This paper proposes a conditional factor model embedded with heterogeneous group structure, called grouped Instrumented Principal Component Analysis (Grouped IPCA) model, to study the enhancement of industry classifcations on the pricing power of frm characteristics. We derive an inferential theory on the alternating least square (ALS) estimators of the grouped IPCA model under an unbalanced panel data. Based on this, we use two BIC-type information criteria to determine the number of latent factors. We further examine the group heterogeneity with a bootstrap test statistics. Simulations are conducted to evaluate both our asymptotic theory and test statistics. In the empirical study, we show that the in-sample performance of Grouped IPCA model excels the IPCA model, and fnd a strong evidence on the incremental pricing power of industries.

We examine the performance of actively managed Chinese mutual Funds over the period 2002-2020. Using the bootstrap-based false discovery technique, we find that 19.25% of Chinese actively managed mutual funds produce positive-alpha, which contrasts with existing studies documented by others in developed markets. Our findings survive a battery of robustness tests. Unlike in developed markets, equilibrium accounting may not hold in China as the Chinese stock market is dominated by retail investors instead of mutual funds, and thus the mutual funds in China can be more skilled at the expense of the retail investors. We find supportive evidence of the applicability of the bootstrap-based false discovery rate method by conducting simulations.

In this paper, we present an entropy-based approach to measure the asymmetry of stock returns. By applying this approach, we use the Bootstrap method that our asymmetry measure exhibits a significantly enhanced ability to detect asymmetry compared to skewness. Moreover, our empirical findings reveal that stocks characterized by higher upside asymmetries, as determined by our innovative entropy measure, exhibit lower average returns across a crosssection of stocks. This supports the conclusions drawn by Han et al. (2018). In contrast, when employing the three-moment skewness measure, the relationship between asymmetry and stock returns remains inconclusive within the Chinese market.

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.

This paper selects 346 green bonds issued in China from 2016 to 2021 as the sample, and the Propensity Score Matching (PSM) method is employed to confirm the existence of ‘green premium’ in the Chinese bond market. On this basis, data on internet media sentiment and print media sentiment are collected from ‘Sina Weibo’ and ‘China Important Newspaper Full Text Database’ by both Web Crawler Technology and Textual Analysis Methods to explore the impact and the mechanism of heterogeneous media sentiments on the ‘green premium’. The results show that both the optimism of internet media and print media can significantly promote the ‘green premium’ of green bonds, and the influence of print media sentiment on the ‘green premium’ is greater than that of internet media sentiment. In addition, the Bootstrap method verifies the mediating effect of print media sentiment in the influence of internet media sentiment on ‘green premium’, indicating that print media sentiment is an important transmission path. Moreover, the results of the heterogeneity test show that the more optimistic the media is, the more significant the ‘green premium’ effect is in the regions with higher institutional environments and financial subsidy policies. The ‘green premium’ of green bonds is most pronounced for higher levels of institutional environment and green bond preferential policies.

本研究对中国股票型私募基金经理的市场择时能力进行了检验，即这些私募基金经理是否具有根据市场情况来调整基金资产组合的市场敞口的能力。相比与公募基金，私募基金的策略和资产组合的调整更加灵活，因此更有利于体现基金经理的择时能力。我们从收益择时、波动择时和流动择时三个维度来对中国的私募基金经理的择时能力进行检验。研究发现，私募基金经理具有一定的收益择时和流动择时能力，但是很少有基金经理具有波动择时能力。即一些私募基金经理可以通过预测市场收益和市场的流动性，来相应调整资产组合的市场敞口，但很少有基金经理可以通过预测市场波动来调整基金的市场风险敞口。同时，我们对回归结果进行了Bootstrap分析，结果表明这些显著的择时能力并不是由于运气因素所带来的。最后，我们也对结果进行了稳健性的检验。我们的研究对于了解中国私募基金经理的择时能力具有一定的帮助，同时，有助于加深理解市场的收益、波动性和流动性在资产管理和投资决策中的作用和重要性

股指期货在资本市场价格发现和风险防范过程中扮演重要角色，科学准确的预测其 收益波动率对充分实现股指期货避险功能具有重要的理论和现实价值。以线性非负模型为 基础，通过幂转换以及不设定扰动项的具体相关结构和分布形式，构建了一个半参数预测 模型来预测高频环境下股指期货市场波动率。模型采用基于极值估计量的两阶段估计法进 行估计，Monte Carlo模拟实验表明该估计方法的渐进性质表现良好。此外，以沪深300 股指期货的5分钟高频交易数据为例，运用滚动时间窗的样本外预测和最新发展起来的具 有Bootstrap特性的MCS检验，在多种稳健损失函数下，实证评价和比较了新构建的半参数 预测模型与其他7类波动率预测模型对高频环境下沪深300股指期货波动率的预测能力。实 证结果表明：在多种稳健损失函数的评价标准下，新构建的股指期货波动率的半参数预测 模型是预测预测能力最好的模型。

本文对2006 年1 月《上市公司股权激励管理办法(试行)》出台后，实施股权 激励方案的42 家上市公司的股权激励效果及其微观机制进行了实证分析。在采用倾向得分匹 配分析法(PSM)和Bootstrap 法克服样本选择偏误和小样本偏误后，我们发现：(1)整体而言， 股权激励能够有效提升经营绩效；(2)最终控制权会显著影响激励效果，民营控股公司的股权 激励能够显著降低代理成本，提高公司的投资支出，进而提升公司绩效，而在国有控股公司 中效果并不明显；(3)不同激励方式会产生不同的效果，相对于以股票为基础的激励方式，以 期权为基础的激励方式能显著降低代理成本，效果更佳；(4)所有权结构也会影响股权激励的 效果，在股权较为分散的公司中，股权激励能显著降低经理人与股东之间的代理成本，激励 的效果较好。

本文在一般息票剥离法（bootstrap method）的基础上进行了尝试性扩展：采用三次样条插值方法，以便可以对任意可得到的国债报价数据进行即期收益率曲线估计。同时利用数学软件Maple对插值方程和收益率曲线节点的非线性联立方程进行了求解。最后，利用该方法以2004年1月9日上海证券交易所的18个国债报价数据（全价）为样本，估计出了我国的国债收益率曲线，然后又根据估计出的国债收益率曲线对010311国债进行了定价，并和当日实际报价进行了比较分析。

It is often documented, based on autocorrelation, variance ratio and power spectrum, that exchange rates approximately follow a martingale process. Because autocorrelation, variance ratio and spectrum check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insigniÞcant. In this paper, we explore whether there exists a gap between serial uncorrelatedness and martingale difference for exchange rate changes, and if so, whether nonlinear time series models admissible in the gap can outperform the martingale model in out-of-sample forecasts. Applying the generalized spectral tests of Hong (1999) to Þve major currencies, we Þnd that the changes of exchange rates are often serially uncorrelated, but there exists strong nonlinearity in conditional mean, in addition to the well-known volatility clustering. To forecast the conditional mean, we consider the linear autoregressive, autoregressive polynomial, artiÞcial neural network and functional-coefficient models, as well as their combination. The functional coefficient model allows the autoregressive coefficients to depend on investment positions via an moving average technical trading rule. We evaluate out-of-sample forecasts of these models relative to the martingale model, using four criteria– the mean squared forecast error, the mean absolute forecast error, the mean forecast trading return, and the mean correct forecast direction. White’s (2000) reality check method is used to avoid data-snooping bias. It is found that suitable nonlinear models, particularly their combination, do have superior predictive ability over the martingale model for some currencies in terms of certain forecast evaluation criteria.