This paper develops a multi-period rational expectations model of stock trading in which investors have differential information concerning the underlying value of the stock. Investors trade competitively in the stock market based on their private information and the information revealed by the market-clearing prices, as well as other public news. We examine how trading volume is related to the information flow in the market and how investors' trading reveals their private information.
The paper analyses the role of investor type (individual-institutional) in trading volume dynamics in securities traded on the Spanish stock market. The results contrast with the evidence found for the US, by showing no sign that differences in investor type generate significant variation in the dynamic behaviour of trading volume, ordinary autocorrelation, or trading in the event of major market movements. Differences in autocorrelation are found when the level of mutual funds ownership of a stock is included in the analysis. These findings are consistent with the view that it is not only the institutional nature, but also the different investment targets of these agents, that can influence trading volume dynamics.
Volume can be the key to understanding what is relly happening in the stock market. Volume is a dynamic aspect of the market, reflective of supply and demand, and thus crowd behaviour. This work gives a complete explanation of volume and how it can be used to trade more effectively. It shows that volume behaviour and changes can provide important clues to price movement, in direct contrast to such established theories as random walk and efficient markets that state that price is essentially unpredictable. The book explains how volume discloses the amount and type of interest in a stock. By examining and understanding the dynamics of volume, the trader can pinpoint the rise, climax and fall of the activity of market participants (behavioural finance), which provides an extremely reliable indicator of price reversal in real time. Knowing this helps the trader get out near stock or market tops and buy near stock or market bottoms.
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
The models of portfolio selection and asset price dynamics in this volume seek to explain the market dynamics of asset prices. Presenting a range of analytical, empirical, and numerical techniques as well as several different modeling approaches, the authors depict the state of debate on the market selection hypothesis. By explicitly assuming the heterogeneity of investors, they present models that are descriptive and normative as well, making the volume useful for both finance theorists and financial practitioners. Explains the market dynamics of asset prices, offering insights about asset management approaches Assumes a heterogeneity of investors that yields descriptive and normative models of portfolio selections and asset pricing dynamics
In this paper we study the dynamic relationship between trading volume, volatility, and stock returns at the international stock markets. First, we examine the role of volume and volatility in the individual stock market dynamics using a sample of ten major developed stock markets. Next, we extend our analysis to a multiple market framework, based on a large sample of cross-listed firms. Our analysis is based on both semi-nonparametric (Flexible Fourier Form) and parametric techniques. Our major findings are as follows. First, we find no evidence of the trading volume affecting the serial correlation of stock market returns, as predicted by Campbell et.al (1993) and Wang (1994). Second, the stock market volatility has a negative and statistically significant impact on the serial correlation of the stock market returns, consistent with the positive feedback trading model of Sentana and Wadhwani (1992). Third, the lagged trading volume is positively related to the stock market volatility, supporting the information flow theory. Fourth, we find the trading volume to have both an economically and statistically significant impact on the price discovery process and the co-movement between the international stock markets. Overall, these findings suggest the importance of the trading volume as an information variable.
The literature on asset market and market microstructure has devoted surprisingly little attention to trading volume. Many economic models of financial markets and market microstructure have been developed to explain the predictability of prices (returns), and information content of it. However, far less attention has been devoted to explain the behavior of trading volume. To fill this gap in the literature, this study tries to expand our understanding of trading volume for an emerging market by empirically estimating econometric models using recently available daily volume data for individual securities listed on the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). The analysis carried out serves several purposes: a) understand the motives for trade & the process by which trades are realized, b) the interaction between price and volume, and c) the roles that risk preferences and market frictions play in determining stock trading activity. Our empirical contributions include: (1) the construction of a volume based index for the Indian equity markets & comprehensive exploratory data analysis of the time-series behavior of trading volume; (2) modeling trading volume series using long-memory models and its forecasting performance; (3) estimation of dynamic price & volume relations using Markov Switching framework; and (4) a new approach for empirically identifying various factors determining the stock trading volume. The empirical result that stock trading volume is a long-memory process, does not affect market efficiency.
For years, traders and investors have been using unproven assumptions about popular patterns such as breakouts, momentum, new highs, new lows, market breadth, put/call ratios and more without knowing if there is a statistical edge. Common wisdom holds that the stock markets are ever changing. But, as it turns out, common wisdom can be wrong. Offering a comprehensive look back at the way the markets have acted over the last two decades, How Markets Really Work: A Quantitative Guide to Stock Market Behavior, Second Edition shows that nothing has changed, that the markets behave the same way today as they have in years past, and that understanding this puts you in a prime position to profit. Written by two top financial experts and filled with charts and graphs that illustrate the market concepts they develop, the book takes a sometimes contrarian view of everything from market edges to historical volatility, and from volume to put/call ratio, giving you all that you need to truly understand how the markets function. Fully revised and updated, How Markets Really Work, Second Edition takes a level-headed, data-driven look at the markets to show how they function and how you can apply that information intelligently when making investment decisions.
