【摘要】：近年來(lái)，作為市場(chǎng)經(jīng)濟(jì)體系的有機(jī)構(gòu)成部分，全球金融市場(chǎng)的規(guī)模急劇擴(kuò)大，重要性日益凸顯。作為一個(gè)新興的市場(chǎng)，中國(guó)金融市場(chǎng)的發(fā)展更是舉世矚目。中國(guó)A股市場(chǎng)市值已躍居全球第二；而中國(guó)的期貨市場(chǎng)經(jīng)過(guò)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(chǎng)，國(guó)內(nèi)首個(gè)金融期貨品種——滬深300指數(shù)期貨也在2010年上市。中國(guó)的黃金市場(chǎng)雖然起步較晚，但隨著國(guó)內(nèi)投資者避險(xiǎn)意識(shí)的覺(jué)醒，現(xiàn)在無(wú)論是交易量還是市場(chǎng)影響力都有了長(zhǎng)足的進(jìn)步。如今在中國(guó)，金融投資已逐步成為個(gè)人、企業(yè)乃至政府的重要理財(cái)工具。 在金融分析和投資決策領(lǐng)域，長(zhǎng)期以來(lái)一直以有效市場(chǎng)假說(shuō)和建立在其基礎(chǔ)之上的資本資產(chǎn)定價(jià)模型為理論基石。然而隨著時(shí)代的發(fā)展，金融市場(chǎng)的分形、混沌等復(fù)雜特性逐漸為人所知。本文即以混沌理論為基礎(chǔ)，對(duì)中國(guó)的股票、期貨、黃金等金融市場(chǎng)進(jìn)行系統(tǒng)的研究，以期揭示這個(gè)新興市場(chǎng)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(gè)方面： 1）中國(guó)金融市場(chǎng)的混沌性檢驗(yàn)。在數(shù)據(jù)預(yù)處理上，采用對(duì)數(shù)線(xiàn)性去趨勢(shì)和收益率兩種方法對(duì)數(shù)據(jù)進(jìn)行了平穩(wěn)化處理。對(duì)于時(shí)間上不連續(xù)的期貨市場(chǎng)品種，新設(shè)計(jì)了最大交易量復(fù)權(quán)法，，保證價(jià)格的連續(xù)性和代表性。然后對(duì)平穩(wěn)化后的序列以R/S分析和BDS檢驗(yàn)以及遞歸圖方法進(jìn)行非線(xiàn)性和確定性檢驗(yàn)。之后再進(jìn)行相空間重構(gòu)，考察其混沌不變量。通過(guò)這些分析，彌補(bǔ)了以前國(guó)內(nèi)期貨市場(chǎng)大部分品種和黃金市場(chǎng)都未進(jìn)行混沌識(shí)別的不足，得出中國(guó)金融市場(chǎng)中普遍存在混沌的結(jié)論。 2）中國(guó)金融市場(chǎng)的噪聲處理研究。主要從兩個(gè)方面研究了中國(guó)金融市場(chǎng)市場(chǎng)的噪聲處理。一是噪聲估計(jì)，以常用的關(guān)聯(lián)積分法、粗糙紋理熵方法、小波法等估計(jì)了我國(guó)金融市場(chǎng)的噪聲水平。并利用小波變換的方差分解功能對(duì)白噪聲的小波系數(shù)方差進(jìn)行分析，提出一種新的噪聲估計(jì)方法。二是噪聲平滑方面，分析了非線(xiàn)性局部平均法和局部投影法，重點(diǎn)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(jìn)行檢驗(yàn)。其后運(yùn)用該方法對(duì)國(guó)內(nèi)金融市場(chǎng)中有代表性的幾個(gè)品種的價(jià)格序列進(jìn)行噪聲平滑處理，驗(yàn)證了有效性。最后，以上證指數(shù)日收盤(pán)價(jià)格序列作為樣本，通過(guò)一天預(yù)測(cè)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(chǎng)的實(shí)際去噪效果。 3）中國(guó)金融市場(chǎng)的混沌預(yù)測(cè)研究。噪聲估計(jì)和平滑處理的基礎(chǔ)上，首先用Lyapunov指數(shù)法對(duì)我國(guó)金融市場(chǎng)的幾個(gè)代表性品種進(jìn)行預(yù)測(cè)實(shí)證。然后研究了Valterra級(jí)數(shù)自適應(yīng)預(yù)測(cè)模型在中國(guó)金融市場(chǎng)的應(yīng)用，并使用遞推最小二乘算法（RLS）來(lái)提高Volterra預(yù)測(cè)模型的預(yù)測(cè)精度。在對(duì)國(guó)內(nèi)幾個(gè)金融市場(chǎng)的實(shí)際預(yù)測(cè)表明，基于Valterra級(jí)數(shù)的自適應(yīng)預(yù)測(cè)模型效果明顯優(yōu)于Lyapunov指數(shù)預(yù)測(cè)法，但是該方法存在穩(wěn)定性差的問(wèn)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(yù)測(cè)器神經(jīng)網(wǎng)絡(luò)應(yīng)用到對(duì)金融市場(chǎng)的預(yù)測(cè)中。在網(wǎng)絡(luò)訓(xùn)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(yù)測(cè)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(yù)測(cè)效果，而且穩(wěn)定性強(qiáng)，是適合中國(guó)金融市場(chǎng)決策分析的有效預(yù)測(cè)方法。 4）中國(guó)金融市場(chǎng)的混沌交易模型和投資組合模型研究。技術(shù)分析是當(dāng)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(shí)了技術(shù)分析的理論基礎(chǔ)。其后把混沌預(yù)測(cè)與技術(shù)分析模型結(jié)合起來(lái)，產(chǎn)生了一些混沌交易模型，包括移動(dòng)平均交易規(guī)則、濾子法則等，并進(jìn)行了實(shí)證分析�；旌辖灰啄Ｐ褪腔谶z傳規(guī)劃，結(jié)合混沌預(yù)測(cè)而構(gòu)建的。對(duì)金融市場(chǎng)的實(shí)證檢驗(yàn)的結(jié)果表明，該模型無(wú)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(xiàn)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(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：F832.51

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