In this study, we investigate whether and how risk disclosure in peer firms’ management discussion and analysis (MD&A) influences analyst earnings forecast accuracy. We find that peer MD&A risk disclosure significantly improves forecast accuracy, demonstrating a positive spillover effect. Moreover, the impact of peer MD&A risk disclosure on analysts’ forecast accuracy strengthens with the comparability and reliability of peer firms’ information, while weakens with the disclosure quality of the focal firm. Finally, peer MD&A risk disclosure also reduces stock price crash risk, providing further evidence that it improves information environment of the focal firm.

我国共同基金市场的规模在近十年急速扩张，而对于基金经理和基金管理公司谁是决定基金业绩的重要因素的问题仍存争议。本文基于1998-2021年的共同基金、基金经理和基金管理公司数据，采用Abowd, Kramarz, and Margolis （AKM）提出的方法和Mover Dummy Variable（MDV）方法，利用移动者提供的流动性，分离识别了基金经理（人才）和基金管理公司（平台）的固定效应。研究发现在决定基金业绩方面，人才比平台更重要，且重要至少9倍。在不同市场环境中，熊市中人才变得更加重要。在基金类型方面，相对于股票型基金，债券型和混合型基金受人才的影响更大。根据业绩分层，在业绩较好的基金中，人才较平台更为重要，而在业绩较差的基金中，平台的重要性相对提高。通过分析不同跳槽方向的基金经理，发现研究结论不受人才与平台之间依据业绩和业绩差异匹配的影响。本文的研究结论对基金从业者、基金管理公司和投资者在选择平台、人才、基金等方面均有指导意义。

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.

Based on the formation and evolution of systemic risk, this study proposes high and low volatility spillover networks and explores the characteristics of the evolution of systemic risk in Chinese financial market, and identifies the source of risk accumulation and risk outbreak, as well as the corresponding contagion mechanisms. Moreover, a new multiscale decomposition method (MVMD) is used to decompose high and low volatility into different time frequency components (short-term and long-term), and the corresponding network is constructed. Upon comparing topological characteristics on each layer from system and individual levels, our results reveal that high and low volatility spillover networks have different network characteristics and evolution behaviors. At the individual level, bond market is always the largest risk net-receivers in the high and low volatility networks, while the futures market and the currency market are respectively risk net-emitters in the high and low volatility networks. Additionally, compared with high volatility network, the low volatility network has greater predictive ability for financial risk. Finally, frequency analysis demonstrates that high-low volatility networks have different spillover intensity and network structure at different time frequencies. The above findings are beneficial for policy makers and investors to formulate appropriate strategies in different evolution of systemic risk and time frequency.

We explore whether Management Discussion and Analysis (MD&A) can provide incremental information to peers. Using Chinese stock market data, we find that positive peers' MD&A tone encourages firms to hold more cash, particularly for industries with fewer institutional investors' site visits. Moreover, this association is moderated by predation risk and decision-making environment. Specifically, this effect is more pronounced for firms which are market followers or financial constrained, and it is also stronger for firms operating under higher economic policy uncertainty or solely in domestic market. Overall, our findings enrich the information channels of peer effects in cash policy.

本文利用EGARCH模型和VAR模型研究了我国沪深300股指期货开市一年来交易量和波动率的动态和因果关系。实证结论表明股指期货的非预期交易量是信息量的有效代理变量，可以很好的解释波动率，同时二者没有显著的Granger 因果关系，因此我国股指期货市场支持了分布混合假说（MDH），证实了该市场是有一定效率的。

非平稳时间序列预测问题一直都是一个难题，本文运用EMD技术将非平稳时间序列分解为一系列imf的和一个残余量。由聚类分析得到若干个cimf，然后通过对每个cimf以及残余量建立神经网络模型进行预测，达到对原时间序列的组合预测。文章的实证结果表明EMD组合预测可以有效解决非平稳的问题，且预测精度达到良好效果。

传统的股利折现模型(CDDM)反映的是持续经营时的企业市场价值，导致企业市场价值定价上的谬误，因为在某些情形下，企业实行有限期经营反而可实现企业市场价值的最大化．通过改进截尾方法与Farrell的持续期限理论，提出修正的股利折现模型(MDDM)，以消除这种谬误．利用MDDM的数学性质，研究了折现率与企业最大化其市场价值的经营期限之间的内在联系．这种内在联系表明，不同于CDDM，无论其所使用的折现率为多大，总假定企业持续经营；MDDM反映出，折现率的选择事实上确定了企业最大化其市场价值的经营期限，该经营期限存在多样性的特征．

本文利用等价保证金理论和OMD方法建立一种新的基金业绩评价体系，经过对我国基金市场的实证分析表明，它比传统的基金业绩评价方法更客观更科学