基于混沌理論的中國金融市場投資決策研究
發(fā)布時間：2018-07-31 15:48
【摘要】：近年來，作為市場經(jīng)濟體系的有機構(gòu)成部分，全球金融市場的規(guī)模急劇擴大，重要性日益凸顯。作為一個新興的市場，中國金融市場的發(fā)展更是舉世矚目。中國A股市場市值已躍居全球第二；而中國的期貨市場經(jīng)過最近十多年的蓬勃發(fā)展，已成為全球第一大商品期貨市場，國內(nèi)首個金融期貨品種——滬深300指數(shù)期貨也在2010年上市。中國的黃金市場雖然起步較晚，但隨著國內(nèi)投資者避險意識的覺醒，現(xiàn)在無論是交易量還是市場影響力都有了長足的進步。如今在中國，金融投資已逐步成為個人、企業(yè)乃至政府的重要理財工具。 在金融分析和投資決策領(lǐng)域，長期以來一直以有效市場假說和建立在其基礎(chǔ)之上的資本資產(chǎn)定價模型為理論基石。然而隨著時代的發(fā)展，金融市場的分形、混沌等復雜特性逐漸為人所知。本文即以混沌理論為基礎(chǔ)，對中國的股票、期貨、黃金等金融市場進行系統(tǒng)的研究，以期揭示這個新興市場的內(nèi)在規(guī)律，探討有效的投資決策方法。 本文研究的主要內(nèi)容包括以下幾個方面： 1）中國金融市場的混沌性檢驗。在數(shù)據(jù)預處理上，采用對數(shù)線性去趨勢和收益率兩種方法對數(shù)據(jù)進行了平穩(wěn)化處理。對于時間上不連續(xù)的期貨市場品種，新設(shè)計了最大交易量復權(quán)法，，保證價格的連續(xù)性和代表性。然后對平穩(wěn)化后的序列以R/S分析和BDS檢驗以及遞歸圖方法進行非線性和確定性檢驗。之后再進行相空間重構(gòu)，考察其混沌不變量。通過這些分析，彌補了以前國內(nèi)期貨市場大部分品種和黃金市場都未進行混沌識別的不足，得出中國金融市場中普遍存在混沌的結(jié)論。 2）中國金融市場的噪聲處理研究。主要從兩個方面研究了中國金融市場市場的噪聲處理。一是噪聲估計，以常用的關(guān)聯(lián)積分法、粗糙紋理熵方法、小波法等估計了我國金融市場的噪聲水平。并利用小波變換的方差分解功能對白噪聲的小波系數(shù)方差進行分析，提出一種新的噪聲估計方法。二是噪聲平滑方面，分析了非線性局部平均法和局部投影法，重點研究了小波軟閾值去噪方法，提出基于小波方差分解的新閾值去噪方法，并用Lorenz、Chen等混沌系統(tǒng)數(shù)據(jù)進行檢驗。其后運用該方法對國內(nèi)金融市場中有代表性的幾個品種的價格序列進行噪聲平滑處理，驗證了有效性。最后，以上證指數(shù)日收盤價格序列作為樣本，通過一天預測再反平穩(wěn)化以比較均方根誤差的方法，比較了各種噪聲平滑方法在金融市場的實際去噪效果。 3）中國金融市場的混沌預測研究。噪聲估計和平滑處理的基礎(chǔ)上，首先用Lyapunov指數(shù)法對我國金融市場的幾個代表性品種進行預測實證。然后研究了Valterra級數(shù)自適應預測模型在中國金融市場的應用，并使用遞推最小二乘算法（RLS）來提高Volterra預測模型的預測精度。在對國內(nèi)幾個金融市場的實際預測表明，基于Valterra級數(shù)的自適應預測模型效果明顯優(yōu)于Lyapunov指數(shù)預測法，但是該方法存在穩(wěn)定性差的問題。一般常用的神經(jīng)網(wǎng)絡(luò)模型多屬于靜態(tài)前饋的處理模式，本文將遞歸預測器神經(jīng)網(wǎng)絡(luò)應用到對金融市場的預測中。在網(wǎng)絡(luò)訓練上，提出用遺傳算法優(yōu)化網(wǎng)絡(luò)的閾值、權(quán)值以及激發(fā)函數(shù)的幅值和斜率。和其他典型的神經(jīng)網(wǎng)絡(luò)預測方法——BP神經(jīng)網(wǎng)絡(luò)、徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)等的比較結(jié)果表明，該方法有較好的預測效果，而且穩(wěn)定性強，是適合中國金融市場決策分析的有效預測方法。 4）中國金融市場的混沌交易模型和投資組合模型研究。技術(shù)分析是當今最為廣泛使用的金融投資分析工具。文章首先在混沌與分形的視角下，重新闡釋了技術(shù)分析的三大假設(shè)，提出混沌分形理論的發(fā)展夯實了技術(shù)分析的理論基礎(chǔ)。其后把混沌預測與技術(shù)分析模型結(jié)合起來，產(chǎn)生了一些混沌交易模型，包括移動平均交易規(guī)則、濾子法則等，并進行了實證分析。混合交易模型是基于遺傳規(guī)劃，結(jié)合混沌預測而構(gòu)建的。對金融市場的實證檢驗的結(jié)果表明，該模型無論在超額收益率還是穩(wěn)定性上都要優(yōu)于傳統(tǒng)的交易規(guī)則模型。最后，文章基于非線性和行為金融理論，提出了基于損失規(guī)避的效用函數(shù)-偏度投資組合模型，發(fā)現(xiàn)該模型的表現(xiàn)要優(yōu)于其他傳統(tǒng)的投資組合模型。
[Abstract]:In recent years, as an organic part of the market economy, the scale of the global financial market has expanded rapidly and its importance has become increasingly prominent. As a new market, the development of China's financial market has attracted worldwide attention. The market value of China's A share market has leaped into second of the world, and China's futures market has developed vigorously after the last more than 10 years, It has become the world's largest commodity futures market, the first domestic financial futures variety - Shanghai and Shenzhen 300 index futures also listed in 2010. Although China's gold market started late, but with the awakening of domestic investors' awareness of risk avoidance, both the trading volume and market impact have made considerable progress. Now, in China, finance Investment has gradually become an important financial tool for individuals, enterprises and even the government.
In the field of financial analysis and investment decision, the capital asset pricing model based on the effective market hypothesis has long been the cornerstone of the theory. However, with the development of the times, the fractal and chaos of the financial market are gradually known. This paper is based on the chaos theory, the stock, futures, yellow of China. The financial markets such as gold are systematically studied in order to reveal the inherent law of this emerging market and explore effective investment decision-making methods.
The main contents of this paper include the following aspects:
1) chaos test in China's financial market. In data preprocessing, the data is stabilized with two methods of logarithmic linear trend and rate of return. R/S analysis, BDS test and recursive graph method are used for nonlinear and deterministic test. Then phase space reconstruction is carried out to investigate its chaotic invariants. Through these analyses, the shortcomings of the previous domestic futures market and the gold market have not been identified, and the general chaos in China's financial market is concluded. Theory.
2) noise treatment in China's financial market. The noise treatment of China's financial market is studied from two aspects. One is noise estimation. The noise level of China's financial market is estimated by using the commonly used correlation integral method, rough texture entropy method, wavelet method and so on. And the wavelet transform is used to analyze the white noise in the wavelet transform. The coefficient variance is analyzed and a new noise estimation method is proposed. Two is the noise smoothing, the nonlinear local mean method and the local projection method are analyzed. The wavelet soft threshold denoising method is studied, and the new threshold de-noising method based on the wavelet variance decomposition is proposed, and the data of the chaotic system such as Lorenz and Chen are tested. Using this method, the price sequence of several representative varieties in domestic financial market is smoothed and smoothed, and the validity is verified. Finally, taking the daily closing price sequence of Shanghai stock index as a sample, the method of comparing the square root error with a day prediction is predicted and compared with the actual method of noise smoothing in the financial market. De-noising effect.
3) chaos prediction in China's financial market. On the basis of noise estimation and smoothing, the Lyapunov index method is used to predict several representative varieties of the financial market in China. Then the application of Valterra series adaptive prediction model in China's financial market is studied, and the recursive least square algorithm (RLS) is used to improve the financial market. The prediction accuracy of the high Volterra prediction model. The actual prediction of several domestic financial markets shows that the effect of the adaptive prediction model based on Valterra series is obviously superior to the Lyapunov index prediction method, but the method has the problem of poor stability. The recursive predictor neural network is applied to the prediction of the financial market. In the network training, the genetic algorithm is proposed to optimize the threshold of the network, the weight and the amplitude and slope of the excitation function, and other typical neural network prediction methods, such as the BP neural network, the radial basis function neural network, and so on. Good prediction effect and strong stability is an effective forecasting method suitable for China's financial market decision analysis.
4) the chaotic trading model and portfolio model in China's financial market. Technical analysis is the most widely used financial investment analysis tool. The article first reinterprets the three hypotheses of technical analysis in the perspective of chaos and fractal, and puts forward that the development of chaotic fractal theory consolidating the theoretical basis of technical analysis. Chaos prediction and technical analysis model are combined to produce some chaotic trading models, including the moving average trading rules, filter rules and so on. The mixed transaction model is based on genetic programming and combined with chaotic prediction. The results of the empirical test on financial markets show that the model is overcharged. The benefit rate or stability is better than the traditional trading rule model. Finally, based on the nonlinear and behavioral finance theory, the utility function bias portfolio model based on loss avoidance is proposed, and it is found that the model is better than the other traditional portfolio model.
【學位授予單位】：南京航空航天大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：F832.51
本文編號：2156057
[Abstract]:In recent years, as an organic part of the market economy, the scale of the global financial market has expanded rapidly and its importance has become increasingly prominent. As a new market, the development of China's financial market has attracted worldwide attention. The market value of China's A share market has leaped into second of the world, and China's futures market has developed vigorously after the last more than 10 years, It has become the world's largest commodity futures market, the first domestic financial futures variety - Shanghai and Shenzhen 300 index futures also listed in 2010. Although China's gold market started late, but with the awakening of domestic investors' awareness of risk avoidance, both the trading volume and market impact have made considerable progress. Now, in China, finance Investment has gradually become an important financial tool for individuals, enterprises and even the government.
In the field of financial analysis and investment decision, the capital asset pricing model based on the effective market hypothesis has long been the cornerstone of the theory. However, with the development of the times, the fractal and chaos of the financial market are gradually known. This paper is based on the chaos theory, the stock, futures, yellow of China. The financial markets such as gold are systematically studied in order to reveal the inherent law of this emerging market and explore effective investment decision-making methods.
The main contents of this paper include the following aspects:
1) chaos test in China's financial market. In data preprocessing, the data is stabilized with two methods of logarithmic linear trend and rate of return. R/S analysis, BDS test and recursive graph method are used for nonlinear and deterministic test. Then phase space reconstruction is carried out to investigate its chaotic invariants. Through these analyses, the shortcomings of the previous domestic futures market and the gold market have not been identified, and the general chaos in China's financial market is concluded. Theory.
2) noise treatment in China's financial market. The noise treatment of China's financial market is studied from two aspects. One is noise estimation. The noise level of China's financial market is estimated by using the commonly used correlation integral method, rough texture entropy method, wavelet method and so on. And the wavelet transform is used to analyze the white noise in the wavelet transform. The coefficient variance is analyzed and a new noise estimation method is proposed. Two is the noise smoothing, the nonlinear local mean method and the local projection method are analyzed. The wavelet soft threshold denoising method is studied, and the new threshold de-noising method based on the wavelet variance decomposition is proposed, and the data of the chaotic system such as Lorenz and Chen are tested. Using this method, the price sequence of several representative varieties in domestic financial market is smoothed and smoothed, and the validity is verified. Finally, taking the daily closing price sequence of Shanghai stock index as a sample, the method of comparing the square root error with a day prediction is predicted and compared with the actual method of noise smoothing in the financial market. De-noising effect.
3) chaos prediction in China's financial market. On the basis of noise estimation and smoothing, the Lyapunov index method is used to predict several representative varieties of the financial market in China. Then the application of Valterra series adaptive prediction model in China's financial market is studied, and the recursive least square algorithm (RLS) is used to improve the financial market. The prediction accuracy of the high Volterra prediction model. The actual prediction of several domestic financial markets shows that the effect of the adaptive prediction model based on Valterra series is obviously superior to the Lyapunov index prediction method, but the method has the problem of poor stability. The recursive predictor neural network is applied to the prediction of the financial market. In the network training, the genetic algorithm is proposed to optimize the threshold of the network, the weight and the amplitude and slope of the excitation function, and other typical neural network prediction methods, such as the BP neural network, the radial basis function neural network, and so on. Good prediction effect and strong stability is an effective forecasting method suitable for China's financial market decision analysis.
4) the chaotic trading model and portfolio model in China's financial market. Technical analysis is the most widely used financial investment analysis tool. The article first reinterprets the three hypotheses of technical analysis in the perspective of chaos and fractal, and puts forward that the development of chaotic fractal theory consolidating the theoretical basis of technical analysis. Chaos prediction and technical analysis model are combined to produce some chaotic trading models, including the moving average trading rules, filter rules and so on. The mixed transaction model is based on genetic programming and combined with chaotic prediction. The results of the empirical test on financial markets show that the model is overcharged. The benefit rate or stability is better than the traditional trading rule model. Finally, based on the nonlinear and behavioral finance theory, the utility function bias portfolio model based on loss avoidance is proposed, and it is found that the model is better than the other traditional portfolio model.
【學位授予單位】：南京航空航天大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：F832.51
【參考文獻】
相關(guān)期刊論文 前10條
1 呂勇,徐金梧,李友榮,楊德斌;基于局部投影和小波降噪的弱沖擊特征信號的提取[J];北京科技大學學報;2004年03期
2 李錟;李鵬;齊中英;;農(nóng)產(chǎn)品期貨價格時間序列R/S分析[J];商業(yè)研究;2006年05期
3 馬明;李松;;基于遺傳算法優(yōu)化混沌神經(jīng)網(wǎng)絡(luò)的股票指數(shù)預測[J];商業(yè)研究;2010年11期
4 徐龍炳;探討資本市場有效性的一種有效方法:分形市場分析[J];財經(jīng)研究;1999年01期
5 李建功;中國期貨市場的混沌研究[J];重慶郵電學院學報(社會科學版);2004年01期
6 王德佳,周福才,朱偉勇,曾文曲;上證綜合指數(shù)混沌模型的動力學特性分析[J];東北大學學報;2002年04期
7 林玲,曾勇,唐小我;移動平均線交易規(guī)則檢驗[J];電子科技大學學報;2000年06期
8 郭雙冰,肖先賜;混沌時間序列的Volterra自適應預測濾波器定階[J];電子與信息學報;2002年10期
9 王炳雪,史忠科,吳方向;時間序列曲線盒維數(shù)的一種快速算法[J];系統(tǒng)工程;2000年04期
10 唐衍偉;陳剛;張晨宏;;中國農(nóng)產(chǎn)品期貨市場價格波動的長程相關(guān)性研究[J];系統(tǒng)工程;2005年12期
相關(guān)博士學位論文 前1條
1 卜云;混沌時間序列自適應預測算法研究[D];電子科技大學;2010年
本文編號：2156057
本文鏈接：http://sikaile.net/guanlilunwen/zhqtouz/2156057.html
最近更新
教材專著
