构建三位一体的中国企业国际化知识学习与共享体系

中国企业走出去参与世界竞争越来越重要,很多中国企业纷纷走出国门进行跨国经营。但是却面临一系列的问题,特别是对复杂的国际、东道国环境的不了解遭受经营失败率很高。为了支持中国企业走出去,政府、大学、企业都应积极参与到建立国际化知识的共享学习体系中,为中国企业提供必要的信息和学习培训的机会,增强企业国际化成功率。     

论电脑学习语言的优势与局限

spects.     

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.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Differentiating two types of learning in contract design: Evidence from the construction industry

This research investigates the learning of inter-organizational contract design in greater depth. Two types of learning, i.e. learning from all past partnerships and learning from one specific partner, are distinguished in terms of their influence on the complexity of three different functions of the contract, namely control, coordination, and adaptation. Contract design capability and interorganizational routines are employed as mediators to explain the two types of learning respectively. Empirical tests using data from the Chinese construction industry reveal that there are significant indirect effects between partner-specific experience and contractual coordination, and between general partnership experience and all the three functions of the contract. This research contributes to the literature by providing more nuanced conclusions regarding the contract learning issue.     

Towards a real-time prediction of waiting times in emergency departments: A comparative analysis of machine learning techniques

Emergency Departments (EDs) can better manage activities and resources and anticipate overcrowding through accurate estimations of waiting times. However, the complex nature of EDs imposes a challenge on waiting time prediction. In this paper, we test various machine learning techniques, using predictive analytics, applied to two large datasets from real EDs. We evaluate the predictive ability of Lasso, Random Forest, Support Vector Regression, Artificial Neural Network, and the Ensemble Method, using different error metrics and computational times. To improve the prediction accuracy, new queue-based variables, that capture the current state of the ED, are defined as additional predictors. The results show that the Ensemble Method is the most effective at predicting waiting times. In terms of both accuracy and computational efficiency, Random Forest is a reasonable trade-off. The results have significant practical implications for EDs and hospitals, suggesting that a real-time performance monitoring system that supports operational decision-making is possible.     

Forecasting crude oil market volatility using variable selection and common factor

This paper aims to improve the predictability of aggregate oil market volatility with a substantially large macroeconomic database, including 127 macro variables. To this end, we use machine learning from both the variable selection (VS) and common factor (i.e., dimension reduction) perspectives. We first use the lasso, elastic net (ENet), and two conventional supervised learning approaches based on the significance level of predictors’ regression coefficients and the incremental R-square to select useful predictors relevant to forecasting oil market volatility. We then rely on the principal component analysis (PCA) to extract a common factor from the selected predictors. Finally, we augment the autoregression (AR) benchmark model by including the supervised PCA common index. Our empirical results show that the supervised PCA regression model can successfully predict oil market volatility both in-sample and out-of-sample. Also, the recommended models can yield forecasting gains in both statistical and economic perspectives. We further shed light on the nature of VS over time. In particular, option-implied volatility is always the most powerful predictor.     

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well-performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well-established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes’ interactions with exogenous factors. To assist related work, we made the code available in a dedicated repository.     

Understanding how heterogeneous agents affect Principal's returns: Perspectives from short-termism and Bayesian learning

We consider a general framework of optimal contract design under the heterogeneity and short-termism of agents. Our research shows that the optimal contract must weigh the agent's information rent, incentive cost, and benefit to overcome the contract's adverse selection and moral hazards. Agents with higher moral levels were more likely to choose higher effort and lower manipulation. Simultaneously, the principal offers lower incentives and receives more significant payoff. We also extend our model to investigate the benefits of Bayesian learning. Furthermore, we compare the principal's returns in general and learning models and find that the learning contract can bring more profit to the principal.     

基于工作过程的数控编程与操作课程学习情境设计

现代制造企业对高职院校学生的职业技能要求越来越高,开展课程教学改革,实施理实一体化教学非常重要。数控编程与加工课程是数控技术专业核心课程,开展基于工作过程的数控编程与操作课程学习情境设计,实施理实一体化教学,有利于提高学生职业技能的培养。