全国煤炭交易中心运营模式研究

全国煤炭交易中心的设立对规范我国煤炭交易市场规则、实施能源宏观调控、提升我国煤炭国际定价话语权具有重要意义。在分析全国煤炭交易中心功能定位和业务的基础上,设计中长期合同邀约、现货挂牌、现货竞价、现货招投标4种交易模式及业务流程,提出依托国家重大战略争取政策支持、加强各方沟通完善综合物流体系、建立银企合作机制与信用体系、完善煤炭交易中心协调机制、增强信息服务与风险防控能力等对策建议。研究成果对优化煤炭供给结构、规范煤炭交易市场和保障国家煤炭能源安全提供了支撑。     

干旱区内陆河流域未利用地开发生态风险评价及预测*——以开都河流域为例

[目的]探析开都河流域在未利用地开发过程中生态风险指数的变化特征,为西北干旱区内陆河流域土地利用结构调整与生态保护修复协调发展提供建议。[方法]文章采用PSR模型构建基于14个指标框架的流域未利用地开发生态风险评价指标体系;通过测度综合生态风险指数法进行时空视角的特征变化与格局划分评价;并运用灰色预测模型前瞻性模糊预测该区域未来4年的生态风险变化态势。[结果]2009—2016年开都河流域未利用地开发生态风险整体呈波动上升趋势,生态风险程度由较低下降至低生态风险水平,随后上升至一般程度。这是因为土地开发利用对生态环境造成压力,但在政府相应生态保护政策的出台落实下又逐步缓解,生态系统结构和功能好转明显,抵御风险能力得以提升。预测结果显示2017—2020年开都河流域生态风险将由一般生态风险程度上升至较高程度,因此需要采取适当的管理措施来消减生态风险发生的可能性。[结论]开都河流域作为沙漠中典型的绿洲生态系统,生态环境较为脆弱,通过未利用地的差别化开发、鼓励零星分散的开发模式以及细分不同地类开发的生态补偿设置等方式路径,以期缓解降低干旱区内陆河流域未利用地开发带来的生态风险。     

关系质量对企业知识创造绩效的作用机理：一个被调节的中介模型

基于嵌入性视角，分别引入知识转移、合作模式作为中介变量和调节变量，深入探究关系质量影响企业知识创造绩效的内在机理。利用277份来自全国多地的制造业及高新技术企业调查问卷，采用多元回归方法进行实证研究。结果表明：经济型和社会型关系质量均正向影响企业知识创造绩效；社会型关系质量通过元素知识和架构知识转移正向影响企业知识创造绩效。在契约治理模式下，经济型关系质量更倾向于通过元素知识转移正向影响企业知识创造绩效；在股权治理模式下，社会型关系质量更倾向于通过架构知识转移正向影响企业知识创造绩效。     

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.     

Exploring the representativeness of the M5 competition data

The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field to identify best practices and highlight their practical implications. However, can the findings of the M5 competition be generalized and exploited by retail firms to better support their decisions and operation? This depends on the extent to which M5 data is sufficiently similar to unit sales data of retailers operating in different regions selling different product types and considering different marketing strategies. To answer this question, we analyze the characteristics of the M5 time series and compare them with those of two grocery retailers, namely Corporación Favorita and a major Greek supermarket chain, using feature spaces. Our results suggest only minor discrepancies between the examined data sets, supporting the representativeness of the M5 data.     

Characteristics and implications of Chinese macroeconomic data revisions

The research examining macroeconomic data for developed economies suggests that an understanding of the nature of data revisions is important both for the production of accurate macroeconomic forecasts and for forecast evaluation. This paper focuses on Chinese data, for which there has been substantial debate about data quality for some time. The key finding in this paper is that, while it is true that the Chinese macroeconomic data revisions are not well-behaved, they are not very different from similarly-timed U.S. macroeconomic data revisions. The positive bias in Chinese real GDP revisions is a result of the fast-growing service sector, which is notably hard to measure in real time. A better understanding of the revisions process is particularly helpful for studies of the forecast errors from surveys of forecasters, where the choice of the vintage for outcomes may have an impact on the estimated forecast errors.     

On the asymmetric impact of macro–variables on volatility

We extend the GARCH–MIDAS model to take into account possible different impacts from positive and negative macroeconomic variations on financial market volatility: a Monte Carlo simulation which shows good properties of the estimator with realistic sample sizes. The empirical application is performed on the daily S&P500 volatility dynamics with the U.S. monthly industrial production and national activity index as additional (signed) determinants. We estimate the Relative Marginal Effect of macro variable movements on volatility at different lags. In the out-of-sample analysis, our proposed GARCH–MIDAS model not only statistically outperforms the competing specifications (GARCH, GJR-GARCH and GARCH–MIDAS models), but shows significant utility gains for a mean-variance investor under different risk aversion parameters. Attention to robustness is given by choosing different samples and estimating the model in an international context (six different stock markets).     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

颠覆性技术识别与预测方法研究进展

为了把握颠覆性技术识别与预测方法研究进展，首先对颠覆性的内涵、特征和运行机制进行论述；然后，通过对已有文献进行系统梳理，将识别与预测方法分为5类，分别展开详述；最后，对各类方法进行比较分析。研究发现，已有颠覆性技术识别与预测方法仍处于发展的初期阶段，所有识别研究方法均围绕颠覆性技术的外部影响或内在特征中的某一方面展开，并未将二者有机结合起来。未来，应综合把握颠覆性技术内外部特征，构建全面、系统的识别与预测框架。     

Influential factors on Chinese airlines’ profitability and forecasting methods

We establish profit models to predict the performance of airlines in the short term using the quarterly profit data collected on the three largest airlines in China together with additional recent historical data on external influencing factors. In particular, we propose the application of the LASSO estimation method to this problem and we compare its performance with a suite of other more modern state-of-the-art approaches including ridge regression, support vector regression, tree regression and neural networks. It is shown that LASSO generally outperforms the other approaches in this study. We concluded a number of findings on the oil price and other influential factors on Chinese airline profitability.