知情交易概率的估计方法比较

知情交易概率PIN模型的极大似然估计,由于似然函数形式复杂,在最优化过程中很容易出现计算溢出的问题。本文提出了PIN模型的广义矩估计,并通过数值模拟比较了这一新方法和以往文献提出的原始极大似然估计、改进极大似然估计在不同情况下的估计精度。模拟结果表明,在某些情况下,广义矩估计比极大似然估计更容易计算得出也更具有精度优势。本文还提出了用bootstrap方法对广义矩估计结果进行误差修正,进一步提高了广义矩估计方法对于知情交易概率PIN的估计精度。     

订单拆分、知情交易概率与风险定价

基于2018-2020年沪深300指数成分股的高频数据,构建累计拆分订单比变量度量市场订单拆分行为,考虑订单拆分行为对知情交易概率及其风险定价能力的影响.实证发现:订单拆分行为增加了不同算法下知情交易概率的差异,这一差异对流动性和订单规模因素具有稳健性.进一步研究发现:不同的订单拆分水平下,知情交易概率与风险定价之间呈现非线性关系,高订单拆分水平降低了知情交易概率的风险定价能力.     

知情交易概率测算在内幕交易监管中的应用

内幕交易定义侧重信息分布状态,知情交易定义侧重信息认知状态,在立法和监管层面,内幕交易包含知情交易的内涵。信息优势是内幕交易监管困境的根源所在,知情交易概率模型从是否拥有信息的两种不同交易行为模式出发．有助于判断潜在的内幕交易行为,根据选取时间段变量、事件变量和财务指标变量的不同,为内幕交易监管选取重点目标提供参考。在监管实务操作中。强化信息核心、改革收盘机制、主张模糊界定等是必要的手段。     

事务所规模、审计行业专长与知情交易概率

外部审计的作用在于提高公司的信息披露透明度,减少信息不对称,以保护投资者利益。本文从知情交易概率视角出发,研究了外部审计在减少信息不对称方面所发挥的作用。研究结果发现,国内"十大"审计的公司,以及由具有审计行业专长的事务所审计的公司,知情交易概率更低,而且行业专长的国内十大(非十大)审计的公司的知情交易概率也低于不具有行业专长的国内十大(非十大)。进一步检验还表明,在机构投资者持股的公司中,事务所规模和审计行业专长能够更有效减少知情交易概率。     

基于面板数据的我国知情交易与股价信息含量研究

知情交易概率是指知情交易委托单占总交易委托单的比重,用于度量我国知情交易强度。R2代表公司回报率能被市场回报率解释的程度,R2越大,股价信息含量越低。在以往的研究基础上,利用面板数据来考察知情交易概率与股价信息含量的关系,发现知情交易概率越低,R2越高,股价信息含量越低。在控制了流动性与部分公司财务指标后,实证结果依然显示我国股价信息含量与知情交易概率存在正相关关系,说明我国知情交易者进行交易时,更有利于公司特质信息进入股票价格。     

信息不对称、公司治理与现金持有价值——基于知情交易概率的非平稳面板数据

本文为了研究中国上市公司信息不对称、公司治理和现金持有价值之间的关系,以沪深上市公司2004-2009年数据为基础,利用知情交易概率作为管理者和投资者之间的信息不对称代理变量,以高管持股比例作为公司治理代理变量,利用公司各种财务指标作为控制变量,发现信息不对称与中国上市公司现金持有价值呈显著负相关,高管持股比例和现金持有价值显著正相关,支持自由现金流理论和委托代理理论,信息不对称程度越高,管理者越可能滥用现金,导致现金持有价值下降,而提高公司治理,加强投资者保护可以提高现金持有价值。     

并购重组前的知情交易行为研究

并购重组中基于内幕信息的知情交易行为既是监管重点,也是学术界关注的热点问题。本文以2006—2020年我国上市公司并购重组事件为样本,考察并购重组前的知情交易行为对并购公告收益的影响。研究发现：并购重组前的知情交易行为引发了主并公司股价的提前反应,从而降低了并购公告时的市场反应,这一现象是由内幕信息泄露引起,且内幕信息主要来源于包括员工在内的公司内部人,而非机构投资者。进一步分析表明,改善信息环境可以有效缓解并购重组的信息泄露问题,体现为知情购买交易的信息泄露效应受到分析师跟踪、审计质量和问询函制度的有效制约。本文研究深化了现有的并购重组内幕交易行为研究,为实施精准监管和防范内幕交易等政策提供了一定参考和依据。     

基于贝叶斯最大后验估计的个人网上银行交易风险管理

以某全国性股份制银行省级分行辖内的零售规模相近的支行网点为样本,基于历史个人网上银行交易额和问卷数据,运用贝叶斯最大后验估计方法,推断未来交易额的合理区间,对相关潜在风险进行预警和识别.结果显示:个人网上银行交易风险多源于钓鱼网站诱使个人账户资金频繁转出,外部欺诈事件在节假日期间多发,源自节假日期间由网络购物导致的电子银行转账交易量的大幅增加,商业银行在节日较多的月份须更加关注网上银行的交易风险.     

中国A股市场上的机构投资者是理性的吗——基于知情交易视角的分析

本文利用2000~2014年沪深两市A股一般上市公司季度数据,研究机构投资者知情交易与特征组合相对超额收益的关系。实证结果显示:第一,当机构投资者知情交易变大时,持有盈余价格比高、利润总额同比增长率高、账面市值比低、价格高、规模大的股票的当期相对超额收益变大,知情机构投资者是理性的。第二,当机构投资者知情交易变大时,持有股息率低、盈余价格比低、利润总额同比增长率低、账面市值比低、价格低、规模小的股票的未来相对超额收益变大,知情机构投资者是非理性的。知情机构投资者并非是完全理性的,由于其更关注当期利益,而忽略长期价值投资,不利于维护股市的稳定。     

Bayesian Estimation of ARMA-GARCH Model of Weekly Foreign Exchange Rates

Three Bayesian methods (Markov chain Monte Carlo, Laplace approximation and quadrature formula) are developed to estimate the parameters of the ARMA-GARCH model. The ARMA-GARCH model is applied to weekly foreign exchange rate data of five major currencies, and their stochastic volatilities are judged by the posterior probabilities of stationarity and other conditions. This revised version was published online in August 2006 with corrections to the Cover Date.     

Stock Return Predictability: A Bayesian Model Selection Perspective

Attempts to characterize stock return predictability have resultedin little consensus on the important conditioning variables,giving rise to model uncertainty and data snooping fears. Weintroduce a new methodology that explicitly incorporates modeluncertainty by comparing all possible models simultaneouslyand in which the priors are calibrated to reflect economicallymeaningful information. Our approach minimizes data snoopinggiven the information set and the priors. We compare the priorviews of a skeptic and a confident investor. The data implyposterior probabilities that are in general more supportiveof stock return predictability than the priors for both typesof investors.     

基于贝叶斯混频模型的中国宏观经济预测研究

本文通过构建中国宏观经济系统的BMF-VAR模型对中国的宏观经济进行预测分析,并与相关模型的预测结果进行比较,来检验BMF-VAR模型对中国宏观经济进行预测时的适用性。研究结果表明,多变量BMF-VAR模型更适合于短期预测,多变量有效信息的增加促进了短期预测精度的提高;两变量BMF-VAR模型在预测精度方面整体上优于相对应的同频模型;通过BMF-VAR模型能够估计出潜在的月度GDP增长率,且相比于通常所使用的规模以上工业增加值月度增长率更加贴近于真实的GDP增长率走势,有效地解决了月度GDP增长率缺失的问题,据此进行决策会更加有效合理,具有重要的理论和现实意义。     

Analyzing determinants of bond yield spreads with Bayesian Model Averaging

This paper analyzes determinants of country default risk in emerging markets, reflected by sovereign yield spreads. The results reported so far in the literature are heterogeneous with respect to significant explanatory variables. This could indicate a high degree of uncertainty about the “true” regression model. We use Bayesian Model Averaging as the model selection method in order to find the variables which are most likely to determine credit risk. We document that total debt, history of recent default, currency depreciation, and growth rate of foreign currency reserves as well as market sentiments are the key drivers of yield spreads.     

Is PIN priced risk?

Several recent papers assume that private information (PIN), proposed by Easley et al. [2002. Is information risk a determinant of asset returns? Journal of Finance 57, 2185–2221; 2004. Factoring information into returns. Working Paper, Cornell University], is a determinant of stock returns. We replicate Easley et al. (2002) and show that while PIN does predict future returns in the sample they analyze, the effect is not robust to alternative specifications and time periods. There is no evidence that PIN factor loadings predict returns or that PIN factor returns reflect future GDP growth. PIN exhibits no association with implied cost of capital derived from analysts’ earnings forecasts. Overall, our findings cast doubt on whether PIN reflects information risk systematically priced by investors.     

A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims

Accurate prediction of future claims is a fundamentally important problem in insurance. The Bayesian approach is natural in this context, as it provides a complete predictive distribution for future claims. The classical credibility theory provides a simple approximation to the mean of that predictive distribution as a point predictor, but this approach ignores other features of the predictive distribution, such as spread, that would be useful for decision making. In this article, we propose a Dirichlet process mixture of log-normals model and discuss the theoretical properties and computation of the corresponding predictive distribution. Numerical examples demonstrate the benefit of our model compared to some existing insurance loss models, and an R code implementation of the proposed method is also provided.     

Data-driven tree structure for PIN models

Probability of informed trading (PIN) models characterize trading with certain types of information through a tree structure. Different tree structures with different numbers of groups for market participants have been proposed, with no clear, consistent tree used in the literature. One of the main causes of this inconsistency is that these trees are artificially proposed through a bottom-up approach rather than implied by actual market data. Therefore, in this paper, we propose a method that infers a tree structure directly from empirical data. More precisely, we use hierarchical clustering to construct a tree for each individual firm and then infer an aggregate tree through a voting mechanism. We test this method on US data from January 2002 for 7608 companies, which results in a tree with two layers and four groups. The characteristics of the resulting aggregate tree are between those of several proposed tree structures in the literature, demonstrating that these proposed trees all reflect only part of the market, and one should consider the proposed empirically driven method when seeking a tree representing the whole market.

     

银行操作风险贝叶斯网络量化控制研究

在银行的风险管理中,操作风险一直是管理难点,具有涉及面广、管理半径长、不易识别、不易控制、不易量化等特点,且在管理工具和量化方法上都没有信用风险、市场风险那么成熟。近年来,银行业因操作风险导致的巨大损失却日益增多。本文利用贝叶斯网络进行建模,量化研究关键风险指标与关键风险诱因的因果关系,正向分析各种操作风险诱因的影响程度,反向分析操作风险指标出现预警后,风险诱因的后验概率如何变化,以期不断优化模型,以更好地管理操作风险。此外,本文还初步探讨了关键风险指标阈值设置方法和控制成本与损失成本的关系。同时,结合银行操作风险管理工作的实践,提出了防范操作风险的建议。