What Are You Saying? Using topic to Detect Financial Misreporting

We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10-K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of Securities and Exchange Commission (SEC) enforcement actions (Accounting and Auditing Enforcement Releases [AAERs]) and irregularities identified from financial restatements and 10-K filing amendments. Our out-of-sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting.     

一种并行化的改进型灰狼分簇算法

针对传统的分簇算法在解决超大规模数据集的分簇问题上不具有高效的时间和空间复杂度且易于陷入局部最优的问题,提出了改进型灰狼分簇算法（Improved Gray Wolf Clustering Algorithm,IGWCA）,将灰狼行为规则与灰狼狩猎策略相融合,同时引入狄利克雷分布（Dirichlet Distribution）实现先验,在基准数据集上完成IGWCA与其他分簇算法的对比分析。实验结果表明IGWCA不仅具有较强的探索和开发能力,还具有较小的分散度。使用Hadoop框架的MapReduce模型实现IGWCA的并行化（IGWCA on MapReduce,IGWCA-MR）,通过F-Measure和平均运行时间验证IGWCA-MR的分簇质量,并在真实数据集上验证了IGWCA-MR的运行时间和加速性能。实验结果证明,IGWCA-MR可以有效解决超大规模数据集的分簇问题,是一种高效的替代算法。     

Tourism destination decisions: the impact of risk aversion and prior experience

This study broadly explores the impact of risk aversion on tourists' destination decisions and, in particular, explores for differences in individuals' leisure and medical tourism destination decisions. The results of this study indicate that risk aversion significantly distinguishes tourists' destination decisions in both leisure and medical tourism in Indonesia, but not in Singapore. All risk-averse groups are less likely to visit Indonesia than Singapore for leisure and medical purposes. By contrast, all risk-averse groups are likely to visit Singapore for leisure purposes, although they remain unlikely to travel to Singapore for medical tourism. In addition, the study found that the impact of prior experience on the likelihood that the two risk-averse groups will travel to Indonesia and Singapore for leisure was significant. Conversely, the effects of prior experience on medical tourism generally do not significantly differ between the two countries.     

Stick-breaking autoregressive processes

This paper considers the problem of defining a time-dependent nonparametric prior for use in Bayesian nonparametric modelling of time series. A recursive construction allows the definition of priors whose marginals have a general stick-breaking form. The processes with Poisson-Dirichlet and Dirichlet process marginals are investigated in some detail. We develop a general conditional Markov Chain Monte Carlo (MCMC) method for inference in the wide subclass of these models where the parameters of the marginal stick-breaking process are nondecreasing sequences. We derive a generalised Pólya urn scheme type representation of the Dirichlet process construction, which allows us to develop a marginal MCMC method for this case. We apply the proposed methods to financial data to develop a semi-parametric stochastic volatility model with a time-varying nonparametric returns distribution. Finally, we present two examples concerning the analysis of regional GDP and its growth.     

Adjustment of Input–Output Tables from Two Initial Matrices

The compilation of the information required to construct survey-based input–output (I–O) tables consumes resources and time to statistical agencies. Consequently, a number of non-survey techniques have been developed in the last decades to estimate I–O tables. These techniques usually depart from observable information on the row and column margins, and then the cells of the matrix are adjusted using as a priori information a matrix from a past period (updating) or an I–O table from the same time period (regionalization). This paper proposes the use of a composite cross-entropy approach that allows for introducing both types of a priori information. The suggested methodology is suitable to be applied only to matrices with semi-positive interior cells and margins. Numerical simulations and an empirical application are carried out, where an I–O table for the Euro Area is estimated with this method and the result is compared with the traditional projection techniques.     

Generalizing patterns of store-type patronage: an analysis across major Chinese cities

A systematic, analytical study is presented of patterns of patronage across store types (including hypermarkets, supermarkets, grocery stores and free/wet markets) in urban China. Using consumer panel data collected from a cumulative sample of almost 5000 households, results are generalized across 8 major cities (from the North, South, East and West of China) and 6 packaged goods categories (including traditional Chinese items like soy sauce and more contemporary items such as packaged laundry detergent). Analyses show that there are definite patterns of store-type patronage in urban China, most of which are predictable from the NBD–Dirichlet model (e.g. double jeopardy and the duplication of purchase rule). These patterns generalize across all the city/category combinations studied in this paper, although some important deviations are also identified (e.g. an excess loyalty premium). It is shown how retailers and planners can make use of the observations and theoretical predictions to assess key measures of patronage.     

Bayesian estimation of truncated data with applications to operational risk measurement

Data insufficiency and reporting threshold are two main issues in operational risk modelling. When these conditions are present, maximum likelihood estimation (MLE) may produce very poor parameter estimates. In this study, we first investigate four methods to estimate the parameters of truncated distributions for small samples—MLE, expectation-maximization algorithm, penalized likelihood estimators, and Bayesian methods. Without any proper prior information, Jeffreys’ prior for truncated distributions is used. Based on a simulation study for the log-normal distribution, we find that the Bayesian method gives much more credible and reliable estimates than the MLE method. Finally, an application to the operational loss severity estimation using real data is conducted using the truncated log-normal and log-gamma distributions. With the Bayesian method, the loss distribution parameters and value-at-risk measure for every cell with loss data can be estimated separately for internal and external data. Moreover, confidence intervals for the Bayesian estimates are obtained via a bootstrap method.     

计量经济学的贝叶斯统计方法

国际上，贝叶斯统计方法越来越受到计量经济学者的喜欢，应用日益广泛；然而我国目前这方面的研究还不多。本文总结了计量经济中贝叶斯方法的特点，介绍了其基本思想、方法和内容，包括先验分布、贝叶斯估计、模型比较和预测等。最后给出了贝叶斯方法的常用计算方法：Gibbs抽样和M-H算法。     

An Empirical Analysis of the Effects of Online Trading on Stock Price and Trading Volume Reactions to Earnings Announcements*

This study provides evidence regarding the effects of online trading on stock price and trading volume reactions to quarterly earnings announcements. We test for differences in stock price and volume reactions to quarterly earnings announcements between a period with a significant amount of online trading (1996‐99) and a period without online trading (1992‐95). We conjecture that online trading has increased the proportion of naive investors in the market. We predict that this will result in (1) a decrease in the average precision of investor information prior to earnings announcements leading to higher earnings response coefficients (ERCs), (2) an increase in differential interpretation of earnings leading to higher trading volume reactions that are unrelated to price change, and (3) a decrease in differential prior precision leading to a decrease in the association between trading volume and absolute price change. We find evidence consistent with all three predictions. Our findings are relevant for assessing the validity of concerns about online trading expressed by regulators and the validity of theoretical models of trade with asymmetrically informed investors.     

跳远运动员赛前心理训练分析与研究

跳远运动员在比赛中发挥的水平,很大程度上取决于其赛前的心理状态。运动员赛前心理应激状态可 分为应激水平低、过度应激、最佳应激三类。加强赛前心理训练可提高运动成绩。