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

【摘要】：谷歌Alpha Go帶來的人工智能的風暴,正在橫掃各個行業(yè),同樣也會對金融投資行業(yè)產生深遠的影響。而現實中量化投資和程序化交易,已經成為很多金融市場中機構投資者的常規(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術界與投資實務界的重視。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)是一種機器學習算法,彌補了傳統(tǒng)神經網絡學習算法的多項不足,在解決模式識別和回歸問題時性能優(yōu)越。對于SVM,國內在金融領域的研究主要用于金融時間序列預測,還沒有與量化擇時策略相結合的研究,而且在研究的過程中主要是側重對SVM方法和應用的研究,往往忽視了策略本身。針對于以上問題,本文通過研究現有的量化擇時策略和SVM算法,結合兩者的優(yōu)勢,構建基于SVM的量化擇時策略。首先,本文介紹量化投資的相關概念,簡要梳理量化投資在國內外的發(fā)展狀況;給出量化擇時策略的定義、分析其特點并對現有的量化擇時策略進行了分類。其次,從機器學習、統(tǒng)計學習理論等六個方面對SVM的相關理論進行較為全面深入的研究。接下來,系統(tǒng)的構建基于SVM的量化擇時策略,主要有兩大部分,一是基于SVM擇時策略的構建,二是策略模型算法的設置。最后,運用中國石油、浦發(fā)銀行、滬深300指數、中證500指數和創(chuàng)業(yè)板指指數的各600組、時間跨度約兩年半的數據進行訓練與測試,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預測模型每日收盤后運行一次,對下一日收盤價進行預測,如果預測出上漲,在當下一日的價格低于前一日收盤價時,全倉買入;如果預測出下跌,當下一日的價格高于前一日收盤價時,清倉賣出;如果預測出沒有變化,就不進行操作,同時加入了止損判斷,也就是說,每日只進行一次交易或不進行交易,整個過程由交易系統(tǒng)自動進行。二是引入支持向量機優(yōu)化算法,系統(tǒng)地構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行優(yōu)化,取得更好的投資效果。在基于SVM擇時策略的構建部分,本文從擇時模型設計的總體思路、預測期限、預測目標、投資范圍、特征指標、買賣時點、模型設置這七個方面構建了擇時策略。在策略模型算法的設置部分,本文對SVM算法以及整個模型算法的各個方面進行具體的設置,主要包括SVM的多分類算法選擇、SVM核函數選取、參數尋優(yōu)、不平衡數據的處理、滾動預測這五個方面的內容。通過研究,本文構建了基于支持向量機的量化擇時策略;使用真實數據進行實證檢驗。通過對模型預測能力的分析、與買入持有策略的對比,以及從不同市場行情下的表現和策略的各項評價指標來看,本論文的量化擇時策略表現優(yōu)異,所構建的基于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用于量化投資策略的構建,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.

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

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