本文關(guān)鍵詞：多分辨小波神經(jīng)網(wǎng)絡(luò)在股票市場預(yù)測中的應(yīng)用　出處：《華中師范大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 上證指數(shù) BP神經(jīng)網(wǎng)絡(luò) 小波分析 多分辨小波神經(jīng)網(wǎng)絡(luò) 預(yù)測

【摘要】：股票市場是國家宏觀調(diào)控、企業(yè)直接融資的重要領(lǐng)域,其收益和風(fēng)險成正比。由于股票價格隨機游走的特征,且受國內(nèi)外經(jīng)濟政治變化、股民心理特征等眾多因素影響,很難進行科學(xué)評測。股票價格影響因素復(fù)雜,呈高度非線性,利用傳統(tǒng)的統(tǒng)計模型對有高噪聲、非線性等眾多特征的股價進行預(yù)測難以取得理想的效果。因此,建立一個準(zhǔn)確度高、精簡實用的評價模型對于證券市場投資及國家宏觀調(diào)控都具有重大的現(xiàn)實意義。目前對于股票價格預(yù)測,絕大多數(shù)投資者采用的依據(jù)股票趨勢、圖形形狀、人氣指標(biāo)進行技術(shù)分析,由于分析方法眾多,缺乏科學(xué)系統(tǒng)的理論支持,且各指標(biāo)的獨立性較強,因此預(yù)測準(zhǔn)確性不高。人工智能是一門集大成的科學(xué),涵蓋了計算機、心理學(xué)、圖像處理等知識,在近年來取得了突破性進展和廣泛的應(yīng)用,神經(jīng)網(wǎng)絡(luò)是人工智能的一個分支,小波神經(jīng)網(wǎng)絡(luò)是基于傳統(tǒng)網(wǎng)絡(luò)之上,引入小波變換對其進行改造,既有神經(jīng)網(wǎng)絡(luò)的非線性逼近、自組織學(xué)習(xí)性、結(jié)構(gòu)簡單等特點,同時兼具小波分析的黑箱辨識能力,能極大增強預(yù)測的效果。2015年股市經(jīng)歷了杠桿瘋牛,千股漲停、跌停、停牌,政府倉促出臺救市措施,注定這是不平凡的一年。本文首先介紹了2015年我國股災(zāi)發(fā)生的詳細(xì)經(jīng)過,從宏觀經(jīng)濟角度分析股災(zāi)的成因,繼而介紹了相關(guān)的知識背景,包括股票市場的基礎(chǔ)知識,現(xiàn)階段股價的預(yù)測方法,神經(jīng)網(wǎng)絡(luò)和小波分析相關(guān)概念,小波神經(jīng)網(wǎng)絡(luò)的基本特征以及具體分類,并對它進行系統(tǒng)闡述。在本文的實證部分中,首先對數(shù)據(jù)進行預(yù)處理,并建立多分辨小波神經(jīng)網(wǎng)絡(luò)模型,根據(jù)樣本數(shù)據(jù)的特性對網(wǎng)絡(luò)各層節(jié)點數(shù)、訓(xùn)練參數(shù)等進行設(shè)置,以2014年至2015年中339個交易日的上證指數(shù)為研究對象,用前311個數(shù)據(jù)對網(wǎng)絡(luò)進行訓(xùn)練,用后28個數(shù)據(jù)做為測試樣本,建立誤差率為主的模型評價標(biāo)準(zhǔn),對2015年股災(zāi)的波動狀況進行分析和預(yù)測。結(jié)果表明,我國的上證指數(shù)并非雜亂無章,而是可預(yù)測的,存在一定的運行規(guī)律；多分辨小波神經(jīng)網(wǎng)絡(luò)對于股價測試樣本的誤差率小,效果優(yōu)良,具有較高的推廣價值。
[Abstract]:Stock market is an important field of national macro-control and direct financing of enterprises. Its income and risk are proportional to each other. Because of the characteristics of stock price random walk, and subject to economic and political changes at home and abroad. Many factors, such as the psychological characteristics of shareholders, are difficult to evaluate scientifically. The influence factors of stock price are complex and highly nonlinear, and the traditional statistical model has high noise. It is difficult to achieve ideal results for forecasting stock price with many characteristics such as nonlinearity. Therefore, the establishment of a high accuracy. The simplified and practical evaluation model is of great practical significance for the investment of the securities market and the national macro-control. At present, the majority of investors use the pattern and shape according to the stock trend for stock price prediction. Because of the large number of analysis methods, lack of theoretical support of scientific system, and the independence of each index, the prediction accuracy is not high. Artificial intelligence is a large science. Covering computer, psychology, image processing and other knowledge, in recent years has made a breakthrough and wide application, neural network is a branch of artificial intelligence, wavelet neural network is based on the traditional network. Wavelet transform is introduced to transform it, which has the characteristics of nonlinear approximation, self-organizing learning, simple structure and so on, and it also has the black box identification ability of wavelet analysis. In 2015, the stock market experienced a leveraged mad cow, a stock price limit, a limit, a suspension, and the government rushed to rescue the market. This paper first introduces the detailed process of the stock market crash in 2015, analyzes the causes of the stock crash from the macroeconomic point of view, and then introduces the relevant knowledge background. Including the basic knowledge of the stock market, the current stock price forecasting methods, neural networks and wavelet analysis related concepts, wavelet neural networks and the basic characteristics of the classification. In the empirical part of this paper, we first preprocess the data, and establish a multi-resolution wavelet neural network model, according to the characteristics of the sample data, the number of nodes in each layer of the network. The training parameters were set up, and the Shanghai Stock Exchange Index (SSE) of 339 trading days from 2014 to middle of 2015 was taken as the research object, and the network was trained with the first 311 data. The last 28 data are used as test samples to establish a model evaluation standard based on error rate to analyze and forecast the fluctuation of stock market in 2015. The results show that the Shanghai Stock Exchange Index in China is not disorderly. It is predictable, and there are certain rules of operation. Multiresolution wavelet neural network has low error rate and good effect for stock price test samples.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：F832.51;TP183

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本文編號：1440517