英文标题：
《Machine learning in sentiment reconstruction of the simulated stock
market》
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作者：
Mikhail Goykhman, Ali Teimouri
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最新提交年份：
2017
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英文摘要：
In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to reconstruct the transition probabilities matrix of the Markov sentiment process and recover the underlying sentiment states from the observed stock price behavior.
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中文摘要：
本文继续研究由情绪驱动过程定义的模拟股票市场框架。我们关注由马尔可夫链类型的买卖交易情绪过程驱动的市场环境。我们应用隐马尔可夫模型和递归神经网络的方法来重建马尔可夫情绪过程的转移概率矩阵，并从观察到的股票价格行为中恢复潜在的情绪状态。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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