基于隐马尔可夫模型的股票价格指数预测
Prediction of Stock Price Index with Hidden Markov Model
由于股票价格受到多种因素的影响，变化规律具有复杂的非线性和随机性，因此，如何精确地预测股票价格有着很大的研究意义。本文基于隐马尔可夫模型结合K-Means聚类算法提出了一种新的股票价格指数预测方法。在此基础上,选取了2014年7月到11月的美国S&P500指数中的收盘价作为研究对象，论述分析了美国S&P500指数的现状以及特点，并且利用这一历史数据对未来的收盘价进行了预测，通过对比时间序列中的ARIMA模型所预测的结果发现该预测方法对于股票价格指数的预测具有一定的有效性和可行性。
he change law of the stock price which is affected by lots of stochastic interference factors is filled with complex nonlinearity and randomicity. Therefore, predicting the stock price accurately is of great research significance. This paper concerns the closing price of S&P 500 index as the research object and the status quo and characteristics of the S&P 500 index is analyzed. A new prediction method on the basis of continuous hidden Markov model with the combination of K-Means clustering algorithm is established. The proposed prediction method for the stock price index has more certain validity and feasibility compared with the Autoregressive Integrated Moving Average Model(ARIMA) in the time series.
何凤霞、黄敬峰
财政、金融自动化技术经济计算技术、计算机技术
随机过程隐马尔可夫模型股票价格指数预测
Stochastic processHidden Markov modelThe stock price indexPrediction
何凤霞,黄敬峰.基于隐马尔可夫模型的股票价格指数预测[EB/OL].(2016-04-15)[2025-08-02].http://www.paper.edu.cn/releasepaper/content/201604-180.点此复制
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