This paper proposes the concepts of ESG information flows and a predictable framework of ESG flows based on AR process, and studies how ESG information flows are incorporated into and affect a dynamic portfolio with transaction costs. Two methods, called the ESG factor model and the ESG preference model, are considered to embed ESG information flows into a dynamic mean-variance model. The dynamic optimal portfolio can be expressed as a traditional optimal portfolio without ESG information and a dynamic ESG preference portfolio, and the impact of ESG information on optimal trading is explicitly analyzed. The rich numerical results show that ESG information can improve the out-of-sample performance, and ESG preference portfolio has the best out-of-sample performance including the net returns, Sharpe ratio and cumulative return of portfolios, and contribute to reducing risk and transaction costs. Our dynamic trading strategy provides valuable insights for sustainable investment both in theory and practice.

The Chinese government has actively traded in the stock market through governmentsponsored institutions, the National Team, since the 2015 market crash. I adopt Koijen and Yogo’s (2019) demand system approach in China’s stock market to understand the impact of government participation. Estimation results indicate the government tilts towards large, less risky, and SOE stocks. During the crash, government participation indeed stabilized the market: the large-scale purchases reduced the cross-sectional market volatility of annual return by 1.8% and raised the market price by 11%. When the market ffuctuation returns to normal, the government acts more like an active investor; its price impact remains high but does not contribute to the cross-sectional volatility. Based on the theoretical framework of Brunnermeier et al. (2020), I investigate the interaction between the Nation Team and retail investors to reveal the government trading strategy. No evidence shows that government participation signiffcantly distorts market information efficiency.

Salience theory has been proposed as a new stock trading strategy. Therefore, to assess the validity of this proposal, a complex decision trading system was constructed based on salience theory, a variational mode decomposition (VMD) model, a bidirectional gated recurrent unit (BiGRU) model, and high-frequency trading. The system selected 30 Chinese new energy concept stocks, ranked the stocks using salience theory, and selected the top and bottom three stocks for two portfolios. Twelve stages were established, after which the VMD and BiGRU models were applied to the predictions. The final predicted returns for the high ST group A (GA) were 194.06% and for the low ST group B (GB) were 165.88%. This paper validated the powerful utility of salience theory and deep learning to analyze Chinas new energy market. And it explains the issues and questions raised by previous researchers.

This paper proposes a novel bond return (price or yield curve) prediction methodology, unifying the classical no arbitrage pricing framework, which is ubiquitous and serves as a fundamental and theoretical building block in mathematical finance, and empirical asset (bond) pricing methodologies, e.g., Bianchi, Büchner, & Tamoni (2021) for treasury bonds and Gu, Kelly, & Xiu (2020) for equities. The methodology can be viewed as a unification of theoretical and empirical asset pricing frameworks. Our method is mathematically and theoretically rigorous, arbitrage-free and meantime enjoys the flexibility offered by the empirical asset pricing framework, i.e., a potentially rich factor structure and accurate function approximations via machine learning regression. Real market back-testing studies show that our predictions are accurate, in the sense that the formulated equally-weighted treasury bond portfolios in China exchange-based markets bear significant positive returns. The average hit rate for yield curve prediction reaches 77.71% across all tenors and the related long-only trading strategy based on the prediction results in an annualized absolute return as high as 12.35% with Calmar ratio achieving 7.31 for equally-weighted portfolios. As a by-product of our prediction framework, spot yield curves can be predicted accurately in an arbitrage-free manner.

In this paper, using news reports and stock trading data from China, we document that stocks covered by the same media platform tend to comove together and refer to it as media-driven comovement. This finding remains significant both by conducting time series regressions of individual stock returns on co-coverage portfolio returns and by calculating the Pearson correlations among stocks that are co-covered by the same media platform. This is a novel type of comovement since it cannot be fully explained by common factors (e.g., additions to market indices) that lead to comovement but accords well with the investment habitat view. Besides, we find no statistically significant relationship between the frequency of co-coverage and the magnitude of comovement. To better illustrate the economic significance of this media-driven comovement, we construct a trading strategy which earns a monthly return of 115 basis point.

Feedback is the trading strategy of irrational investors. Positive feedback traders' buying or selling behavior is decided by the stock price of the preceding period. On the presence of positive feedback trading, stock market returns show characteristics which are different from these assumed in the classical financial theory, and thus make the stock market fluctuate extraordinarily. This essay establishes a model with the participation of positive feedback trader and rational trader,. The irrational traders adopt feedback trading strategy. Based on the model, we used Monte Carlo simulation method to produce a time series. We computed the kurtosis, tail-index and the autocorrelation coefficient of the time series and compared with the relevant quantity of Shanghai stock market index time series. It turned out that our model can provide a powerful expression to the fat-tail and autocorrelation characteristics of stock market return.

This paper studies sequential portfolio choices by MPS-risk-averse investors in a continuous time jump-diffusion framework. It is shown that the optimal trading strategies for MPS risk averse investors, if they exist, must be located on a so-called `temporal efficient frontier' (t.e.f.). The t.e.f. is found not to coincide with the local instantaneous frontier --- the continuous time analogue of Markowitz's mean-variance frontier. This observation is potentially useful in understanding the existence of documented financial anormally in empirical finance --- MPS risk averse investors may not wish to invest along the local instantaneous Markowitz's mean-variance frontier, but instead hold portfolios on the t.e.f.. The optimal portfolio on the t.e.f. could well fall strictly within the instantaneous local Markowitz's efficient frontier. Our observations on mutual fund separation are also profound and interesting. In contrast to the classical two-fund separation along the line of Black (1972) and Tobin (1958), our study shows that MPS-risk-averse investors' optimal trading strategy is target rate specific. Precisely, investors with different target rates may end up investing into different managed mutual funds, each involving a specific set of separating portfolios. Our theoretic findings are, nevertheless, much in line with the real world phenomena on the existence of various types of mutual funds offered by different financial institutes, each aiming to attract demand from some specific groups of investors --- a picture that is in sharp contrast to the theoretical prediction made by Black (1972) and Tobin (1958). Finally, our study sheds light on the difference between expected utility and MPS-risk-averse investors concerning their trading behavior in sequential time frame. Even though these two groups of investors may end up holding a common risky portfolio in each spot market, the differences between their trading behaviors are most reflected through the portfolio weights assigned to each of the separating portfolios within the time frame and across states. Precisely, the portfolio weights corresponding to investors respectively from the two groups are associated with recognizable different time patterns. We showed that such difference in trading behavior would be also reflected from the time patterns of the instantaneous returns and the volatilities of the funds respectively managed by investors from these two groups.

摘要：作者对实际使用的对冲模型提出研究，在本文中建立了一个在指数.指数期货.指数期权上的进行无套利对冲的前沿模型，揭开了对冲基金使用数量模型神秘的面纱。通过比较从市场指数和指数期货中估计得出的风险中性密度函数与从隐含在指数期权市场的风险中性密度函数，发现对冲机会，设计对冲交易策略。我们这里采用的估计方法为非参数方法,是对BS模型的拓展。 Abstract: In this paper, the authors make a research on the field of hedge model, after the investigation of some hedge funds in U.S. ,we establish a frontier no-arbitrage hedge model for index.index futures and index options. We compare the risk neutral density estimated from cross section of index market to the risk neutral density inferred from the time series of index and index future markets. The methods we used are nonparametric method and hypothesis test. We can find the arbitrage opportunity and design the hedge trading strategy.

China’s Stock Market is the most important emerging market awaiting for investigation by both academics and industrials. We study the pro…tability of long position in winner-based threshold momentum strategies after accounting for the trans-action cost. We …nd substantial pro…ts (double to octuple the money every year) in daily threshold trading strategies when trading cost is not accounted. However, at very low level of trading cost, say 0.2%, all pro…ts disappear. We employ a model that rebalance the portfolio carefully to save the transaction cost, but the trading rules still fail to profit at a reasonable level of trading cost. Thus, the momentum pro…ts may not compete with the trading cost.

Abstract: China is the most important emerging market awaiting for investigation by both academics and industrials. We study the profitability of long position in winner-based threshold momentum strategies after accounting for the transaction cost. We find substantial profits (double to octuple the money every year) in daily threshold trading strategies when trading cost is not accounted. However, at very low level of trading cost, say 0.2%, all profits disappear. We employ a model that rebalance the portfolio carefully to save the transaction cost, but the trading rules still fail to profit at a reasonable level of trading cost. Thus, the momentum profits may not compete with the trading cost.