Untangling the nexus of stock price and trading volume: evidence from the Chinese stock market

This paper explores the linear and non-linear causal relationship between stock price and trading volume in China. The empirical results substantiate that there is a long-run level equilibrium relationship between the stock price and trading volume in China. The results from the linear causality tests indicate that there is unidirectional causality running from price to volume for the case of Shanghai B and Shenzhen B shares in the short-run, but there is a bidirectional causal relation between price and volume for the case of Shanghai A share and Shenzhen A share. In the results of the non-linear Granger causality, evidence shows that there is neutral price-volume relation for Shanghai B share. However, there is a bidirectional non-linear price-volume causal relation for the case of Shanghai A share and Shenzhen A share. For the case of Shenzhen B share, there is a unidirectional non-linear Granger causal relationship running from the stock price to the trading volume.Price

Empirical regularities of opening call auction in Chinese stock market

We study the statistical regularities of opening call auction using the ultra-high-frequency data of 22 liquid stocks traded on the Shenzhen Stock Exchange in 2003. The distribution of the relative price, defined as the relative difference between the order price in opening call auction and the closing price of last trading day, is asymmetric and that the distribution displays a sharp peak at zero relative price and a relatively wide peak at negative relative price. The detrended fluctuation analysis (DFA) method is adopted to investigate the long-term memory of relative order prices. We further study the statistical regularities of order sizes in opening call auction, and observe a phenomenon of number preference, known as order size clustering. The probability density function (PDF) of order sizes could be well fitted by a $q$-Gamma function, and the long-term memory also exists in order sizes. In addition, both the average volume and the average number of orders decrease exponentially with the price level away from the best bid or ask price level in the limit-order book (LOB) established immediately after the opening call auction, and a price clustering phenomenon is observed.Comment: 11 pages, 6 figures, 3 table

Do Chinese stock markets share common information arrival processes?

According to the Mixture of Distributions Hypothesis (MDH), returns volatility and trading volume are driven by a common news arrival variable. Consequently, these two variables should be correlated. This paper extends, and to some extent, globalises the concept of a common information arrival process by hypothesising that this variable drives daily price (returns) volatility and trading volume changes in different financial markets. An implication is that returns volatility in one stock market should show positive and contemporaneous correlation with returns volatility in another stock market. This paper tests this implication using data from three separate, but geographically close, stock markets (Shenzhen, Shanghai and Hong Kong). A problem in the usual testing procedure is the likelihood that the news arrival process has long memory. This means that both volatility and volume (or external volatility) will have long memory and consequently, contemporaneous correlation between these variables is likely to be incorrectly rejected in cases where the test equation does not account for long memory. This paper uses fractionally integrated GARCH (FIGARCH) to test and account for long memory. The analysis finds that there is contemporaneous correlation between returns volatility in these stock markets and confirms the presence of long memory effects.mixture of distributions hypothesis, news arrival process, FIGARCH, volatility, long memory

Preferred numbers and the distribution of trade sizes and trading volumes in the Chinese stock market

The distribution of trade sizes and trading volumes are investigated based on the limit order book data of 22 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. We observe that the size distribution of trades for individual stocks exhibits jumps, which is caused by the number preference of traders when placing orders. We analyze the applicability of the "$q$-Gamma" function for fitting the distribution by the Cram\'{e}r-von Mises criterion. The empirical PDFs of trading volumes at different timescales $\Delta{t}$ ranging from 1 min to 240 min can be well modeled. The applicability of the $q$-Gamma functions for multiple trades is restricted to the transaction numbers $\Delta{n}\leqslant8$. We find that all the PDFs have power-law tails for large volumes. Using careful estimation of the average tail exponents $\alpha$ of the distribution of trade sizes and trading volumes, we get $\alpha>2$, well outside the L{\'e}vy regime.Comment: 7 pages, 5 figures and 4 table

Stock Prices in a Speculative Market: The Chinese Split-Share Reform

In 2005-2006 China reformed its stock market by eliminating non-tradable shares. The regulator set general guidelines and then assigned responsibility for implementation to each company. We derive relations that should have been followed by the prices of stocks and exploit a company-level data set to compare the actual and the theoretical price reactions. We find evidence for abnormal returns both before the beginning of the reform and during the reform. Cross-sectionally, abnormal returns are associated mainly with turnover and compensation. This shows that in a speculative market, investors do not properly react to unambiguous corporate actions.Speculation, Chinese Stock Market, Market segmentation, Event study, Market Efficiency

Quantifying bid-ask spreads in the Chinese stock market using limit-order book data: Intraday pattern, probability distribution, long memory, and multifractal nature

The statistical properties of the bid-ask spread of a frequently traded Chinese stock listed on the Shenzhen Stock Exchange are investigated using the limit-order book data. Three different definitions of spread are considered based on the time right before transactions, the time whenever the highest buying price or the lowest selling price changes, and a fixed time interval. The results are qualitatively similar no matter linear prices or logarithmic prices are used. The average spread exhibits evident intraday patterns consisting of a big L-shape in morning transactions and a small L-shape in the afternoon. The distributions of the spread with different definitions decay as power laws. The tail exponents of spreads at transaction level are well within the interval $(2,3)$ and that of average spreads are well in line with the inverse cubic law for different time intervals. Based on the detrended fluctuation analysis, we found the evidence of long memory in the bid-ask spread time series for all three definitions, even after the removal of the intraday pattern. Using the classical box-counting approach for multifractal analysis, we show that the time series of bid-ask spread does not possess multifractal nature.Comment: 8 EPJ pages including 7 eps figure

Empirical properties of inter-cancellation durations in the Chinese stock market

Order cancellation process plays a crucial role in the dynamics of price formation in order-driven stock markets and is important in the construction and validation of computational finance models. Based on the order flow data of 18 liquid stocks traded on the Shenzhen Stock Exchange in 2003, we investigate the empirical statistical properties of inter-cancellation durations in units of events defined as the waiting times between two consecutive cancellations. The inter-cancellation durations for both buy and sell orders of all the stocks favor a $q$-exponential distribution when the maximum likelihood estimation method is adopted; In contrast, both cancelled buy orders of 6 stocks and cancelled sell orders of 3 stocks prefer Weibull distribution when the nonlinear least-square estimation is used. Applying detrended fluctuation analysis (DFA), centered detrending moving average (CDMA) and multifractal detrended fluctuation analysis (MF-DFA) methods, we unveil that the inter-cancellation duration time series process long memory and multifractal nature for both buy and sell cancellations of all the stocks. Our findings show that order cancellation processes exhibit long-range correlated bursty behaviors and are thus not Poissonian.Comment: 14 pages, 7 figures and 5 table