本文選題：支持向量機 + 非支配解�。� 參考：《哈爾濱工業(yè)大學(xué)》2014年博士論文

【摘要】：世界范圍內(nèi)的金融市場正處于一個迅速發(fā)展的歷史時期，近年來，交易系統(tǒng)的研究與應(yīng)用在國外得到了快速的發(fā)展，在交易中占有越來越大的比重。根據(jù)紐約證券交易所公布的數(shù)據(jù)，截至2011年5月20日當周該交易所的日均程序化交易占比為28.6%。 成功的交易系統(tǒng)能夠產(chǎn)生穩(wěn)定和超額的回報，根據(jù)美國權(quán)威交易系統(tǒng)評選雜志2011年發(fā)布的交易系統(tǒng)排名，前三名模型年收益率均在200%以上。 交易系統(tǒng)通常包含有追求最大收益率的阿爾法模型和以控制風(fēng)險敞口規(guī)模為主的風(fēng)險控制模型。但當前國內(nèi)外對交易系統(tǒng)的研究主要傾向于交易信號的設(shè)計和挖掘，試圖以一致的方法在任何的趨勢中獲利,沒有注意到交易系統(tǒng)本身所存在的缺陷。在對交易系統(tǒng)的風(fēng)險控制模型的研究較少，將阿爾法模型和風(fēng)險控制模型兩者結(jié)合在一起的研究尚不多見。 為解決上述研究存在的不足，本文以傳統(tǒng)技術(shù)分析中的均線交易系統(tǒng)為基礎(chǔ)，使用支持向量機（SVM）、多目標優(yōu)化算法中的非支配解和風(fēng)險管理的VaR方法，構(gòu)建了交易系統(tǒng)中重要的兩個模型：阿爾法模型和風(fēng)險控制模型，形成了基于非線性方法和VaR的均線交易系統(tǒng)。 傳統(tǒng)的均線交易系統(tǒng)在趨勢市場中具有明顯的贏利效應(yīng)，但在橫盤市場中卻反復(fù)虧損。針對交易系統(tǒng)的這個缺陷，本文首先利用SVM分類器對市場進行趨勢識別，使用RAVI等5種趨向技術(shù)指標將股票價格時間序列映射到高維特征空間，構(gòu)建了支持向量機分類器對趨勢進行分類和過濾，對不利于均線系統(tǒng)交易的橫盤趨勢進行過濾（空倉），以上證指數(shù)為研究對象，將5-60日均線作為基本參數(shù)，改進基于趨勢跟隨的均線交易系統(tǒng)，建立了基于SVM分類器的均線交易系統(tǒng)。 在這個基礎(chǔ)上，進一步優(yōu)化參數(shù)。在參數(shù)優(yōu)化過程中，為防止出現(xiàn)參數(shù)的過度擬合，將交易系統(tǒng)中常用且重要的兩個評價指標，最大收益與連續(xù)最大回撤作為目標，使用了多目標優(yōu)化算法中的非支配解的方法。經(jīng)過優(yōu)化，完成了對交易模型中的一個重要的組成部分-阿爾法模型的構(gòu)建。 為建立風(fēng)險控制模型，本文以5-60日均線交易系統(tǒng)為研究對象，建立了非特定時間動態(tài)VaR模型。用蒙特卡羅方法產(chǎn)生了近3000個交易收益率數(shù)據(jù)、分析了非特定時間動態(tài)VaR收益率分布特征，并進行了模型準確性檢驗；在使用非特定時間動態(tài)VaR模型進行風(fēng)險管理后，，研究結(jié)果表明可以優(yōu)化交易策略。因此研究完成了對非特定時間動態(tài)VaR模型-風(fēng)險控制模型的構(gòu)建。 最后將阿爾法模型與風(fēng)險控制模型組合起來，構(gòu)建了基于非線性方法和VaR的均線交易系統(tǒng)。為了將非特定時間動態(tài)VaR模型引入，首先使用威爾科克森秩和檢驗的方法驗證了使用SVM前后，交易系統(tǒng)所生成的收益率序列的VaR值在置信條件下是沒有統(tǒng)計差別的。然后通過對參數(shù)優(yōu)化后的均線交易系統(tǒng)進行動態(tài)VaR建模求解。結(jié)果表明，基于非線性方法和VaR的均線交易系統(tǒng)可以有效地提高收益和降低風(fēng)險。 將非線性方法和VaR方法與投資交易相結(jié)合，有利于推動非線性科學(xué)在投資領(lǐng)域的應(yīng)用，同時基于非線性方法和VaR的均線交易系統(tǒng)的構(gòu)建也為投資者提供了一整套科學(xué)的投資方法，豐富了投資的研究方法，為程序化交易在中國股市的應(yīng)用提供經(jīng)驗證據(jù)。
[Abstract]:The worldwide financial market is in a rapid development period in recent years, the research and application of the trading system has been rapid development in foreign countries, and play more and more important role in the transaction. According to the data released by the New York stock exchange, the daily program trading as of May 20, 2011 week the exchange ratio 28.6%.
The successful trading system can generate stable and excess returns. According to the authoritative trading system of the United States, the annual ranking of the top three models is over 200%, which is selected by the magazine in 2011.
The trading system usually contains a risk control model with Alfa model in pursuit of the maximum rate of return and to control the risk exposure of the size of the main design and mining. But the current research on the trading system at home and abroad mainly tend to trading signals, trying to consistent in any trend of profit, note that no defects of transaction the system itself. In the study of risk control model for the trading system will be less, the Alfa model and risk control model of them in combination with the research is still rare.
In order to solve the deficiency of the existing research, this paper is based on the average transaction system of traditional analysis, using support vector machine (SVM), a multi-objective optimization method of VaR non dominated solutions and risk management algorithm, constructs two important models of trading system: Alfa model and risk control model, formation the average transaction system based on VaR and nonlinear methods.
The average transaction system with traditional profit effect obvious trend in the market, but the market has repeatedly sideways loss. In order to overcome the defect of the trading system, this paper use the SVM classifier for recognition of the market trend, the use of RAVI and other 5 kinds of trends in technical indicators of stock price time series will be mapped into high dimensional feature space, construction the support vector machine classifier to classify and filter the trend, to filter the sideways trend is not conducive to the average system transactions (short), with the Shanghai Composite Index as the research object, the 5-60 day moving average as the basic parameters, the improved moving average trading system based on trend following, a moving average trading system based on SVM classifier.
On this basis, further parameter optimization. In the optimizing process, to prevent over fitting parameters, the trading system in common and important two evaluation index, the maximum income and continuous maximum retracement as the target, using the multi-objective optimization method of non dominated solution algorithm. After optimization, complete the construction of an important part of the transaction model of the Alfa model.
In order to establish the risk control model, based on the 5-60 day average trading system as the research object, to establish the non specific time dynamic VaR model. Using Monte Carlo method produced nearly 3000 trading return data, the analysis of non specific time dynamic VaR return distribution, and the accuracy of the model test in the use of non specific dynamic time; the VaR model of risk management, the results of the study show that can optimize the trading strategy. So the research done on the construction of risk control model of non specific time dynamic VaR model.
The Alfa model and risk control model together, constructs a nonlinear method and moving average trading system based on VaR. In order to introduce non specific time dynamic model of VaR, Kekesen will first use rank sum test method is verified using SVM before and after the trading system generated returns VaR values in confidence conditions there is no statistical difference. Then through the dynamic VaR modeling and solving of moving average trading system after optimization. The results show that the average transaction system nonlinear method and VaR can effectively improve the yield and reduce risk based.
The nonlinear method and the VaR method and the combination of investment transactions, to promote the application of Nonlinear Science in the field of investment, at the same time, based on average trading system nonlinear method and VaR also provides a set of scientific methods of investment for investors, enrich the research methods of investment, for program trading to provide empirical evidence in the application Chinese the stock market.

【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：F830.91;F224

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