An Easily Implemented Framework for Forecasting Ticket Sales to Performing Arts Events

This paper presents an easily used framework for modeling ticket sales to performing arts and entertainment events. Unlike existing efforts in this area, our framework allows us to: (1) model demand for events that consist of more than a single performance; (2) account for the influence of promotional effort on ticket sales; and (3) account for sellouts of some performances. The framework is applied to ticket sales for a university theater company, where it predicts ticket sales well in both an estimation and holdout sample. We discuss how the framework has influenced the company's marketing decisions.     

煤层瓦斯的赋存因素及涌出量预测

煤矿安全生产的一个重大问题就是矿井瓦斯,瓦斯涌出量的准确预测是煤矿通风系统优化及制定合理瓦斯防治措施的前提条件,禹州市富山煤矿矿井瓦斯随着煤层埋藏深度的增加,瓦斯涌出量将会增大,采区正常生产期间为1个回采工作面、2个煤巷掘进工作面,因此,计算得出生产采区相对瓦斯涌出量为4.43m3/t,绝对瓦斯涌出量为2.77m3/min。     

Excessive Volatility is Also a Feature of Individual Level Forecasts

The excessive volatility of prices in financial markets is one of the most pressing puzzles in social science. It has led many to question economic theory, which attributes beneficial effects to markets in the allocation of risks and the aggregation of information. In exploring its causes, we investigated to what extent excessive volatility can be observed at the individual level. Economists claim that securities prices are forecasts of future outcomes. Here, we report on a simple experiment in which participants were rewarded to make the most accurate possible forecast of a canonical financial time series. We discovered excessive volatility in individual-level forecasts, paralleling the finding at the market level. Assuming that participants updated their beliefs based on reinforcement learning, we show that excess volatility emerged because of a combination of three factors. First, we found that submitted forecasts were noisy perturbations of participants’ revealed beliefs. Second, beliefs were updated using a prediction error based on submitted forecast rather than revealed past beliefs. Third, in updating beliefs, participants maladaptively decreased learning speed with prediction risk. Our results reveal formerly undocumented features in individual-level forecasting that may be critical to understand the inherent instability of financial markets and inform regulatory policy.     

Return prediction and stock selection from unidentified historical data

The experimental approach was applied to test the value of historical return series in technical prediction. Return sequences were randomly drawn cross-sectionally and over time from S&P500 records and participants were asked to predict the 13th realization from 12 preceding returns. The hypothesis that predictions (nominal or real) are randomly assigned to historical sequences is rejected in permutation tests, and the best-stock portfolios by experimental predictions significantly outperform the worst-stock portfolios in joint examination of mean return and volatility. The participants dynamically adjust their predictions to the observed series and switch from momentum riding to contrarian extrapolation when recent trends get extreme. The implicit tuning of predictions to specific series captures variabilities that could not be inferred by schematic statistical forecasting.     

内蒙古经济发展与工业污染关系的动态分析

本文结合内蒙古区情,对内蒙古经济发展和环境污染关系进行动态分析。首先,根据研究目的进行指标的选取。其次,构造经济发展综合指数,利用灰色预测模型对经济发展综合指数进行两期预测,进一步说明内蒙古经济发展情况。再次,运用单位根检验得出序列一阶差分后均平稳,属于同阶单整序列,满足协整检验的条件,由协整检验得出工业三废增速和经济增长率存在长期协整关系,通过用差分后的数据建立VAR模型进行脉冲响应分析和方差分解分析,得出经济发展和工业环境污染的双向作用机制：经济增长率与工业三废排放量增速互相有不同程度的正向冲击,工业三废之间作为有机整体互相影响从而加速工业环境的恶化。最后结合分析结果提出走新型工业化道路的对策。     

财务预警模型中的样本配比分析及其对策研究

上市公司财务预警模型受到不同配对比例的下采样影响较大,2007—2008年上市公司财务数据的分析结果表明：配对比例过高,ST公司的识别率太低;配对比例过低,模型识别结果变异太大,结果不可靠;而现代统计学中针对不平衡数据的统计方法SMOTO方法和Bagging算法均能较好地克服样本比例不均衡的影响,上述数据的实证研究结果显示：基于上述两种方法的财务预警模型在测试集上对正常公司和ST公司都取得了较好的稳定识别率。     

股价波动序列的综合预测法研究

股票市场中股票价格的波动是一个非线性混沌时间序列,其参数是随时间变化的。笔者提出的多层递阶一灰色预测综合预测法是运用多层递阶法,通过辨识时变参数,建立时变参数动态预测模型,并在此基础上进一步运用灰色预测方法,通过对时变参数的预测来预测股票价格的波动。实例表明：多层递阶一灰色预测综合预测法有较好的预测精度。     

Fundamentals and Exchange Rate Prediction Revisited

This paper measures latent fundamental exchange rates with independent component‐based rates constructed from a cross‐section of exchange rates and then uses their deviations from exchange rates to forecast. Empirical results indicate that the independent component‐based model and its Taylor rule and purchasing power parity augmented models are superior to the random walk in predicting exchange rates. These results are robust to several scenarios and are likely to be observed if the U.S. sources and the recursive scheme are applied. Our results reveal that information regarding the third moment of exchange rate changes is helpful to explain exchange rate movements.     

一种提高薄互层储集层分辨率的新方法

目前，薄储集层横向追综、对比的困难，在于薄互层结构的高频信息经地层吸收，采集和接收后变得很弱，甚至被强干扰掩没，从记录上很难对薄互层储集层作追踪对比。本文提出了一种在复数域提高地震记录分辨率的处理方法，实际资料处理表明；它既提高了地震记录的分辨率又不降低其信噪比，是一种既经济又理想的处理方法。     

基于时间序列的BP神经网络预测鄂尔多斯市的GDP

国内生产总值(GDP)是制定经济发展战略、规划、年度计划以及各种宏观经济政策的重要依据,研究GDP预测方法具有重要的现实意义。在对鄂尔多斯市GDP数据序列分析的基础上,考虑到BP神经网络可以通过简单非线性函数的复合能够实现对于复杂函数的映射,本文采用BP神经网络方法来预测鄂尔多斯市的GDP,取得了较好的预测效果。