本文選題：量化投資 + 選股策略�。� 參考：《天津商業(yè)大學》2017年碩士論文

【摘要】：近年來,由于股票市場的不斷發(fā)展,量化投資技術(shù)越來越受到投資者的關(guān)注,我國的量化投資體系也逐漸走向成熟。隨著股市規(guī)則的不斷完善,上市股票的數(shù)量及與之相關(guān)的數(shù)據(jù)在不斷的增加,而股票的這些數(shù)據(jù)多且復雜,卻又隱含著很多有用的信息,那么如何從這些海量的數(shù)據(jù)中發(fā)現(xiàn)有用的信息,用常規(guī)的方法顯然已經(jīng)無法解決,而近些年發(fā)展起來的數(shù)據(jù)挖掘技術(shù)則可以幫助我們從那些海量的股票數(shù)據(jù)中挖掘出我們所需要的數(shù)據(jù)信息,通過對這些數(shù)據(jù)進行分析、建模得到我們想要的信息。本文主要討論了基于數(shù)據(jù)挖掘的量化選股模型。首先我們根據(jù)兩個條件對2013年-2015年滬深市場類全部A股的3000多支股票進行初步篩選:一是連續(xù)3年凈資產(chǎn)收益率穩(wěn)定且不小于10%,并剔除ST等公司股票;二是主營業(yè)務(wù)增長率與凈利潤增長率基本一致并且在10%以上。經(jīng)過篩選,51支基本面較好的股票被保留。其次,我們選取了財務(wù)數(shù)據(jù)中能夠反映公司盈利、償債、成長等能力的17個重要指標作為數(shù)據(jù)分析的基礎(chǔ),考慮到因子之間存在重疊性、相關(guān)性,并且若模型解釋變量太多則容易出現(xiàn)主次不分等問題,因此我們對這些指標做了主成分分析。通過主成分分析,在保留原數(shù)據(jù)絕大部分信息的同時,我們選出了無相關(guān)性的五個綜合指標,進而達到了降維的目的。在眾多的數(shù)據(jù)挖掘的算法中,聚類分析是特別容易理解而且已經(jīng)被證明在選股方面是很有效的一種方法,所以本文選擇了K均值聚類來研究選股策略,并且對K的選取做了對比,通過R軟件選出了最優(yōu)的K,從而將選股問題演變?yōu)檫x類問題。事實證明,針對我們的數(shù)據(jù),當K取5時聚類效果最好,因此我們選出了7支股票作為最終選股結(jié)果,通過wind平臺調(diào)出已選股票的歷史K線,發(fā)現(xiàn)所選的股票的整體走勢幾乎都可以跑贏大盤,而且未來有上升的趨勢,事實證明文章所做的工作對股票投資者分析選擇股票具有一定的參考作用。
[Abstract]:In recent years, due to the continuous development of the stock market, the quantitative investment technology has attracted more and more attention of investors, and the quantitative investment system of our country has gradually matured. As the rules of the stock market continue to improve, the number of listed stocks and their related data are constantly increasing, and these data of stocks are many and complex, but contain a lot of useful information. So, how to find useful information from these massive amounts of data is obviously not solved by conventional methods. The data mining technology developed in recent years can help us to mine the data information we need from the massive stock data. Through the analysis of these data, we can model the information we want. This paper mainly discusses the quantitative stock selection model based on data mining. Firstly, according to two conditions, we preliminarily screen more than 3000 A-share stocks in Shanghai and Shenzhen stock market from 2013 to 2015: first, the return of net assets is stable and not less than 10% for three consecutive years, and the stock of St and other companies are excluded; Second, the main business growth rate and net profit growth rate is basically consistent and above 10%. After screening, 51 stocks with better fundamentals were retained. Secondly, we select 17 important indicators in the financial data that can reflect the company's profitability, debt service, growth and so on as the basis of the data analysis, considering the overlap and correlation among the factors. And if there are too many variables explained by the model, the primary and secondary problems are easy to occur, so we do the principal component analysis of these indexes. Through principal component analysis, we select five uncorrelated synthetic indexes while retaining most of the original data, and then achieve the goal of dimensionality reduction. Among the many algorithms of data mining, clustering analysis is especially easy to understand and has been proved to be a very effective method in stock selection, so this paper chooses K-means clustering to study stock selection strategy. By comparing the selection of K, the optimal K is selected by R software, and the stock selection problem is transformed into a class selection problem. It turns out that for our data, when K takes 5, the clustering effect is the best, so we select 7 stocks as the final stock selection result, and through the wind platform, we call out the historical K line of the selected stock. It is found that the overall trend of the selected stocks can almost outperform the market, and there is an upward trend in the future. The facts show that the work done in this paper has a certain reference role for stock investors to analyze and select stocks.
【學位授予單位】：天津商業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13;F832.51

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本文編號：1856257