本文選題：數(shù)據(jù)挖掘 + 量化選股�。� 參考：《安徽工業(yè)大學》2017年碩士論文

【摘要】：截止到2013年底,全球所有對沖基金管理的資金規(guī)模已經超過27千億美元。使用量化投資方式管理的各類基金和資管產品的資金量大約占到全球總交易量的3成左右,在全球各種大型的證券交易所中,各類量化投資方式貢獻了將近50%的成交量。量化交易策略的構建,首先要對證券期貨市場上的信息進行統(tǒng)計分析,然后對量化模型進行歷史數(shù)據(jù)的回測,回測效果好且穩(wěn)定的模型才會投入到實盤操作中。本文針對量化交易的實際應用,設計了一種基于數(shù)據(jù)挖掘方法的量化交易系統(tǒng),所用的主要開發(fā)工具為數(shù)值計算軟件MATLAB,設計了量化選股、策略回測、時序分析和組合管理4大核心模塊,以支持簡單的交易決策。本文主要內容和結果如下:(1)利用多因子模型進行選股并以此構建投資組合,組合能在較長周期范圍內大幅跑贏業(yè)績比較基準,具有穩(wěn)步上升的超額收益。使用SVM算法構建股票漲跌分類器,并對所有股票的未來漲跌進行預測。更重要的是提出了將層次聚類與K-均值聚類疊加,構造兩階段聚類模型,最終選出了盈利能力最強的股票類別。(2)利用滬深300股指期貨合約的5分鐘收盤數(shù)據(jù)對經典的雙均線趨勢策略進行回測,通過參數(shù)掃描法優(yōu)化均線參數(shù),在樣本內測試集得到了較高的年化夏普比率。(3)利用ARIMA模型和灰色預測模型預測個股股價走勢,馬氏預測模型和SVM回歸模型用來預測大盤指數(shù)走勢。結果顯示對于個股的預測結果誤差較大,但對于大盤指數(shù)的預測精度較高。(4)運用均值-方差模型,對過去一段時間期望收益率最高的20只股票進行資產配置,設置個股、行業(yè)大類配置比例,確定當組合風險最小時,各股票的權重占比。運用績效評估指標,對3只具有代表性的公募基金進行分析比較,并評估其整體表現(xiàn)。利用VAR模型的3種計算方法,計算了組合的風險價值。
[Abstract]:By the end of 2013, all hedge funds managed more than $2 trillion and 700 billion in the world. The amount of funds and management products managed by quantitative investment accounts for about 3 of the global trading volume. In all the large stock exchanges around the world, a variety of quantitative investment methods have contributed nearly 50%. The construction of quantitative transaction strategy is to make a statistical analysis of the information on the stock and futures market, and then to test the historical data of the quantitative model, and to return the good and stable model to the actual operation. In this paper, a quantized method based on data mining is designed to quantify the actual application of the transaction. Trading system, the main development tool used for the numerical computing software MATLAB, designed the quantitative selection of stock selection, strategy back test, time series analysis and combination management 4 core modules to support simple transaction decision-making. The main content and results are as follows: (1) using multi factor model into stock selection and building investment portfolio, combination can be in a long week. The SVM algorithm is used to build the stock fluctuation classifier and predict the future rise and fall of all stocks. The more important thing is to put forward a hierarchical clustering and K- mean clustering to construct the two stage clustering model, and finally select the most profitable stock. Category. (2) using the 5 minute closing data of the Shanghai and Shenzhen stock index futures contract in 5 minutes, the classic dual trend strategy is measured, the parameters are optimized by the parameter scanning method, and the higher annual SHARP ratio is obtained in the sample test set. (3) the ARIMA model and the grey pretest model are used to predict the stock price trend, martensitic prediction model and SVM The regression model is used to predict the trend of the big plate index. The results show that the prediction results for the stock are more accurate, but the prediction accuracy is higher for the big plate index. (4) the average variance model is used to configure the assets of the 20 stocks with the highest expected return on the past period of time, set up a stock, the proportion of the industry large category, and determine the combination wind. The risk is the hour, the weight of each stock is accounted for. By using performance evaluation index, 3 representative public funds are analyzed and compared, and their overall performance is evaluated. By using 3 methods of VAR model, the risk value of the combination is calculated.
【學位授予單位】：安徽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13;F724.5

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本文編號：2059319