本文關(guān)鍵詞： 數(shù)據(jù)挖掘 分類算法 數(shù)據(jù)庫 模型選股 股票分析　出處：《電子科技大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：伴隨著計(jì)算機(jī)、信息技術(shù)的飛速發(fā)展，信息數(shù)據(jù)的存儲(chǔ)和獲取技術(shù)也得到了前所未有的發(fā)展，各個(gè)行業(yè)領(lǐng)域都產(chǎn)生了大量數(shù)據(jù)，而從這些數(shù)據(jù)中怎樣提取到對我們有用的數(shù)據(jù)，僅僅按照常規(guī)的方法已很難去解決，而近年來產(chǎn)生的數(shù)據(jù)挖掘則技術(shù)則可以發(fā)現(xiàn)那些隱藏在海量數(shù)據(jù)的、具有一定規(guī)律的、對我們有用的信息數(shù)據(jù)，所謂數(shù)據(jù)挖掘是一種應(yīng)用模型發(fā)現(xiàn)知識(shí)、提取有用數(shù)據(jù)的過程，我們可以用這些數(shù)據(jù)進(jìn)行分析和預(yù)測。 股票市場是我國市場經(jīng)濟(jì)不可缺少的組成部分，在經(jīng)濟(jì)發(fā)展中起著不可代替的作用，如何能夠比較正確的分析和預(yù)測股票未來的走勢，對金融投資方面來說有著非常重要的意義。但是股票的價(jià)格走向是受很多因素的影響，所以說炒股是一個(gè)有著非常不確定性的復(fù)雜過程。對它建立某種固模型是有一定困難的，同時(shí)股票相關(guān)的數(shù)據(jù)越來越龐大，而這些數(shù)據(jù)中常常包含著股票價(jià)格走勢的規(guī)律性。而近些年來新興發(fā)展起來的據(jù)挖掘技術(shù)則是一種可以滿足從這種海量數(shù)據(jù)之中，獲取有價(jià)值的數(shù)據(jù)的新的數(shù)據(jù)處理技術(shù)，因而如何對股票數(shù)據(jù)利用挖掘技術(shù)進(jìn)行分析和處理，并做出趨勢預(yù)測具有重大的理論和實(shí)際的意義。 本論文主要討論了基于現(xiàn)金流的選股模型，將其理論數(shù)量化抽象生成一套關(guān)于選擇股票的公式分類規(guī)則，并對選擇的樣本數(shù)據(jù)進(jìn)行預(yù)處理，轉(zhuǎn)換構(gòu)造挖掘所需要指標(biāo)和屬性，然后利用數(shù)據(jù)挖掘技術(shù)中的公式分類技術(shù)和數(shù)據(jù)庫中的一些聚集函數(shù)，結(jié)合SQL語句對股票數(shù)據(jù)進(jìn)行分析、預(yù)測，并對挖掘結(jié)果進(jìn)行了必要的檢驗(yàn)。根據(jù)實(shí)際結(jié)果證明從模型獲得的公式分類算法進(jìn)行選股是可行的。用戶可以根據(jù)這類規(guī)則快速的選擇出有投資價(jià)值的個(gè)股，然后進(jìn)一步的去分析和預(yù)測或直接投資。 最后根據(jù)所論證挖掘模型以及分類所用算法設(shè)計(jì)并開發(fā)了一個(gè)實(shí)際的模型分析選股平臺(tái)，并對其功能和性能等進(jìn)行了必要的測試，它可以對股票數(shù)據(jù)進(jìn)行多維的分析預(yù)測，作為投資者的投資決策的輔助工具，是利用數(shù)據(jù)挖掘技術(shù)結(jié)合華爾街著名選股模型理論，分析大量與股票相關(guān)的信息數(shù)據(jù)，，并做出未來走勢預(yù)測，具有一定實(shí)用意義。
[Abstract]:With the rapid development of computer and information technology, the storage and acquisition technology of information data has been developed unprecedented. A large number of data have been produced in various fields, and how to extract the useful data from these data. It is difficult to solve only according to the conventional method, but the data mining technology produced in recent years can find the information data which is hidden in the massive data, has certain regularity, and is useful to us. Data mining is a process of applying model to discover knowledge and extract useful data, which can be used for analysis and prediction. The stock market is an indispensable part of our market economy and plays an irreplaceable role in economic development. How can we correctly analyze and predict the future trend of the stock market? It is very important for financial investment. But the price trend of stock is influenced by many factors, so stock speculation is a complicated process with very uncertainty. It is difficult to build some kind of solid model for it. At the same time, stock data are getting bigger and larger, and they often contain the regularity of stock price movements. And the newly developed data mining technology in recent years is a way to satisfy this huge amount of data. New data processing technology for obtaining valuable data, so how to analyze and process stock data using mining technology and make trend prediction has great theoretical and practical significance. This paper mainly discusses the stock selection model based on cash flow, and abstracts the theoretical quantification to generate a set of formula classification rules about stock selection, and preprocesses the selected sample data, and transforms and constructs the indexes and attributes needed for mining. Then using the formula classification technology in the data mining technology and some aggregation functions in the database, combined with the SQL statement to analyze and predict the stock data, According to the actual results, it is proved that it is feasible to select stocks by the formula classification algorithm obtained from the model. Users can quickly select the stocks with investment value according to this kind of rules. Then further analysis and prediction or direct investment. Finally, a practical model analysis and stock selection platform is designed and developed according to the demonstrated mining model and the algorithm used in classification, and its function and performance are tested, which can be used for multidimensional analysis and prediction of stock data. As an assistant tool for investors' investment decision, it is of practical significance to use data mining technology combined with Wall Street famous stock selection model theory to analyze a large number of information data related to stocks and to predict the future trend.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP311.13;F832.51

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本文編號：1512755