基于IOWA算子的我国旅游总收入组合预测

以2000～2010年我国旅游总收入为样本区间,在回归预测、指数预测与二次移动平均预测三种单项预测方法的基础上,以预测误差平方和最小为准则,建立IOWA算子组合预测模型。对该模型的预测有效度进评价结果表明该组合预测模型能有效提高预测精度。     

销售组合预测模型构建及实证分析

预测销售是企业确保销售指标制定的先决条件,准确的销售预测不仅有助于提高企业及其产品的竞争力,减少销售风险,也是企业提高市场应变能力的有效手段。本文构建了组合预测模型,然后运用单项预测模型和组合预测模型对a公司的销售情况进行了预测分析和比较。实践证明,组合预测的预测精度比单项预测更高、可靠性更强是企业制定生产计划和销售计划的科学依据。     

基于时间序列模型的农产品市场价格短期预测研究

为了对农产品市场价格波动的进一步了解,及早采取措施应对价格波动频繁带来的一系列不利影响,有效指导农民把握时机增收增产,本文以全国黄瓜月度批发市场价格为预测目标,综合利用季节虚拟变量法、CensusX12法、ARMA法等建立短期预测模型,并根据模型预测误差大小赋予不同的权重值来建立这三个不同短期预测模型的组合预测,运用组合模型对农产品价格做出合理预测。实证分析结果表明：组合模型能够从一定程度上减小预测误差提高模型的预测精度。总体上随着预测周期变长精度下降。在2009年的评估预测中,所建立的3个单一短期预测模型平均绝对误差百分比（MAPE）分别为13.53％,11.77％和11.07％,ARMA法建立的短期预测模型精度最高。在实证分析的基础上,采用组合预测方法对2012年7月到11月这五个月的黄瓜价格进行了预测,并将预测值与收集到的实际值做了对比,结果表明,除了8月的预测误差较大为33％外．其余各个月的误差均不超过10％,预测结果较为理想。     

组合预测模型在工业经济预测中的应用

论文研究了工业经济预测问题,以四川省近4年景气调查数据与工业生产、效益统计数据为基础,选取多种数据挖掘算法分别建立各自的经济预测模型,对四川省2006年第4季度的工业产品销售收入指标进行预测,分析了各种预测方法的优劣,并用多元回归方法对几种模型进行拟合,求得最终的组合预测模型。经过实证研究,该组合预测模型取得了令人满意的效果。     

组合预测模型在工业经济预测中的应用

论文研究了工业经济预测问题,以四川省近4年景气调查数据与工业生产、效益统计数据为基础,选取多种数据挖掘算法分别建立各自的经济预测模型,对四川省2006年第4季度的工业产品销售收入指标进行预测,分析了各种预测方法的优劣,并用多元回归方法对几种模型进行拟合,求得最终的组合预测模型.经过实证研究,该组合预测模型取得了令人满意的效果.     

最优加权组合法在四川省GDP预测中的运用研究

在国民经济发展的过程中,国内生产总值(GDP)是衡量一个国家经济总体经济运行状况的重要指标。GDP预测对国家的经济政策和措施的制定具有战略性意义。单一的GDP预测方法,由于模型本身的特点和适应条件的限制,测算结果只包含了所研究系统的部分信息,无法准确反映系统的全部信息和其真正的发展规律。考虑用最优加权组合法将多个单一模型的优势综合起来,组成一个预测模型,就有可能比较合理地描述系统的整体情况。文章运用最优加权组合预测法对四川省1978—2014年的GDP走势进行预测。预测结合定性、定量方法,考虑采用指数回归方法、时间序列预测方法建立四川省GDP的单一预测模型,运用SPSS,Excel,Eviews等专业软件分别对这两种预测方法进行操作。采用最小二乘法进行权重分配并进行组合预测。结果发现,相对于单一预测模型,组合预测的误差最小,预测结果更可靠。     

组合预测模型在货运量预测中的应用

本文对灰色预测模型和ARIMA预测模型进行组合,建立了组合模型,并应用于货运量的预测,实证预测表明,组合模型的预测精度优于单一的预测模型,预测结果与实际货运量拟合较好。     

基于ARIMA—SVM组合模型的我国农产品价格预测研究

为准确把握国内农产品价格波动规律,提高农产品价格预测精度,构建农产品价格自回归移动平均与支持向量机（ARIMA—SVM）组合预测模型,以ARIMA模型揭示农产品价格线性变动规律,以SVM模型揭示非线性变动规律,并结合1999—2011年我国农产品价格指数月度数据,使用组合模型和ARIMA、SVM单个模型对农产品价格进行预测。预测结果显示：组合模型比单个ARIMA、SVM模型预测精度高,能够提高农产品价格预测的准确性,是一种有效的农产品价格预测模型。     

基于灰色支持向量机模型的基金波动率预测

探讨灰色系统与最小二乘支持向量机组合预测模型在波动率上的应用的可行性,通过对灰色模型进行残差修正和背景值修正以及对最小二乘支持向量机进行参数寻优,来提高组合预测模型的预测精度和推广泛化能力。经波动率预测的实证分析得出建立的组合模型比支持向量机模型有较好的预测效果。     

剩余收益模型与股票未来回报

本文采用Hou et al.(2012)公司基本面盈余预测模型并结合剩余收益模型对上市公司的内在价值进行估计,并分析内在价值与市价比率(V/P)与股票未来回报之间的关系。我们发现基于V/P分组的投资组合,在未来一至三年规模调整的持有超额回报套利分别达到15.2%、37.9%和55.9%;在控制了市账比等因素以后,V/P对股票未来回报仍然具有显著的预测作用。本文的研究克服了以往文献中运用证券分析师盈余预测进行剩余收益模型估值的内在局限,并提供了我国资本市场背景下切实可行的基于剩余收益模型估值的投资组合策略。     

Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect

Future markets play vital roles in supporting economic activities in modern society. For example, crude oil and electricity futures markets have heavy effects on a nation’s energy operation management. Thus, volatility forecasting of the futures market is an emerging but increasingly influential field of financial research. In this paper, we adopt big data analytics, called Extreme Gradient Boosting (XGBoost) from computer science, in an attempt to improve the forecasting accuracy of futures volatility and to demonstrate the application of big data analytics in the financial spectrum in terms of volatility forecasting. We further unveil that order imbalance estimation might incorporate abundant information to reflect price jumps and other trading information in the futures market. Including order imbalance information helps our model capture underpinned market rules such as supply and demand, which lightens the information loss during the model formation. Our empirical results suggest that the volatility forecasting accuracy of the XGBoost method considerably beats the GARCH-jump and HAR-jump models in both crude oil futures market and electricity futures market. Our results could also produce plentiful research implications for both policy makers and energy futures market participants.     

预测中国宏观经济变量:专家与模型的组合预测

本文使用组合预测方法,探究以“朗润预测”为代表的专家预测以及计量模型对于中国宏观经济变量的预测效果,并研究对不同预测进行组合预测是否有助于改进预测效果。本文发现,对我国CPI和GDP的增长率,专家预测效果总体上优于模型预测。从原因看,一方面,专家在预测时已经考虑了计量模型的预测信息;另一方面,在经济出现“拐点”的时期,专家通过对实际经济环境和政策的把握,得出更准确的经济预测。组合预测有助于提升预测精度,对专家预测进行组合得到的预测效果优于大多数的专家预测,“模型—专家”组合预测的效果也优于所有的模型和大部分专家预测。     

Multi-population mortality forecasting using tensor decomposition

ABSTRACT

In this paper, we formulate the multi-population mortality forecasting problem based on 3-way (age, year, and country/gender) decompositions. By applying the canonical polyadic decomposition (CPD) and the different forms of the Tucker decomposition to multi-population mortality data (10 European countries and 2 genders), we find that the out-of-sample forecasting performance is significantly improved both for individual populations and the aggregate population compared with using the single-population mortality model based on rank-1 singular value decomposition (SVD), or the Lee–Carter model. The results also shed lights on the similarity and difference of mortality among different countries. Additionally, we compare the variance-explained method and the out-of-sample validation method for rank (hyper-parameter) selection. Results show that the out-of-sample validation method is preferred for forecasting purposes.     

VIX term structure forecasting: New evidence based on the realized semi-variances

Considering the asymmetric volatility response to positive and negative shocks, this paper investigates VIX term structure forecasting by incorporating the realized upside and downside semi-variances based on high-frequency data, named good volatility and bad volatility, into the discrete-time GARCH-type model. We derive the risk-neutral model specification and calculate the analytical expression of VIX term structure, then estimate parameters by the maximum likelihood method. The MoP strategy (momentum of predictability, Wang et al., 2018) is extended for VIX term structure forecasting. Our empirical results show that incorporating high frequency data and considering the asymmetric shocks of realized variance are necessary to get much more accurate forecasting. The application of MoP strategy demonstrates the superior forecasting ability of integrating the advantages of multiple individual models. The evaluation of economic significance further confirms the superiority of our newly proposed model and the combination of multiple individual models, and results are robust under the alternative realized measure.     

Stock market return predictability: A combination forecast perspective

Based on traditional macroeconomic variables, this paper mainly investigates the predictability of these variables for stock market return. The empirical results show the mean combination forecast model can achieve superior out-of-sample performance than the other forecasting models for forecasting the stock market returns. In addition, the performances of the mean combination forecast model are also robust during different forecasting windows, different market conditions, and multi-step-ahead forecasts. Importantly, the mean combination forecast consistently generates higher CER gains than other models considering different investors' risk aversion coefficients and trading costs. This paper tries to provide more evidence of combination forecast model to predict stock market returns.     

高频数据下基于组合预测思想的统计套利策略创新设计与实证研究

采用两只股票的日数据和5种高频数据,借鉴组合预测思想,综合利用协整模型和新卡尔曼滤波模型,与统计套利策略具体目标相结合,设计出新统计套利组合策略,实证分析数据频率、策略选择对统计套利效果的影响。结果表明：运用高频数据及引入卡尔曼滤波模型均有效,但卡尔曼滤波模型与协整模型不存在明显优劣之分,选择组合策略是必要的;组合策略收益性显著优于采取单一模型的套利策略;组合策略下的套利组合随数据频率提高,收益率波动性更小、更稳定;组合策略接近市场中性,能很好地免疫市场风险。     

基于VaR模型对我国外汇储备规模适度性的研究

本文根据外汇储备的用途,建立了一个关于固定汇率制度下一国外汇储备规模的需求模型,在此基础上结合模型提出了建立我国外汇储备适度规模的估计方法,并进行了举例说明,从中可以发现模型对提高我国外汇储备规模的管理水平是有益的。     

Evaluating the performance of futures hedging using factors-driven realized volatility

The complexity and uncertainty of the financial market mainly stem from the rich market internal transaction information and a wide range effect of external factors. To this end, this paper proposes the combination factors-driven forecasting method to predict realized volatilities of the CSI 300 index and index futures. Based on the volatilities predicted by the proposed method, we further evaluate the ex-ante hedging performance in comparison to the conventional HAR model as well as GARCH-type models. The empirical results indicate that the factors-driven realized volatility model significantly dominates the other commonly used models in terms of hedging effectiveness. Furthermore, the superiority of the proposed method is robust in different market conditions, including significant rising or falling and abnormal market fluctuations in the COVID-19 pandemic, and in different index markets. Therefore, this paper improves the prediction accuracy of volatility by integrating market internal transaction information and external factor information, and the proposed method in this paper can be used by investors to obtain an excellent hedging effect.     

促进我国节约能源的税收政策取向

我国现行税收政策在促进节能方面存在着诸多不足.根据国家节约能源的现实要求,借鉴国外能源税收政策的经验,建议运用税收政策调整能源价格、完善现有能源税收优惠政策、增强现有税种的能源保护功能以及开征有利于节约能源的限制性税种等.     

我国能源消费影响因素实证分析

随着经济的快速发展,能源的消费量也在不断上升.能源消费水平是衡量一个国家或地区现代化水平的重要标志.保持能源的稳定供应,不断提高能源利用率,是支持经济增长的重要保证.为了分析影响能源消费需求的因素,本文选取了国内生产总值GDP、人均生活电力消费、能源加工转换率、年末总人口数和工业品出厂价格指数-原材料、燃料作为主要的参考指标,利用EVIEWS3.1进行分析和检验,并建立最终的数学模型.根据最终的数学模型可以对能源消费进行分析与预测.最后,本文在做出结论的基础上提出了一些政策性的建议.