本文選題：擇時策略 + 支持向量機　； 參考：《西安工業(yè)大學(xué)》2017年碩士論文

【摘要】：谷歌Alpha Go帶來的人工智能的風(fēng)暴,正在橫掃各個行業(yè),同樣也會對金融投資行業(yè)產(chǎn)生深遠(yuǎn)的影響。而現(xiàn)實中量化投資和程序化交易,已經(jīng)成為很多金融市場中機構(gòu)投資者的常規(guī)操作模式。量化投資以其理性客觀、決策效率高、信息處理能力強等特點越來越受到學(xué)術(shù)界與投資實務(wù)界的重視。而量化擇時策略是量化投資策略的一個重要分支。支持向量機(SVM)是一種機器學(xué)習(xí)算法,彌補了傳統(tǒng)神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法的多項不足,在解決模式識別和回歸問題時性能優(yōu)越。對于SVM,國內(nèi)在金融領(lǐng)域的研究主要用于金融時間序列預(yù)測,還沒有與量化擇時策略相結(jié)合的研究,而且在研究的過程中主要是側(cè)重對SVM方法和應(yīng)用的研究,往往忽視了策略本身。針對于以上問題,本文通過研究現(xiàn)有的量化擇時策略和SVM算法,結(jié)合兩者的優(yōu)勢,構(gòu)建基于SVM的量化擇時策略。首先,本文介紹量化投資的相關(guān)概念,簡要梳理量化投資在國內(nèi)外的發(fā)展?fàn)顩r;給出量化擇時策略的定義、分析其特點并對現(xiàn)有的量化擇時策略進(jìn)行了分類。其次,從機器學(xué)習(xí)、統(tǒng)計學(xué)習(xí)理論等六個方面對SVM的相關(guān)理論進(jìn)行較為全面深入的研究。接下來,系統(tǒng)的構(gòu)建基于SVM的量化擇時策略,主要有兩大部分,一是基于SVM擇時策略的構(gòu)建,二是策略模型算法的設(shè)置。最后,運用中國石油、浦發(fā)銀行、滬深300指數(shù)、中證500指數(shù)和創(chuàng)業(yè)板指指數(shù)的各600組、時間跨度約兩年半的數(shù)據(jù)進(jìn)行訓(xùn)練與測試,分析驗證策略的有效性。本文研究的創(chuàng)新性工作主要有兩方面:一是對于量化擇時策略進(jìn)行了系統(tǒng)的梳理,并建立了自己的量化擇時策略。本文量化擇時策略的思路是:策略選擇在我國股票市場運行,SVM預(yù)測模型每日收盤后運行一次,對下一日收盤價進(jìn)行預(yù)測,如果預(yù)測出上漲,在當(dāng)下一日的價格低于前一日收盤價時,全倉買入;如果預(yù)測出下跌,當(dāng)下一日的價格高于前一日收盤價時,清倉賣出;如果預(yù)測出沒有變化,就不進(jìn)行操作,同時加入了止損判斷,也就是說,每日只進(jìn)行一次交易或不進(jìn)行交易,整個過程由交易系統(tǒng)自動進(jìn)行。二是引入支持向量機優(yōu)化算法,系統(tǒng)地構(gòu)建和檢驗了量化擇時策略。采用SVM算法,可以將量化擇時策略進(jìn)行優(yōu)化,取得更好的投資效果。在基于SVM擇時策略的構(gòu)建部分,本文從擇時模型設(shè)計的總體思路、預(yù)測期限、預(yù)測目標(biāo)、投資范圍、特征指標(biāo)、買賣時點、模型設(shè)置這七個方面構(gòu)建了擇時策略。在策略模型算法的設(shè)置部分,本文對SVM算法以及整個模型算法的各個方面進(jìn)行具體的設(shè)置,主要包括SVM的多分類算法選擇、SVM核函數(shù)選取、參數(shù)尋優(yōu)、不平衡數(shù)據(jù)的處理、滾動預(yù)測這五個方面的內(nèi)容。通過研究,本文構(gòu)建了基于支持向量機的量化擇時策略;使用真實數(shù)據(jù)進(jìn)行實證檢驗。通過對模型預(yù)測能力的分析、與買入持有策略的對比,以及從不同市場行情下的表現(xiàn)和策略的各項評價指標(biāo)來看,本論文的量化擇時策略表現(xiàn)優(yōu)異,所構(gòu)建的基于SVM量化擇時策略是有效的。本論文的研究對于將支持向量機方法應(yīng)用于量化投資策略的構(gòu)建,對于完善和優(yōu)化量化擇時策略,對于量化投資實踐具有一定的指導(dǎo)和參考意義。
[Abstract]:The storm of artificial intelligence brought by Google Alpha Go is sweeping across all industries, and it will also have a far-reaching impact on the financial investment industry. In reality, quantitative investment and procedural transactions have become the conventional mode of operation for institutional investors in many financial markets. Quantitative investment is rational and objective, efficient in decision-making, and information processing. The characteristics of ability and ability are becoming more and more important in academic circles and investment practice circles. Quantitative timing strategy is an important branch of quantitative investment strategy. Support vector machine (SVM) is a kind of machine learning algorithm, which makes up many shortcomings of traditional neural network learning algorithm, and has superior performance in solving pattern recognition and regression problems. For SVM, The domestic research in the financial field is mainly used in the financial time series prediction, and there is no research on the combination of the quantitative timing strategy, and in the process of the study, the main focus is on the study of the SVM method and application, often ignoring the strategy itself. In view of the above problems, this paper studies the existing quantitative timing strategy and SVM algorithm. Combining the advantages of the two, this paper constructs a quantitative timing strategy based on SVM. Firstly, this paper introduces the related concepts of quantitative investment, briefly combs the development of quantitative investment at home and abroad, gives the definition of quantitative timing strategy, analyzes its characteristics and classifies the existing quantitative timing strategies. Secondly, from machine learning and statistical learning theory. In the following six aspects, a more comprehensive and in-depth study of the related theories of SVM is carried out. Next, the system builds a quantitative timing strategy based on SVM, including two major parts. One is based on the construction of the SVM timing strategy and the two is the setting of the strategy model algorithm. Finally, it uses CNPC, Pufa Bank, Shanghai and Shenzhen 300 index, CSI 500 index and entrepreneurship. The 600 groups of the index index, the time span of about two and a half years of data training and testing, analysis and validation of the effectiveness of the strategy. The innovative work of this study mainly has two aspects: first, the quantitative timing strategy is systematically combed, and the establishment of their own quantitative timing strategy. This paper quantifies the strategy of timing strategy is: Strategy Choose to run in our stock market. The SVM forecast model runs once a day to predict the closing price of the next day. If the price is predicted to rise and the price of the next day is lower than the closing price of the previous day, the whole warehouse is bought. If the forecast is down and the price of the next day is higher than the closing price of the previous day, the warehouse will be sold out; if predicted, if it is predicted Without change, the operation is not carried out, and the stop loss judgment is added, that is to say, only one transaction or no transaction is carried out every day. The whole process is automatically carried out by the transaction system. Two, the support vector machine optimization algorithm is introduced, and the quantitative timing strategy is constructed and tested systematically. The SVM algorithm can be used to optimize the timing strategy. In the construction part of the SVM timing strategy, this paper constructs the timing strategy from seven aspects: the overall idea of the timing model design, the prediction period, the forecast target, the investment scope, the characteristic index, the time point of the sale and the model setting. In the setting part of the strategy model algorithm, this paper is on the SVM algorithm and the whole model. All aspects of the algorithm are set up, mainly including the selection of SVM multi classification algorithm, SVM kernel function selection, parameter optimization, unbalance data processing, rolling prediction. This paper constructs a quantitative timing strategy based on support vector machine, and uses real data to verify the five aspects. The analysis of model forecasting ability, compared with the buying and holding strategy, and the evaluation indexes of performance and strategy in different market quotations, the quantitative timing strategy of this paper is excellent. The construction of the SVM quantization timing strategy is effective. The research of this paper applies the support vector machine method to quantitative investment. The construction of strategy is of guiding and referential significance for improving and optimizing quantitative timing strategies and quantifying investment practices.

【學(xué)位授予單位】：西安工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F832.51

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