英文标题：
《A Modified Levy Jump-Diffusion Model Based on Market Sentiment Memory
  for Online Jump Prediction》
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作者：
Zheqing Zhu and Jian-guo Liu and Lei Li
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最新提交年份：
2017
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英文摘要：
  In this paper, we propose a modified Levy jump diffusion model with market sentiment memory for stock prices, where the market sentiment comes from data mining implementation using Tweets on Twitter. We take the market sentiment process, which has memory, as the signal of Levy jumps in the stock price. An online learning and optimization algorithm with the Unscented Kalman filter (UKF) is then proposed to learn the memory and to predict possible price jumps. Experiments show that the algorithm provides a relatively good performance in identifying asset return trends.
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中文摘要：
在本文中，我们提出了一个改进的Levy跳跃扩散模型，该模型具有股票价格的市场情绪记忆，其中市场情绪来自使用推特上的推特实现的数据挖掘。我们将具有记忆的市场情绪过程作为利维跳升股价的信号。然后，提出了一种基于无迹卡尔曼滤波器（UKF）的在线学习和优化算法来学习记忆并预测可能的价格跳跃。实验表明，该算法在识别资产收益趋势方面具有较好的性能。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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