Residual income,non‐earnings information,and information content

We extend Ohlson's (1995) model and examine the relationship between returns and residual income that incorporate analysts' earnings forecasts and other non‐earnings information variables in the balance sheet, namely default probability and agency cost of a debt covenant contract. We further divide the sample based on bankruptcy (agency) costs, earnings components and growth opportunities of a firm to explore how these factors affect the returns–residual income link. We find that the relative predictive ability for contemporaneous stock price by considering other earnings and non‐earnings information is better than that of models without non‐earnings information. If the bankruptcy (agency) cost of a firm is higher, its information role in the firm's equity valuation becomes more important and the accuracy of price prediction is therefore higher. As for non‐earnings information, if bankruptcy (agency) cost is lower, the information role becomes more relevant, and the earnings response coefficient is hence higher. Moreover, the decomposition of unexpected residual income into permanent and transitory components induces more information than that of the unexpected residual income alone. The permanent component has a larger impact than the transitory component in explaining abnormal returns. The market and industry properties and growth opportunity also have incremental explanatory power in valuation. Copyright © 2008 John Wiley & Sons, Ltd.     

泄漏检测信号滤波技术比较

通过泄漏检测模型试验分析测量信号中的噪声来源，在对比研究传统小波去噪、改进神经网络去噪、最小二乘拟合去噪等方法在实测数据中去噪效果的基础上，借鉴神经网络反向传播学习算法的思路，提出了信号预滤波结合闽值自学习小波去噪的综合滤波方法。该方法通过对恒定状态下带噪压力信号阈值自学习使得重构信号与期望输出均方误差最小来获得单一工况下的最佳去噪阈值，再将此阈值用于同一工况下整个时间段的去噪，这样根据不同工况下得到的最佳阈值可以获得最优输出。数值计算结果比较表明该方法对噪声的抑制作用明显，比传统小波去噪、改进神经网络去噪等方法效果更好。     

Using accounting‐based information on young firms to predict bankruptcy

This study analyzes the nonlinear relationships between accounting‐based key performance indicators and the probability that the firm in question will become bankrupt or not. The analysis focuses particularly on young firms and examines whether these nonlinear relationships are affected by a firm's age. The analysis of nonlinear relationships between various predictors of bankruptcy and their interaction effects is based on a structured additive regression model and on a comprehensive data set on German firms. The results of this analysis provide empirical evidence that a firm's age has a considerable effect on how accounting‐based key performance indicators can be used to predict the likelihood that a firm will go bankrupt. More specifically, the results show that there are differences between older firms and young firms with respect to the nonlinear effects of the equity ratio, the return on assets, and the sales growth on their probability of bankruptcy.     

Perspectives on forecasting research in accounting and finance

This paper offers some perspectives on forecasting research in accounting and finance. It is maintained that many common areas of forecasting research exist. Yet, most research has focused upon a particular (Box-Jenkins) technique and a particular (reported earnings) variable, virtually neglecting numerous other relevant forecasting research topics. This symposium issue includes papers which address several of these neglected research topics. The eight papers constituting the issue are classified into three categories: (1) univariate time-series modelling; (2) multivariate time-series modelling; and (3) comparison of experts' forecasts with those of statistical models. Following a summary of the papers, some suggestions for future research are offered.     

基于反馈机制的抗混合噪声的盲图像源分离方案

针对基于稀疏成分分析的盲图像源分离算法无法解决混合噪声问题,本文提出一种采用反馈机制的盲源分离算法.通过小波域稀疏成分分析和置零反馈的方法,逐次分离出各支路信号.实验结果表明,该方法无需大量的迭代运算,与传统稀疏成分分析法相比,能有效地分离高斯白噪声参与的混合图像,与经典快速独立成分分析法相比,取得了更高的分离精度.     

基于轮廓小波的图像信号去噪算法研究

在非下采样Contourlet变换域中,针对憎水性图像相关特性,分析了图像有用信息与干扰噪声,提出了基于非下采样Contourlet变换复合绝缘子憎水性图像去噪算法,对变换后低频分量中含有光照不均匀成分采用B样条曲面进行近似,得到补偿后低频分量;对多分辨率多方向性带通分量中乘性噪声应用非线性扩散有选择滤波,最后对修正后系数重构.实验结果表明:与同态滤波相比,此算法不仅对憎水性图像光照不均匀部分最佳补偿,而且图像的细节、边缘信息得到有效的保留甚至加强,为后续憎水性图像分析与理解提供了良好的基础.     

Quarterly GPPA earnings data: Time-series properties and predictive ability results in the airlines industry

This paper provides preliminary results regarding the impact of general purchasing power adjustments (GPPA) on quarterly earnings for 24 firms from the airlines industry. Findings indicated that GPPA transformations do not substantially alter the time-series properties of quarterly earnings data. Two Box-Jenkins models, (100) × (100) and (100) × (110) were identified as possible parsimonious models for the airlines industry. It is recommended that these structures be considered as viable candidates for earnings expectations models in market studies testing for informational content of GPPA earnings. The predictive findings demonstrated that predictions of historical cost quarterly earnings were significantly more accurate than GPPA predictions for four of the five horizons tested.     

利用AR模型参数和BP神经网络辨识微震信号

利用AR模型参数和BP神经网络,针对矿山微震信号具有频带较宽、谱成分丰富的特性,提出了时不同频率范围的信号和噪声进行滤波处理的方法.利用该方法可将噪声与信号分离以及将不同频段信号分解,从而达到滤波的目的.实验结果表明,利用AR模型参数和BP神经网络能够有效去除微震异常信号的噪声,可应用于微震信号的预处理和微震预测.     

Forecasting price delay and future stock returns: The role of corporate social responsibility

This paper investigates the role of corporate social responsibility (CSR) performance in forecasting companys' stock prices and future returns. The forecasting analysis identifies a negative association between CSR performance and proxies of price delay. The negative CSR–delay association is weak for state‐owned enterprises (SOEs) because of their politically oriented motivation of CSR activities, but significantly strong for non‐SOEs. Furthermore, we find that forecasting delayed firms is expected to have higher future returns. In particular, the returns premium is most attributable to the CSR component of delay, compared with the non‐CSR component. Taken together, these results suggest that CSR performance plays a positive role in enhancing stock price efficiency, and a potential explanation is that CSR performance can be considered as additional information for equity predictions.     

The detection of earnings manipulation: the three‐phase cutting plane algorithm using mathematical programming

The primary goal of this study was to propose an algorithm using mathematical programming to detect earnings management practices. In order to evaluate the ability of this proposed algorithm, the traditional statistical models are used as a benchmark vis‐à‐vis their time series counterparts. As emerging techniques in the area of mathematical programming yield better results, application of suitable models is expected to result in highly performed forecasts. The motivation behind this paper is to develop an algorithm which will succeed in detecting companies that appeal to financial manipulation. The methodology is based on cutting plane formulation using mathematical programming. A sample of 126 Turkish manufacturing firms described over 10 financial ratios and indexes are used for detecting factors associated with false financial statements. The results indicate that the proposed three‐phase cutting plane algorithm outperforms the traditional statistical techniques which are widely used for false financial statement detections. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of false financial statements and highlight the importance of financial ratios/indexes such as Days' Sales in Receivables Index (DSRI), Gross Margin Index (GMI), Working Capital Accruals to Total Assets (TATA) and Days to Inventory Index (DINV). Copyright © 2009 John Wiley & Sons, Ltd.     

The decomposition of forecast in seasonal arima models

This paper presents a procedure to break down the forecast function of a seasonal ARIMA model in terms of its permanent and transitory components. Both depend on the initial values at the forecast origin, but their structures are fixed and independent of this origin. The permanent component is an estimate of the long-run projection of the corresponding economic variable and the transitory element describes the approach towards the permanent one. Within the permanent component a distinction is made between the factors that depend on the initial conditions of the system and those that are deterministic. The procedure is compared to other methods presented in the literature and illustrated in an example.     

基于DWT-CT的遥感影像数字水印方案

提出一种基于DCT和DWT的遥感影像数字水印方案。首先对水印图像进行Arnold置乱预处理,以破坏像素之间的空间相关性,增强算法鲁棒性;鉴于小波变换良好的局部性和时频分析特性以及多分辨率分析特性,而离散余弦变换具有较好的聚能效应,算法将载体遥感影像进行DWT变换,并选取其子带图像HLl进行DCT变换;最后自适应的将数字水印嵌入所选子块的DC系数上。实验证明本文算法有效提高了水印的不可见性,对JEPG压缩、滤波和叠加噪声等攻击具有较好的鲁棒性。     

Revisions in analysts' earnings forecasts: evidence of non‐linear adaptive expectations

Prior studies use a linear adaptive expectations model to describe how analysts revise their forecasts of future earnings in response to current forecast errors. However, research shows that extreme forecast errors are less likely than small forecast errors to persist in future years. If analysts recognize this property, their marginal forecast revisions should decrease with the forecast error's magnitude. Therefore, a linear model is likely to be unsatisfactory at describing analysts' forecast revisions. We find that a non‐linear model better describes the relation between analysts' forecast revisions and their forecast errors, and provides a richer theoretical framework for explaining analysts' forecasting behaviour. Our results are consistent with analysts' recognizing the permanent and temporary nature of forecast errors of differing magnitudes. Copyright © 2000 John Wiley & Sons, Ltd.     

Harris Laplace特征区域 Contourlet域遥感图像水印算法

针对现有遥感图像水印算法抵抗旋转、剪切、水平镜像及联合几何攻击能力差的问题,提出一种局部化遥感图像数字水印新方法.算法首先对水印图像进行 Arnold置乱预处理,并将其行扫描形成一维向量作为待嵌入的水印信息;在宿主遥感图像中提取 Harris Laplace特征点,依据特征点确定水印嵌入特征区域;规范所选特征区域,对其进行归一化后实施 Contourlet变换,利用奇偶量化方法将数字水印嵌入选定的变换系数内.实验证明算法对滤波、噪声、JEPG压缩等常规信号处理具备较好鲁棒性,并且能够更好地抵抗旋转、缩放、平移及其联合等几何攻击     

企业在市场需求及预测不确定下的多期定价研究

建立多阶段线性需求一价格模型,运用动态规划思想,针对耐用品市场需求量不确定及耐用品生产厂商市场信息预测不准的问题展开探讨。得到了如果消费者对耐用品价格的预期与耐用品生产厂商对市场的预期不一致,耐用品需求量的波动及厂商掌握信息量的多少将对耐用品生产厂商的最优定价具有影响等结论。并根据现实市场状况,对信息不对称下的耐用品定价模型的经济含义给予分析,结果表明模型对耐用品生产厂商的市场决策具有理论指导意义。     

Adaptive prediction for stochastic processes with markov jump parameters

This paper is concerned with the adaptive prediction for stochastic processes with abruptly changing parameters modelled as a finite-state Markov chain. The Markov transition matrix is assumed to be known. For the coloured noise disturbance case, it is shown that the optimal prediction algorithm requires a bank of elemental predictors running in parallel with its number growing exponentially with time. If the noise disturbance is white, it is found that the number of the elemental predictors required increases exponentially with the prediction ahead step instead of time. A suboptimal predictor is proposed with substantial reduced storage and computational requirements. Simulation examples show the good performance of the proposed algorithms.     

基于四元数表达的模糊矢量形态学算子

由于经典的形态学算子不能直接拓展到彩色图像处理中,提出一类基于四元数表达的模糊矢量形态学算子.该算子利用四元数表达彩色图像并结合其分解特性,将彩色图像的模值、四元数的垂直分量和角度信息作为词典编纂顺序的3个边缘分量.3个分量一方面去除了彩色图像多通道数据的相关性,另一方面更便于词典编纂顺序等级的划分.并利用模糊数学解决了现有词典编纂顺序存在的等级判定难以深层进入的问题,给出了基于四元数表达的模糊矢量排序算法.与现有的矢量排序算法相比,新的矢量排序算法同时考虑了各分量的作用,并将判定进行到词典编纂顺序的最后一层,实现了矢量的正确排序.基于新的矢量排序规则,定义了矢量形态学算子,并将新的矢量形态学算子应用于彩色图像滤波、分割中.实验结果表明,与现有的矢量形态学算子相比,新的矢量形态学算子在平滑彩色图像噪声的同时保留了图像细节,并能正确地分割多类彩色图像,具有较高的鲁棒性及实用性.     

Are Analysts' Loss Functions Asymmetric?

Despite displaying a statistically significant optimism bias, analysts' earnings forecasts are an important input to investors’ valuation models. Understanding the possible reasons for any bias is important if information is to be extracted from earnings forecasts and used optimally by investors. Extant research into the shape of analysts' loss functions explains optimism bias as resulting from analysts minimizing the mean absolute forecast error under symmetric, linear loss functions. When the distribution of earnings outcomes is skewed, optimalforecasts can appear biased. In contrast, research into analysts' economic incentives suggests that positive and negative earnings forecast errors made by analysts are not penalized or rewarded symmetrically, suggesting that asymmetric loss functions are an appropriate characterization. To reconcile these findings, we exploit results from economic theory relating to the Linex loss function to discriminate between the symmetric linear loss and the asymmetric loss explanations of analyst forecast bias. Under asymmetric loss functions optimal forecasts will appear biased even if earnings outcomes are symmetric. Our empirical results support the asymmetric loss function explanation. Further analysis also reveals that forecast bias varies systematically across firm characteristics that capture systematic variation in the earnings forecast error distribution. Copyright © 2011 John Wiley & Sons, Ltd.     

TD_LTE移动终端NC系统VAD模块的改进和实现

经过详细研究信号和噪声时频域的不同特性,提出了一种改进的VAD算法。改进前的VAD算法是一种静态的语音停顿周期检测算法,在信噪比很低时,检测性能变差;改进后的VAD算法在信号的全频带,低频带和高频带动态跟踪信号的短时功率包络,多次门限比较后,当出现语音停顿周期时,将做出基于帧的判决。为了验证改进VAD算法在TD_LTE手机NC系统中的应用能力,测试了在不同噪声、不同信噪比情况下的NC系统指标,并与传统NC系统做了比较。经评测,含新VAD算法的改进后NC算法能提高信噪比,并帮助TD_LTE移动终端顺利通过入网测试。     

一种基于谱相减法的声信号时延估计

利用信号增强算法,结合广义互相关时延估计原理,提出一种声信号时延估计算法。在噪声背景下先采用谱相减法从带噪信号中估计出原始声信号,提高信噪比,利用四元立体传声器阵列和广义互相关算法原理,对相关数据进行加权处理得到较高精度的时廷估计值。该算法可以有效抑制空间加性噪声,得到有效声信号准确的时延估计。实验仿真结果表明了该算法的可行性,在信噪比较低的情况下,与单一使用广义互相关函数相比,极大地提高了时延估计算法的准确程度。