本文選題：大數(shù)據(jù) + 股票　； 參考：《沈陽(yáng)師范大學(xué)》2017年碩士論文

【摘要】：股票市場(chǎng)是一個(gè)非常龐大而復(fù)雜的系統(tǒng),其功能主要是對(duì)已經(jīng)發(fā)行的股票進(jìn)行轉(zhuǎn)讓、買賣和流通;其狀況如何與國(guó)民經(jīng)濟(jì)的發(fā)展息息相關(guān)。在中國(guó),伴隨著國(guó)民經(jīng)濟(jì)的迅猛崛起,股票市場(chǎng)也在蓬勃的發(fā)展,越來(lái)越多的股民懷著支持國(guó)家建設(shè)與投資理財(cái)?shù)臒崆橥渡碛诠墒挟?dāng)中。因此,關(guān)于股市的研究具有重要的意義。雖說(shuō)這方面的研究工作已經(jīng)很多,但大多結(jié)果都不夠令人滿意,其主要不足之處大多在于以下兩點(diǎn):一、受到信息攝取量和儲(chǔ)存水平的限制與計(jì)算水平的限制,用于支撐研究的數(shù)據(jù)量較小;二、沒(méi)有充分地考慮利率與內(nèi)部收益率的影響。當(dāng)代計(jì)算機(jī)與互聯(lián)網(wǎng)絡(luò)的發(fā)展,使得我們有能力克服這兩個(gè)不足之處,做出更加理想的科研成果。本文試從擴(kuò)大數(shù)據(jù)量與重視利息率和收益率的視域出發(fā)建構(gòu)三項(xiàng)探討股市奧秘的方法并進(jìn)行一定的實(shí)證分析。文章共五章。第一章介紹股票的意義與背景和大數(shù)據(jù)方法的意義與背景,以及相關(guān)的研究動(dòng)態(tài),并說(shuō)明筆者所要進(jìn)行的工作與文章的結(jié)構(gòu)。第二章首先簡(jiǎn)介行為金融學(xué)的內(nèi)容與意義,以及相關(guān)研究動(dòng)態(tài);然后從大數(shù)據(jù)、機(jī)器學(xué)習(xí)和行為金融學(xué)的角度出發(fā),通過(guò)設(shè)計(jì)一個(gè)算法定義出一個(gè)表現(xiàn)按照某種初級(jí)炒股行為買賣一支股票的收益狀況的隨機(jī)變量R,并進(jìn)行有關(guān)研究。基于這支股票的一定的折現(xiàn)歷史數(shù)據(jù)運(yùn)用所設(shè)計(jì)的算法求出R的一個(gè)樣本;根據(jù)該樣本做出一個(gè)經(jīng)驗(yàn)分布;再根據(jù)該經(jīng)驗(yàn)分布建立其近似解析分布的一個(gè)序,并進(jìn)而給出一種求優(yōu)化近似解析分布的方法�；赗的生成方式及相關(guān)探討展示它的意義,并揭示出一些值得進(jìn)一步研究的問(wèn)題。第三章先從消除利率與內(nèi)部收益率影響的視角出發(fā),定義一種現(xiàn)值均線,而后建立一種根據(jù)長(zhǎng)短現(xiàn)值均線預(yù)測(cè)股價(jià)與股指走勢(shì)的方法,并基于過(guò)去多年的歷史數(shù)據(jù)進(jìn)行實(shí)證分析。第四章先簡(jiǎn)介馬爾科夫鏈的有關(guān)基礎(chǔ)知識(shí),定義一種擬內(nèi)部收益率,而后建立一種基于擬內(nèi)部收益率折現(xiàn)歷史數(shù)據(jù)與馬爾科夫鏈預(yù)測(cè)股票與股指趨勢(shì)的方法,并根據(jù)一定的歷史數(shù)據(jù)進(jìn)行實(shí)證分析。第五章是結(jié)論和展望,總結(jié)性的描述所做工作;指不足,并提出有待進(jìn)一步研究的問(wèn)題,及筆者對(duì)于未來(lái)的展望。所做工作可為炒股提供一定的啟示,可為從大數(shù)據(jù)、機(jī)器學(xué)習(xí)和行為金融學(xué)的角度出發(fā)進(jìn)一步研究股市提供一定的啟示。
[Abstract]:The stock market is a very large and complex system, whose function is mainly to transfer, trade and circulate the issued stock, and how its status is closely related to the development of the national economy. In China, with the rapid rise of the national economy, the stock market is also booming, more and more investors are devoted to the stock market with the enthusiasm of supporting national construction and investment management. Therefore, the research on the stock market has important significance. Although much research has been done in this area, most of the results are not satisfactory, and the main shortcomings lie in the following two points: first, limited by the level of information intake and storage and limited by the level of calculation, The amount of data used to support the research is small; second, the influence of interest rate and internal rate of return is not fully considered. With the development of computer and Internet, we have the ability to overcome these two deficiencies and make more ideal scientific research results. This paper tries to construct three methods to probe into the mystery of stock market from the view of enlarging the amount of data and paying attention to the interest rate and yield. The article consists of five chapters. The first chapter introduces the significance and background of the stock and the significance and background of the big data method, and the related research trends, and explains the work to be carried out and the structure of the article. The second chapter introduces the content and significance of behavioral finance and the related research trends, and then from the perspective of big data, machine learning and behavioral finance, By designing an algorithm, we define a random variable R, which represents the income of buying and selling a stock according to some primary stock speculation, and carry on the relevant research. Based on the discounted historical data of this stock, a sample of R is obtained by using the algorithm designed, an empirical distribution is made according to the sample, and an order of approximate analytic distribution is established according to the empirical distribution. Furthermore, a method to optimize the approximate analytic distribution is given. Based on the R generation method and related discussion, the significance of R is demonstrated, and some problems worthy of further study are revealed. In the third chapter, from the perspective of eliminating the influence of interest rate and internal rate of return, we define a present value mean line, and then establish a method to predict the trend of stock price and stock index based on the long and long present value average. And based on the past years of historical data for empirical analysis. Chapter four introduces the basic knowledge of Markov chain, defines a kind of quasi-internal rate of return, and then establishes a method to predict the trend of stock and stock index based on discounted historical data of quasi-internal yield and Markov chain. And according to certain historical data for empirical analysis. The fifth chapter is the conclusion and prospect, the summary description of the work done, pointing to the shortcomings, and put forward the problems to be further studied, and the author's prospects for the future. The work can provide some enlightenment for stock speculation and further study of stock market from the point of view of big data machine learning and behavioral finance.
【學(xué)位授予單位】：沈陽(yáng)師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.51

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