本文關(guān)鍵詞：基于數(shù)據(jù)挖掘的套利選股模型的研究及設(shè)計(jì)　出處：《復(fù)旦大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 對(duì)沖套利 數(shù)據(jù)挖掘 決策樹(shù) 神經(jīng)網(wǎng)絡(luò)

【摘要】：隨著我國(guó)計(jì)劃經(jīng)濟(jì)向市場(chǎng)經(jīng)濟(jì)的轉(zhuǎn)軌,中國(guó)證券市場(chǎng)在十幾年的時(shí)間里迅速發(fā)展,至今滬深兩個(gè)交易所已有超過(guò)兩千五百家上市公司。隨著股指期貨,ETF等金融衍生品的出現(xiàn)及全流通的時(shí)代來(lái)臨,中國(guó)的證券市場(chǎng)將進(jìn)一步發(fā)展與完善。在這個(gè)越來(lái)越成熟的市場(chǎng),各種先進(jìn)的、創(chuàng)新的、定量的研究方法將有越來(lái)越大的用武之地。同時(shí)伴隨著做空機(jī)制的出現(xiàn),國(guó)外成熟市場(chǎng)的交易方式對(duì)沖、套利等新的交易手段逐步走入投資者的眼中。但對(duì)于此類(lèi)交易方式的數(shù)據(jù)模型卻少之又少。 數(shù)據(jù)挖掘作為一項(xiàng)新興的技術(shù),最近年來(lái),在諸多領(lǐng)域得到了成功的應(yīng)用,主要包括：市場(chǎng)、銷(xiāo)售、客戶(hù)資源管理等領(lǐng)域。但是將數(shù)據(jù)挖掘運(yùn)用于選股模型構(gòu)建,投資組合盈利預(yù)測(cè)乃至將其應(yīng)用于投資模型等方面的研究還不是很多。 本文將數(shù)據(jù)挖掘方法引入到證券選股投資領(lǐng)域,重點(diǎn)討論利用決策樹(shù)算法對(duì)上市公司一攬子股票的阿爾法值進(jìn)行數(shù)據(jù)挖掘,得出短期內(nèi)阿爾法值跑贏大盤(pán)的一攬子股票,利用與期貨或融資融券的對(duì)沖形成套利模型。在建立完模型以后,又采用“數(shù)據(jù)拆分”確定這些預(yù)測(cè)效果的穩(wěn)健可靠。數(shù)據(jù)挖掘可以和現(xiàn)在已經(jīng)應(yīng)用較廣泛的統(tǒng)計(jì)學(xué)和計(jì)量經(jīng)濟(jì)學(xué)方法相結(jié)合,以取得更好的定量分析的效果。使投資者在目前越發(fā)成熟的市場(chǎng)中獲取穩(wěn)定的投資收益。
[Abstract]:With the transition from planned economy to market economy, China's securities market has developed rapidly in more than ten years. So far, there are more than 2,500 listed companies in Shanghai and Shenzhen stock exchanges. ETF and other financial derivatives and the advent of the era of full circulation, China's securities market will be further developed and improved. In this increasingly mature market, all kinds of advanced, innovative. Quantitative research methods will be more and more useful. At the same time, with the emergence of short-selling mechanism, the trading methods of foreign mature markets will be hedged. New trading methods, such as arbitrage, have gradually come into the eye of investors. But there are few data models for such transactions. Data mining as a new technology, in recent years, has been successfully applied in many fields, including: market, sales. However, the application of data mining in stock selection model construction, portfolio profit prediction and even its application to investment model is not much. In this paper, the data mining method is introduced into the field of stock selection and investment, and the decision tree algorithm is used to mine the alpha value of the listed companies. In the short run, the alpha value outperforms the large stock market, using the hedge with futures or margin to form arbitrage model. After the establishment of the model. Data mining can be combined with the widely used statistical and econometric methods. In order to achieve better results of quantitative analysis, so that investors in the current increasingly mature market to achieve a stable return on investment.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP311.13;F832.51

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本文編號(hào)：1429208