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双向拍卖市场中强化学习代理的作用

The Effect of Reinforcement Learning Agents in Double-Auction Markets
课程网址: http://videolectures.net/amlcf09_raynor_terladam/  
主讲教师: Khoa Minh Nguyen, Imon Palit, Neil Raynor
开课单位: 埃塞克斯大学
开课时间: 2009-08-21
课程语种: 英语
中文简介:
ARCH和GARCH等几个时间序列模型已经开发出来,可以使用资产回报数据预测波动性。然而,这些方法忽略了市场波动的一个关键来源:金融新闻。类似地,资产定价模型通常通过跳转过程描述新信息的到达,但是底层跳转过程的特征仅仅是粗略地与底层新闻源相关。我们在这篇论文中的目标是表明,统计学习的最新进展使我们能够对新闻对资产价格的影响进行更精确的分析。在本文中,我们证明了新闻发布的信息可以用来预测日内异常收益,并且具有较高的准确性。我们形成了一个文本分类问题,在这个问题中,如果新闻发布后某个(固定)时间内的绝对回报出现跳跃,新闻稿就会被标记为正数。首先,使用支持向量机以类似于[1]的方式预测异常收益。给定一个新闻稿,我们预测在接下来的10,20,…,使用文本或过去的绝对回报,每次250分钟。我们的实验分析了许多层面的可预测性,并证明了重要的初始日内可预测性,减少了整个交易日。其次,我们将文本信息与资产价格时间序列进行优化组合,利用多核学习(MKL)显著提高分类性能。本文采用解析中心切割平面法(ACCPM)求解结果mkl问题。ACCPM对于目标函数和梯度难以评价,但其可行集足够简单,能够有效计算分析中心的问题特别有效。此外,由于不存在条件问题,ACCPM可以比其他一阶方法实现更高的精度目标。
课程简介: Several time series models such as ARCH and GARCH have been developed to forecast volatility using asset returns data. However, these methods ignore one key source of market volatility: financial news. Similarly, asset pricing models often describe the arrival of novel information by a jump process, but the characteristics of the underlying jump process are only coarsely, if at all, related to the underlying news source. Our objective in this paper is to show that recent advances in statistical learning allow a much more refined analysis of the impact of news on asset prices. In this paper, we demonstrate that information from press releases can be used to predict intraday abnormal returns with relatively high accuracy. We form a text classification problem where press releases are labeled positive if the absolute return jumps at some (fixed) time after the news is made public. First, abnormal returns are predicted using support vector machines in similar fashion to [1]. Given a press release, we predict whether or not an abnormal return will occur in the next 10, 20, ..., 250 minutes using either text or past absolute returns. Our experiments analyze predictability at many horizons and demonstrate significant initial intraday predictability that decreases throughout the trading day. Second, we optimally combine text information with asset price time series to significantly enhance classification performance using multiple kernel learning (MKL).We use an analytic center cutting planemethod (ACCPM) to solve the resultingMKL problem. ACCPM is particularly efficient on problems where the objective function and gradient are hard to evaluate but whose feasible set is simple enough so that analytic centers can be computed efficiently. Furthermore, because it does not suffer from conditioning issues, ACCPM can achieve higher precision targets than other first-order methods.
关 键 词: 双向拍卖市场; 学习代理
课程来源: 视频讲座网
最后编审: 2020-07-31:yumf
阅读次数: 35