本文選題：符號時間序列分析 + 最小生成樹　； 參考：《天津大學(xué)》2014年碩士論文

【摘要】：隨著國家經(jīng)濟的發(fā)展，中國與世界各國的經(jīng)濟聯(lián)系越來越密切，同時全球金融市場的波動對我國經(jīng)濟的影響越來越大。采取科學(xué)的方法對股票市場的結(jié)構(gòu)以及金融市場收益與波動進行分析，對于認(rèn)識與分散金融風(fēng)險具有重要的意義。在金融市場風(fēng)險的度量與預(yù)測方面，金融計量學(xué)方法已經(jīng)取得了豐碩的研究成果，但是金融系統(tǒng)是一個典型的非線性系統(tǒng)，金融計量學(xué)的方法很難全面而又準(zhǔn)確地把握波動的本質(zhì)。因此需要有新的方法和視角來認(rèn)識與研究金融市場結(jié)構(gòu)與金融波動等有關(guān)問題。作為金融計量學(xué)方法的補充，本文試圖將符號時間序列分析法引入到非線性金融系統(tǒng)的結(jié)構(gòu)與波動分析中。 本文將符號時間序列分析法引入到股市網(wǎng)絡(luò)結(jié)構(gòu)分析中，，基于交易量和收益兩個變量建立符號序列，通過符號序列編碼序列之間的歐式距離建立距離矩陣，從而得到最小生成樹和分層樹。結(jié)合分層樹和最小生成樹的特點對網(wǎng)絡(luò)結(jié)構(gòu)進行分析,文中以滬深300指數(shù)成分股進行了實證分析。在金融收益與波動預(yù)測方面，引入生物信息學(xué)中的序列比對方法與符號時間序列分析方法相結(jié)合，提出一種新的預(yù)測方法。通過數(shù)據(jù)的符號化、選取合適的模式長度，基于符號比對的方式在歷史符號序列中搜索與當(dāng)前模式相似度最高的歷史模式，然后將此歷史模式用于下一個值的預(yù)測。文中以上證綜指高頻數(shù)據(jù)為樣本，對收益以及波動進行了實證分析。 本文第一章闡述了文章研究背景、意義以及涉及到的研究方法的研究現(xiàn)狀，梳理了文章結(jié)構(gòu)并且提出了本文的創(chuàng)新點。第二章概述了文中用到的理論基礎(chǔ)，包括時間序列符號化、網(wǎng)絡(luò)結(jié)構(gòu)分析法、基于模式匹配的預(yù)測方法。第三章提出了基于符號時間序列分析法的股票市場網(wǎng)絡(luò)結(jié)構(gòu)分析，并以滬深300指數(shù)成分股為例進行了實證分析。第四章提出基于模式匹配的金融收益與波動的預(yù)測方法，并對文中用到的基于序列比對的相似性度量進行了詳細(xì)描述，且以上證綜指采樣間隔為20分鐘的高頻數(shù)據(jù)為樣本進行了實證分析。第五章總結(jié)了本文的工作，以及指出對未來研究中需要進一步改進的方向。
[Abstract]:With the development of national economy, the economic relationship between China and other countries is getting closer and closer. At the same time, the fluctuation of global financial market has more and more influence on China's economy.It is of great significance to analyze the structure of the stock market and the return and fluctuation of the financial market by adopting scientific methods to understand and disperse the financial risks.In the aspect of measurement and prediction of financial market risk, financial metrology has made great achievements, but the financial system is a typical nonlinear system.The method of financial metrology is difficult to grasp the essence of volatility comprehensively and accurately.Therefore, new methods and perspectives are needed to understand and study financial market structure and financial volatility.As a supplement of financial metrology, this paper attempts to introduce symbolic time series analysis into the structural and volatility analysis of nonlinear financial systems.In this paper, the symbolic time series analysis method is introduced into the stock market network structure analysis. The symbol sequence is established based on the two variables of trading volume and income, and the distance matrix is established by the Euclidean distance between the symbol sequence coding sequences.Thus, the minimum spanning tree and the hierarchical tree are obtained.Combined with the characteristics of hierarchical tree and minimum spanning tree, the network structure is analyzed, and the empirical analysis is carried out with the index of Shanghai and Shenzhen 300.In the aspect of forecasting financial returns and volatility, a new forecasting method is proposed by combining the methods of sequence alignment and symbolic time series analysis in bioinformatics.Through the symbolization of the data, the appropriate pattern length is selected, and the historical pattern with the highest similarity to the current pattern is searched in the historical symbol sequence based on symbol alignment, and then the historical pattern is used to predict the next value.Taking the high frequency data of Shanghai Composite Index as the sample, the paper makes an empirical analysis on the return and volatility.The first chapter describes the background, significance and research status of the research methods involved, combs the structure of the article and puts forward the innovative points of this paper.The second chapter summarizes the theoretical basis used in this paper, including time series symbolization, network structure analysis, pattern matching based prediction methods.In chapter 3, the network structure analysis of stock market based on symbolic time series analysis is proposed, and the empirical analysis of CSI 300 index component stock is given.In chapter 4, a method of forecasting financial returns and volatility based on pattern matching is proposed, and the similarity measures based on sequence alignment are described in detail.Based on the high frequency data with sampling interval of 20 minutes in Shanghai Composite Index, the empirical analysis is carried out.The fifth chapter summarizes the work of this paper and points out the direction of further improvement in the future research.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F832.51

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