-share and B-share markets. We document a reverting pattern in the CAR series around earnings announcement in A share market while the behavior of the CAR series in B share market is quite similar to that found in developed markets. We argue that the difference may be due to that some of the A share investors overreact to the information before the earnings announcement. Additionally, abnormally high volume occurs around the earnings announcement, in both A-share and B-share markets, however, contrary to abnormally high volume several days before the announcement in B-share market, abnormally low volume exists several days prior to the announcement in A-share market. Through cross-sectional analysis we find that abnormal trading volume on the announcement day, taken as an index of the surprise of earnings announcement, and the responsiveness of the market are positively correlated, and that the average return before the announcement is negatively correlated with the CAR after the announcement, which supports the A-share investors' overreaction to earnings announcement. We also find some evidence that A-share investors tend to be influenced by the market conditions. In the third essay we review the literature on herding behavior in financial market and build a new empirical model based on stock trading volume to detect the overall market herding behavior. With the model we find that in the Chinese stock market there is herding when the market moves up and there is no or little evidence of herding when the market moves down. For comparison we also extend the test to other international markets. Based on the empirical results we document with the Chinese market data we suggest canceling t