股票預(yù)測(cè)論文：遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)在股市預(yù)測(cè)中的應(yīng)用

1、. ：.；股票預(yù)測(cè)論文：遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)在股市預(yù)測(cè)中的運(yùn)用【中文摘要】股票是市場(chǎng)經(jīng)濟(jì)的產(chǎn)物,現(xiàn)已成為金融市場(chǎng)中不可或缺的組成部分,在推助國(guó)民經(jīng)濟(jì)安康開展、籌措企業(yè)資金需求、社會(huì)財(cái)富再分配以及個(gè)人投資理財(cái)中發(fā)揚(yáng)著重要作用。但股票價(jià)錢受企業(yè)運(yùn)營(yíng)情況、政策走勢(shì)、經(jīng)濟(jì)大環(huán)境等諸多要素的影響,投資股市面臨宏大風(fēng)險(xiǎn)。對(duì)于占股市絕大多數(shù)的中小投資者來(lái)說(shuō),在進(jìn)展股票投資活動(dòng)時(shí)需求一種有效的分析方法來(lái)輔助決策,從而最大限制的降低風(fēng)險(xiǎn),添加收益。對(duì)股市價(jià)錢預(yù)測(cè)的方法很多,傳統(tǒng)的預(yù)測(cè)模型大多建立在長(zhǎng)期、大樣本的數(shù)據(jù)統(tǒng)計(jì)分析根底之上,對(duì)數(shù)據(jù)分布規(guī)律性和數(shù)據(jù)本身的完好性要求較高,中長(zhǎng)期的股市預(yù)測(cè)較為準(zhǔn)確。但股

2、市是一個(gè)復(fù)雜的多變量非線性動(dòng)態(tài)系統(tǒng),傳統(tǒng)方法對(duì)股市短期價(jià)錢走勢(shì)的預(yù)測(cè)存在很大局限性。人工神經(jīng)網(wǎng)絡(luò)具有良好的非線性逼近才干和對(duì)雜亂信息的綜合處置才干,其特性與股票市場(chǎng)的研討難點(diǎn)相對(duì)應(yīng),可以抑制傳統(tǒng)方法中的缺乏,在短期預(yù)測(cè)中準(zhǔn)確度較高。近年來(lái),國(guó)內(nèi)外很多學(xué)者將人工神經(jīng)網(wǎng)絡(luò)運(yùn)用于股市預(yù)測(cè)研討,獲得了較好的效果。因此本文選擇運(yùn)用廣泛、算法成熟的BP神經(jīng)網(wǎng)絡(luò)來(lái)研討股票價(jià)錢的預(yù)測(cè)。首先詳細(xì)論述了BP神經(jīng)網(wǎng)絡(luò)的根本原理和操作方法。對(duì)BP神經(jīng)網(wǎng)絡(luò)在實(shí)踐運(yùn)用中存在的缺陷進(jìn)展分析,針對(duì)這些缺乏引入遺傳算法來(lái)優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的初始權(quán)值,從而處理網(wǎng)絡(luò)初始權(quán)值難設(shè)定的問題,有效降低了預(yù)測(cè)誤差并提高了網(wǎng)絡(luò)的收斂速度。為了

3、驗(yàn)證本文算法的穩(wěn)定性和適用性,在實(shí)驗(yàn)中選擇了上證A股的皖通高速和中國(guó)石化兩支不同類型的股票數(shù)據(jù)作為實(shí)驗(yàn)樣本。由于本文進(jìn)展股票價(jià)錢的短期預(yù)測(cè),思索到股票價(jià)錢前后的關(guān)聯(lián)性,將股票延續(xù)三天的開盤價(jià)、最高價(jià)、最低價(jià)、收盤價(jià)、成交量和MA5作為一個(gè)輸入樣本,第四天開盤價(jià)、最高價(jià)、最低價(jià)、收盤價(jià)、成交量和MA5作為輸出樣本,以此滾動(dòng)建立訓(xùn)練樣本。首先建立BP網(wǎng)絡(luò)進(jìn)展訓(xùn)練,然后用遺傳算法優(yōu)化BP網(wǎng)絡(luò),經(jīng)過(guò)選擇、交叉和變異操作找到最優(yōu)順應(yīng)度值個(gè)體,將最優(yōu)個(gè)體運(yùn)用在對(duì)BP網(wǎng)絡(luò)的權(quán)值和閾值的優(yōu)化,然后再對(duì)同一樣本進(jìn)展訓(xùn)練。對(duì)比優(yōu)化前后的預(yù)測(cè)結(jié)果可以發(fā)現(xiàn)：遺傳算法優(yōu)化BP網(wǎng)絡(luò)可以大幅提升兩支股票的預(yù)測(cè)精度,同時(shí)網(wǎng)絡(luò)

4、的收斂速度加快。實(shí)驗(yàn)結(jié)果闡明：遺傳算法具有優(yōu)化訓(xùn)練BP網(wǎng)絡(luò)的才干,將遺傳算法優(yōu)化的BP網(wǎng)絡(luò)模型運(yùn)用于股票價(jià)錢預(yù)測(cè)是可行的、有效的。實(shí)驗(yàn)中也發(fā)現(xiàn)該算法只提升原有BP網(wǎng)絡(luò)的預(yù)測(cè)精度,并不能把預(yù)測(cè)誤差較大的BP神經(jīng)網(wǎng)絡(luò)優(yōu)化為可以準(zhǔn)確預(yù)測(cè)的BP神經(jīng)網(wǎng)絡(luò)。下一階段將結(jié)合其他的算法進(jìn)展研討,實(shí)現(xiàn)更好的預(yù)測(cè)效果。另外算法的穩(wěn)定性和成熟性有待進(jìn)一步的改良和驗(yàn)證?！居⑽恼縎tock, as a product of the market economy, has now become an integral part of the financial markets, playing a very impo

5、rtant role in pushing the national economy to a healthy development, satisfying financial capital needs of enterprises and helping social redistribution of wealth and personal financial investment. But stock prices are affected numerous factors, rather like business conditions, the policy trend, eco

6、nomic environment and many others. Investment in the stock market is facing great risk. To ensure the vast majority of small and medium-sized investors a maximized risk and increased revenue, an effective analytical method to assist decision-making is essential and necessary when stock investment ac

7、tivities are under progress.Despite many ways to forecasts the price on the stock market, the traditional forecasting models are mostly based on a long-term, statistical analysis of large amounts of data, which has raised tough requirements of distribution regularity and integrity. Due to the fact t

8、hat the stock market is a complex multi-variable nonlinear dynamic system, there is a big limitation of the traditional method of forecasting stock price of short-term movements. The artificial neural network has good nonlinear approximation ability as well as the capabilities to handle unclear, dis

9、ordered and complex information. Its characteristics are proper and appropriate to overcome shortcomings of traditional approaches, thereby reaching a higher accuracy in the short term. Many scholars home and abroad have recently applied artificial neural network prediction into the stock market, an

10、d achieved good results and effects.Therefore, this article focuses on widely used, proven mature BP neural network to forecast stock prices. Its relevant mathematical theory, technology and method are discussed in detail; the current situation and the problems caused by its application home and abr

11、oad are analyzed. In order to solve the problems that arbitrary initial values of BP network affect the accuracy Problem, we find BP genetic algorithm to optimize BP neural network. After several experiments we find convergence network speed, once optimized, has been improved greatly.To verify the s

12、tability and practicality of this algorithm in the experiment, we chose as the experimental samples the Shanghai A shares of Anhui Expressway Company Limited and Sinopec stock, two different types of data. As stock prices are forecast short-term, opening price, ceiling price, bottom price, trading v

13、olume and MA5 of three consecutive days are taken as an input sample, while the counterparts of the 4th day as the output sample. Training samples are to be established, then to optimize and finally find out the best individual by choosing, crossing and operating. Comparing the results, we can find

14、Genetic Algorithm optimization BP network predicts more accurate of the two stocks than ever before, also it has a significant improvement of the convergence of training network speed.The findings are that, the Genetic Algorithm has the ability to optimize BP network. The application of BP network m

15、odel upgraded by genetic algorithm into stock price prediction is feasible and effective. Additionally, its also found that the algorithm only improves the prediction accuracy of the original BP network, but inefficient to reduce the error of BP neural network. The next stage will combine with other

16、 algorithms to achieve better prediction results. And the stability and the maturity of Genetic Algorithm remain to be further improved and verified.【關(guān)鍵詞】股票預(yù)測(cè) 神經(jīng)網(wǎng)絡(luò) BP算法 遺傳算法【英文關(guān)鍵詞】stock forecast neural networks back propagation algorithm genetic algorithm【目錄】遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)在股市預(yù)測(cè)中的運(yùn)用摘要3-5Abstract5-6目錄7

17、-9第一章 緒論9-161.1 股票預(yù)測(cè)研討的背景和意義9-101.2 國(guó)內(nèi)外研討現(xiàn)狀10-121.2.1 國(guó)外研討現(xiàn)狀111.2.2 國(guó)內(nèi)研討現(xiàn)狀11-121.3 基于BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)存在的問題12-131.4 本文的研討思緒131.5 主要研討內(nèi)容和組織構(gòu)成13-16第二章 股票分析根本實(shí)際16-232.1 股票價(jià)錢預(yù)測(cè)存在的問題16-172.2 股市預(yù)測(cè)的實(shí)際根底17-182.3 股票價(jià)錢預(yù)測(cè)方法的分析18-192.4 股市常規(guī)變量和技術(shù)目的19-222.4.1 股市常規(guī)變量19-202.4.2 股市常用技術(shù)目的20-222.5 本章小結(jié)22-23第三章 BP人工神經(jīng)網(wǎng)絡(luò)及遺傳算法23-

18、363.1 人工神經(jīng)網(wǎng)絡(luò)實(shí)際23-263.2 人工神經(jīng)網(wǎng)絡(luò)構(gòu)造26-273.2.1 分層型神經(jīng)網(wǎng)絡(luò)構(gòu)造263.2.2 互聯(lián)型神經(jīng)網(wǎng)絡(luò)構(gòu)造26-273.3 神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)方式273.4 BP神經(jīng)網(wǎng)絡(luò)27-323.4.1 BP神經(jīng)網(wǎng)絡(luò)定義27-283.4.2 BP神經(jīng)網(wǎng)絡(luò)計(jì)算步驟28-303.4.3 BP神經(jīng)網(wǎng)絡(luò)的優(yōu)缺陷30-323.5 遺傳算法32-343.5.1 遺傳算法組成部分32-333.5.2 遺傳算法運(yùn)算流程33-343.5.3 遺傳算法的優(yōu)點(diǎn)343.6 本章小結(jié)34-36第四章 GA-BP算法預(yù)測(cè)股票價(jià)錢36-574.1 BP神經(jīng)網(wǎng)絡(luò)設(shè)計(jì)36-384.1.1 BP網(wǎng)絡(luò)層數(shù)確實(shí)定364.1.2 輸入層和輸出層的設(shè)計(jì)36-374.1.3 確定隱藏層神經(jīng)元數(shù)37-384.2 BP網(wǎng)絡(luò)的Matlab實(shí)現(xiàn)38-494.2.1 實(shí)驗(yàn)樣本數(shù)據(jù)的選取38-414.2.2 樣本數(shù)據(jù)的歸一化處置41-434.2.3 B