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博碩士論文 etd-0620107-163418 詳細資訊
Title page for etd-0620107-163418
論文名稱
Title
利用技術分析比較不同股票市場之實證研究
The empirical study of applying Technical Analysis on DJI, HSI and Taiwan Stock Market
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
139
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2007-06-08
繳交日期
Date of Submission
2007-06-20
關鍵字
Keywords
資料探勘、動態貝氏網路、股票預測、技術分析
stock market prediction, technical analysis, dynamic bayesian network, data mining
統計
Statistics
本論文已被瀏覽 6069 次,被下載 7910
The thesis/dissertation has been browsed 6069 times, has been downloaded 7910 times.
中文摘要
股票市場在現今的資本市場上的角色日益重要,同時由於全球化時代的來臨,促進了全球資本的融通與流動的便利性。而股票市場的發達,引起了學術界的高度興趣與廣泛的研究,而股票預測也漸漸被各領域的研究者所重視,從而發展出兩個截然不同的股市預測方法:基本分析(fundamental analysis) 與技術分析 (technical analysis)。本研究的目的在於了解全球其中三個最有影響力的資本市場,包括: 台灣加權指數 (IDXWT)、香港恆生指數 (HSI) 與美國道瓊工業平均指數 (DJI)在使用技術分析的時候,對各股市有效的技術指標為何。本研究結合動態貝氏網路 (Dynamic Bayesian Network) 與技術分析技術,希望可以針對每一個市場找到一組或多組相對有效的技術指標,能夠指引技術分析投資者方向,從而解決現有大量技術指標造成投資人不容易選擇之問題,本研究基於王聖中“應用動態貝氏網路於投資決策支援"[Wan05] 之研究,透過預測不同的市場與加入不同的技術指標,望能得到更好的投資策略與投資報酬。 本研究總共分成三個實驗,實驗一為以台灣加權指數為我們的預測目標,實驗二以香港恆生指數而實驗三則把目標定在美國道瓊工業平均指數。每一個實驗皆以同樣的結構的動態貝氏網路,但不同的指標組合作為預測指標 (predictors)。 透過本研究之實驗結果發現,在台灣加權指數中,以十五天之移動平均(Moving Average) 作為趨勢判斷的情況下,共有兩組指標組合與動態貝氏網路組合能得到最好的投資報酬率,四年之總合投資報酬率為47.95%;當使用二十天之移動平均時,指標組合MTM ,MACD ,Support/ Resistant 之投資報酬率為60.21%;而香港恆生指數則以指標組合Support/ Resistant, MACD, KD 為最佳,四年之投資報酬率為60.06%;最後美國道瓊工業平均指數則以指標組合 MACD、MTM 與 RSI_20等五組組合最佳,四年之投資報酬皆為25.83%。以上之結果皆能打敗買入持有投資策略、相對強弱指標(RSI)與隨機指標(KD)。證明本方法加上其他指標後,能有效打敗市場普遍之操作方法,作為投資人的一個參考指標。
Abstract
Stock Market is always being the most important role in modern capital market. And Stock Market is becoming one the most popular investment tools these days. Because of the Globalization of capital markets, the spreading of capital becomes faster and easier. The development of capital markets evoke the interesting of scholars and the field of stock market prediction attract scholars and researchers from different background. There are two approaches of predicting stock market - fundamental analysis and technical analysis. The purpose of my work was to predict three stock markets in the world, namely Taiwan Weighted Index (IDXWT), Hong Kong Hang Seng Index (HSI) and Dow Jones Industrial Average (DJI) using technical analysis and Dynamic Bayesian Network (DBN).This thesis is based on Wang’s thesis [Wan05] “Investment Decision Support with Dynamic Bayesian Networks”. According to different characteristic of 3 stock markets, we divide 3 different markets into 3 experiments. For each market, we expect we can find the best indicators and trading signals. The first experiment involves Taiwan Weighted Index as our prediction target; the second one uses Hong Kong Hang Seng Index and the third experiment employs Dow Jones Industrial Average. As a result, Taiwan Stock market (both 15-day and 20-day Moving Average)can make higher returns than buy-and-hold, RSI_6 and KD. And we also have the same conclusion of Hang Seng Index and Dow Jones Industrial Average. The best return from 15-day MA and 20-day MA of Taiwan Stock market is 47.95% and 60.21%, respectively. Moreover, the best result of Hang Seng Index is 60.06% for 4 years and 25.83% for Dow Jones Industrial Average. All of the best results can make higher returns than each of their buy-and-hold, RSI_6 and KD. In the conclusion, we may say that this paper can provide a direction to investors while they are using these technical indicators to predict these particular stock markets.
目次 Table of Contents
Table of Contents Abstract....................................................................................i
中文摘要................................................................................iii
Table of Contents................................................................v
Lists of figures....................................................................vii
Lists of tables....................................................................viii
1. Introduction.....................................................................1
1.1 Background..................................................................1
1.2 Motivations and Objective..........................................3
1.3 Problem Description..................................................4
2. Literature Review..........................................................6
2.1 Technical Analysis.....................................................6
2.2 Overview of Efficient Market Hypothesis................8
2.3 Overview of Data mining technique in Stock market...............................................................................10
2.3.1 Artificial neural network........................................10
2.3.2 Generic Algorithms................................................11
2.3.3 Other data mining technique for predicting stock market................................................................................12
2.4 Overview of Bayesian Network and Dynamic Bayesian Network...........................................................13
2.4.1 Representation......................................................13
2.4.2 Inference and Learning........................................14
2.4.3 Representation of DBN........................................15
2.4.4 Inference and Learning in DBN..........................17
2.5 Related Work.............................................................18
3. Concept model and experimental design..............20
3.1 Wang’s concept model and results......................21
3.1.1 Signaling theory of Wang.....................................21
3.1.2 Concept model......................................................29
3.1.3 Summary of Wang’s work...................................30
3.2 Parameters of experiment......................................31
3.2.1 Stock markets study.............................................31
3.2.2 Indicators study.....................................................32
3.3 Introduction of experiments...................................33
4. Experimental design & results................................37
4.1 Experimental design...............................................38

4.1.1 Experiment 1 (Taiwan Stock market)................38

4.1.2 Experiment 2 (Hong Kong Stock market).........39

4.1.3 Experiment 3 (Dow Jones Industrial Average)............................................................................39

4.2 Experimental results...............................................40
4.2.1 Generalizing configurations...............................40
4.2.2 Indicator set configuration and combination...41
4.2.3 The results of experiment 1 (Taiwan Stock market)..............................................................................42
4.2.4 The results of experiment 2 (Hong Kong Stock market)..............................................................................79
4.2.5 The results of experiment 3 (Dow Jones Industrial Average)..........................................................91
4.3 Discussions of experimental results..................106 4.3.1 Dynamic Bayesian Network as a modern data mining tool for predicting stock market.....................106
4.3.2 Effect of different input indicators on prediction performance...................................................................106
4.3.3 Effect of span size of MA to determinate the trend of different stock markets............................................109
5. Conclusions and future research.........................110
5.1 Findings and conclusion......................................111
5.2 Contribution.............................................................112
5.3 Limitation and further research...........................112
6. References................................................................114
Appendix A Complete indicator set being used in this paper..................................................................................i
Appendix B ......................................................................iv
Appendix C.......................................................................v
Appendix D......................................................................vii
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