本文選題：量化投資 + 支持向量機(jī)��； 參考：《西南交通大學(xué)》2017年碩士論文

【摘要】：量化投資是一種的更理想的投資方式,它以數(shù)據(jù)為基礎(chǔ)、以模型為核心、以程序化交易為手段,且具有總體收益穩(wěn)定、持倉時(shí)間較短、交易標(biāo)的較多等特點(diǎn)。同一般的傳統(tǒng)投資方法相比較,量化投資具有如下的優(yōu)點(diǎn)。首先,量化投資依賴于更加客觀的投資邏輯。量化投資從決策產(chǎn)生及策略執(zhí)行都是通過計(jì)算機(jī)程序來實(shí)現(xiàn),這樣可以消除人為情緒所造成的不良后果。另外,量化投資的指令更加精確、交易更加快速。量化投資在利用計(jì)算機(jī)收集、處理歷史數(shù)據(jù)的特性下,能夠更加高效、更加全面地分析數(shù)據(jù),不錯(cuò)過每一個(gè)可能盈利的機(jī)會(huì)。在中國,量化投資還處于發(fā)展起步階段,整個(gè)金融投資領(lǐng)域,量化投資所占的比重甚至不到十分之一,說明量化投資在國內(nèi)的股票市場(chǎng)仍有非常廣闊的發(fā)展前景。隨著中國金融市場(chǎng)的不斷完善和發(fā)展,不斷地推進(jìn)金融改革和金融創(chuàng)新,量化投資在中國金融市場(chǎng)這片樂土上必將茁壯成長。整個(gè)投資過程都有量化投資技術(shù)的身影,其中包括量化選股、風(fēng)險(xiǎn)控制、算法交易等,本文針對(duì)股票收益率和波動(dòng)的預(yù)測(cè)建立量化選股策略。首先,基于兩個(gè)隨機(jī)擾動(dòng)都服從尖峰厚尾的T分布的SV模型的基礎(chǔ)上,構(gòu)建了雙T-SV模型�；趥鹘y(tǒng)的先驗(yàn)分布假設(shè),推導(dǎo)了雙T-SV模型的MCMC估計(jì)過程,并將其用于HS300指數(shù)的實(shí)證分析。通過與傳統(tǒng)的SV模型簇的DIC準(zhǔn)則對(duì)比,證實(shí)了雙T-SV模型能夠更加準(zhǔn)確刻畫我國金融收益率波動(dòng)時(shí)變性、聚集性。針對(duì)股票的收益率,本文通過對(duì)六大類因子的選取,主成分分析預(yù)處理,利用前6個(gè)主成分來作為輸入,以每只股票的后五個(gè)交易日的累積收益作為輸出,建立支持向量機(jī)回歸模型,基于技術(shù)面、成長面等因子的支持向量機(jī)回歸模型成功預(yù)測(cè)股票的五日的累積收益率。結(jié)合支持向量機(jī)預(yù)測(cè)與雙T-SV模型,構(gòu)建了量化投資策略。該投資策略以雙T-SV模型預(yù)測(cè)的收益率波動(dòng)σ與支持向量機(jī)的預(yù)測(cè)收益率r相結(jié)合來進(jìn)行選股,在HS300股票市場(chǎng)中選出r-λ*σ0(其中λ為風(fēng)險(xiǎn)厭惡因子,0≤λ≤1)的股票加入備選股池,并且在備選股池中選出r-λ*σ值最大的50只股票加入買入集,每五日調(diào)倉。根據(jù)量化策略在HS300股票市場(chǎng)上進(jìn)行回測(cè)的結(jié)果,其年化收益率可超過33%,累計(jì)收益達(dá)到117.8%,并且夏普比率達(dá)到0.83。在裸多的情況下,其資金收益率遠(yuǎn)遠(yuǎn)超過滬深300指數(shù),說明了策略的優(yōu)越性。
[Abstract]:Quantitative investment is a more ideal way of investment. It is based on data, takes model as the core, takes programmed transaction as the means, and has the characteristics of stable overall income, short position time and many trading targets.Compared with conventional investment methods, quantitative investment has the following advantages.First, quantitative investment depends on more objective investment logic.Quantitative investment is realized by computer program from decision generation and strategy execution, which can eliminate the adverse consequences caused by artificial emotion.In addition, quantitative investment orders are more accurate, trading faster.Under the characteristic of using computer to collect and process historical data, quantitative investment can analyze the data more efficiently and comprehensively, and not miss every possible profit opportunity.In China, quantitative investment is still in the initial stage of development. The proportion of quantitative investment in the whole field of financial investment is less than 1/10, which indicates that quantitative investment still has a very broad development prospect in the domestic stock market.With the continuous improvement and development of China's financial market, financial reform and financial innovation are constantly promoted, and quantitative investment is bound to thrive in this happy land of China's financial market.There are quantitative investment techniques in the whole investment process, including quantitative stock selection, risk control, algorithm trading, etc. In this paper, quantitative stock selection strategy is established for stock return and volatility prediction.Firstly, based on the SV model of T distribution with two random disturbances, a double T-SV model is constructed.Based on the traditional prior distribution hypothesis, the MCMC estimation process of the double T-SV model is derived and applied to the empirical analysis of the HS300 index.By comparing with the DIC criterion of the traditional SV model cluster, it is proved that the double T-SV model can more accurately describe the volatility and aggregation of the financial yield in China.According to the return rate of stock, this paper uses the first six principal components as input and the cumulative income of the last five trading days of each stock as the output through the selection of six categories of factors and the preprocessing of principal component analysis.The support vector machine regression model is established, and the support vector machine regression model based on technical and growth factors is used to successfully predict the cumulative return rate of stocks for five days.Combining support vector machine prediction with double T-SV model, a quantitative investment strategy is constructed.The investment strategy combines the return fluctuation 蟽 predicted by double T-SV model with the predicted return rate r of support vector machine to select the stocks with r- 位 * 蟽 0 (where 位 is risk aversion factor 0 鈮，

本文編號(hào)：1769099