We construct firm-level indicators to capture the engagement in the Belt and Road Initiative (BRI, henceforth) via textual analysis. We find that higher firm engagement in BRI predicts higher stock returns in the subsequent 12 months. The top 10% high-BRI firms have 12.42% higher annual returns than bottom 10% low-BRI firms in China A-Share market. Additionally, two fundamental channels of increased earnings and reduced liabilities explain the higher expected returns of high-BRI firms. Furthermore, we reveal that the phenomenon is more pronounced among non-state-owned enterprises. For large-cap firms, BR Report is a more effective indicator for predicting future stock returns, while BR Beta performs better for small-cap firms. These findings contribute to the measurement of firm engagement in BRI and its impact on the stock market.

We examine the performance of actively managed Chinese mutual Funds over the period 2002-2020. Using the bootstrap-based false discovery technique, we find that 19.25% of Chinese actively managed mutual funds produce positive-alpha, which contrasts with existing studies documented by others in developed markets. Our findings survive a battery of robustness tests. Unlike in developed markets, equilibrium accounting may not hold in China as the Chinese stock market is dominated by retail investors instead of mutual funds, and thus the mutual funds in China can be more skilled at the expense of the retail investors. We find supportive evidence of the applicability of the bootstrap-based false discovery rate method by conducting simulations.

We apply various test portfolios and alternative statistical methodologies to evaluate the performance of eleven prominent asset pricing models. To compile the test portfolios, we construct 105 anomalies in China and apply the 23 significant anomalies as test assets for model comparison. The results indicate that in the time-series test and anomalies explanation, the Hou et al. (2019) five-factor q model exhibits the best overall performance. The pairwise cross-sectional R^2s and the multiple model comparison tests affirm that the Hou et al. (2019) five-factor q model, the Fama and French (2018) six-factor (FF6) model and the Kelly et al. (2019) five-factor Instrumented Principal Component Analysis (IPCA5) model stand out as the top performers. Notably, the performance of the five-factor q model is insensitive to variations in experimental design.

This study examines whether sell-side analysts have the ability to disseminate information consistent with anomaly prescriptions in China. I adopt 192 trading-based and accounting-based anomaly signals to identify undervalued and overvalued stocks. Analysts tend to give more (less) favorable recommendations and earnings forecasts to undervalued (overvalued) stocks. On analyzing the information content, I find that analyst recommendations and earnings forecasts are consistent with accounting-based information rather than trading-based information. Analysts make recommendations and earnings forecasts consistent with anomalies, especially when firms experience relatively bad firm-level information. Additionally, undervalued (overvalued) stocks are associated with high (low) analyst coverage. The results indicate that analysts may contribute to mitigating anomaly mispricing and improving market efficiency in China.

Using daily news tone data between 2017 and 2020, we examine whether news tones can predict stock returns in Chinese A-share market. We first document that the news tones significantly and positively predict the cross-sectional stock returns over next day and over the next 12-weeks. When we separate the news into online news and paper news, the online news exhibit strong predictive power for future returns, while the printed news only displays marginal predictive power. We hypothesize that the online news is more related to firm fundamentals, while the paper news is more linked to political aspects of firm information. Our results using earnings surprises and SOE subsamples provide supportive evidence for the hypothesis.

Over the past 150 years, finance has evolved into one of the world’s most globalized, digitized and regulated industries. Digitalization has transformed finance but also enabled new entrants over the past decade in the form of technology companies, especially FinTechs and BigTechs. As a highly digitized industry, incumbents and new entrants are increasingly pursuing similar approaches and models, focusing on the economies of scope and scale typical of finance and the network effects typical of data, with the predictable result of the emergence of increasingly large digital finance platforms. We argue that the combination of digitization, new entrants (especially BigTechs) and platformization of finance – which we describe as FinTech 4.0 and mark as beginning in 2019-2020 – brings massive benefits and an increasing range of risks to broader sustainable development. The platformization of finance poses challenges for societies and regulators around the world, apparent most clearly to date in the US and China. Existing regulatory frameworks for finance, competition, data, and technology are not designed to comprehensively address the challenges to these trends to broader sustainable development. We need to build new approaches domestically and internationally to maximize the benefits of network effects and economies of scope and scale in digital finance while monitoring and controlling the attendant risks of platformization of finance across the existing regulatory silos. We argue for a principles-based approach that brings together regulators responsible for different sectors and functions, regulating both on a functional activities based approach but also – as scale and interconnectedness increase – addressing specific entities as they emerge: a graduated proportional hybrid approach, appropriate both domestically in the US, China and elsewhere, as well as for cross-border groups, building on experiences of supervisory colleges and lead supervision developed for Globally Systemically Important Financial Institutions (G-SIFIs) and Financial Market Infrastructures (FMIs). This will need to be combined with an appropriate strategic approach to data in finance, to enable the maximization of data benefits while constraining related risks.

Based on a DSGE model embedded with a stock market, we inspect interconnection between China's financial markets and macroeconomic cycles. We find consumption, investment and capacity utilization display significant and positive responses to stock market booms triggered by financial and confidence shocks, however, inflation responds insignificantly. We perceive a counteractive and significant reaction of China's monetary policy rule to credit-to-GDP gap at business cycle frequency. We decompose stock price into fundamental value influenced by the financial shock and speculative bubble driven by the confidence shock, and the confidence shock's contribution to stock price fluctuations is estimated to be about 14.8%. Model validation based on the DSGE-VAR framework indicates no serious structural model misspecification.

Existing research does not find significant momentum profits in many emerging markets including China. We propose an alternative momentum strategy which groups stocks into return intervals rather than percentiles. We apply the method to the China A-share market and find economically significant momentum profits in weekly returns, but not in monthly returns. The weekly momentum lasts for about 1 year. More than half of the profit is realized in the first 3 weeks. We apply the method to other Asian equity markets and find significant weekly momentum in Hong Kong, Taiwan, Korea, Thailand, and Indonesia. These findings suggest that momentum may exist in different formats in different markets. Existence of momentum in a closed equity market like China supports momentum is pervasive in short-term stock returns.

This paper examines whether there is evidence of spillovers of volatility from the Chinese stock market to its neighbours and trading partners, including Australia, Hong Kong, Singapore, Japan and USA. China's increasing integration into the global market may have important consequences for investors in related markets. In order to capture these potential eects, we explore these issues using an Autoregressive Moving Average (ARMA) return equation. A univariate GARCH model is then adopted to test for the persistence of volatility in stock market returns, as represented by stock market indices. Finally, univariate GARCH, multivariate VARMA-GARCH, and multivariate VARMA-AGARCH models are used to test for constant conditional correlations and volatility spillover eects across these markets. Each model is used to calculate the conditional volatility between both the Shenzhen and Shanghai Chinese markets and several other markets around the Pacic Basin Area, including Australia, Hong Kong, Japan, Taiwan and Singapore, during four distinct periods, beginning 27 August 1991 and ending 17 November 2010. The empirical results show some evidence of volatility spillovers across these markets in the pre-GFC periods, but there is little evidence of spillover eects from China to related markets during the GFC. This is presumably because the GFC was initially a US phenomenon, before spreading to developed markets around the globe, so that it was not a Chinese phenomenon.

In this paper we compare counterfactual corporate bond issuing dates to actual issuing dates in order to test the ability of firms to time the credit market. The 50 most active bond issuing financial firms and the 50 most active industrial firms are studied using one week, one month, and one quarter windows. The ability to time firm-specific CDS prices is studied from January 2002 - October 2009. The ability to time the risk-free rate (10 year US government bond) is studied from January 1988 - October 2009. We find that: firms do not successfully time the risk-free rate or the credit spreads. There is no evidence of CDS timing ability over one week or one month, but there is some borderline evidence at one quarter. For a typical bond issue, the firm loses about 1% of the face value of the bond relative to a 1 month window, due to their inability to time the market. If the firms could improve their market timing, they could save many hundreds of millions of dollars. Since there is a degree of statistical predictability in the data, we find it surprising that these firms are not able to do a better job of timing the credit market.