创业板上市公司IPO定价机制研究

本文在回顾新股发行制度改革的基础上,以新询价制为切入点,运用我国创业板市场2009年10月-2011年4月上市的新股为样本,从新股的询价区间及定价调整的角度对创业板询价制下的IPO抑价进行了实证研究。研究表明:询价制加强了新股定价的市场化,切合了新股发行折价的风险-收益对等的市场原则;机构投资者参与询价的经验不足,定价能力有待进一步提高。     

上市公司财务预测信息的现状分析和框架设计

目前我国证券市场处于一种“新兴市场”的历史阶段,上市公司的财务预测信息披露存在一些问题,财务预测信息披露的规范制度远远没有完善起来,财务预测信息披露的规范建设和框架设计迫在眉睫。一、上市公司财务预测信息披露的现状简析我国关于上市公司财务预测信息披露的法律规范,证监会在1994年才开始规定在招股说明书中,应包括经注册会计师审核的盈利预测。在新股发行过程中,证监会要求上市公司初次上市时必须披露盈利预测,并以盈利预测作为新股发行价的确定基础。1997年证监会发布了《关于做好1997年股票发行工作的通知》,对股票发行定价规…     

基于模糊优化模型下的IPO估值区间确定

针对投资者对新股内在价值的异质评估,论文采用可比公司估值方法,运用模糊优化评估模型对新股和同行业可比公司进行综合价值评估,确定新股的估值区间,并进行了实证研究.通过实证结果与初步询价区间比较表明,在预期需求过剩的前提下,机构投资者为追求更高的回报和规避"赢者诅咒",在初步询价阶段,有有意压低询价区间的激励;运用该评估方法确定新股估值区间,可以在一定程度上消除机构投资者有意压低询价区间而导致的部分抑价.     

我国新股发行制度存在的问题及改进建议

本文在分析证券市场发行新股存在的发行溢价率畸高、制度设计偏向机构投资者和影响金融系统稳定等问题的基础上,指出要实现新股发行市场化定价必须在新股发行体制、市场参与主体、新股上市交易制度等方面进行改进,以保障广大投资者的利益。     

我国新股发行制度存在的问题及改进建议

本文在分析证券市场发行新股存在的发行溢价率畸高、制度设计偏向机构投资者和影响金融系统稳定等问题的基础上,指出要实现新股发行市场化定价必须在新股发行体制、市场参与主体、新股上市交易制度等方面进行改进,以保障广大投资者的利益。     

财务分析师盈余预测特性研究——中国A股市场的实证检验

本文以我国A股上市公司2001年度的预测盈余数据为样本,实证分析了我国财务分析师的预测精确度、预测修正情况及预测倾向性等预测特性。研究表明,当前我国分析师的预测盈余具有较高的精确度;其修正行为能进一步改善预测效果;其预测行为具有显著的乐观倾向。同时还发现,虽然分析师及其提供的服务尚未得到投资者的完全认同,但市场确实对分析师预测盈余的公布做出了反应。     

上市公司股权再融资方式的比较研究

配股、公开增发新股和定向增发新股是中国上市公司股权再融资的主要方式。中国上市公司股权再融资的方式经历了由配股向公开增发新股,再到流行定向增发新股的演变过程,本文着重探讨定向增发对公司的影响,定向增发新股融资引入了机构投资者,可以强化对上市公司的监管,从而降低代理成本,提高上市公司的业绩。并且,相对于配股、公开增发新股而言,定向增发新股融资的手续更简单,门槛更低,因此,定向增发新股融资是中国上市公司股权再融资的最佳选择。     

中国股票发行市场IPO定价效率研究

本文采用异质性随机前沿分析方法对中国股票发行市场的IPO定价效率进行了测评,模型把定价分为前沿部分(效率部分)和非效率部分,得出的主要结论有以下几点:第一,模型的效率部分均与新股发行价格显著相关,说明企业的内在价值因素是正向影响IPO价格的重要因素;第二,影响IPO非效率部分的因素主要有:询价机构投资者的报价行为、新股发行定价的行政管制以及新股发行所处的市场氛围;第三,第三阶段的改革措施使得新股发行呈"去市场化"的态势,新股发行定价存在着较为严格的价格管制。     

信息不对称对IPO抑价影响机理研究——基于信息披露视角

我国新股发行规定,拟上市公司在上市前需要向公众发布有资质的承销商撰写的招股说明书。通过分析不同类型的信息披露对IPO抑价的影响因素:叙事前瞻性信息披露、金融前瞻性预测和预测(未来的财务信息)以及风险因素危害公司的竞争地位等,探寻信息不对称对IPO抑价影响机理,以期丰富IPO抑价理论的研究,对于我国完善新股发行体制具有一定的实践意义。     

机构交叉所有权对管理层自愿盈利预测的影响——基于产品市场竞争的调节作用

文章基于2010—2018年我国A股全行业2627家上市公司样本,探讨了机构交叉所有权、产品市场竞争对管理层自愿盈利预测的影响。研究结果表明:一是机构交叉所有权通过降低专有成本和内部化披露外部性两种机制提高企业披露管理层自愿盈利预测的意愿。二是产品市场竞争强度越小,机构交叉所有权对企业披露管理层自愿盈利预测的激励效应越明显。从机构交叉所有权的视角研究影响企业管理层自愿盈利预测的因素,有助于完善股权结构影响企业信息披露决策的路径,在为我国上市公司合理决策提供丰富依据的同时,也为机构投资者的组合投资决策提供一个新的研究视角。     

Forecasting performance of multivariate time series models with full and reduced rank: an empirical examination

Since Quenouille's influential work on multiple time series, much progress has been made towards the goal of parameter reduction and model fit. Relatively less attention has been paid to the systematic evaluation of out-of-sample forecast performance of multivariate time series models. In this paper, we update the hog data set studied by Quenouille (and other researchers who followed him). We re-estimate his model with extended observations (1867–1966), and generate recursive one- to four-steps-ahead forecasts for the period of 1967 through 2000. These forecasts are compared to forecasts from an unrestricted vector autoregression, a reduced rank regression model, an index model and a cointegration-based error correction model. The error correction model that takes into account both nonstationarity of the data and rank reduction performs best at all four forecasting horizons. However, differences among competing models are statistically insignificant in most cases. No model consistently encompasses the others at all four horizons.     

Forecasting welfare caseloads: The case of the Japanese public assistance program

Forecasting welfare caseloads has grown in importance in Japan because of their recent rapid increase. Given that the forecasting literature on welfare caseloads only focuses on US cases and utilizes limited classes of forecasting models, this study employs multiple alternative methods in order to forecast Japanese welfare caseloads and compare forecasting performances. In pseudo real-time forecasting, VAR and forecast combinations tend to outperform the other methods investigated. In real-time forecasting, however, a simple version of forecast combinations seems to perform better than the remaining models, predicting that welfare caseloads in Japan will surpass 1.7 million by the beginning of 2016, an approximately 20% increase in five years from the beginning of 2011.     

Forecasting commodity prices out-of-sample: Can technical indicators help?

Economic variables are often used for forecasting commodity prices, but technical indicators have received much less attention in the literature. This paper demonstrates the predictability of commodity price changes using many technical indicators. Technical indicators are stronger predictors than economic indicators, and their forecasting performances are not affected by the problems of data mining or time changes. An investor with mean–variance preference receives utility gains of between 104.4 and 185.5 basis points from using technical indicators. Further analysis shows that technical indicators also perform better than economic variables for forecasting the density of commodity price changes.     

Forecasting realized volatility of agricultural commodities

We forecast the realized and median realized volatility of agricultural commodities using variants of the heterogeneous autoregressive (HAR) model. We obtain tick-by-tick data on five widely-traded agricultural commodities (corn, rough rice, soybeans, sugar, and wheat) from the CME/ICE. Real out-of-sample forecasts are produced for between 1 and 66 days ahead. Our in-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump components and incorporate leverage effects offer better fitting in the predictive regressions. However, we demonstrate convincingly that such HAR extensions do not offer any superior predictive ability in their out-of-sample results, since none of these extensions produce significantly better forecasts than the simple HAR model. Our results remain robust even when we evaluate them in a Value-at-Risk framework. Thus, there is no benefit from including more complexity, related to the volatility decomposition or relative transformations of the volatility, in the forecasting models.     

Forecasting demands for inpatient services in a large public health care delivery system

This paper studies the use of autoregressive integrated moving average (ARIMA) time-series models for forecasting demands for inpatient services in a large public health care delivery system. Here, demands are measured in terms of monthly admissions and patient days by services and forecasts are made yearly. This paper emphasizes the implementation aspect of ARIMA models when they are used on a large scale basis in an institutional setting, and compares forecasts with actuals. For forecasting patient days, the adequacy of an indirect approach using the formula L = λW is also evaluated. Finally, we briefly describe how the forecasts are used in the context of resource allocation.     

Forecasting Errors: the Track Record

This Briefing Paper is the last of a series of three about forecasting. In this one we examine our forecasting record; it complements the February paper in which we analysed the properties of our forecasting model in terms of the error bands attached to the central forecast.
There are many ways of measuring forecasting errors, and in the first part of this Briefing Paper we describe briefing how we have tackled the problem. (A more detailed analysis can be found in the Appendix.) In Part II we report and comment upon the errors in our forecasts of annual growth rates and show how our forecasting performance has improved over the years. In Part III we focus on quarterly forecasts up to 8 quarters ahead, and compare our forecasting errors with measurement errors in the oficial statistics; with the estimation errors built into our forecast equations; and with the stochastic model errors we reported last February. A brief summary of the main conclusions is given below.     

Anticipating special events in Emergency Department forecasting

Accurate daily forecast of Emergency Department (ED) attendance helps roster planners in allocating available resources more effectively and potentially influences staffing. Since special events affect human behaviours, they may increase or decrease the demand for ED services. Therefore, it is crucial to model their impact and use them to forecast future attendance to improve roster planning and avoid reactive strategies. In this paper, we propose, for the first time, a forecasting model to generate both point and probabilistic daily forecast of ED attendance. We model the impact of special events on ED attendance by considering real-life ED data. We benchmark the accuracy of our model against three time-series techniques and a regression model that does not consider special events. We show that the proposed model outperforms its benchmarks across all horizons for both point and probabilistic forecasts. Results also show that our model is more robust with an increasing forecasting horizon. Moreover, we provide evidence on how different types of special events may increase or decrease ED attendance. Our model can easily be adapted for use not only by EDs but also by other health services. It could also be generalised to include more types of special events.     

南京市人口规模预测与空间分布引导研究

人口规模预测是城市总体规划编制工作中的基础性工作,本研究结合南京城市总体规划修编对城市人口规模进行了多方案、多角度的预测与校核,并针对现状问题和城市发展目标提出了相应的人口发展策略和空间布局建议,为科学确定城市用地规模和用地布局提供了支撑.     

Applicability of the M5 to Forecasting at Walmart

The M5 Forecasting Competition, the fifth in the series of forecasting competitions organized by Professor Spyros Makridakis and the Makridakis Open Forecasting Center at the University of Nicosia, was an extremely successful event. This competition focused on both the accuracy and uncertainty of forecasts and leveraged actual historical sales data provided by Walmart. This has led to the M5 being a unique competition that closely parallels the difficulties and challenges associated with industrial applications of forecasting. Like its precursor the M4, many interesting ideas came from the results of the M5 competition which will continue to push forecasting in new directions.In this article we discuss four topics around the practitioners view of the application of the competition and its results to the actual problems we face. First, we examine the data provided and how it relates to common difficulties practitioners must overcome. Secondly, we review the relevance of the accuracy and uncertainty metrics associated with the competition. Third, we discuss the leading solutions and their implications to forecasting at a company like Walmart. We then close with thoughts about a future M6 competition and further enhancements that can be explored.     

Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters

Evidence from a large and growing body of empirical literature strongly suggests that there have been changes in the inflation and output dynamics in the United Kingdom. The majority of these papers base their results on a class of econometric models that allows for time-variation in the coefficients and volatilities of shocks. While these models have been used extensively for studying evolving dynamics and for structural analysis, there has been little evidence that they are useful for forecasting UK output growth and inflation. This paper attempts to fill this gap by comparing the performances of a wide range of time-varying parameter models in forecasting output growth and inflation. We find that allowing for time-varying parameters can lead to large and statistically significant gains in forecast accuracy.