本文關(guān)鍵詞：基于時(shí)間價(jià)值的神經(jīng)網(wǎng)絡(luò)的股票價(jià)格預(yù)測　出處：《廣東財(cái)經(jīng)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 時(shí)間價(jià)值 神經(jīng)網(wǎng)絡(luò) 股票價(jià)格預(yù)測 BP網(wǎng)絡(luò)模型

【摘要】：股票屬于一種高風(fēng)險(xiǎn)、高收益的投資，已成為現(xiàn)代生活中不可缺少的一部分，因此投資者們時(shí)刻關(guān)心股市，分析股市，研究價(jià)格趨勢。股票市場中隨機(jī)因素很多，導(dǎo)致股票價(jià)格波動(dòng)表現(xiàn)出很強(qiáng)的不確定性，以致傳統(tǒng)預(yù)測技術(shù)的效果并不理想。于是，建立一個(gè)合理的股票價(jià)格預(yù)測模型，具有重要的理論意義和實(shí)踐價(jià)值。 本文通過深入分析股票原理，比較常見的股票預(yù)測方法，探討B(tài)P神經(jīng)網(wǎng)絡(luò)在股票預(yù)測上的可行性。從原理上講，神經(jīng)網(wǎng)絡(luò)是對股票交易的歷史數(shù)據(jù)學(xué)習(xí)后實(shí)現(xiàn)對未來股票價(jià)格的預(yù)測。具體而言，BP網(wǎng)絡(luò)通過對股票的歷史數(shù)據(jù)的學(xué)習(xí),不斷地修正相應(yīng)的權(quán)值、閥值，最終建立一個(gè)相對合理的模型。本文研究的是投機(jī)的超短線股票交易，與傳統(tǒng)的投資理念有明顯區(qū)別；預(yù)測的結(jié)果是來源于多次預(yù)測結(jié)果的分析，而非特指某一次預(yù)測。 本文提出了一種創(chuàng)新的研究思想——引入基于時(shí)間價(jià)值的動(dòng)態(tài)權(quán)重誤差函數(shù)，設(shè)計(jì)出一種基于時(shí)間價(jià)值的神經(jīng)網(wǎng)絡(luò)模型。本文認(rèn)為：BP模型通過引入動(dòng)態(tài)權(quán)重的方法，可以改變了原來BP模型單純的擬合訓(xùn)練集數(shù)據(jù)，更靈活地?fù)駜?yōu)而達(dá)到預(yù)測效果。據(jù)此，本文采用MATLAB軟件選定醫(yī)藥行業(yè)的股票進(jìn)行仿真實(shí)驗(yàn)。實(shí)證結(jié)果表明:與傳統(tǒng)預(yù)測方法和BP神經(jīng)網(wǎng)絡(luò)相比，本文提出的模型準(zhǔn)確率較高，明顯降低預(yù)測誤差，進(jìn)一步提高了網(wǎng)絡(luò)的泛化能力和模型預(yù)測精度，優(yōu)化了股票價(jià)格預(yù)測效果。為了驗(yàn)證模型的經(jīng)濟(jì)和社會效益，，本文設(shè)計(jì)了一種現(xiàn)實(shí)中可實(shí)現(xiàn)的模擬交易操作（T+0模型），驗(yàn)證了基于時(shí)間價(jià)值的BP模型的價(jià)值。
[Abstract]:Stock is a kind of high risk, high yield investment, has become an indispensable part of modern life, so investors always care about the stock market, analysis of the stock market. Research on price trend. There are many random factors in stock market, which lead to strong uncertainty of stock price fluctuation, so the effect of traditional forecasting technology is not ideal. It is of great theoretical and practical value to establish a reasonable forecasting model of stock price. This paper discusses the feasibility of BP neural network in stock forecasting by deeply analyzing the stock principle and comparing the common stock forecasting methods. Neural network is to realize the prediction of the future stock price after learning the historical data of stock trading. Specifically, the BP network constantly modifies the corresponding weights and thresholds by learning the historical data of the stock. Finally, a relatively reasonable model is established. This paper studies the speculative ultra-short term stock trading, which is obviously different from the traditional investment concept. The result of prediction comes from the analysis of multiple prediction results, not from a particular prediction. In this paper, an innovative research idea is proposed, which is to introduce the dynamic weight error function based on time value. A neural network model based on time value is designed. This paper holds that the original BP model can change the original BP model by introducing the dynamic weight method. According to this, the MATLAB software is used to select the stocks of the pharmaceutical industry for simulation experiment. The empirical results show that: compared with the traditional forecasting method and BP neural network. The model presented in this paper has a high accuracy, obviously reduces the prediction error, further improves the generalization ability of the network and the prediction accuracy of the model, and optimizes the forecasting effect of stock price, in order to verify the economic and social benefits of the model. In this paper, a realistic simulation transaction operation model is designed, which verifies the value of BP model based on time value.
【學(xué)位授予單位】：廣東財(cái)經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP183;F830.91

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