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1. Prediction of Stock Price Index with Hidden Markov Model | |||
HE Fengxia,HUANG Jingfeng | |||
Mathematics 08 April 2016 | |||
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Abstract:The 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. | |||
TO cite this article:HE Fengxia,HUANG Jingfeng. Prediction of Stock Price Index with Hidden Markov Model[OL].[ 8 April 2016] http://en.paper.edu.cn/en_releasepaper/content/4683378 |
2. Selection of Covariates for estimating causal effects in a Given Diagram | |||
ZHANG San-Feng, LI Si | |||
Mathematics 26 December 2014 | |||
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Abstract:Graphical models with the corresponding linear structural equation model can be used to describe a causal-effect relationship. A common problem is argued about which covariates in the diagram should be used to estimate the causal effect of a control plan. The covariates selection in this paper is back-door criterion. Unfortunately the result of selection of covariates is not unique as we shown in the following part. In this paper, we evaluated the asymptotic variance of the estimated causal effect with different plans and give a comparison between them. | |||
TO cite this article:ZHANG San-Feng, LI Si. Selection of Covariates for estimating causal effects in a Given Diagram[OL].[26 December 2014] http://en.paper.edu.cn/en_releasepaper/content/4625554 |
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