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.

I conduct an exploratory study about the feasibility of factor timing in the Chinese stock market, covering 24 representative and well-identiffed risk factors in ten categories from the literature. The long-short portfolio of short-term reversal exhibits strong and statistically signiffcant out-of-sample predictability, which is robust across various models and all types of predictors. However, such results are not evident in the prediction of all other factors’ long-short portfolios, as well as all factors’ long-wing and short-wing portfolios. The high exposure to the market beta, together with the unpredictability of the market return, explains these failures to some degree. On the other hand, a simple investment strategy based on predicted returns of the reversal factor’s long-short portfolio obtains a signiffcant return three times higher than the simple buy-and-hold strategy in the sample period, with a signiffcant annualized 20.4% CH-3 alpha.

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.

Existing research has found cross-sectional seasonality of stock returns—the periodic out- performance of certain stocks during the same calendar months or weekdays. We hypoth- esize that assets’ different sensitivities to investor mood explain these effects and imply other seasonalities. Consistent with our hypotheses, relative performance across individ- ual stocks or portfolios during past high or low mood months and weekdays tends to recur in periods with congruent mood and reverse in periods with noncongruent mood. Furthermore, assets with higher sensitivities to aggregate mood—higher mood betas— subsequently earn higher returns during ascending mood periods and earn lower returns during descending mood periods.

The Chinese stock market is known for high synchronicity and the market portfolio represents a prominent risk factor to investors in the Chinese stock market. We conjecture that as a result, stocks with high exposure to market risk in China earn higher returns. Indeed, we find that CAPM beta is positively related to daily and monthly stock returns in the Chinese stock market. To substantiate our argument, we further show that the betareturn relation is stronger during periods when market risk is high. Moreover, we find that market risk is priced only during the day but not overnight in the Chinese stock market. We explore the effect of several unique trading rules in China and show evidence that the “T+1” trading rule is likely the cause.

We track the fundamental informed traders' (FITs) behavior and show the fundamental momentum effect in the Chinese stock market. We train the deep learning model with a set of fundamental characteristics to extract fundamental implied component from realized returns. The fundamental part characterizes the price movement driven by FITs. Fundamental momentum differentiates from the fundamental trend and is not quality minus junk (QMJ) factor. Underreaction bias helps explain the strategy, as it generates stronger profit during periods of low investor sentiment and aggregate idiosyncratic volatility. Fundamental momentum is not sensitive to changing beta and robust in subsamples and machine learning models.

We study the relationship between common factor betas and the expected overnight versus intraday stock returns. Using data from the Chinese A-share markets, we find that the Fama-French five-factor betas and expected returns exhibit contrasting relationships overnight versus intraday. The market, value, and profitability factors earn positive beta premiums overnight and negative premiums intraday, while the size and investment factors’ beta premiums behave oppositely. The night and day factor beta premium differentials are more muted among stocks with higher investor sophistication and vary across macroeconomic conditions. The contrasting day and night beta premiums extend to some other common factors and Chinese B shares, and vary their signs for some factors in the U.S. market.

We extend the ex-ante mean-variance (SVIX) asset pricing models of Martin (2017) and Martin-Wagner (2019) to a mean-variance-asymmetry (AVIX) framework by incorporating higher-moment and co-moment risk in asset pricing. Our proposed AVIX model is risk-neutral with left-tail asymmetries in returns to correct the SVIX approach's downside bias. We derive an option implied market beta of a stock as the weighted average of the betas of SVIX and AVIX. Empirically, the implied beta has significant predictability of risk/return relationship We develop an investible portfolio (MKT*) that mimics realized outcomes on the implied market index adjusted for volatility asymmetry.

This study provides a comprehensive analysis of the effects of Computer-based Trad-ing (CBT) on Treasury bond expected returns. We document a strong relationship between bond expected returns and the overall intensity at which CBT takes place in the Treasury market. Investing in bonds with the largest beta to the aggregate CBT intensity and shorting those with the smallest generates large and significant returns. Those returns are not due to compensation for facing conventional sources of risk or to transaction costs. Our results are consistent with capital-flow based explanations implied by asset pricing models with institutional investors.

The adequate evaluation of mutual fund performance and of the fund managers’ ability to add value is an issue to which it has been given special attention in the recent financial literature. One of the traditional evaluation measures most commonly used is Carhart's alpha. However, one of the main problems of the evaluation methods that use the beta of the portfolios as a measure of risk and, therefore, Carhart's alpha is its sensitivity to the definition of the market portfolio. In this work we study the importance of defining the market portfolio using Carhart's alpha for a sample of UK mutual funds, and the influence of this market portfolio in the funds´ excess returns and in the performance ranking classification of the fund sample.