• 详情 A Filter to the Level, Slope, and Curve Factor Model for the Chinese Stocks
    This paper studies the Level, Slope, and Curve factor model under different tests in the Chinese stock market. Empirical asset pricing tests reveal that the slope factor in the model represents either reversal or momentum effect for the Chinese stocks. Further tests on individual stocks demonstrate that the Level, Slope, and Curve model using effective predictor variables outperforms other common factor models, thus a filter in virtue of multiple hypothesis testing is designed to identify the effective predictor variables. In the filter models, the cross-section anomaly factors perform better than the time-series anomaly factors under different tests, and trading frictions, momentum, and growth categories are potential drivers of Chinese stock returns.
  • 详情 Motivated Extrapolative Beliefs
    This study investigates the relationship between investors’ prior gains or losses and their adoption of extrapolative beliefs. Our findings indicate that investors facing prior losses tend to rely on optimistic extrapolative beliefs, whereas those experiencing prior gains adopt pessimistic extrapolative beliefs. These results support the theory of motivated beliefs. The interaction between the capital gain overhang and extrapolative beliefs results in noteworthy mispricing, yielding monthly returns of approximately 1%. Motivated extrapolative beliefs comove with investors’ survey expectations and trading behavior, and help explain momentum anomalies. Additionally, households are susceptible to this belief distortion. Institutional investors can avoid overpriced stocks associated with motivated (over-)optimistic extrapolative beliefs.
  • 详情 Factors in the Cross-Section of Chinese Corporate Bonds: Evidence from a Reduced-Rank Analysis
    We investigate the cross-sectional factors of Chinese corporate bond returns via the reducedrank regression analysis (RRA) proposed by He et al. (2022). We collect 37 individual bond characteristics in the extant literature using a new dataset and construct 40 factor portfolios. Empirically, we find that the four-factor models created by RRA outperform the traditional factor models, PCA, and PLS factor models, both in-sample and out-of-sample. Among the 40 factors, the bond market factor is the most substantial predictor of future bond returns. In contrast, other factors provide limited incremental information for the cross-sectional pricing. Therefore, it is necessary to find more new bond factors. We further find that stock market anomalies do not improve the explanatory power of the RRA factor models. In particular, stock market anomalies can only partially explain the systematic part of bond returns in the RRA framework and have almost no explanatory power for the idiosyncratic component.
  • 详情 Anomalies and Expected Market Return—Evidence from China A-Shares
    This paper is the first study to systematically discuss the predictive power of crosssectional asset pricing anomalies on aggregate market excess return time series in the Chinese A-share market. The paper summarizes the anomalies and uses linear methods with different shrinkage techniques to extract predictive information from highdimensional long-short anomaly portfolio returns datasets. We find that long-short anomaly portfolio returns show highly significant out-of-sample predictive power of aggregate market excess returns, both statistically and economically. Unlike similar studies on U.S. stocks, the predictive power stems from stronger limits of arbitrage in the short-leg when using bid-ask spread as a proxy but from stronger limits of arbitrage in the long-leg when idiosyncratic volatility or market capitalization is used as proxies.
  • 详情 The Behaviour of Chinese Government Bond Yield Curve Before and During the COVID-19 Pandemic
    The aim of the study is to investigate the behaviour of the Chinese government bond yield curve before and during the COVID-19 pandemic. Its methodology comprises the techniques of time series analysis, correlation analysis and dimensionality reduction. The main empirical results show that in the pandemic period, the behaviour of the Chinese government bond yield curve differs significantly from that before the outbreak of COVID-19. This is evidenced by the weaker correlations among the analysed yields, the presence of anomalies, heterogeneous behaviour and probable arbitrage opportunities at the long-term end of the studied yield curve, as well as the significant changes in the main factors of its dynamics. The research also reveals that prior to the COVID-19 pandemic, portfolios composed of Chinese government bonds could be well protected against interest rate risk even by using traditional parallel shift immunization techniques. However, after the outbreak of the COVID-19 pandemic the use of such techniques would be relatively effective for portfolios of Chinese government bonds with maturities between 1 and 5 years, while portfolios that include Chinese government bonds with maturities greater than 7 years should be either hedged against all the three factors of the yield curve dynamics or be used only for arbitrage strategies.
  • 详情 Do Analysts Disseminate Anomaly Information in China?
    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.
  • 详情 Impacts of CME changing mechanism for allowing negative oil prices on prices and trading activities in the crude oil futures 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.
  • 详情 Risk factor analysis of industrial bonds based on multifactor model: Evidence from China
    In this paper, we identify cross-sectional anomalies in excess returns of industrial bonds at the issuer and secondary market levels, and find that liquidity, risk, and historical return variables can generate cross-sectional excess returns that cannot be explained by traditional bond factors. We also introduce a risk premium factor that is economically and statistically significant in industrial bonds based on the risk characteristics prevalent in credit bonds and that cannot be explained by long-standing bond market factors. We show that the newly identified risk factor outperforms the other anomalies considered in this paper in explaining the cross-sectional returns of industrial bonds.
  • 详情 Disruptive Dependency Theory and the Equity Premium Puzzle: A NEW ANSWER TO THE EQUITY PREMIUM PUZZLE
    The equity premium puzzle, properly termed the American Equity Premium Puzzle, is one of the most significant empirical anomalies in finance, as it pertains to the observation that the expected return on equities has been consistently higher than that of bonds for many years, and that this premium is excessive. This paper presents one answer to the Equity Premium Puzzle, viz., the Disruptive Dependency Theory. The Disruptive Dependency Theory states that the world can be viewed in terms of “core” and “periphery” nations. Thus, there is a "core" set of nations in the world that are strong and a "periphery" that is relatively weak. This has been the state of the world since the end of the Second World War. The nations in the "core" are the strong nations. This includes the United States, China, Russia, France and the United Kingdom. What constitutes the "periphery" is a bit nebulous, but certainly the weakest nations such as island nations (Vanuatu, Togo, Jamaica, Antigua & Barbuda) belong the periphery. The nations in the core use the following to exert their influence on the nations in the periphery: (a) political strategies; (b) economic strategies; (c) social and cultural strategies; (d) technological strategies. Disruptive innovation has emerged as one of the chief strategies. With the rise of disruptive innovation, they are able to "disrupt" existing business in a very large number of periphery nations, thereby a very small number of individuals are becoming super-rich billionaires while the rest of the world remains still quite poor. According to this theory, it is the power differential of nations that historically resulted in the equity premium for stocks being excessively high. This paper explores the implications of the Disruptive Dependency Theory and its potential contribution to understanding the Equity Premium Puzzle.
  • 详情 Salience Theory Based Factors in China
    We have developed two novel salience factors — PMOR and PMOV based on the stock’s salient return and salient trading volume (as proposed by Cosemans and Frehen, 2021, and Sun et al., 2023). Notably, these factors cannot be accounted for by existing factor models in China. When we integrate the salience trading volume factor — PMOV into Liu et al. (2019)’s Chinese three-factor model, the resulting four-factor model outperforms other models including the Chinese four-factor model in explaining 33 significant anomalies in China.