英文标题：
《Stock Market Trend Analysis Using Hidden Markov Models》
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作者：
G. Kavitha, A. Udhayakumar, D. Nagarajan
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最新提交年份：
2013
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英文摘要：
Price movements of stock market are not totally random. In fact, what drives the financial market and what pattern financial time series follows have long been the interest that attracts economists, mathematicians and most recently computer scientists [17]. This paper gives an idea about the trend analysis of stock market behaviour using Hidden Markov Model (HMM). The trend once followed over a particular period will sure repeat in future. The one day difference in close value of stocks for a certain period is found and its corresponding steady state probability distribution values are determined. The pattern of the stock market behaviour is then decided based on these probability values for a particular time. The goal is to figure out the hidden state sequence given the observation sequence so that the trend can be analyzed using the steady state probability distribution( ) values. Six optimal hidden state sequences are generated and compared. The one day difference in close value when considered is found to give the best optimum state sequence.
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中文摘要：
股票市场的波动并不完全是随机的。事实上，推动金融市场的因素以及金融时间序列的模式一直吸引着经济学家、数学家和最近的计算机科学家[17]。本文提出了一种利用隐马尔可夫模型（HMM）对股票市场行为进行趋势分析的方法。这种趋势一旦在特定时期出现，将来肯定会重演。找出某一时期股票收盘价的日差，并确定其相应的稳态概率分布值。然后根据特定时间的这些概率值来决定股市行为的模式。目标是找出给定观测序列的隐藏状态序列，以便使用稳态概率分布（）值分析趋势。生成并比较了六个最优隐状态序列。当考虑到一天的闭合值差异时，可以得出最佳状态序列。
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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：Mathematics 数学
二级分类：Probability 概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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