發(fā)布時(shí)間：2018-01-04 02:40

  本文關(guān)鍵詞：基金經(jīng)理投資風(fēng)險(xiǎn)行為機(jī)理分析和預(yù)測(cè)　出處：《東北大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 基金經(jīng)理 投資風(fēng)險(xiǎn)行為 多元線性回歸 神經(jīng)網(wǎng)絡(luò) 機(jī)理分析

【摘要】：近些年,開放式基金的規(guī)模逐年增加,已經(jīng)逐漸成為中國(guó)金融市場(chǎng)重要組成部分。基金經(jīng)理作為基金投資管理的主要負(fù)責(zé)人,其投資風(fēng)險(xiǎn)行為對(duì)于基金的長(zhǎng)期業(yè)績(jī)的穩(wěn)定性具有重要影響作用。針對(duì)基金經(jīng)理的投資風(fēng)險(xiǎn)行為的機(jī)理分析和預(yù)測(cè),可以給投資者和監(jiān)管者的決策提供參考依據(jù),對(duì)于開放式基金的長(zhǎng)期良性地發(fā)展具有重要意義。國(guó)內(nèi)外大多數(shù)學(xué)者,采用基金業(yè)績(jī)的波動(dòng),即基金業(yè)績(jī)的標(biāo)準(zhǔn)差,作為基金經(jīng)理投資風(fēng)險(xiǎn)的衡量標(biāo)準(zhǔn)。研究發(fā)現(xiàn),基金經(jīng)理會(huì)根據(jù)自己歷史的相對(duì)排名對(duì)將來的投資風(fēng)險(xiǎn)進(jìn)行調(diào)整。本文在此基礎(chǔ)上,基于大量開放式基金運(yùn)行數(shù)據(jù)和特征,分別采用了多元回歸方法和神經(jīng)元網(wǎng)絡(luò)方法對(duì)基金經(jīng)理投資風(fēng)險(xiǎn)行為進(jìn)行了機(jī)理分析和預(yù)測(cè)。實(shí)證檢驗(yàn)表明了方法的有效性和普遍性。首先,從職業(yè)生涯角度對(duì)基金經(jīng)理投資風(fēng)險(xiǎn)行為機(jī)理進(jìn)行分析,基金經(jīng)理出于自利性的原因,在進(jìn)行投資和做決策的出發(fā)點(diǎn)更多的是自己職業(yè)生涯考慮,以此為依據(jù)決定下期是否增大或減小投資風(fēng)險(xiǎn),前期排名較低的基金經(jīng)理面臨更大的雇傭風(fēng)險(xiǎn),在下期選擇高風(fēng)險(xiǎn)策略,而前期排名較高的基金經(jīng)理沒有職業(yè)壓力,為了維持勞動(dòng)力市場(chǎng)上的聲譽(yù),在下期選擇低風(fēng)險(xiǎn)的策略。其次,針對(duì)中國(guó)2011開放式股票型基金的面板數(shù)據(jù),選取基金相對(duì)業(yè)績(jī)排名等因素,利用多元線性回歸方法對(duì)基金經(jīng)理風(fēng)險(xiǎn)進(jìn)行回歸擬合,通過逐步回歸方法求解出回歸系數(shù),建立回歸預(yù)測(cè)模型,利用2012年開放式股票型基金運(yùn)行數(shù)據(jù)進(jìn)行了驗(yàn)證。最后,為了提高預(yù)測(cè)精度,分別采用了基于BP神經(jīng)網(wǎng)絡(luò)和RBF神經(jīng)網(wǎng)絡(luò)方法來預(yù)測(cè)基金經(jīng)理風(fēng)險(xiǎn)行為。通過兩種方法的對(duì)比分析顯示,RBF神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型預(yù)測(cè)準(zhǔn)確性和穩(wěn)定性方面都有顯著性的提高。
[Abstract]:In recent years, open-end fund scale increased year by year, has gradually become an important part of the financial market. Chinese fund managers as the main person in charge of fund investment management, which plays a very important role in the stability of investment risk for long-term performance of the fund's behavior. The mechanism analysis and prediction of investment risk behavior for fund managers, can provide a reference for investors and regulators of the decision, the open-end fund's long-term healthy development has important significance. Most scholars at home and abroad, the fund performance fluctuations, namely fund performance standard deviation, as the fund manager's investment risk measure. The study found that the fund manager will according to the relative ranking of their own history to adjust the future the risk of investment. On this basis, a large number of open-end fund operation data and based on the features, using multiple back The regression method and neural network method on fund managers' investment risk behavior is analyzed and forecasted. The empirical test shows the effectiveness and universality of the method. First, analyze the mechanism of fund managers' investment risk behavior from the perspective of occupation career, the fund manager for reasons of self benefit, the starting point of investment and decision making the more of their own occupation career to consider, as a basis for deciding whether to increase or decrease the risk of investment, the lower ranked fund managers face a greater risk of employment, in the next higher risk strategy, the pre ranking fund managers have no higher occupation pressure, in order to maintain the reputation of the labor market, low risk the strategy in the next issue. Secondly, according to the panel data of 2011 China open stock fund, selected fund relative performance ranking factors, using multiple linearregression To the method of risk to the fund manager by regression fitting, stepwise regression method to calculate the regression coefficient, regression prediction model was verified by the 2012 open stock fund operation data. Finally, in order to improve the prediction accuracy, respectively based on BP neural network and RBF neural network method to predict the fund manager's risk behavior. By comparing the two methods of analysis shows that the prediction model prediction accuracy and stability has significantly improved RBF neural network.

【學(xué)位授予單位】：東北大學(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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