How corporate growth affects the choice between relationship-based debt and public debt remains an unsettled issue in the literature. For high-growth firms, the banking relationship mitigates asset-substitution and underinvestment problems due to debt financing. Close relationships, however, work against funding competition and facilitate holdup behavior by banks. This paper suggests an effective mechanism for high-growth firms, namely competition from equity, to curb banks’ rent extraction when public debt becomes more costly. According to the generalized Myers-Majluf view in the recent literature, new equity issues by high-growth firms actually reduce or even reverse the adverse-selection discount because asymmetric information about these firms’ valuations arise largely from growth rather than from assets-in-place. Our evidence from Japanese data for 1983 to 1997 shows that the relation between loan-to-debt ratio and growth, initially significantly negative, is indeed reversed toward the high end of growth spectrum and turns significantly positive. Consistent with our explanation, fast-growing high-flyers raise more new equity than do other firms. These results not only confirm the existence of both costs and benefits of monitored debt, but also explain why high-growth firms enjoy the benefits without fearing holdup behavior by banks.

We investigate how the market for corporate control (external governance) and shareholder activism (internal governance) interact. Looking at equity prices from 1990 to 2001, we find that these mechanisms are strong complements. A portfolio that buys firms with the highest level of takeover vulnerability and shorts firms with the lowest level of takeover vulnerability generates an annualized abnormal return of 10 - 15% only when public pension fund (blockholder) ownership is high as well. A similar portfolio created to mimic the importance of internal governance generates annualized abnormal returns of 8%, though only in the presence of ‘high’ vulnerability to takeovers. Further, we show that the complementary relation exists for firms with lower industry-adjusted leverage and is stronger for smaller firms. The complementary relation is confirmed using accounting measures of profitability. Using data on acquisitions, firm level Q’s and accounting performance, we explore possible interpretations, providing preliminary evidence for a risk effect as well.

We propose a model of project selection and design of managerial compensation contract that features adverse selection and moral hazard. Our model generates the rather intuitive result that the ex ante probability of a specific project being selected (or, equivalently, its manager being hired) is increasing in the type of the project/manager. Ex post, however, the most capable manager (i.e., the one with the highest type) is not necessarily the one who will be hired to run a project. Basically, when the managers’ types are not identically distributed, picking the most capable manager or selecting the most promising project may actually be inconsistent with the provision of optimal incentives to alleviate the inherent agency problems. Therefore, our model offers a rational explanation to the phenomenon that apparently more capable candidates are occasionally passed over in recruitment and job promotion situations. Our analysis also holds obvious implications for firms’ capital budgeting decisions. If the severity of the principal-agent conflict is sufficiently great (say, between the headquater and the divisional manager) and if the verification of the true project type (the NPV value) by the headquarter is sufficiently costly, we may well see instances where corporate headquarters rationally allocate scarce resources to a lower-NPV project ahead of a higher-NPV project.

Using a new statistical procedure suitable to test efficient market hypothesis in presence of volatility clustering, we find significant evidence against the weak form of efficient market hypothesis for both Shanghai and Shenzhen stock markets, although they have become more efficient at the later stage. We also find that Share A markets are more efficient than Share B markets, but there is no clear evidence on which stock market, Shanghai or Shenzhen, is more efficient. These findings are robust to volatility clustering, a key feature of high-frequency financial time series. They have important implications on predictability of stock returns and on efficacy of capital asset pricing and allocation in Chinese economy.

This paper reports the effort of using agent-based mix-game model to predict financial time series. It introduces simple generic algorithm into the prediction methodology, and gives an example of its application to forecasting Shanghai Index. The results show that this prediction methodology is effective and agent-based mix-game model is a potential good model to predict time series of financial markets.

This paper investigates a two-factor affine model for the credit spreads on corporate bonds. The Þrst factor can be interpreted as the level of the spread, and the second factor is the volatility of the spread. The riskless interest rate is modeled using a standard two-factor affine model, thus leading to a four-factor model for corporate yields. This approach allows us to model the volatility of corporate credit spreads as stochastic, and also allows us to capture higher moments of credit spreads. We use an extended Kalman Þlter approach to estimate our model on corporate bond prices for 108 Þrms. The model is found to be successful at Þtting actual corporate bond credit spreads, resulting in a signiÞcantly lower root mean square error (RMSE) than a standard alternative model in both in-sample and out-of-sample analyses. In addition,key properties of actual credit spreads are better captured by the model.

This paper develops a reduced form model of interest rate swap spreads. The model accommodates both the default risk inherent in swap contracts and the liquidity difference between the swap and Treasury markets. We use an extended Kalman Þlter approach to estimate the model parameters. The model Þts the swap rates well. We then solve for the implied general collateral repo rates and use them to decompose the swap spreads into their default risk and liquidity components. This exercise shows that the default risk and liquidity components of swap spreads behave very differently: although default risk accounts for the largest share of the levels of swap spreads, the liquidity component is much more volatile. In addition, while the default risk component has been historically positive, the liquidity component was negative for much of the 1990s and has become positive since the Þnancial market turmoils in 1998.

We provide a comprehensive analysis of the out-of-sample performance of a wide variety of spot rate models in forecasting the probability density of future interest rates. While the most parsimonious models perform best in forecasting the conditional mean of many financial time series, we find that the spot rate models that incorporate conditional heteroskedasticity and excess kurtosis or heavy-tails have better density forecasts. GARCH significantly improves the modeling of the conditional variance and kurtosis, while regime switching and jumps improve the modeling of the marginal density of interest rates. Our analysis shows that the sophisticated spot rate models in the existing literature are important for applications involving density forecasts of interest rates.

We try to explain the severe under-pricing of 523 A-share IPOs in the Chinese markets from 1997 to 2001 using institutional characteristics, absolute value, and relative value of IPO. We find that relative values of IPO are critical determinants of the severe under-pricing of A-share IPOs in China. We also find that relaxing government regulation of offering price increases under-pricing, and thus conclude that the severe under-pricing of A-share IPOs in China is not caused by the government regulation of offering price. We propose a relative value theory to explain why relaxing government regulation of offering price results in higher under-pricing and find some support for the theory.

In this paper we use artificial neural network (ANN) to empirically analyze the liquidity values of the non-floating shares and the influencing factors to China’s stock market in the background of China’s listed companies split share stricture reform. We try to use a proportion which the company’s non-floating shareholders offer compensation to the floating shareholders to test the liquidity values of the non-floating shares and use MATLAP establish a feed-forward BP neural network model to analyze and forecast according to the data of the companies which have announced and actualized their split stricture reform plans. In expansion analysis, we use the perturbation method to measure the influence of these parameters on the liquidity values of the non-floating. As result, the character of the shares, the share structure and the ratio of the shares by the principal shareholder held are the main influencing factors.