本文選題：價格預測　切入點：函數(shù)型數(shù)據(jù)　出處：《廈門大學》2014年碩士論文　論文類型：學位論文

【摘要】：股票價格的預測模型可以幫助交易者做更好的交易策略,但是因為股票價格受到很多方面因素的影響,要建立一個合適的模型去模擬股票價格的波動是不容易的。時間序列模型在預測方面的應用受到了廣泛的認可,因此,以往有很多研究股票價格預測的文章都用到了時間序列模型。但是很多時間序列模型,如著名的ARIMA模型,通常需要假設樣本數(shù)據(jù)本身或變換后有平穩(wěn)性和線性性,這一假設并不一定能滿足,因此人們要尋找新的模型,可以適用于更寬松的假設,減少由于不滿足模型假設而引起預測結果出現(xiàn)較大偏差的情形。文獻上有Wang and Leu(1996)利用神經(jīng)網(wǎng)絡方法來建模并進行非線性擬合和預測,但這類模型多用于低頻數(shù)據(jù)的分析。隨著金融市場的迅速發(fā)展,證券市場中的交易越來越頻繁,交易量也越來越大,相應的證券價格的變化也越來越頻繁,所以傳統(tǒng)的用低頻數(shù)據(jù)來做證券市場的研究已經(jīng)很難滿足市場發(fā)展的需求,人們開始轉向對時間間隔更小而數(shù)據(jù)量更大的高頻數(shù)據(jù)的研究。高頻的股票交易數(shù)據(jù)蘊含更豐富的信息,因而對建模選用的模型的靈活性和適用性有更高的要求。 結合自回歸模型和非參數(shù)回歸思想,本文提出了一個新的混合模型以預測未來股票的開盤價。該模型的自回歸部分,反映了過去開盤價的信息,該模型的非參數(shù)部分,是對前一個交易日的日內交易價格與一個未知函數(shù)作積分所得。由于利用了前一個交易日的日內交易價格的綜合信息,故有望能提高我們對未來股價的預測能力。我們對混合模型中的未知函數(shù)不作任何參數(shù)形式的設定。通過對日間交易價格進行函數(shù)型主成分分析,我們可以巧妙地擬合混合模型非參數(shù)部分。最后我們用滬深300指數(shù)的數(shù)據(jù)進行實證分析。分析結果顯示,本文提出的混合模型相比于傳統(tǒng)的自回歸模型有更好的預測表現(xiàn)。
[Abstract]:The forecasting model of stock price can help traders to make better trading strategy, but because the stock price is affected by many factors, It is not easy to establish a suitable model to simulate the fluctuation of stock price. The application of time series model in forecasting is widely accepted, so, In the past, many researches on stock price forecasting used time series models, but many time series models, such as the famous ARIMA model, usually need to assume that the sample data itself or the transformed data are stable and linear. This assumption is not necessarily satisfied, so people are looking for new models that can be applied to more relaxed assumptions. In the literature, Wang and Leuer (1996) uses neural network method to model and carry out nonlinear fitting and prediction. With the rapid development of the financial market, the transactions in the securities market are more and more frequent, the trading volume is also increasing, and the corresponding securities prices are changing more and more frequently. Therefore, the traditional use of low-frequency data to do securities market research has been very difficult to meet the needs of market development. People begin to study the high-frequency data with smaller interval and larger amount of data. The high-frequency stock trading data contain more information, so the flexibility and applicability of the models used in modeling are higher. Combined with the idea of autoregressive model and nonparametric regression, this paper presents a new mixed model to predict the opening price of future stocks. The autoregressive part of the model reflects the information of the past opening price, and the non-parametric part of the model. Is obtained by integrating the intraday trading price of the previous trading day with an unknown function. As a result of the use of comprehensive information on the intraday trading price of the previous trading day, It is expected to improve our ability to predict future stock prices. We do not set the unknown functions in the mixed model in any parameter form. We can fit the non-parametric part of the hybrid model skillfully. Finally, we use the data of the Shanghai and Shenzhen 300 index to carry on the empirical analysis. The results show that the hybrid model proposed in this paper has better prediction performance than the traditional autoregressive model.
【學位授予單位】：廈門大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F224;F832.51

【參考文獻】

相關碩士學位論文 前1條

1 田慶波;中國股市高頻數(shù)據(jù)的波動性研究[D];山東財經(jīng)大學;2012年

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本文編號：1571421