本文關(guān)鍵詞： BP神經(jīng)網(wǎng)絡(luò) GARCH模型 短期預(yù)測　出處：《蘭州大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著中國金融市場與國際接軌,金融衍生品市場初步建成,金融投資工具在多樣化、高杠桿的條件下也帶來了巨大的金融風(fēng)險(xiǎn).復(fù)雜多變的金融市場上對(duì)于金融投資分析工具的要求也就更高,催生出了多種對(duì)于股票價(jià)格預(yù)測的方法.對(duì)于不同的數(shù)據(jù)以及不同的市場環(huán)境需要不同分析方法.神經(jīng)網(wǎng)絡(luò)算法所具有的分布式存儲(chǔ)數(shù)據(jù)以及學(xué)習(xí)反饋機(jī)制的特點(diǎn)使得它在預(yù)測等方面有獨(dú)到的作用.本文中選取中國銀行股票收盤價(jià),采用BP神經(jīng)網(wǎng)絡(luò)(即前饋模型)和GARCH模型的方法對(duì)股票價(jià)格進(jìn)行了預(yù)測,通過對(duì)比分析得出結(jié)論BP神經(jīng)網(wǎng)絡(luò)在隱含層節(jié)點(diǎn)數(shù)為5時(shí)對(duì)于市場數(shù)據(jù)擬合度最好；而GARCH模型在對(duì)股票價(jià)格預(yù)測方面也是有效的,主要是因?yàn)橹袊y行股票數(shù)據(jù)具有尖峰厚尾和平穩(wěn)性特征.最終得出結(jié)論兩種預(yù)測方法都能夠?qū)χ袊y行股票短期價(jià)格進(jìn)行預(yù)測,但BP神經(jīng)網(wǎng)絡(luò)預(yù)測方法優(yōu)于GARCH模型的預(yù)測方法.
[Abstract]:With China's financial market in line with international standards, the financial derivatives market has been initially established, and financial investment instruments are diversifying. Under the condition of high leverage, it also brings great financial risks. The requirements for financial investment analysis tools in complex and changeable financial markets are even higher. Different analysis methods are needed for different data and different market environment. The distributed storage data and learning feedback mechanism of neural network algorithm are special. In this paper, the closing price of Bank of China stock is selected. The method of BP neural network (i.e. feedforward model) and GARCH model are used to predict the stock price. Through comparative analysis, it is concluded that BP neural network has the best fit for market data when the number of hidden layer nodes is 5:00. The GARCH model is also effective in forecasting the stock price, mainly because the bank of China stock data has the characteristics of peak, thick tail and stability. Finally, it is concluded that both of the two forecasting methods can predict the short-term price of Bank of China stock. But BP neural network is better than GARCH model.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP183;F832.51

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本文編號(hào)：1509044