This paper analyzes the impact of changes in the securities transaction tax (STT) rate on the local A-shares market in China. We find that, on average, a 22-base-point- increase in the STT rate is associated with about a 28% drop in trading volume, while a 17-base-point- reduction in the STT rate is associated with about a 89% increase in trading volume in the Chinese A-shares market. Both the increases and reductions in the STT rate result in a significant increase in the market return volatility. Besides, the increases in the STT rate have mixed effects on market efficiency, either improving or curbing it. The reductions usually either make the market less efficient or have not effect on it. The empirical results together show that levying the STT on trading is not an effective tool to regulate stock market, at least in this emerging market.

China launched her warrant market in August 2005 in the split share structure reform of listed companies. As up to now, equity trading on margin and short-sale of any form are still prohibited in China. This warrant market enables investors to trade on information that otherwise might be prohibitively expensive to trade on. The Chinese warrant market created top trading volume and turnover with only a handful of different warrants traded. This paper first studies the Chinese warrant market. Empirical evidence shows that the market prices of warrants are much higher systematically than the Black-Scholes prices with historical volatility. Moreover, the paper documents ample evidence that the one-dimensional diffusion model does not apply well in the Chinese warrant market. The prices of a warrant and its underlying asset do not support the monotonicity, perfect correlation and option redundancy properties. The paper also studies the cumulated gains of a delta-hedged warrant portfolio. In the Chinese warrant market, the cumulated delta-hedged gains for almost all expired warrants are negative. The negative gains are mainly driven by the volatility risk, and the trading values of the warrants for puts and the market risk for calls. The investors are trading some other risks in addition to the underlying risk.

We study whether firm name has an effect on firm valuation. Some Chinese firms listed on U.S. stock exchanges have the word "China" or "Chinese" included in their company names ("China-name stocks"), whereas others do not ("non-China-name stocks"). During the China stock market boom in 2007, we find that China-name stocks significantly outperform non-China-name stocks. This is not due to differences in firm characteristics, risk, or liquidity. We also find a significant increase in both abnormal returns and trading volumes of existing China-name stocks to the listing events of new Chinese initial public offerings. This "China-name effect" is largely consistent with the hypothesis that optimistic investor sentiment during the China stock market boom drives up China-name stocks more than non-China-name stocks.

Motivated by how transaction amount constrain trading volume and price volatility in stock market, we, in this paper, study the relation between volume and price if amount of transaction is given. We find that accumulative trading volume gradually emerges a kurtosis near the price mean value over a trading price range when it takes a longer trading time, regardless of actual price fluctuation path, time series, or total transaction volume in the time interval. To explain the volume-price behavior, we, in terms of physics, propose a transaction energy hypothesis, derive a time-independent transaction volume-price probability wave equation, and get two sets of analytical volume distribution eigenfunctions over a trading price range. By empiric test, we show the existence of coherence in stock market and demonstrate the model validation at this early stage. The volume-price behaves like a probability wave.

It is documented that technical analysis is a highly pervasive activity among stock traders and security analysts in China. This paper uses eight years’ data on daily stock prices and trading volume of thirty-nine companies listed on the Shenzhen Stock Exchange to examine the usefulness of technical analysis. Very weak evidence in support of return predictability is generated either by considering returns alone or by the use of volume in conjunction with returns. The results not only cast doubt on the ability of technical analysis to predict future price movements in China’s stock markets, but also challenge the views of market inefficiency for China that are based on aggregate market data rather than individual company data.

A substantial literature in institutional herding examines reasons for and evidence of correlated trading across institutional investors, but little has been written about the extent to which individual investor trading is correlated or why. We document that the trading of individuals is highly correlated and surprisingly persistent. Furthermore, we find that the systematic trading of individual investors is driven by their own decisions―trades they initiated―rather than by passive reactions to institutional herding. We discuss why this correlation is unlikely to stem from the same motivations as institutional herding. Correlated trading by individual is a necessary condition for the trading biases of individual investors to affect asset prices, since the trades of any particular individual are likely to be small. The preferences for buying some stocks while selling others must be shared by many individual investors if these preferences are to affect prices. We analyze trading records for 66,465 households at a large national discount broker between January 1991 and November 1996 and 665,533 investors at a large retail broker between January 1997 and June 1999. Using a variety of empirical approaches, we document that the trading of individuals is more coordinated than one would expect by mere chance. For example, if individual investors are net buyers of a stock this month, they are likely to be net buyers of the stock next month. In additional analyses, we present four stylized facts about the trading of individual investors: (1) they buy stocks with strong past returns; (2) they also sell stocks with strong past returns, though this relation is stronger than that for buys at short horizons (one to two quarters), but weaker at long horizons (up to 12 quarters); (3) their buying is more concentrated in fewer stocks than selling; and (4)they are net buyers of stocks with unusually high trading volume.

Motivated by the behavior of asset prices, trading volume, and price volatility during episodes of asset price bubbles, we present a continuous-time equilibrium model in which overconfidence generates disagreements among agents regarding asset fundamentals. With shortsale constraints, an asset buyer acquires an option to sell the asset to other agents when those agents have more optimistic beliefs. As in a paper by Harrison and Kreps, agents pay prices that exceed their own valuation of future dividends because they believe that in the future they will find a buyer willing to pay even more. This causes a significant bubble component in asset prices even when small differences of beliefs are sufficient to generate a trade. In equilibrium, bubbles are accompanied by large trading volume and high price volatility. Our analysis shows that while Tobin’s tax can substantially reduce speculative trading when transaction costs are small, it has only a limited impact on the size of the bubble or on price volatility.

In this paper, the author observes a stationary transaction volume distribution over a trading price range in intraday transactions on individual stocks by studying relationship between the volume and price of transaction through amount of transaction in stock market. The transaction or accumulated trading volume gradually emerges kurtosis near the price mean value over a price range when it takes a longer trading time, regardless of actual price fluctuation path, time series, or total transaction volume in the time interval. The volume/price behaves a probability wave toward an equilibrium price, driven by a restoring force that can be represented by a linear potential. In terms of physics, the author derives a time-independent transaction volume/price probability wave equation and gets two sets of analytical transaction volume distribution eigenfunctions over a price range when supply or demand quantity varies. By fitting and testing the functions with intraday real transaction volume distribution over a price range on a considerable number of individual stocks in Shanghai 180 Index, the author shows the existence of relative equilibrium in stock market and demonstrates the model validation at this early stage. It concludes that either General Equilibrium Theory or Price Random Walk Hypothesis is an extreme conditional case in the probability wave model. Thus, the author attempts to offer a unified micro and dynamic probability wave theory on transaction volume/price in financial market. 本文作者通过成交金额研究股票市场中的成交量与价格之间关系时，观察到每只股票全天的成交量（即累计交易量）在交易价格区间有一种平稳的分布关系。随着交易时间的延长，累计交易量在交易价格区间逐渐显现出在成交价格均值附近峰化的分布特征。这一特征与体系在此间交易价格涨落的路径、时间序列或总成交量的大小无关。成交量价的运动表现为能够用线性势表示的中心力的作用下，围绕体系某一均衡价格运动的几率波。由此，作者用物理的方法推导出不显含时间变量的证券成交量价的波动方程并且得到当供求关系变化时，两组解析的成交量随价格变化的分布函数。用该函数与上证180指数中一些股票在全天真实的成交量随价格的分布进行拟合和检验，作者初步证明了在股票市场中存在相对均衡并且验证了该模型的有效性。其结论是：无论一般均衡理论还是价格波动的随机游走假说都是几率波模型在极端条件下的一个特例。这样，作者试图提出一个适用于描述金融市场中统一、微观和动态的成交量价几率波理论。

Financial market is a typical complex system because it is an open trading system and behaved by a variety of interacting agents. The consequence of the interaction appears quite complex and nonlinear. Therefore, how to observe this system and find a simplified methodology to describe it is, probably, a key to understand and solve the problem. In this paper, the author observes a stationary transaction volume distribution over a trading price range, studied the relationship between the volume and price of transaction through the amount of it in stock market. The probability of accumulated trading volume (i.e. actual supply/demand quantity or transaction volume) that distributes over a trading price range gradually emerges kurtosis near a transaction price mean value in a transaction body system when it takes a longer trading time, regardless of actual trading price fluctuation path, time series, or total transaction volume in the time interval. The volume and price behaves a probability wave toward an equilibrium price, driven by an actual supply/demand quantity restoring or regressive force that can be represented by a linear potential (an autoregressive item in mathematics). In terms of physics, the author derives a time-independent security transaction probability wave differential equation and obtains an explicit transaction volume distribution function over the price, the distribution of absolute zero-order Bessel eigenfunctions, in a stable transaction body system when its supply and demand quantity is dynamic. By fitting and testing the function with intraday real transaction volume distributions over the price on a considerable number of individual stocks in Shanghai 180 Index, the author demonstrates its validation at this early stage, and attempts to offer a micro and dynamic transaction volume/price (actual supply/demand quantity and price) probability wave theory.

In this paper we study how a firm’s capital structure choice affects informed trading of its securities in the secondary markets and consequently, the information efficiency of its security prices. We identify two new factors as the potential determinants of the firm’s optimal capital structure policy: the liquidity premium caused by informed trading and, perhaps more importantly, the improved operating efficiency due to information revelation from its security prices. We show that, from these two perspectives, the optimal debt level is achieved at the point where there is no informed trading in the bond market and the informed traders are just about to trade in the bond market. Thus, the cost of debt financing differs in nature from that of the existing models. This has very different implications for the significance of the cost of debt financing and for financial system design. Our model can also explain the relative trading volumes in debt and equity markets.