国家高新区设立能够提升城市全要素生产率吗？——基于261个地级市TFP分解数据的路径分析

国家高新区是培育经济增长新动能，实现区域经济高质量发展的重要政策载体，文章利用2001-2014年我国261个城市面板数据，通过渐进双重差分法研究国家高新区设立对城市全要素生产率的影响。研究发现：研究期内国家高新区的设立对城市全要素生产率造成显著的负面影响；从TFP的分解项来看，高新区设立促进了所在城市规模效率提高，却抑制了技术进步和技术效率提高；异质性分析结果表明上述作用具有显著城市等级异质性，在高行政等级城市设立高新区对城市全要素生产率具有促进作用。进一步运用工业企业数据库、微观土地转让数据、城市专利数据等进行机制分析发现，企业间资源错配和土地价格扭曲降低了技术效率，而技术创新下降和产业升级钝化是抑制技术进步的主要机制。     

社会心理服务驱动下信访“分类治理”的理路——以河南省西平县为例

学界对于信访分类治理的研究大多基于因“人”分类或因“事”分类展开,形成了信访分类治理研究的基本范式。然而,社会心理服务嵌入信访治理体系,为信访分类治理提供了新的视野:因“心”分类,即按照信访人“心理诉求——利益诉求”的两分原则,将信访人分为利益型、心理型、耦合型和无意识型四种。本文以西平县信访治理实践为个案,提炼出由“心”而治的信访分类治理模型,并对该模型的治理理路进行了分析。研究发现:地方政府对信访频次、情绪和事件的三维叠加形成了信访分类治理的参照维度,而地方政府对治理技术的灵活性运用则构成了信访分类治理的动态实践。本文为信访分类治理过程中信访人的心理诉求与利益诉求间的关系提供经验事实,并为社会心理服务驱动下的信访分类治理理路提供分析框架。然而,受限于研究区域和案例数量,本文提炼出来的由“心”而治的信访分类治理模式还有待进一步验证和完善。     

Trading activity and price discovery in Bitcoin futures markets

This study examines the impact of trading activities on price discovery in the Bitcoin futures markets. We find that trades of hedgers are positively correlated with the modified information shares in both CME and CBOE futures markets, suggesting that their trading promotes futures market efficiency. Retailers’ trading activity relates negatively to the price discovery of the CME Bitcoin futures and thus destabilizes the market. Speculators exert positive (negative) impact on the price discovery in the CME (CBOE) Bitcoin futures. Our finding that CME’s Bitcoin futures exhibit superior price discovery than CBOE’s provides plausible justification for CBOE’s decision in March 2019 to suspend further listings of Bitcoin futures contracts.     

创新生态系统视角下价值共创如何影响企业创新绩效——一个有调节的中介模型

以创新生态系统为视角，引入创新资源互动与生态系统规范机制为中介变量和调节变量，探究价值共创如何影响企业创新绩效。基于307家企业样本数据，运用结构方程模型、Bootstrap和层级回归方法对数据进行分析。研究发现：创新生态系统中，价值共创不完全直接影响企业创新绩效；创新资源互动部分中介价值共创对企业创新绩效的影响；生态系统规范机制部分正向调节价值共创对创新资源互动的影响。最后提出，创新生态系统调控方与企业要差异化地制定提升创新绩效的实施方案，营造活跃的创新资源互动氛围，形成并完善系统内生态规范机制。     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

股价信息含量与投资-股价敏感性——基于融资融券的准自然实验

本文利用2006-2015年间的数据研究了融资融券对投资-股价敏感性的影响。利用融资融券作为股价信息含量的一个外生冲击变量,本文研究发现,我国A股市场确实存在反馈效应,融资融券政策的实施增强了标的公司投资-股价敏感性,这个结论在采用倾向得分模型(PSM)配对后依然成立,说明融资融券使股价融入了更多有利于管理层投资决策的信息。其次,融资融券对投资-股价敏感性的影响在机构投资者比例高、流动性高、处于新兴行业的这类管理层反馈效应更强的股票组中更显著。参照以往研究考虑了融资约束对反馈效应的调节作用,同样发现融资融券对投资-股价敏感性的影响在国有企业和规模较大的企业组中更显著。最后,融资融券交易规模越大,投资对股价的敏感性越强。     

Combining wavelet decomposition with machine learning to forecast gold returns

This paper combines the discrete wavelet transform with support vector regression for forecasting gold-price dynamics. The advantages of this approach are investigated using a relatively small set of economic and financial predictors. I measure model performance by differentiating between a statistically-motivated out-of-sample forecasting exercise and an economically-motivated trading strategy. Disentangling the predictors with respect to their time and frequency domains leads to improved forecasting performance. The results are robust compared to alternative forecasting approaches. My findings on the relative importances of such wavelet decompositions suggest that the influences of short-term and long-term trends are not stable over the full evaluation period.     

基于技术轨道的制造企业服务化机理研究

基于技术轨道理论与制造企业服务化间的关系，认为技术轨道对制造企业服务化的影响机理包括3类：驱动机理、拉动机理和差异化机理。制造企业服务化转型内在机理是技术轨道驱动下投入服务化、企业边界变化以及用户需求、产出、政策拉动下与技术轨道的协同演化过程，且通过技术轨道形成服务化转型差异。制造企业对自身技术轨道的识别有利于确定发展所需主要技术来源及技术演化轨迹，把握实现服务化的关键机理。     

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.     

Building responsive supervision over smaller banks in Europe: an insight from the Principal-Agent perspective

Abstract

The Single Supervisory Mechanism (SSM) is an administrative arrangement of national assistance to the ECB in exercising its exclusive supervisory competence. It sets two systems of assistance: ECB Direct Supervision (for large banks) and Indirect Supervision (for smaller and medium-sized banks) supervision. This paper analyzes dynamics between the ECB Banking Supervision as the principal and NCAs as its agents in the system of ECB Indirect Supervision. It identifies six formal (statutory) accountability and control mechanisms which are put at the ECB’s disposal to monitor the way how the NCAs carry SSM supervisory tasks when exercising the ECB’s exclusive supervisory competences under the SSM Regulation.