Prediction of stock price movement based on daily high prices

Prediction of stock close price movements has attracted a lot of research interest. Using machine learning techniques, especially statistical classifiers, for day ahead forecasting of the movement of daily close prices of a broad range of several hundreds of liquid stocks is generally not very successful. We suspect that one of the reasons for failure is the relatively high volatility of prices in the last minutes before the market closes. There have been some attempts to use less volatile daily high prices instead, but the studies concentrated only on a specific non-statistical machine learning approach on a small number of specific securities. We show that incorporating statistical classifiers for day ahead daily high price movement predictions in to some simple portfolio management techniques significantly increases their performance. Tests performed on S&P 500 stocks show that such a strategy is robust, i.e. the difference in reliability for different stocks does not vary significantly, and that such a strategy greatly outperforms the S&P 500 index and several other benchmarks while increasing the risk only by a small amount.     

Accounting‐Based Probabilistic Prediction of ROE,the Residual Income Valuation Model and the Assessment of Mispricing in the Swedish Stock Market

Using Swedish stock market data, this study investigates whether an investment strategy based on publicly available accounting information can generate abnormal investment returns. The strategy involves two steps. First, an accounting‐based probabilistic prediction model of changes in the medium‐term book return on owners' equity (ROE) is estimated. Second, market expectations of changes in medium‐term ROE are assessed through observed stock prices and the residual income valuation model. Stock market positions over 36‐month holding periods are taken when the accounting‐based predictions of ROE and the market expectations differ. Over the period 1983–2003, the investment strategy generated values of Jensen's alpha corresponding to an average monthly excess return for a hedge position of up to 0.8% for a sample of manufacturing companies. In the main this hedge return was caused by strong positive returns to the long positions, and additional analyses show that the returns appear to have been affected by a positive market sentiment bias (i.e., positive ROE surprises being associated with stronger price reactions than negative ROE surprises) making out‐of‐sample inferences somewhat dubious. Furthermore, most of the investment returns accrued over holding periods up to around 1995, with no indications of market mispricing over the last third (1995–2003) of the investment period. The empirical results are consistent with market investors having become more sophisticated in their use of publicly available accounting information over time.     

国际投机资本冲击风险预警指标体系的构建

在全球金融危机的背景下,国际投机资本的频繁流动严重影响了我国经济的稳定性.通过选取1996～2007年能够反映我国经济面临风险的12个指标,确定各自的权重,用KLR信号分析法实证论述近年来我国的风险变化情况,找出影响风险变化的重要指标.研究发现,国际投机资本这一潜在的危险正在加重,因此,可以在利用风险预警机制的同时加强对跨境资本流动的监管,以保证国民经济的均衡发展.     

The associations among accounting program attributes, 150-hour status, and CPA exam pass rates

This study contributes to the extant literature by providing a better understanding of the associations among attributes of schools providing accounting education, 150-hour status, and CPA exam pass rates. We model program-level pass rates as a function of the state’s 150-hour status and several program-specific attributes, including: the level of AACSB accreditation, student selectivity, the percentage of candidates with advanced degrees, and faculty research productivity. We examine the average pass rates of 520 accounting programs that provided 43,711 first-time candidates for CPA exams given in 1998 and 1999. Similar to most prior studies we find that candidates from more selective schools, candidates with advanced degrees, and candidates who attended schools located in jurisdictions with the 150-hour requirement fully in place have higher average pass rates. However, we also find a significant decline in average pass rates during the 150-hour transition period, and in contrast to the strong positive effects of accreditation reported by [Grant, C. T., Ciccotello, C. S., &; Dickie, M. (2001). Barriers to professional entry: how effective is the 150-hour rule? Journal of Accounting and Public Policy, 21, 71–93], we find only weak evidence of an association between program-level pass rates and college-level or separate AACSB accounting program accreditation.     

如何把握国有企业党组织党员队伍建设的基本要求

国有企业党组织党员队伍建设得当,能够为企业的发展建设注入更多活力,推动企业朝着更好的方向发展,使得企业上下团结一心,有效地解决企业生产经营活动中所面临的矛盾与纠纷。那么,在当前复杂的经济环境下,国有企业究竟应当如何做好党员队伍建设工作,把握好党员队伍建设的基本要求呢?论文对相关问题进行详细分析和论述。     

基于赢得值法的混凝土坝工期预测方法研究

混凝土坝工程是复杂巨系统工程,施工周期长,在项目实施过程中对项目进度/费用联合分析、集成管理非常重要。文章正是在对项目费用/进度进行联合分析的基础上,提出了基于网络计划技术与挣值法联合预测的方法,对于实时控制混凝土坝工程的施工进度以及预测工程完工总工期有一定的理论意义,并通过实例分析验证了方法的可行性。     

基于灰色关联理论的旅游业影响因子分析——以浙江省为例

利用灰色关联分析法对浙江省2005-2010年的旅游产业进行了定量分析,发现浙江省旅游业影响因子从大到小的关联排序为:国内生产总值、人均国内生产总值、城镇家庭收入、旅客周转量、客运量、在校大学生人数、星级饭店数、运输线路距离、广播电视覆盖率。通过建立GM(1,1)模型,对浙江省国内旅游收入和国际创汇进行了短期预测。     

基于改进灰色模型的手机回收量预测及其空间分布研究

我国的手机行业发展迅速,随之带来的则是大量的废旧手机,这些废旧手机有着丰富的贵金属,合理回收并利用可以带来收益、减少环境污染。但手机的回收量有着高度不确定性,这对企业回收以及优化逆向物流的网络带来很大挑战。基于此,文章通过改良的灰色预测模型对废旧手机回收量进行预测,并对其空间分布进行了研究,为后续废旧手机逆向物流网络的设计与优化提供一定参考。     

改进的非等间距GM(1,1)模型的优化

针对传统非等间距GM(1,1)模型的不足,本文分析了传统非等间距GM(1,1)模型的基本原理,分析了传统非等间距GM(1,1)模型与白化方程之间的差异,提出了改进累加序列的方式来优化非等间距GM(1,1)模型,并建立了一种新的累加方式并应用于某大坝沉降。分析了本文中的非等间距GM(1,1)模型与传统非等间距GM(1,1)模型之间的差异。实例表明了改进非等间距GM(1,1)模型精度较高,更适合于工程实例。     

我国航空运输的市场预测

本文论述了目前我国航空运输业的现状及面临的问题，阐述了航空运输的市场预测对我国航空运输业发展的帮助和作用，并介绍了航空运输量预测的一般方法。以我国航空货邮运输量预测为例，对我国近十年来航空货部运输量的发展变化进行了详细的分析和对比。通过三种数学模型预测了我国2015年的航空货邮运输量。