臺灣博碩士論文加值系統

本篇論文的主旨乃是在對Ready(2002)所稱之技術分析無效說提出質疑，此外希望提供一技術策略來使投資人能達到較佳的獲利。資料偏差為近幾年在財務研究領域上所遭遇到一個很重要的問題，因此本文在作法採用了迥異於先前財金學者所使用的技術分析法－「日本蠟燭」法、或稱之為「陰線陽線」法，資料樣本是採用自西元1934年至西元2000年長達逾五十年的美國道瓊工業指數股票日報酬。這樣的研究設計即可避免資料偏差所帶來的問題。另外，為了使結果的可信度增加，使用自西元1987至西元2000年期間英國的FTSE 100、日本的NIKKEI 225、以及台灣的台灣發行量加權股價指數日報酬來做頑強性檢定。研究結果除了否定Ready(2002)所稱之技術分析無效說，並指出最佳的超額報酬乃是利用所揀選出的五十種陰線陽線圖加上十天期動能的技術策略所獲得的報酬，並且此報酬在近幾年來皆優於單獨使用移動平均法或是基因演算法所獲得的報酬。本篇論文的主要貢獻在於，不僅從學術研究面重申技術分析的有效性，亦在實務投資面提供投資人另一個可以幫助他們增加投資報酬與降低市場風險的投資策略。

The purpose of this article is to question the statement made by Ready(2002) on uselessness of technical trading rules, and to provide a technical trading strategy that can be conducive to earning abnormal returns for the investors. This article adopts technical trading rules called “Japanese Candlesticks” - which is different from those used by previous financial academics - utilizing the predictability of daily stock returns for Dow Jones Industrial Average Index in the United States for more than fifty years from 1934 to 2000. It provides solution to the crucial issue: “data snooping bias”, which has been generally encountered during financial studies in recent years. Furthermore, FTSE 100 in the United Kingdom, NIKKEI 225 in Japan, and Taiwan Stock Exchange Capitalization Weighted Stock Index in Taiwan R.O.C from 1987 to 2000 are all used for robustness. In conclusion, the outcome of the research negates the theory on uselessness of technical trading rules presented by Ready(2002). And the best rules for gaining the excess returns after testing are using strategies with fifty selected candle patterns plus ten-day momentum. Also such returns gained are superior to those solely using the rule of moving average strategies and rule from genetic algorithms in the recent years. This article not only reaffirms the usefulness of technical trading rules academically, but practically provides the investors with an alternative strategy which can bring substantial value for them. The investors can even learn how candlestick charting may be used to improve returns and help reduce the risk on market.

Contents
Chapter 1 Introduction 1
1.1. Market efficiency and uselessness of technical trading rules 2
1.2. The rejection of market efficiency 3
1.3. Market inefficiency and usefulness of technical trading rules 5
1.4. Data snooping and market efficiency reaffirmation 5
1.5. The article in brief and market inefficiency reaffirmation 8
Chapter 2 Technical Trading Rules 10
2.1. A historical background to Japanese Candlesticks 10
2.2. Candlesticks charting 12
2.3. Basic Candlesticks 15
2.4. Candle pattern analysis 18
2.4.1. Reversal patterns 19
2.4.2. Continuation patterns 21
Chapter 3 Data and Fitness Measure 22
3.1. Data 22
3.2. Variables and returns measuring 28
Chapter 4 Results 32
4.1. Results for using pure Japanese Candlesticks 32
4.2. Results for Japanese Candlesticks with momentum 37
4.3. Results for comparing technical trading rules 43
Chapter 5 Conclusions 48
Appendix 50
References 60
List of Figures
Figure 1 Charts Difference between the Bar and the Candlesticks 14
Figure 2 Basic Candlesticks of the Japanese candlesticks 16
List of Tables
Table 1 Summary Literature Review between Market Efficiency
and Market Inefficiency 7
Table 2 76 Types Candle Patterns Classifying to Reversal and
Continuation Patterns 20
Table 3 Summary Statistics for Daily Returns 24
Table 4 A Summary of Excess Returns with Different One-way
Transaction Costs in Each Country 30
Table 5 A Summary of the Test Results for Trading Rules of
Japanese Candlesticks 34
Table 6 50 Types Candle Patterns Classifying to Reversal and
Continuation Patterns 38
Table 7 A Summary of the Test Results for Trading Rules of
Japanese Candlesticks Plus Momentum 40
Table 8 Comparison of the Test Results among Three Trading Rules 44

Alexander, S. S., 1961. Price movements in speculative markets: trends or random walks. Industrial Management Review 2, pp.7—26.
Alexander, S. S., 1964. Price movements in speculative markets: trends or random walks, no 2, in P. Cootner, ed.: The Random Character of Stock Market Prices, MIT Press, Cambridge, pp.338—372.
Allen, F., and R. Karjalainen, 1999. Using genetic algorithms to find technical trading rules. Journal of Financial Economics 51, pp. 245—271
Brock, W., J. Lakonishok, and B. LeBaron, 1992. Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance 47, pp. 1731-1764.
Chopra, Navin, Josef Lakonishok, and Jay R. Ritter, 1992. Performance measurement methodology and the question of whether stocks overreact. Journal of Financial Economics 31, pp. 235-268.
Cutler, D. M., J. M. Poterba, and L. H. Summers , 1991. Speculative dynamics. Review of Financial Studies 58, pp. 529-546.
De Bondt, W. F. M., and R. H. Thaler, 1985. Does the stock market overreact? Journal of Finance 40. pp. 793-805.
Fama, E. F. 1965. The behavior of stock-market prices. Journal of Business 38, pp. 34-105.
Fama, E. F., and M. E. Blume, 1966. Filter rules and stock-market trading. Journal of Business 39, Part 2: supplement on security prices, pp. 226-241.
Fama, E. F., and K. R. French, 1986. Permanent and temporary components of stock prices. Journal of Political Economy 98. pp. 246-274.
Fama, E. F., and K. R. French, 1988. Permanent and temporary components of stock prices. Journal of Political Economy 96. pp. 246-273.
French, K. R., and R. Roll, 1986. Stock return variances: The arrival of information and the reaction of traders. Journal of Financial Economics 17. pp. 5-26.
James, F. E., 1968. Monthly moving averages－an effective investment tool? Journal of Financial and Quantitative Analysis 3, Special issue: random walk hypothesis, pp. 315-326.
Jegadeesh, N., 1990. Evidence of predictable behavior of securities returns. Journal of Finance 45. pp. 881-898.
Jegadeesh, N., and S. Titman, 1993. Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance 48. pp. 65-91.
Jensen, M. C., and G. A. Benington, 1970. Random walks and technical theories: some additional evidence. Journal of Finance 25, Papers and proceedings of the twenty-eighth annual meeting of the American Finance Association New York, N.Y. December, 28-30, 1969, pp. 469-482.
Lehmann, Bruce N., 1990. Fads, martingales, and market efficiency. Quarterly Journal of Economics 105. pp 1-28.
Levy, R. A., 1967. Relative strength as a criterion for investment selection. Journal of Finance 22, pp. 595-610.
Lo, Andrew W., and Craig A. MacKinlay, 1990a. When are the contrarian profits due to stock market overreaction? Review of Financial Studies 3. pp. 175-206.
Murphy, John J, 1998. Technical Analysis of the Financial Markets. New York :New York Institute of Finance. pp. 297-317.
Nison, Steve, 1991. Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Technique of the Far East. New York :New York Institute of Finance.
Poterba, J. M., and L. H. Summers, 1988. Mean reversion in stock prices: Evidence and implications. Journal of Financial Economics 22. pp. 27-59.
Ready, M. J., 2002. Profits from technical trading rules. Financial Management, pp. 43-61.
Rosenberg, B., K. Reid, and R. Lanstein, 1985. Persuasive evidence of market inefficiency. Journal of portfolio management 11, pp. 9-16.
Sullivan, R., A. Timmermann, and H. White, 1999. Data-snooping, technical trading rule performance, and the bootstrap. Journal of Finance 54, pp. 1647-1691.
Sweeney, R. J., 1988. Some New Filter Rule Tests: Methods and Results. Journal of Financial and Quantitative Analysis 23, pp. 285-300.
Van Horne, J. C., and G.. C. Parker, 1967. The Random Walk Theory: An Empirical Test. Financial Analysts Journal, pp. 87-92.