The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

The Chinese government has actively traded in the stock market through governmentsponsored institutions, the National Team, since the 2015 market crash. I adopt Koijen and Yogo’s (2019) demand system approach in China’s stock market to understand the impact of government participation. Estimation results indicate the government tilts towards large, less risky, and SOE stocks. During the crash, government participation indeed stabilized the market: the large-scale purchases reduced the cross-sectional market volatility of annual return by 1.8% and raised the market price by 11%. When the market ffuctuation returns to normal, the government acts more like an active investor; its price impact remains high but does not contribute to the cross-sectional volatility. Based on the theoretical framework of Brunnermeier et al. (2020), I investigate the interaction between the Nation Team and retail investors to reveal the government trading strategy. No evidence shows that government participation signiffcantly distorts market information efficiency.

The rise of automated trading systems has made stock trading more accessible and convenient, reducing the link between traditional illiquidity measures and stock returns. However, empirical data in China’s stock market shows conflicting results. We find a significantly positive correlation between intraday illiquidity and future returns in China’s stock market. We offer that the pricing ability of this intraday illiquidity originates from the correlation between trading activity and intraday return. This finding provides compelling out-of-sample evidence for the debate regarding the pricing of the Amihud (2002) measure in the U.S. market. Additionally, we create an intradayreturn illiquidity factor that outperforms Liu, Stambaugh, and Yuan (2019) sentiment factors in China’s stock market.

This study investigates and compares the effects of the Coronavirus Disease 2019 (COVID-19) pandemic, the Chicago Mercantile Exchange (CME)'s negative price suggestion on prices and trading activities in the crude oil futures market to discuss the cause of negative crude oil futures prices. Through event studies, our results show that the COVID-19 pandemic no longer impacts crude oil futures prices in April after controlled market risk, while the CME’s negative prices suggestion can explain the crude oil futures price changes around and around even after April 8 to some degree. Moreover, our study uncovers anomalies in prices and trading activities by analyzing returns, trading volume, open interest, and illiquidity measures using vector autoregressive (VAR) models. The results imply that CME’s allowing negative prices strengthens the price impact on trading volume and makes illiquidity risk matter. Our results coincide with the following lawsuit evidence of market manipulation.

Fluctuations in investor demand dramatically affect firms' valuation and access to capital. To quantify its real impact, we develop a dynamic investment model that endogenizes both the demand- and supply-side of capital. Strong investor demand elevates equity prices and dampens price impacts of issuance, facilitating investment and financing, while weak investor demand instead incentivizes firms to optimally repurchase shares at favorable prices, which can crowd out investment, especially among firms with liquidity constraints. We estimate the model using indirect inference by matching the endogenous relationship between investors' portfolio holdings and firm characteristics. Our estimation suggests that investor demand substantially distorts firms' real investment decisions and impedes the efficient capital allocation across firms. Eliminating excess demand reduces dispersion in the marginal product of capital by 10.74% and TFP losses by 16.20%. Investor demand also influence firm size distributions and generates a heavy right tail---large excess demand provides firms with market power and opportunities to profit from their financial market activities, contributing to the emergence of superstar firms.

We examine the process of stock prices adjusting to information conveyed by the trading process. Using the price impact of a trade to measure its information content, our analysis shows that the weekly price impact of market transactions has significant cross-sectional predictive power for returns in the subsequent week. The effect is sensitive to the level of informational asymmetry and is not due to excess liquidity demands or variations in rational risk premia. This finding suggests that prices may slowly incorporate trading information. We then characterize the key channel through which price underreaction occurs. We find that the price impact contains information that is not fully captured by public order flows and that a lead-lag effect exists regarding the arrival of information to different groups of investors. Hong and Stein’s (1999) gradual-information-diffusion theory seems the most likely explanation for price underreaction.

Using block trades data on the Shanghai Stock Exchange (SSE) from 2003 – 2009, we study the pricing mechanisms of block buys and sells. We show that block trades are priced at discount (premium) for sells (buys). The discount/ premium varies depending on the characteristics of the stocks traded, the complexity of the trades, and also on whether the trades are internalized. We also study permanent and temporary price impact of the trades. As expected, seller-initiated trades do not seem to be information related as there is no significant information content. On the contrary, the prices decline after buyer-initiated trades, suggesting that buyers do not possess private information which leads to a permanent shift in prices. Temporary price impacts of all trades are large in magnitude and statistically significant, reflecting compensation for locating counterparties and the cost of negotiating terms. This suggests that the information platform on SSE for locating counterparties is yet to be fully developed to help reduce the transaction cost of block trades.

A fundamental question in finance is whether and how removing market frictions is associated with efficiency gains. We study this question using share issue privatization in China that took place through the split share structure reform. Prior to the reform, domestic A-shares were divided into tradable and non-tradable shares with identical cash flow and voting rights. Under the reform, holders of the non-tradable shares negotiated a compensation plan with holders of the tradable shares in order to make their shares tradable. We hypothesize that efficiency gains in terms of better risk sharing play an important role in the determination of compensation. We show that the size of compensation is positively associated with both the gain in risk sharing and the price impact of more shares coming to the market after the reform, and is negatively associated with the bargaining power of holders of non-tradable shares and firm performance. Our study highlights the role of risk sharing in China’s share issue privatization.

The paper examines the price impact of mutual funds in the Chinese equity market from 2000 to 2007. We find there is strong positive correlation between stock returns and mutual fund holding and trading, and the price impact is more significant in mutual-fund buying than mutual-fund selling. Our findings support the hypothesis that the price impact is due to the information advantage of mutual funds.

This paper considers informed traders’ trading strategies in a bear market. Known as stealth trading, one strategy of informed traders’ is to use medium-size trades, which tend to contain more information than small- and large-size ones and thus to have stronger impact on stock price movement. Using the tick-by-tick data of Shanghai 180 Index Component Stocks, we document the strong pattern of stealth trading in Chinese stock market during the period of June 1, 2004 to May 31, 2005, which is: (1) an order-driven market; (2) a market that has limit orders only; (3) a bear market; (4) a market with no corresponding derivative market; (5) a market with short-selling constraints; (6) an emerging market. The results extend the empirical evidence on the stealth trading by documenting the fact that price movements are mainly due to the medium-size trades. We find that the pattern in a bear market is highly consistent with that in a bull market. First, we observe that the per-transaction stock price changes in different trade-size categories exhibit a clear U-shape and only the price changes induced by medium-size trades are consistent with the market movement direction. We formally test the stealth trading as well as four alternative hypotheses, and conclude that stealth trading hypothesis can correctly explain this phenomenon. Second, the evidence shows that the medium- size trades have stronger impacts on price change s in the stocks whose price movements are highly consistent with the market (in our study, it refers to those stocks with severely low cumulative return in the sample period). Third, we further document that there is strong interaction between stealth trading hypothesis and order imbalance hypothesis. However, after controlling the effect of order imbalance, the stealth trading hypothesis still holds, but the magnitude is much lower. It is suggested that the follow-up researchers take into consideration the effect of order imbalance, when confirming the existence or the magnitude of the stealth trading.