發(fā)布時(shí)間：2018-03-12 17:47

  本文選題：模糊時(shí)間序列　切入點(diǎn)：K均值算法　出處：《吉林財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：股票市場(chǎng)作為證券行業(yè)至關(guān)重要的組成部分,備受投資者的關(guān)注。尋求有效的股票分析方法,降低投資者的風(fēng)險(xiǎn),具有重大實(shí)踐意義和理論價(jià)值。然而,由于股票市場(chǎng)受股票內(nèi)在價(jià)值、市場(chǎng)因素、政治因素以及宏觀經(jīng)濟(jì)運(yùn)行狀況等諸多因素影響,各因素間沒(méi)有確定規(guī)則,且股票市場(chǎng)每天產(chǎn)生大量數(shù)據(jù),這些均給股票市場(chǎng)研究帶來(lái)一定難度。數(shù)據(jù)挖掘結(jié)合數(shù)據(jù)庫(kù)、統(tǒng)計(jì)學(xué)和人工智能等多門學(xué)科,它能夠從大量的原始數(shù)據(jù)中挖掘出隱含的有價(jià)值的信息。股票市場(chǎng)的特征決定了應(yīng)用數(shù)據(jù)挖掘方法對(duì)股市分析和預(yù)測(cè),具有較強(qiáng)的可行性和現(xiàn)實(shí)性。本文通過(guò)收集整理股票指標(biāo)及上市公司財(cái)務(wù)數(shù)據(jù),利用數(shù)據(jù)挖掘中的分類和聚類方法,針對(duì)不同問(wèn)題提出一系列改進(jìn)的算法,對(duì)我國(guó)股市進(jìn)行分析與預(yù)測(cè),主要內(nèi)容如下:(1)針對(duì)模糊時(shí)間序列模型處理數(shù)據(jù)時(shí)的不確定性和缺乏客觀性,提出基于密度峰值算法的模糊時(shí)間序列模型,并將其應(yīng)用于股票價(jià)格的預(yù)測(cè)中。(2)由于人工魚(yú)群算法能夠獲取全局最優(yōu)解,克服K均值算法對(duì)初始化聚類中心敏感且易陷入局部極值的問(wèn)題,提出基于核函數(shù)人工魚(yú)群的K均值算法,將其應(yīng)用于股票市場(chǎng)的聚類分析中。(3)針對(duì)股票數(shù)據(jù)高維性和復(fù)雜性的特點(diǎn),提出基于因子分析法和OPTICS-Plus算法的股票分類模型,實(shí)現(xiàn)對(duì)股票市場(chǎng)的分類。該算法有效地消除數(shù)據(jù)的冗余性,提高聚類的性能和收斂速度。(4)鑒于上市公司財(cái)務(wù)數(shù)據(jù)的高維性和冗余性等特點(diǎn),提出基于Lasso方法和Logistic回歸的上市公司財(cái)務(wù)預(yù)警模型,判斷上市公司財(cái)務(wù)狀況是否發(fā)生危機(jī),達(dá)到預(yù)警的效果。本文應(yīng)用數(shù)據(jù)挖掘中的分類和聚類算法預(yù)測(cè)我國(guó)股票市場(chǎng)的價(jià)格,對(duì)股票市場(chǎng)分類,以及預(yù)警上市公司經(jīng)營(yíng)狀況。仿真實(shí)驗(yàn)結(jié)果表明,本文提出的方法能夠較為有效地分析和預(yù)測(cè)我國(guó)股市,幫助投資者合理地做出決策。
[Abstract]:As an important part of the securities industry, the stock market has attracted the attention of investors. It is of great practical and theoretical value to seek effective stock analysis methods to reduce the risk of investors. Because the stock market is influenced by many factors, such as the intrinsic value of the stock, market factors, political factors and macroeconomic operating conditions, there are no definite rules among the various factors, and the stock market produces a large amount of data every day. All of these bring some difficulties to stock market research. Data mining combines database, statistics and artificial intelligence. It can extract hidden valuable information from a large number of raw data. The characteristics of stock market determine the application of data mining to the analysis and prediction of stock market. Through collecting and arranging stock index and financial data of listed company, using the classification and clustering method in data mining, this paper puts forward a series of improved algorithms for different problems. The main contents of the analysis and prediction of Chinese stock market are as follows: (1) aiming at the uncertainty and lack of objectivity in processing data in fuzzy time series model, a fuzzy time series model based on density peak algorithm is proposed. The artificial fish swarm algorithm can obtain the global optimal solution, and overcome the problem that K-means algorithm is sensitive to initialization clustering center and easily fall into local extremum. This paper presents a K-means algorithm based on kernel function artificial fish swarm, and applies it to the clustering analysis of stock market. Aiming at the characteristics of high dimension and complexity of stock data, a stock classification model based on factor analysis and OPTICS-Plus algorithm is proposed. The algorithm effectively eliminates the redundancy of data, improves the performance and convergence speed of clustering. (4) in view of the characteristics of high dimension and redundancy of financial data of listed companies, this algorithm can effectively eliminate the redundancy of data, improve the performance of clustering and speed of convergence. This paper puts forward a financial early warning model of listed companies based on Lasso method and Logistic regression to judge whether the financial situation of listed companies is in crisis and achieve the effect of early warning. In this paper, the classification and clustering algorithm in data mining is used to predict the price of stock market in China. The simulation results show that the method proposed in this paper can effectively analyze and predict the stock market in China and help investors to make reasonable decisions.
【學(xué)位授予單位】：吉林財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13;F832.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張艷麗;尤曉琳;強(qiáng)薇;鄭雨晗;李鈞濤;;基于LASSO的企業(yè)財(cái)務(wù)危機(jī)預(yù)警與關(guān)鍵指標(biāo)選擇[J];河南師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年03期

2 呂少娟;張桂珠;;一種融合K-means算法和人工魚(yú)群算法的聚類方法[J];計(jì)算機(jī)應(yīng)用與軟件;2015年09期

3 董倩;;改進(jìn)遺傳算法優(yōu)化模糊均值聚類中心的圖像分割[J];吉林大學(xué)學(xué)報(bào)(理學(xué)版);2015年04期

4 陳皓;宋博;朱慧芳;趙旺果;;基于ARIMA模型對(duì)平安銀行股票價(jià)格的分析與預(yù)測(cè)[J];商;2015年17期

5 王U，

本文編號(hào)：1602648