Starting from document research, this paper analyzes the mechanism of the risk spillover effect from developed capital markets to the Chinese capital market. After that, this paper conducts an empirical study on the risk spillover effect of developed capital markets on the Chinese capital market by using the DCC-GARCH model. Then the impact degree of global major stock market fluctuations on the Chinese stock market is measured. The analysis shows that there exists a significant risk spillover effect of developed capital markets on the Chinese capital market, but the effect began to weaken after the financial crisis and the size of the spillover effect can be affected by macro factors such as geographical locations, foreign trade, and foreign investment.

We propose a Capital Asset Pricing Model where investors exhibit prospect preferences. In equilibrium, we find that agents seek an optimal trade-off between expected returns, variance, and skewness. All assets in the economy are then priced by a three-factor model, which augments the security market line with two factors that respectively capture positive and negative coskewness with the market portfolio. Using U.S. stock market data, we find evidence consistent with these predictions. In additional tests, we find that the results are stronger among stocks traded by less sophisticated investors. Overall, prospect preferences have a substantial effect on stock prices.

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.

We propose a new investor sentiment index by estimating the differences in variance,skewness, and kurtosis from realized stock returns and option implied moments. We show that our index cannot be explained by risk factors such as market risk, firm size, value, or profitability. Furthermore, we present evidence that this correlation can be exploited for momentum strategies, which perform significantly better during high-stimulation periods. In fact, our methodology can be extended to a daily sentiment measure and stock-specific sentiment indices.

Using the Boehmer, Jones, Zhang, and Zhang (2021) algorithm, we identify a broad swath of marketable retail investor orders in the U.S. market during the pandemic. The marketable retail trading volumes rapidly rise from $325 billion in 2019 to $852 billion at mid-2020, and stay high for the next two years. The retail order flows positively predict cross-sectional returns over various horizons, and are associated with wider future effective spreads and higher future volatilities, as well as less market participations by high frequency traders and short-sellers. We find supportive evidence for informed and uninformed retail hypotheses.

We provide an easy method to identify purchases and sales initiated by retail investors using recent, widely available U.S. equity transactions data. Individual stocks with net buying by retail investors outperform stocks with negative imbalances by approximately 10 basis points over the following week. Less than half of the predictive power of marketable retail order imbalances is attributable to order flow persistence; contrarian trading (a proxy for liquidity provision) and public news sentiment explain little of the remaining predictability. There is suggestive (but only suggestive) evidence that retail marketable orders contain firm-level information that is not yet incorporated into prices.

Using multiple short sale measures, we examine the predictive power of short sales for future stock returns in 38 countries from July 2006 to December 2014. We find that the days-to-cover ratio and the utilization ratio measures have the most robust predictive power for future stock returns in the global capital market. Our results display significant cross-country and cross-firm differences in the predictive power of alternative short sale measures. The predictive power of shorts is stronger in countries with non-prohibitive short sale regulations and for stocks with relatively low liquidity, high shorting fees, and low price efficiency.

Using a director-level dataset of board proposal voting by independent directors of public companies, we analyze the effects of career concerns and current reputation stock on independent directors in their voting behavior. Younger directors and directors in their second (and last) terms, who have stronger career concerns, are more likely to be aligned with investors rather than the managers. Their dissenting behavior is eventually rewarded in the market place in the form of more outside career opportunities. Directors with higher reputation stocks (measured by positive news media mentioning and the number of directorships) are also more likely to dissent. Finally, we find that career concerns are significantly stronger among directors who already enjoy higher reputation.

I show that issuing credit cards helps the bank compete for retail deposits in China. When credit card growth increases by 1%, retail deposit growth is expected to rise by 0.2% with regard to peers next year. This effect is stronger for small joint-stock banks compared with big state-owned banks. This is realized by introducing new credit card holders to visit the branch and open a savings account. DID test shows that after a shock that tightened new account opening, banks with higher credit card growth experienced a harsher decline in retail deposit growth. This paper highlights the customer introducing benefit of credit card promotion, which can provide an alternative explanation for the intensified competition in the credit card market in China. It also unveils the strategy that small banks can use to compete for the deposits of big state-owned banks, who intrinsically has more branches and retail customers.

This paper proposes a new forecasting framework for the stock market that combines machine learning algorithms with several technical analyses. The paper considers three different algorithms: the Random Forests (RF), the Gradient-boosted Trees (GBT), and the Deep Neural Networks (DNN), and performs forecasting tasks and statistical arbitrage strategies. The portfolio weight optimization strategy is also proposed to capture the model's return and risk information from output probabilities. The paper then uses the stock data in the Chinese A-share market from January 1, 2011, to December 31, 2020, and observes that all three machine learning models achieve significant returns in the Chinese stock market. The DNN achieves an average daily return of 0.78% before transaction costs, outperforming the 0.58% of the RF and 0.48% of the GBT, far exceeding the general market level. The performance of the weighted portfolio based on the ESG score is also improved in all three machine learning strategies compared to the equally weighted portfolio. These results help bridge the gap between academic research and professional investments and offer practical implications for financial asset pricing modelling and corporate investment decisions.