We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7) and China. The quantile autoregression model (QAR) enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.

We study the cross-sectional effects of investor sentiment on stock price crash risk from the perspective of investor behavioural biases. We develop a firm-specific investor sentiment measure, and find that stocks with stronger investor sentiment are more prone to a future price crash. The positive relation is more pronounced for stocks eligible for margin trading, as higher investor sentiment induces greater margin buy/cover by optimistic investors. Short interest moderates the impact of optimistic sentiment on crash risk. The positive relation is also particularly prominent for stocks with a more speculative appeal, especially for those with lower institutional ownership.

This paper analyzes the effects of corporate venture capital (CVC) and independent venture capital (IVC) backing on technological innovation and value creation in invested companies. Using data on Chinese listed companies, we show that CVC investment can induce higher firm value than can IVC investment. By considering different dimensions of complementary assets, technological fit, geographical proximity, and product market relations, we explore the mechanism through which CVC contributes to innovation and value creation in invested companies in China.

This article imports a behavioural perspective into a team context to study the effort-coordination problem among agents. Specifically, we investigate how the presence of optimism bias impacts the severity of the free-rider problem, the organizational structure of the team and the compensation contracts offered to agents in equilibrium. The results indicate that all agents become more reluctant to exert effort and the team welfare decreases when some of its agents are optimistic, suggesting that optimism aggravates the free-rider problem in teams. Appointing a team leader makes all agents work harder, and the team benefits more by having the optimistic agent as its leader. These findings are in sharp contrast to the effects of overconfidence as identified in the literature. It is advisable to pay the optimistic agents less than the rational agents. Encouragingly, optimistic agents can learn about their own bias in the long run, leaving their team without too much detriment of optimism.

The Zero-Price Probability (ZPP) model is applied to evaluate the credit risk of listed companies in China and its performance is compared to that of the Kealhofer-McQuown-Vasicek (KMV) model. The sample includes 34 financially distressed companies and a comparison group of 34 financially healthy companies. The performances of ZPP and KMV models are compared using various descriptive statistics and statistical tests. The empirical analyses show that the ZPP model is superior to the KMV model in terms of discriminatory power. Compared to the KMV model, the ZPP model performs much better in distinguishing between financially challenged and healthy firms. Among different specifications of the ZPP model, the naïve constant variance zero-price probability model outperforms those with generalized autoregressive conditional heteroskedasticity specifications. This article is among the very first studies that provide evidence on the performance of the ZPP model.