A COMPARATIVE ANALYSIS OF THE PREDICTIVE ABILITY OF ADAPTIVE FORECASTING,RE-ESTIMATION,AND RE-IDENTIFICATION USING BOX-JENKINS TIME-SERIES ANALYSIS ON QUARTERLY EARNINGS DATA

This paper assesses the predictive ability of the Box-Jenkins methodology when utilized in an on-going setting. Three procedures are utilized to update the original forecasts generated from the Box-Jenkins models: adaptive forecasting, re-estimation, and re-identification. The results indicate that constant monitoring of the structure and parameters of the time-series models are necessary through time. It appears that adaptive forecasting techniques are insufficient to update BJ time-series models when used in conjunction with quarterly earnings data. Re-estimation is recommended as each new observation becomes available. Re-identification procedures are recommended on a less frequent basis.     

Some Empirical Findings on Short-Term Forecasting: Technique Complexity and Combinations

The purpose of this research is to determine if prior findings that favor simple forecasting techniques and technique combinations hold true in a short-term forecasting environment, where demand data can be quite volatile. Twenty-two time series of daily data from a real business setting are used to test one-period ahead forecasts, the epitome of short-term forecasting. The time series vary systematically as to data volatility and forecast difficulty. Forecast accuracy is measured in terms of both mean absolute percentage error (MAPE) and mean percentage error (MPE).     

Improving the accuracy and operational predictability of project cost forecasts: an adaptive combination approach

Conducting an early warning forecast to detect potential cost overrun is essential for timely and effective decision-making in project control. This paper presents a forecast combination model that adaptively identifies the best forecast and optimises various combinations of commonly used project cost forecasting models. To do so, a forecast error simulator is formulated to visualise and quantify likely error profiles of forecast models and their combinations. The adaptive cost combination (ACC) model was applied to a pilot project for numerical illustration as well as to real world projects for practical implementation. The results provide three valuable insights into more effective project control and forecasting. First, the best forecasting model may change in individual projects according to the project progress and the management priority (i.e. accuracy, outperformance or large errors). Second, adaptive combination of simple, index-based forecasts tends to improve forecast accuracy, while mitigating the risk of large errors. Third, a post-mortem analysis of seven real projects indicated that the simple average of two most commonly used cost forecasts can be 31.2% more accurate, on average, than the most accurate alternative forecasts.     

考虑影响因素的隐马尔可夫模型在经济预测中的应用

定量预测方法分为因果预测法和时间序列预测法,因果预测法利用预测变量与其他变量之间的因果关系进行预测,时间序列预测法是根据预测变量历史数据的结构推断其未来值。由于因果预测法只利用某个变量与其他变量之间的因果关系,但缺少描述变量自身时间序列结构的功能;而时间序列预测法只能描述变量自身序列的结构,但没有考虑其他相关因素的影响,因此本文提出基于观测向量序列的隐马尔可夫模型(HMM)预测方法,该方法能同时考虑变量自身序列结构以及相关因素的影响。首先介绍HMM基本理论;其次,在模型训练、隐状态序列估计的基础上,提出基于观测向量序列HMM预测算法;最后分别进行仿真实验和实证研究,结果表明该方法的有效性。     

Managing finished-goods inventory under capacitated delayed differentiation

Delayed differentiation or postponement is widely advocated to mitigate conflicts between product diversity and inventory cost savings. Manufacturers practicing postponement often suffer from severely constrained finishing capacities and noticeable finishing lead times. Therefore, inventories are still needed for finished products. Using the concept of inventory shortfall, this paper studies base-stock inventory models with and without demand forecasting and provides a computationally efficient method to set optimal inventory targets for finished products under capacitated postponement. Computations show inventory-saving benefit quickly vanishes after the capacity reaches a certain level. The value of forecasted advance-demand information (ADI) to postponement is justified, but can easily be overstated. Finishing capacities usually force manufacturers to build ahead according to demand forecast. When capacity limitation becomes severe, intuitions often guide producers to build to forecast even more than finishing lead times ahead. Results of this research indicate that these intuitions may be invalid and build to forecast more than finishing lead times ahead may not be a good practice. Further studies reveal that under capacitated postponement the forecasted advance-demand information is useful only when the variance of demand forecast errors is less than that of demands, and show that the optimal forecast lead time can be obtained in the same way as if the capacity is unlimited.     

Combining Judgmental and Statistical Forecasts: An Application to Earnings Forecasts

This study investigated the accuracy of combinations of statistical and judgmental forecasts of annual accounting earnings. Combined forecasts were generated as equally weighted (i.e., simple averages) and unequally weighted combinations of individual forecasts from time-series models of quarterly and annual earnings (statistical forecasts) and security analysts' forecasts of quarterly and annual earnings (judgmental forecasts). The effect of the number of individual forecasts combined on the accuracy of the combined forecasts was also examined. The empirical results indicated that, on the average, combined forecasts were more accurate than individual forecasts. The results also indicated that although analysts' forecasts are based on a wider information set, the accuracy of their forecasts could be improved by combining them with forecasts generated from statistical models. Even if the best individual forecast could be identified in advance, gains in accuracy could be achieved by using combinations of two other forecasting methods. Several of the combined forecasts were superior to the most accurate individual forecast. Forecasts combined by using unequal weights derived from a regression model proved more accurate than equally weighted combinations. Forecasting accuracy improved and the variability of accuracy across different combinations decreased as the number of forecasts in the combination increased.     

基于修剪平均的神经网络集成时序预测方法

由于复杂时序存在结构性断点和异常值等问题，往往导致预测模型训练效果不佳，并可能出现极端预测值的情况。为此，本文提出了基于修剪平均的神经网络集成预测方法。该方法首先从训练数据中生成多组训练集，然后分别训练多个神经网络预测模型，最后将多个神经网络的预测结果使用修剪平均策略进行集成。相较于简单平均策略而言，修剪平均策略不容易受到极值的影响，能够使集成模型获得鲁棒性强的预测效果。在实证研究中，本文构造了两种神经网络集成预测模型，分别为基于修剪平均的自举神经网络集成模型(Trimmed Average based Bootstrap Neural Network Ensemble, TA-BNNE)和基于修剪平均的蒙特卡洛神经网络集成模型(Trimmed Average based Monte Carlo Neural Network Ensemble, TA-MCNNE)，并采用这两种模型对NN3竞赛数据集进行预测，结果表明在常规和复杂数据集上，修剪平均策略比简单平均策略具有更好的预测精度。此外，本文将所提出的集成模型与NN3的前十名模型进行比较，发现两种模型在全部数据集上均超过了第6名，在复杂数据集上的表现均超过了第1名，进一步验证本文所提方法的有效性。     

Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative Methods

In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network models incorporating the fundamental accounting variables can generate more accurate forecasts of future earnings than the models assuming a linear combination of these same variables. We investigate four types of models: univariate‐linear, multivariate‐linear, univariate‐neural network, and multivariate‐neural network using a sample of 283 firms spanning 41 industries. This study shows that the application of the neural network approach incorporating fundamental accounting variables results in forecasts that are more accurate than linear forecasting models. The results also reveal limitations of the forecasting capacity of investors in the security market when compared to neural network models.     

Overstated Quarterly Earnings and Analysts' Earnings Forecast Revisions*

A primary purpose of accounting is to provide information for decision makers. Accounting misstatements may have a detrimental effect on decision making. The Securities and Exchange Commission (SEC) identifies earnings overstatements as being particularly troublesome to users, as indicated by SEC Accounting and Auditing Enforcement Releases' emphasis on earnings' overstatement errors. This research investigates how security analysts' forecast revisions are affected by accounting earnings overstatement errors, which become known only after the analysts released their revised annual earnings forecasts. The paper investigates the clarifying role that additional information plays in analysts' revisions. The results show that analysts draw significantly different conclusions from earnings containing (unknown) overstatement errors than from accurately reported earnings. In essence, the analysts identify some of the overstatement, at least on average, by making an adjustment that effectively ignores 21 percent of the overstatement error.     

Sales Forecasting with Financial Indicators and Experts' Input

We present a method for forecasting sales using financial market information and test this method on annual data for US public retailers. Our method is motivated by the permanent income hypothesis in economics, which states that the amount of consumer spending and the mix of spending between discretionary and necessity items depend on the returns achieved on equity portfolios held by consumers. Taking as input forecasts from other sources, such as equity analysts or time‐series models, we construct a market‐based forecast by augmenting the input forecast with one additional variable, lagged return on an aggregate financial market index. For this, we develop and estimate a martingale model of joint evolution of sales forecasts and the market index. We show that the market‐based forecast achieves an average 15% reduction in mean absolute percentage error compared with forecasts given by equity analysts at the same time instant on out‐of‐sample data. We extensively analyze the performance improvement using alternative model specifications and statistics. We also show that equity analysts do not incorporate lagged financial market returns in their forecasts. Our model yields correlation coefficients between retail sales and market returns for all firms in the data set. Besides forecasting, these results can be applied in risk management and hedging.     

INDIVIDUAL ITEM FORECASTING MODEL EVALUATION*

A computer simulation experiment was conducted to evaluate and compare seven individual item forecasting models across five different demand patterns. Results indicate the best model varies depending upon the demand pattern, the time period forecast, the noise level of the demand pattern, and to a lesser extent the measure of forecast error. Across all demand patterns, exponential double smoothing was best for the long run forecast and at least second best for the short run regardless of noise level in the demand patterns. Analysis of models within a demand pattern yielded, in most cases, several models as ranking equally well. The adaptive model developed here did not perform as well as some other models. For example, it ranked no better than third on a step function demand pattern.     

Forecasting: A hybrid approach

It has recently been shown that the Box-Jenkins approach to forecasting time series is superior to an econometric approach over a relatively short horizon. The results here support this contention. A combination of the two approaches, however, proves to be clearly superior to either one separately. By taking account of changes in economic and weather related variables in a time series model, improved forecasts are obtained.     

小波包与混沌集成的股票市场预测新方法

基于小波包变换和混沌理论提出了一种股票市场建模及其预测的新方法,既能刻划时间序列的规律,又能捕捉混沌状态的特征.首先,应用小波包变换对上证综指和深证成指日收益率序列进行三层分解,分别得到第三层从低频到高频八个频率成分的时序,并在此基础上作进一步分析,结果表明中国股市存在混沌特性;然后,应用混沌理论分别建立从低频到高频八个时序的预测模型,分别对八个时序进行预测;最后,基于小波包理论对混沌模型预测的结果予以重构,实现对原始收益率序列的预测.与现有方法比较,结果表明该方法具有较高的精度,有极大的应用范围.     

Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

This paper addresses aggregation in integer autoregressive moving average (INARMA) models. Although aggregation in continuous-valued time series has been widely discussed, the same is not true for integer-valued time series. Forecast horizon aggregation is addressed in this paper. It is shown that the overlapping forecast horizon aggregation of an INARMA process results in an INARMA process. The conditional expected value of the aggregated process is also derived for use in forecasting. A simulation experiment is conducted to assess the accuracy of the forecasts produced by the aggregation method and to compare it to the accuracy of cumulative h-step ahead forecasts over the forecasting horizon. The results of an empirical analysis are also provided.     

短期电价预测的组合混沌方法

电力市场中,电价的变化呈现的是一种非线性的、动态开放的过程,传统的方法已很难提高其预测精度。为此,本文提出一种基于小波变换、计量经济学模型和径向基函数网络的组合混沌预测方法。首先利用小波变换将原电价序列分解、重构成概貌序列和细节序列;在此基础上,针对不同的子序列建立不同的模型,并进行预测;最后将所有子序列的预测结果求和,作为最终的预测值。对西班牙电力市场短期电价的预测表明,该方法具有很高的预测精度。     

机构投资者持股对信息披露的治理作用研究——以管理层盈余预告为例

本文以我国A股上市公司2004-2007年盈余预告披露数据为例,实证检验了机构投资者对信息披露的治理作用。结果发现:(1)随着机构投资者持股比例的增加,管理层采取的盈余预告精确性提高(更具体的形式和更小的误差),及时性也增强;(2)银行、财务公司类机构、一般基金类机构对管理层盈余预告选择的积极治理作用相对较强,而养老、保险类机构对管理层盈余预告选择的积极治理作用则相对较弱;(3)处于不同持股规模时,管理层盈余预告的精确性、及时性均随着机构投资者整体持股比例增大而提高。但是,机构投资者持股比例的提高易导致了管理层盈余预告的乐观态度倾向;(4)股权分置改革后,机构投资者持股对管理层盈余预告披露选择的积极治理作用比股权分置改革前有所增强。建议大力发展机构投资者规模和专业素质以优化投资者结构,促进我国资本市场的健康发展。     

集团军山地进攻作战减员预计模型

收集了我军以往作战军减员的经验数据,建立军减员率的时间序列模型,提出计算机模拟的方法。在此基础上,着重分析影响减员的多种因素,对交战双方武器装备数量及技术等级,作战地区的地形和气候条件等因素进行了定量描述。运用专家咨询方法筛选了社会经济行为等"软"指标,用群体层次分析法确定各指标的权重,建立了量化指标体系,并运用该指标体系对我军今后主要作战对象进行了量化。结合以上因素对计算机模拟生成的数据进行修正,建立相应的调整算法。     

基于集成支持向量机的企业财务业绩分类模型研究

要想正确预测公司财务业绩,首先必须选择合适的预测方法。现有文献所采用的财务业绩预测模型普遍存在着泛化能力不强的问题。本文提出用支持向量机方法来预测我国上市公司的财务业绩。为了提高预测准确率,本文还用AdaBoost算法对支持向量机进行了改进(集成支持向量机)。在支持向量机核函数的选择上,我们采用了实验法,即对每个核函数及其相关参数的预测效果都进行了测算,以期找出最适用的预测模型。实证结果表明,径向基核函数(rbf)的效果最好,支持向量机方法预测准确率远远高于其它方法。     

Application of support vector machines in financial time series forecasting

This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial time series forecasting. The objective of this paper is to examine the feasibility of SVM in financial time series forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. The experiment shows that SVM outperforms the BP neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE), directional symmetry (DS) and weighted directional symmetry (WDS). Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast financial time series.     

基于小波分析的石油价格长期趋势预测方法及其实证研究

本文将小波方法引入到油价长期趋势的预测中,利用小波多尺度分析的功能,提出了一种可以较为准确地根据油价时序列预测其未来长期走势的方法。这种方法的优点在于可以准确地提取油价的长期趋势,从总体上把握油价的非线性波动特征,从而能够很好地利用油价时间序列的历史数据,开展对未来一段时期内的多步预测。实证研究中,对Brent油价开展了时间跨度为1年的趋势预测,并将预测结果与ARIMA、GARCH、Holtwinters等方法得到的结果进行了比较,表明了基于小波分析的长期趋势预测法的预测能力是其他方法所不能比拟的,反映了本文所建立的石油价格长期趋势预测方法的有效性。