本文關鍵詞：基于宏觀經濟指標和人工智能方法的上證綜合指數(shù)預測，由筆耕文化傳播整理發(fā)布。

        隨著我國市場經濟的高速發(fā)展和證券市場的逐步完善,越來越多的投資者參與到股票市場當中,希望通過股票投資來分享經濟增長的成果。但股票市場是一個復雜的市場,它不僅要受到國內經濟、政治、心理各方面的影響,也要受到國際經濟和政治等方面的影響,同時這些因素之間又以復雜的形式相互影響著。所以通過對股票市場的詳盡剖析,建立一個穩(wěn)定并且相對準確的股票預測模型,對廣大投資者,特別是中小投資者具有重要的實用價值。對于成熟資本市場來說,股市的走勢會受到宏觀經濟運行的影響,而股市也是經濟運行情況的“晴雨表”,所以可以通過宏觀經濟指標來對股市的走勢做出預測。但我國股票市場是不是我國經濟的“晴雨表”呢?自從2005年股權分置改革以后,制約我國股票市場發(fā)展的根本性的制度問題得到了解決,我國股市像脫韁的野馬走上了快速發(fā)展的道路。健全的法律體系逐步建成,立體化的市場結構也逐漸明確,股市逐步向成熟資本市場的方向發(fā)展,股市經濟“晴雨表”的功能也越來越明顯。而且現(xiàn)有的研究成果也表明,我國股市在一定程度上還是反映了我國經濟運行的整體狀況,個別宏觀經濟變量對股價變動的解釋能力很強。這就奠定了使用宏觀經濟指標來預測股市價格走勢的基礎。在股市價格預測中最主要的方法是以基本分析技術分析為代表的傳統(tǒng)分析法和以時間序列為代表的計量模型法。傳統(tǒng)分析法在實踐中使用比較多,它對股市的預測主要取決于使用者自己的經驗,不具有客觀性。以時間序列為代表的股市預測方法主要在學術研究中使用,這些方法往往對樣本要求比較高,而且在處理非線性問題時時間序列模型就顯得力不從心。在這樣的背景之下,近年來快速發(fā)展的人工智能方法得到了金融研究者的關注。人工智能方法就是模仿人腦學習知識的原理來讓計算機自動的學習客觀事物存在的內部規(guī)律。人工智能由于其較強的學習能力已經在多個領域得到廣泛的應用,包括分類問題、模式識別和信號處理等。在金融領域,由于人工智能方法具有較強的非線性擬合能力,所以也得到了廣泛的應用。利用人工智能方法預測股市就是給出與股票價格相關的變量,然后通過人工智能的方法自動的發(fā)現(xiàn)變量與股票價格之間的關系,從而利用這種關系來預測股票價格的變動。在人工智能方法中最常用的就是神經網(wǎng)絡方法和支持向量機方法。神經網(wǎng)絡方法種類較多,在眾多方法中由于BP神經網(wǎng)絡即誤差反向傳播網(wǎng)絡具有優(yōu)良的網(wǎng)絡性能所以得到了廣泛的應用。在以往利用宏觀經濟指標預測股市價格走勢的研究中大部分的學者主要使用計量模型的方法,很少有人使用人工智能方法。而使用人工智能方法預測股市時的學者們又很少使用宏觀變量,大多都是使用股市技術指標來對股市短期走勢做出預測,很少有學者利用宏觀經濟變量結合人工智能方法對股市的中長期走勢做出預測。在這樣的背景下,本文以上證指數(shù)作為我國股票市場的代表,利用宏觀經濟指標,使用人工智能方法對上證指數(shù)的走勢做出預測。上證指數(shù)樣本主要選取2005年股權分置改革以后的數(shù)據(jù)。宏觀經濟變量主要選取2005年以后的月度數(shù)據(jù)。在人工智能方法中本文主要使用神經網(wǎng)絡和支持向量機兩種方法。在神經網(wǎng)絡方法中,BP神經網(wǎng)絡由于具有良好的擬合能力和容錯能力成為使用最為廣泛的神經網(wǎng)絡模型之一,但是BP神經網(wǎng)絡模型又有自己的局限性,本文在前人提出的改進BP神經網(wǎng)絡的基礎之上提出了使用貝葉斯正則算法和提前停止算法相結合的方法來改進標準的BP神經網(wǎng)絡。在提出改進的BP神經網(wǎng)絡模型之后,本文分別使用改進的BP神經網(wǎng)絡和支持向量機方法對上證指數(shù)做出預測,并對兩種方法的預測結果做出比較。文章具體安排如下：第一章為前言部分,主要介紹文章研究的背景、意義以及研究思路和方法。第二章為宏觀經濟與股市預測部分,該部分主要包括以下幾個方面內容,首先是宏觀經濟指標介紹,該部分介紹了反映宏觀經運行情況的幾個重要宏觀經濟指標,這幾個宏觀經濟指標同時也是后文實證過程中將會用的變量。隨后介紹了目前在股市預測中主要使用的三種方法：傳統(tǒng)法、時間序列法和人工智能法。第一種方法在實際操作中使用比較廣泛,而后兩種方法在理論研究方面使用比較多。最后介紹了宏觀經濟與股市的關系以及兩者關系的國內外研究狀況。第三章為神經網(wǎng)絡部分,該部分從神經網(wǎng)絡的研究現(xiàn)狀入手,先后依次介紹了神經網(wǎng)絡的相關理論,特點及其分類,之后著重介紹了本文研究的BP神經網(wǎng)絡的學習算法以及特點,然后根據(jù)BP神經網(wǎng)絡的學習特點,對目前針對其性能的改進方法做介紹,這些方法主要解決了標準BP神經網(wǎng)絡收斂速度慢,精度不高的問題。之后在前人的基礎之上提出本文改進BP神經網(wǎng)絡的算法,即使用貝葉斯正則算法和提前停止算法相結合改進標準BP神經網(wǎng)絡。第四章為支持向量機部分,該部分從支持向量機的國內外研究現(xiàn)狀出發(fā),主要介紹了支持向量機的相關理論,通過統(tǒng)計學習理論與傳統(tǒng)機器學習理論的對比,說明統(tǒng)計學習理論的結構風險最小化很好的解決了傳統(tǒng)機器學習中經驗風險最小化的缺陷,進而介紹了建立在統(tǒng)計學習理論基礎之上的支持向量機理論,并對核函數(shù)做簡要介紹。第五章為實證部分,在前述理論的基礎之上,本章分別利用貝葉斯正則算法和提前停止算法相結合的BP神經網(wǎng)絡和支持向量機對上證指數(shù)的月度收盤價進行預測,并對預測結果做評價。通過對上證指數(shù)預測的實證研究,本文得出以下幾個結論：第一、利用宏觀經濟指標并結合人工智能方法對上證指數(shù)的中長期走勢做預測是具有可行性的,在實證研究中兩種人工智能方法都得到了比較理想的預測效果。第二、確定BP神經網(wǎng)絡隱藏層神經元個數(shù)是網(wǎng)絡結構設計中的重點,本文采用如下的確定方法,限制隱藏神經元最小最大個數(shù),然后利用窮舉法遍歷最小最大個數(shù)之間的所有情況,將其作為隱藏神經元個數(shù),然后選擇誤差輸出最小的網(wǎng)絡作為本文的BP神經網(wǎng)絡模型；第三、本文通過對使用貝葉斯正則算法的BP神經網(wǎng)絡的研究,發(fā)現(xiàn)貝葉斯正則算法雖然可以提高網(wǎng)絡的泛化能力,但是根據(jù)實證研究發(fā)現(xiàn),網(wǎng)絡過多的訓練次數(shù)可能導致使用貝葉斯正則算法的BP神經網(wǎng)絡出現(xiàn)過擬合的現(xiàn)象,最終導致網(wǎng)絡的泛化能力下降；第四、經過實證研究發(fā)現(xiàn),使用貝葉斯正則算法和提前停止算法相結合的BP神經網(wǎng)絡可以有效的防止單獨使用貝葉斯正則算法出現(xiàn)的過擬合現(xiàn)象,從而提高了網(wǎng)絡的泛化能力。在使用提前停止算法中過早的根據(jù)驗證樣本輸出誤差提前停止網(wǎng)絡的訓練可能會造成網(wǎng)絡訓練不充足造成網(wǎng)絡精度不夠,本文實證研究發(fā)現(xiàn)在驗證樣本集誤差連續(xù)上升6次的時候提前停止網(wǎng)絡訓練,網(wǎng)絡可以達到比較理想的效果；第五、通過改進的BP神經網(wǎng)絡與支持向量機對上證指數(shù)收盤價預測效果比較可知,支持向量機的預測效果要好于BP神經網(wǎng)絡。支持向量機預測有堅實的統(tǒng)計學習理論基礎,所以網(wǎng)絡預測效果比較好,表現(xiàn)比較穩(wěn)定。相對支持向量機,BP神經網(wǎng)絡的穩(wěn)定性不是很高,在選定網(wǎng)絡結構后網(wǎng)絡需要通過反復的訓練才可能達到比較理想的效果。本文可能的創(chuàng)新點：第一、通過實證證明使用貝葉斯正則算法的BP神經網(wǎng)絡在充分訓練的狀況下可能造成網(wǎng)絡過擬合,造成網(wǎng)絡泛化能力下降；第二、提出了貝葉斯正則算法與提前停止算法相結合的方法來改進BP神經網(wǎng)絡；第三、在使用提前停止算法時,提出當驗證樣本誤差連續(xù)上升6次時停止對網(wǎng)絡的訓練,此時得到的網(wǎng)絡性能比較好；第四、設計良好的BP神經網(wǎng)絡的預測誤差精度可以接近使用支持向量機模型的誤差精度。雖然本文在利用宏觀經濟指標結合人工智能方法預測上證指數(shù)的問題上做出了嘗試性研究,但鑒于目前人工智能方法還是一個比較新的學科,其在金融預測領域的應用也處在探索階段,并且本人的理論功底還不夠扎實,知識結構還不夠全面,所以在問題的研究中肯定會存在諸多不足之處,敬請各位專家學者批評指正,本人必定在以后的工作和學習中努力學習、積極探索。謝謝各位評審老師和答辯老師!

    With the rapid development of China’s market economy, more and more investors participate in the stock market, which they hope to share the fruits of economic growth through equity investment. But the stock market is a complex market, it is not only affected by domestic economic political and psychological factors, but also affected by international economic and political factors,at the same time these factors often affect each other. So through detailed analysis the stock market to establishment a stable and relatively accurate stock prediction model has important practical value for investors especially for small investors.In this paper, we use the Shanghai Composite Index as a representative of the stock market in China, using macroeconomic indicators and artificial intelligence methods to predict the trend of SZZS. The SZZS sample were mainly choose from samples that after the split share structure reform and the Macroeconomic variables selected monthly data. In this paper, two artificial intelligence methods mainly used are neural networks and support vector machine. In the neural network, BP neural network is one of the most widely used neural networks, it has good fitting ability and fault tolerance, although BP neural network has many advantage, it also has its own limitations, as local minimize.So in this paper, we use Bayesian regulation algorithms and early stopping methods to improve the generalization ability of BP neural network. After propose the improved BP neural network model, we then use the improved BP neural network and support vector machine to predict the SZZS and make a compare the results of two methods. The paper is organized as follows:Chapter one is the introduction section, mainly introduces the article background, significance, and research methods.Chapter two is the macroeconomic and stock market forecast, in this part, we first introduce the macroeconomic indicators, these indicators also will be used is in the after text.Then introduced the three methods currently used in the stock market prediction, the traditional method, the method of time series and artificial intelligence. The first method is more widely used in practice; the latter two methods are more used in theoretical research. Finally, introduce the relationship between macroeconomic and stock market, and research situation in domestic and foreign country.Chapter three mainly introduces neural network.First we introduce the theory of the Neural Network and their characteristics and classification. Then we focus on the BP neural network learning algorithm and point out that standard BP neural network convergence was slow, and the accuracy is not high. Then we use the Bayesian regularization algorithm combine with early-stopping algorithm to improve the standard BP neural network.Chapter four is about the support vector machine, this part start from the research status of the support vector machine, and then explains the structure risk minimization is better than empirical risk minimization through comparison between statistical learning theory and machine learning. After this we introduce the theory of support vector machine which based on statistical learning theory. At the end several kernel functions are introduced brieflyChapter five is the empirical research, on the basis of the foregoing theory, we use the proposed improved BP neural network and support vector machine to predict the monthly closing price on the SZZS.Through the Empirical research on the SZZS forecast, we draw the following conclusions:First, the most important part in BP neural network designing is choosing the number of the neurons in hidden layer, in this paper, we first choose the minimum and maximum number of the neurons in hidden layer, then we try every condition between minimum and maximum and calculate the error of the network output, at the end we select the network that have minimum error as our model.Second, although Bayesian regularization algorithm can improve the network generalization ability, we found that when training network, too much of training to the BP neural network that used Bayesian regularization algorithm may make the network too strong to have enough generalization ability.Third, through empirical research,we found that the method that using the Bayesian regularization algorithm combine with the early stopping algorithm can effectively prevent over fitting that appear in BP neural network separately using the Bayesian regularization algorithm during the BP neural network training. When using the early stopping method we found that we can stop network training when error of the validation sample continuous increase6times, the network can achieve the desired results.Fourth, empirical research that using improved BP neural network and support vector machine on SZZS closing price prediction shows that the support vector machine prediction is better than BP neural network. Support vector machines have a solid statistical learning theory, so the network prediction results were better, the performance is relatively stable. The performance of BP neural network is not very stable, if you want to get a better result you must try many times.This article may innovations:First, to prove through empirical research that adequate training to the BP neural network that only using Bayesian regularization algorithm may cause network over-fitting and decline of network generalization; Second using the method that combine Bayesian regularization algorithm with the early stopping algorithm to improve the BP neural network’s performance; Third, When using the early stopping method, we can stop network training when error of the validation sample continuous increase6time, the network can achieve the desired results. Fourth, well-designed BP neural network prediction error accuracy is close to the accuracy of support vector machine model error.

        基于宏觀經濟指標和人工智能方法的上證綜合指數(shù)預測

摘要4-8Abstract8-101. 前言13-162. 宏觀經濟與股市預測16-22    2.1 宏觀經濟16-17    2.2 股市預測17-20        2.2.1 傳統(tǒng)預測方法17-19        2.2.2 計量方法19-20        2.2.3 人工智能方法20    2.3 宏觀經濟與股市20-223. 神經網(wǎng)絡理論22-40    3.1 研究概述22-24        3.1.1 國外研究概述22-23        3.1.2 國內研究概述23-24    3.2 神經網(wǎng)絡理論介紹24-29        3.2.1 神經網(wǎng)絡簡介24-27        3.2.2 神經網(wǎng)絡的學習27        3.2.3 神經網(wǎng)絡特點27-29    3.3 BP神經網(wǎng)絡29-34        3.3.1 BP網(wǎng)絡學習算法的數(shù)學推導30-33        3.3.2 BP網(wǎng)絡性能分析33-34    3.4 BP神經網(wǎng)絡的改進方法34-39        3.4.1 傳統(tǒng)方法34-37        3.4.2 提高網(wǎng)絡泛化能力的BP神經網(wǎng)絡的改進方法37-39    3.5 本文提出的改進BP神經網(wǎng)絡的方法39-404. 支持向量機理論40-49    4.1 研究概述40-41        4.1.1 國外研究概述40-41        4.1.2 國內研究概述41    4.2 支持向量機核心知識41-49        4.2.1 傳統(tǒng)機器學習理論41-43        4.2.2 統(tǒng)計學習理論43-45        4.2.3 支持向量機45-495. 機器學習方法在上證指數(shù)預測中的實證研究49-75    5.1 實證環(huán)境簡介49-50    5.2 基于BP神經網(wǎng)絡的上證指數(shù)預測50-69        5.2.1 選取變量50-58        5.2.2 樣本預處理58-59        5.2.3 網(wǎng)絡結構設計59-64        5.2.4 網(wǎng)絡的訓練與預測64-69    5.3 基于支持向量機的上證指數(shù)預測69-73    5.4 BP神經網(wǎng)絡結果與支持向量機誤差結果比較73    5.5 實證結論73-756. 總結與展望75-77    6.1 總結75    6.2 展望75-77參考文獻77-79附錄79-82后記82-83致謝83

  本文關鍵詞：基于宏觀經濟指標和人工智能方法的上證綜合指數(shù)預測，由筆耕文化傳播整理發(fā)布。