This paper examines how liquidity is priced in the Chinese corporate bond market. Unlike the pervasive liquidity premium across bonds traded in the interbank market, we document a puzzle in the liquidity-yield relation in the exchange market. Before the first significant credit event in March 2014, there was a "liquidity discount" in this market-the more illiquid a corporate bond, the lower its yield spread. This liquidity discount disappears after the first bond default event. The liquidity premium, i.e., a positive relation between illiquidity and yield, emerged when the credit condition for corporate bonds worsened after April 2018. We find that retail investors' yield-chasing behavior in the exchange market is the driving factor behind the liquidity discount effect. The liquidity discount is stronger for bonds favored by retail investors and during the period with a high intensity of yieldchasing behavior. Our empirical analysis further shows that our findings are not due to two alternative hypotheses based on the market microstructure effects of liquidity concentration and nonsynchronous trading.

This study investigates the link between market arbitrage risk and liquidity risk pricing in a conditional asset pricing framework. We estimate comparative models both at the portfolio and firm level in the Chinese A- and B-shares to test behavioral hypotheses with respect to foreign ownership restrictions and market segmentation. Results show that conditional liquidity premium and risk betas exhibit pronounced asymmetry across share classes which could be attributed to differentiated levels of market mispricing. Specifically, stocks with a greater degree of information asymmetry and retail ownership are more sensitive to liquidity risks when the market arbitrage risk increase. Further policy impact analysis shows that China’s market liberalization efforts, contingent upon its recent stock connect programs, conditionally reduce the price of liquidity risk for connected stocks.

This study highlights asymmetries in liquidity risk pricing from the perspective of heterogeneously informed traders facing changing levels of preference uncertainty. We hypothesize that higher illiquidity premium and liquidity risk betas may arise simultaneously in circumstances where investors are asymmetrically informed about their trading counterparts’ preferences and their financial firms’ timely valuations of assets . We first test the time-varying state transition patterns of IML, a traded liquidity factor of the return premium on illiquid-minus-liquid stocks, using a Markov regime-switching framework. We then investigate how the conditional price of the systematic risk of the IML fluctuate over time subject to changing levels of preference uncertainty. Empirical results from the Chinese stock market support our hypotheses that investors’ sensitivity to the IML systematic risk conditionally increase in times of higher preference uncertainty as proxied by the stock turnover and order imbalance. Further policy impact analyses suggest that China’s market liberalization efforts, contingent upon its recent stock connect and margin trading programs, reduce the conditional price of liquidity risk for affected stocks by helping the incorporation of information into stock prices more efficiently. Tighter macroeconomic funding conditions, on the contrary, conditionally increase the price of liquidity that investors require.

Until recently, Chinese companies publicly listed in domestic stock exchanges had two classes of stock: tradable and non-tradable shares. These two classes of stock had the same voting, cash flow, and all other legal rights except that non-tradable shares cannot be transferred at the open markets. From 2005 to mid-2007, Chinese government completed the ownership reform, so-called the Split Share Structure Reform (SSSR), to convert all non-tradable shares into tradable shares. Under this reform process, the holders of non-tradable shares had to negotiate with those of tradable shares to determine how much liquidity premium, or the compensation ratio, non-tradable shareholders have to pay to tradable shareholders in order to obtain the liquidity right. This paper starts with a theoretical model to identify the fundamental factors, including price discount before and after the SSSR reform, the percentage of non-tradable shares in total shares, the volatility of tradable share price, and the lockup period, that should determine the compensation ratio. We show that those factors except price discount before the reform are statistically significant in determining the compensation ratio proposed by non-tradable shareholders. We further show that the agency problems also reveal themselves in the compensation ratios. Specifically, when a firm is controlled by a governmental agency, the compensation is higher. However, the compensation is lower when more concentrated in the top ten holders, especially when shares are held by mutual funds. Thus, the evidence is consistent with the notion that the agency problem exists in China’s fund managers. Finally, we show that the existence of agency problems also reduce the importance of fundamental factors in determining the compensation ratios.

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.