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Systematically Analyzing the Financial Securities Through Quantitative Methods

Author: ZhaoShiJun
Tutor: XuBingZhen
School: Ningbo University
Course: Theoretical Physics
Keywords: Copula function ARMA Dynamic Correlation Hurst exponent Support Vector Machine
CLC: F830.9
Type: Master's thesis
Year: 2011
Downloads: 60
Quote: 0
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Abstract


In the financial industry , the time series can be said to be particularly important information , the so-called time series refers to a series of observational data is arranged in chronological order , observations equidistant or non - equidistant time interval sampling . For financial securities analysis , often to the past history of the data or information is based on the trend of the future or Change provides predictive information . Such as stocks or futures opening price , closing price , highest price , lowest price, the volume of transactions , trading volume . Can be used as a time sequence to be analyzed. Analysis of these time series , its processing and transformation , a new set of indicators , including : KDJ , MACD , RSI , etc. [ 1 ] , the use of these indicators to predict the future trend of the securities is relatively common method . Recently being generated in the country , a new quantitative research methods . The article focuses on the decomposition of the time series , noise reduction, the correlation between the related feature amount time series study ( including Copula function tail of a sequence of binary time correlation multivariate GARCH model [4 ] ) , followed by the memory of the time series by fractal analysis of financial time series often exhibit complex morphology and detailed features , due to a number of nonlinear factors , Hurst exponent by moving its trend for analysis. As well as the spectrum analysis of time series , the determination time sequence cycle . Autoregressive moving average model ( ARMA ) , the integrity of autoregressive moving average (ARIMA) time series linear regression prediction of nonlinear time series regression prediction by neural networks and support vector machines . Variables fit by linear or non-linear analysis , and thus the analysis variable dependent on financial time series is concerned , the regression analysis was used to analyze the sequence of changes in trends and trends . By mathematics , physics , computer intelligent time series systematic analysis and research , so as to predict the future trend of the time series .

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CLC: > Economic > Fiscal, monetary > Finance, banking > Finance, banking theory > Financial market
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