【摘要】：股市是資本市場的重要組成部分,它是宏觀經(jīng)濟最靈敏的晴雨表,經(jīng)濟的波動往往最先體現(xiàn)在股價漲跌行情中。此外,它在增加資本流動性,協(xié)調(diào)社會資源配置,提供投資渠道等方面也有舉足輕重的作用。經(jīng)過二十余年的迅速發(fā)展,我國股市已具有相當(dāng)規(guī)模,截止2012年12月底,我國滬深兩市市值合計達(dá)36973.7億美元①,在世界各大證券交易所市值排名中占居第三位。 盡管我國股市在不斷完善,相關(guān)法律法規(guī)也逐步健全,但仍存在一些特有的問題,如股票流通限制、政府導(dǎo)向性強、投機性高、投資者非理性等問題,因此經(jīng)常表現(xiàn)出比世界成熟資本市場更大的波動性。而股票價格的非正常波動不僅會給投資者帶來不必要損失,同時也會危害證券市場的健康發(fā)展以及我國整個經(jīng)濟體系的穩(wěn)定性。由此可見,對我國股票價格指數(shù)進(jìn)行深入研究具有重要意義。 由于金融時間序列往往同時具有線性和非線性特點,因此本文提出了將差分自回歸移動平均(ARMA)模型與神經(jīng)網(wǎng)絡(luò)(NN)相結(jié)合的混合模型,同時本文分別運用ARIMA模型、NN模型、ARIMA-GARCH族模型和ARIMA-NN混合模型,對我國1996年12月16日至2009年12月31日的上證綜指和深證成指收盤價格數(shù)據(jù)建立時間序列預(yù)測模型,并用2010年1月4日至2012年12月31日數(shù)據(jù)用于驗證所建模型的準(zhǔn)確性,通過比較預(yù)測結(jié)果,得出混合模型較其他模型更能提高我國股票價格預(yù)測準(zhǔn)確率的相關(guān)結(jié)論。同時根據(jù)ARIMA-GARCH族模型結(jié)果,分析得出我國股票市場的波動性特征,并據(jù)此提出我國股市未來發(fā)展的相關(guān)建議。
[Abstract]:The stock market is an important part of the capital market, it is the most sensitive barometer of the macro economy, the economic fluctuations are often reflected in the stock price ups and downs. In addition, it also plays an important role in increasing capital mobility, coordinating social resource allocation and providing investment channels. After more than 20 years of rapid development, China's stock market has a considerable scale. By the end of December, 2012, the market value of Shanghai and Shenzhen stock markets amounted to 3.69737 trillion US dollars, ranking third among the world's major stock exchanges. Although China's stock market is constantly improving and relevant laws and regulations are gradually improving, there are still some unique problems, such as restrictions on the circulation of stocks, strong government guidance, high speculation, irrational investors, and so on. As a result, it often shows greater volatility than the world's mature capital markets. The abnormal fluctuation of stock price will not only bring unnecessary losses to investors, but also endanger the healthy development of the securities market and the stability of the whole economic system of our country. Thus, it is of great significance to study the stock price index in China. Because financial time series are always linear and nonlinear at the same time, a hybrid model combining differential autoregressive moving average (ARMA) model with neural network (NN) is proposed, and the ARIMA model is used in this paper. NN model, ARIMA-GARCH family model and ARIMA-NN mixed model are used to predict the closing price of Shanghai Composite Index and Shenzhen Composite Index from December 16, 1996 to December 31, 2009. The data from January 4, 2010 to December 31, 2012 are used to verify the accuracy of the proposed model. By comparing the prediction results, it is concluded that the hybrid model can improve the accuracy of stock price prediction in China better than other models. At the same time, according to the results of ARIMA-GARCH family model, the characteristics of volatility of Chinese stock market are analyzed, and the relevant suggestions for the future development of Chinese stock market are put forward.
【學(xué)位授予單位】：天津財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F832.51;F224

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