本文選題：β系數(shù) + 量化投資策略　； 參考：《重慶工商大學(xué)》2017年碩士論文

【摘要】：自從馬科維茨的投資組合理論提出到現(xiàn)在已經(jīng)有了六十多年,該理論在很多方面已經(jīng)取得了很大的進(jìn)步和發(fā)展。比如:從單期研究到多期的研究,通過各種方法簡化均值-方差模型的求解過程,對風(fēng)險(xiǎn)衡量方法的修正以及對原假設(shè)條件的逐漸放松等方面。在實(shí)踐方面,投資組合理論問世以后,它開始為金融機(jī)構(gòu)和投資者所廣泛采用;金融學(xué)也開始了它在實(shí)際投資中的量化階段。本文給出了基于定量的投資組合的管理方法,該方法主要包括兩個(gè)階段,一是行業(yè)和股票優(yōu)選階段,二是對股票進(jìn)行投資組合,主要是確定所選股票最優(yōu)投資權(quán)重的。上述兩個(gè)階段主要在本研究的第3章和第4章進(jìn)行論述。第3章選取申銀萬國一級分類行業(yè)指數(shù)2014年6月3日至2015年9月30日這樣一個(gè)涵蓋市場上升階段和下跌階段的完整投資周期數(shù)據(jù)為樣本。通過對這些樣本數(shù)據(jù)的檢驗(yàn),得出了這些一級分類行業(yè)指數(shù)受市場態(tài)勢的影響;并計(jì)算了行業(yè)指數(shù)的上升β系數(shù)(up-marketβ,+?)和下降β系數(shù)(down-marketβ,?-)。最后,根據(jù)上升β系數(shù)和下降β系數(shù)的比值構(gòu)建了一個(gè)指標(biāo)并從中選出了6個(gè)優(yōu)質(zhì)的行業(yè)。第4章引入?yún)⒖紩r(shí)間窗口長度L和持有期限窗口長度H兩個(gè)外生的時(shí)間參數(shù)構(gòu)建了動(dòng)態(tài)的均值-方差投資組合模型,并利用遍歷法求解最優(yōu)時(shí)間窗口參數(shù)。然后采用了第三章優(yōu)選行業(yè)的代表性股票進(jìn)行動(dòng)態(tài)投資組合實(shí)證。在假定投資者風(fēng)險(xiǎn)容忍水平一定的情形下,以投資者效用最大化為衡量標(biāo)準(zhǔn),利用MATLAB軟件程序來尋求收益最優(yōu)的外生參數(shù)及每次調(diào)整資產(chǎn)的權(quán)重。最后,利用計(jì)算所得最優(yōu)參數(shù)進(jìn)行投資,并通過多項(xiàng)業(yè)績評價(jià)指標(biāo)(包括投資期年收益率、風(fēng)險(xiǎn)調(diào)整收益率和預(yù)測收益率等)對比分析動(dòng)態(tài)投資組合策略和被動(dòng)投資的收益情況。結(jié)果表明本文的動(dòng)態(tài)投資組合策略在風(fēng)險(xiǎn)調(diào)整后的收益率以及預(yù)測收益率等方面表現(xiàn)均優(yōu)于被動(dòng)投資。總之,本文研究為投資者提供了一種定量的投資組合管理方法,具有一定的理論意義及實(shí)用價(jià)值:一方面,通過實(shí)證分析驗(yàn)證了我國股市的非有效性;另一方面,為投資者如何分配各股票投資權(quán)重提供了有益的借鑒。
[Abstract]:It has been more than 60 years since Markowitz's portfolio theory was put forward. It has made great progress and development in many aspects. For example, from single-period study to multi-period study, the solution process of mean-variance model is simplified by various methods, the risk measurement method is modified, and the original assumptions are gradually relaxed. In practice, portfolio theory began to be widely used by financial institutions and investors, and finance began its quantitative stage in actual investment. In this paper, a quantitative portfolio management method is presented. The method mainly includes two stages, one is the industry and the stock selection stage, the other is the stock portfolio, which is mainly to determine the optimal investment weight of the selected stock. The above two stages are mainly discussed in chapters 3 and 4 of this study. The third chapter selects the whole investment cycle data from June 3, 2014 to September 30, 2015, which covers both the rising and falling stages of the market. Based on the test of these sample data, the influence of market situation on the industry index is obtained, and the rising 尾 coefficient of industry index is calculated by up-market 尾. And decreasing 尾 -market. Finally, according to the ratio of rising 尾 coefficient and decreasing 尾 coefficient, an index was constructed and six high quality industries were selected. In chapter 4, the dynamic mean-variance portfolio model is constructed by introducing two exogenous time parameters: the reference window length L and the holding term window length H, and the optimal time window parameters are solved by traversal method. Then we use the third chapter to select the representative stocks in the industry to carry out dynamic portfolio demonstration. Under the assumption that the level of investor risk tolerance is constant, taking the maximization of investor utility as the criterion, the MATLAB software program is used to find the optimal exogenous parameters of income and the weight of assets adjusted every time. Finally, using the calculated optimal parameters to invest, and through a number of performance evaluation indicators (including the annual rate of return on the investment period, Risk adjusted rate of return and forecast rate of return are compared to analyze the dynamic portfolio strategy and the return of passive investment. The results show that the dynamic portfolio strategy performs better than passive investment in terms of risk-adjusted return rate and prediction rate of return. In short, this paper provides a quantitative portfolio management method for investors, which has certain theoretical significance and practical value: on the one hand, it verifies the non-validity of China's stock market through empirical analysis; on the other hand, For investors how to allocate the weight of each stock investment provides a useful reference.
【學(xué)位授予單位】：重慶工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F224;F832.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉德彬;馬超群;周忠寶;劉文斌;;均值-方差下動(dòng)態(tài)投資組合效率評價(jià)模型[J];系統(tǒng)工程;2016年07期

2 郝靜;張鵬;;具有基數(shù)約束的多階段均值-方差投資組合優(yōu)化[J];中國科學(xué)技術(shù)大學(xué)學(xué)報(bào);2016年02期

3 清華大學(xué)國家金融研究院課題組;陳曉升;王佳音;管清友;楊柳;;中國股災(zāi)反思[J];中國經(jīng)濟(jì)報(bào)告;2016年01期

4 王佳;金秀;苑瑩;王旭;;基于動(dòng)態(tài)參照點(diǎn)的損失厭惡投資組合優(yōu)化模型[J];運(yùn)籌與管理;2015年06期

5 王建華;孫曼曼;王傳美;童恒慶;;基于分塊矩陣的均值-方差投資組合模型[J];統(tǒng)計(jì)與決策;2014年22期

6 李仲飛;姚海祥;;不確定退出時(shí)間和隨機(jī)市場環(huán)境下風(fēng)險(xiǎn)資產(chǎn)的動(dòng)態(tài)投資組合選擇[J];系統(tǒng)工程理論與實(shí)踐;2014年11期

7 張鵬;張衛(wèi)國;;多階段均值—半方差模糊投資組合決策研究[J];華南理工大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2014年05期

8 姚海祥;姜靈敏;馬慶華;簡e，

本文編號(hào)：1824854