The impact of transaction duration,volume and direction on price dynamics and volatility

We explore the role of trade volume, trade direction, and the duration between trades in explaining price dynamics and volatility using an Asymmetric Autoregressive Conditional Duration model applied to intraday transactions data. Our results suggest that volume, direction and duration are important determinants of price dynamics, while duration is also an important determinant of volatility. However, the impact of volume and direction on volatility is marginal after controlling for duration, and the impact of volume on volatility appears to be confined to periods of infrequent trading.     

Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks

We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific price changes.     

Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy

Science and technology for renewable and sustainable energy are indispensable for our future society and economics. To meet the goal of sustainable energy development, there is a growing body of research efforts world wide. The planner of energy research has to grasp the broader coverage of scientific and technological research, and make decisions on effective investment in promising and emerging technologies especially under circumstances of limited resources. In this paper, we track emerging research domains in energy research by using citation network analysis. Our analysis confirms that the fuel cell and solar cell are rapidly growing domains in energy research. We further investigate the detailed structure of these two domains by clustering publications in these domains. Each citation cluster has characteristic research topics, and there is a variety of growth trends among the clusters. By using citation network analysis, we can track emerging research domains among a pile of publications efficiently and effectively.     

基于数据挖掘的经济预测模型可行性研究

在企业预测和规划中,对数据的处理是必须的,但面临着数据量大,部分数据无法满足要求,决策者需要从各种约束条件构成的庞大数据库中分析其变动情况,找出需要数据的具体困难。因此,如何利用已知数据,从中挖掘出有用的数据,成为在企业发展规划和预测工作中思考的难题。本文介绍了经济预测技术的现状,提出了基于数据挖掘的经济预测模型以及相关的实施方法,对其具体实施提出了一些可行的方案,为经济预测技术的研究提供了一种新的思路。     

声誉如何影响证券分析师的预测行为？

声誉被认为是抑制分析师机会主义行为的有效机制.本文选取2003-2012年分析师盈余预测和新财富最佳分析师榜单数据,采用倍差法研究明星分析师上榜前后预测特征变化.实证结果表明,虽然明星分析师上榜前的预测准确性和一贯性均优于非明星分析师,但是上榜后明星分析师的这两个特征却低于非明星分析师.研究结论说明我国的明星分析师上榜后研究质量下滑.本文对进一步完善最佳分析师评选机制提供了经验证据和理论启示.     

A new approach to bad news effects on volatility: the multiple-sign-volume sensitive regime EGARCH model (MSV-EGARCH)

In this paper, using daily data for six major international stock market indexes and a modified EGARCH specification, the links between stock market returns, volatility and trading volume are investigated in a new nonlinear conditional variance framework with multiple regimes and volume effects. Volatility forecast comparisons, using the Harvey-Newbold test for multiple forecasts encompassing, seem to demonstrate that the MSV-EGARCH complex threshold structure is able to correctly fit GARCH-type dynamics of the series under study and dominates competing standard asymmetric models in several of the considered stock indexes.

“剪刀差”问题的对策研究

本文首先指出了传统的房地产销售预测方法的不足。为了改进原有的预测方法 ,作者结合影响单个房地产销售量各项因素的特殊性和模糊性 ,运用模糊数学的理论与方法 ,建立了新的数学模型并将其成功地运用于实践 ,取得了较好的效果 ,其理论与方法具有较高的参考和应用价值。