成功大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	林永哲 Lin, Yung-Che Eric
論文名稱：	利用逐步預測力優劣檢定探討技術分析在期貨市場之績效持續性 Examining the Performance Persistence of Technical Analysis in Futures Markets with the SSPA Test
指導教授：	顏盟峯 Yen, Meng-Feng
學位類別：	碩士 Master
系所名稱：	管理學院 - 會計學系 Department of Accountancy
論文出版年：	2011
畢業學年度：	99
語文別：	英文
論文頁數：	57
中文關鍵詞：	技術分析、交易規則、資料探勘誤差、逐步預測力優劣檢定、定態拔靴
外文關鍵詞：	technical analysis, trading rule, data snooping bias, SSPA test, stationary bootstrap
相關次數：	點閱：110 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

為檢驗技術分析其績效持續性，本篇研究採用大量交易規則（20,970條）應用到五個期貨市場，並利用逐步預測力優劣檢定分別於六個有部分重複的樣本期間內作探討，同時將交易成本及保證金都考慮在內以求貼近現實交易環境。透過將樣本期間內依三年、一年半、一年和六個月做滾動，重複檢驗最近期的優質和劣質交易規則，我們追蹤所挑選出來的規則在樣本外期間的表現。經由挑選樣本期間內相較於無風險利率顯著較為優質和劣質的交易規則，並依循優質交易規則在樣本期間外的訊號和劣質交易規則在樣本期間外的反向訊號從事交易行為。逐步預測力優劣檢定能在五個市場的所有樣本內期間找出眾多顯著為劣的交易規則，僅在Euro FX期貨市場少數樣本期間內發現極少的顯著優質交易規則。逐步預測力優劣檢定係用來解決因使用同一資料來聯合檢定眾多模型時所產生之資料探勘誤差，在其定態拔靴過程中，本篇研究同時採用四個參數值，並針對1%、5%和10%的顯著水準進行全盤式的考量。

To examine the superior performance persistence of technical analysis, we apply a large universe of 20,970 technical trading rules to six overlapping in-sample periods in five futures markets using the stepwise test for superior predictive ability (SSPA test). By rolling over the in-sample window every three-year, one-and-half-year, one-year, and six-month, we analyze the performance of these designated technical trading rules in the out-of-sample periods. The transaction cost and the margin are taken into account for a practical manner. We determine significantly outperforming and underperforming trading rules compared to the risk-free rate in each in-sample period, and utilize trading signals (from outperformers) and contrarious trading signals (from underperformers) to conduct trading activity in the out-of-sample periods. The SSPA test successfully identifies numerous underperforming trading rules for all of five futures markets and a few outperforming trading rules for the Euro FX futures market. The SSPA test is adopted to resolve the common concern of data snooping bias raised from applying the same data set to numerous models (technical trading rules). The four values of smooth parameter we conduct for the geometric distribution for the stationary bootstrap block size of the SSPA test and all of 1%, 5%, and 10% significance levels we analyze make our study more complete.

CONTENTS
CHAPTER 1 INTRODUCTION	1
1.1 RESEARCH BACKGROUND	1
1.2 MOTIVATION AND OBJECTIVES	3
1.3 MAJOR FINDINGS	3
1.4 CONTRIBUTION	4
CHAPTER 2 LITERATURE REVIEW	5
2.1 PROFITABILITY OF TECHNICAL ANALYSIS SUFFERS FROM THE DATA SNOOPING BIAS	6
2.2. THE PROPOSITION OF THE BRC AND ITS APPLICATION TO THE DJIA	8
2.3 THE APPLICATION OF THE BRC TO THE FIFTEEN FUTURES MARKETS	8
2.4 THE PROPOSITION OF THE SPA TEST AND THE STEPWISE-RC	9
2.5 THE APPLICATION OF THE SPA TEST TO THE TEN FUTURES MARKETS	10
2.6 THE PROPOSITION OF THE SSPA TEST BASED ON THE SPA TEST	11
2.7 THE APPLICATION OF THE SPA TEST TO THE TWENTY FUTURES MARKETS	12
CHAPTER 3 DATA AND METHODOLOGY	13
3.1 TRADING RULES	13
3.1.1 Filter Rules (FR)	13
3.1.2 Support and Resistance Rules (SR)	14
3.1.3 Channel Breakout Rules (CB)	15
3.1.4 Moving Average Rules (MA)	15
3.1.5 On-Balance Volume Average Rules (OBV)	17
3.2 DATA	18
3.3 APPROACHES OF EVALUATION	24
3.3.1 The BRC	24
3.3.2 The SPA Test	25
3.3.3 The SRC	27
3.3.4 The SSPA test	27
3.4 COMPUTATION OF RETURNS	29
CHAPTER 4 EMPIRICAL RESULTS	32
CHAPTER 5 CONCLUSIONS	50
5.1 SUGGESTIONS	51
REFERENCES	55

 
TABLE OF CONTENTS
TABLE 1 THE SUMMARY STATISTIC FOR EACH OF THE FIVE FUTURES MARKETS	22
TABLE 2 DESCRIPTIVE STATISTICS OF DAILY EXCESSIVE RETURNS ON FUTURES MARKETS IN DIFFERENT PERIODS.	23
TABLE 3 PANEL A THE IDENTIFIED BEST/WORST RULES AND CRITICAL VALUES FOR EACH ROUND OF THE SSPA TEST.	35
TABLE 3 PANEL B THE IDENTIFIED BEST/WORST RULES AND CRITICAL VALUES FOR EACH ROUND OF THE SSPA TEST.	37
TABLE 3 PANEL C THE IDENTIFIED BEST/WORST RULES AND CRITICAL VALUES FOR EACH ROUND OF THE SSPA TEST.	39
TABLE 3 PANEL D TABLE 3 PANEL D THE IDENTIFIED BEST/WORST RULES AND CRITICAL VALUES FOR EACH ROUND OF THE SSPA TEST.	41
TABLE 3 PANEL E THE IDENTIFIED BEST/WORST RULES AND CRITICAL VALUES FOR EACH ROUND OF THE SSPA TEST.	43
TABLE 4 PANEL A THE EXCESSIVE RETURNS IN OUT-OF-SAMPLE PERIODFROM THE EQUAL-WEIGHTS PORTFOLIO CONSISTS OF IDENTIFIED BEST/WORST RULES FROM IN-SAMPLE PERIOD.	45
TABLE 4 PANEL B THE EXCESSIVE RETURNS IN OUT-OF-SAMPLE PERIODFROM THE EQUAL-WEIGHTS PORTFOLIO CONSISTS OF IDENTIFIED BEST/WORST RULES FROM IN-SAMPLE PERIOD.	46
TABLE 4 PANEL C THE EXCESSIVE RETURNS IN OUT-OF-SAMPLE PERIODFROM THE EQUAL-WEIGHTS PORTFOLIO CONSISTS OF IDENTIFIED BEST/WORST RULES FROM IN-SAMPLE PERIOD.	47
TABLE 4 PANEL D THE EXCESSIVE RETURNS IN OUT-OF-SAMPLE PERIODFROM THE EQUAL-WEIGHTS PORTFOLIO CONSISTS OF IDENTIFIED BEST/WORST RULES FROM IN-SAMPLE PERIOD.	49
APPENDIX A PARAMETERS OF TECHNICAL TRADING RULES	52
APPENDIX B THE IN-SAMPLE AND OUT-OF-SAMPLE PERIODS FOR FUTURES MARKETS.	53


                                    

Alexander, S. S., 1961. Price movements in speculative markets: Trends or random walks, Industrial Management Review 2, 7-26.
Blume, L., D. Easley, and M. O'Hara, 1994. Market Statistics and Technical Analysis: The Role of Volume, The Journal of Finance 49, 153-181.
Brock, W., J. Lakonishok, and B. LeBaron, 1992. Simple Technical Trading Rules and the Stochastic Properties of Stock Returns, The Journal of Finance 47, 1731-1764.
Chang, P. H. K., and C. L. Osler., 1999. Methodological Madness:Technical Analysis and the Irrationality of Exchange-Rate Forecasts, Economic Journal 109, 636-661.
Fama, E. F., 1970. Efficient capital markets: a review of theory and empirical work, The Journal of Finance 25, 383-417.
Fama, E. F., and M. E. Blume, 1966. Filter Rules and Stock-Market Trading, The Journal of Business 39, 226-241.
Hamilton, W. P., 1922. The Stock Market Barometer (Harper and Brothers Publishers, London).
Hansen, P. R., 2005. A Test for Superior Predictive Ability, Journal of Business and Economic Statistics 23, 365-380.
Hansen, P. R., and A. Lunde, 2005. A forecast comparison of volatility models: does anything beat a GARCH (1,1)?, Journal of Applied Econometrics 20, 873-889.
Hsu, P.-H., Y.-C. Hsu, and C.-M. Kuan, 2010. Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias, Journal of Empirical Finance 17, 471-484.
Hsu, P.-H., and C.-M. Kuan, 2005. Reexamining the Profitability of Technical Analysis with Data Snooping Checks, Journal of Financial Econometrics 3, 606-628.
Huang, W.-L., 2011. A Study of Predictability of Technical Trading Rules in Global Futures Markets, master thesis, NCKU
Irwin, S. H., C. R. Zulauf, M. E. Gerlow, and J. N. Tinker, 1997. A performance comparison of a technical trading system with ARIMA models for soybean complex prices, Advances in Investment Analysis and Portfolio Management 4, 193–203.
Jensen, M. C., and G. A. Benington, 1970. Random Walks and Technical Theories: Some Additional Evidence, The Journal of Finance 25, 469-482.
Lesmond, D. A., J. P. Ogden, and C. A. Trzcinka, 1999. A new estimate of transaction costs, Review of Financial Studies 12, 1113-1141.
Lesmond, D. A., M. J. Schill, and C. Zhou, 2004. The illusory nature of momentum profits, Journal of Financial Economics 71, 349-380.
Lin, W. -C., 2010. Examining the Performance of Technical Analysis in Futures Markets with the SSPA Test and the BRC, master thesis, NCKU
Lo, A. W., 2004. The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective, Journal of Portfolio Management 30, 15-29.
Lo, A. W., and A. C. MacKinlay, 1990. When are Contrarian Profits Due to Stock Market Overreaction?, The Review of Financial Studies 3, 175-205.
Locke, P. R., and P. C. Venkatesh, 1997. Futures market transactions costs., Journal of Futures Markets 172, 229–245.
Lukac, L. P., B. W. Brorsen, and S. H. Irwin, 1986. A Comparison of Twelve Technical Trading Systems with Market Efficiency Implications, Station bulletin - Dept. of Agricultural Economics, Purdue University, Agricultural Experiment Station 495, 71.
Lukac, L. P., B. W. Brorsen, and S. H. Irwin, 1988. A test of futures market disequilibrium using twelve different technical trading systems, Applied Economics 20, 623-639.
Marshall, B. R., R. H. Cahan, and J. M. Cahan, 2008. Can commodity futures be profitably traded with quantitative market timing strategies?, Journal of Banking and Finance 32, 1810-1819.
Newey, C. J., P. A. Weller, and J. M. Ulrich, 2009. The adaptive markets hypothesis: Evidence from the foreign exchange market, Journal of Financial and Quantitative Analysis 44, 467-488.
Newey, W. K., and K. D. West, 1987. A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica 55, 703-708.
Olson, D., 2004. Have trading rule profits in the currency markets declined over time?, Journal of Banking and Finance 28, 85-105.
Politis, D. N., and J. P. Romano, 1994. The Stationary Bootstrap, Journal of the American Statistical Association 89, 1303-1313.
Romano, J. P., and M. Wolf, 2005. Stepwise Multiple Testing as Formalized Data Snooping, Econometrica 73, 1237-1282.
Sullivan, R., A. Timmermann, and H. White, 1999. Data-Snooping, Technical Trading Rule Performance, and the Bootstrap, The Journal of Finance 54, 1647-1691.
Sweeney, R. J., 1986. Beating the Foreign Exchange Market, The Journal of Finance 41, 163-182.
Sweeney, R. J., 1988. Some new filter rule tests: Methods and results, ournal of Financial and Quantitative Analysis 23, 285-300.
Taylor, M. P., and H. Allen, 1992. The use of technical analysis in the foreign exchange market, Journal of International Money and Finance 11, 304-314.
Wang, C., and M. Yu, 2004. Trading activity and price reversals in futures markets, Journal of Banking and Finance 28, 1337-1361.
White, H., 2000. A Reality Check for Data Snooping, Econometrica 68, 1097-1126.
Yen, M.-F , and Hsu, Y.-L., 2010. Profitability of Technical Analysis in Financial and Commodity Futures Markets - A Reality Check, Decision Support Systems 50, 128-139.

校內：2021-12-31公開
校外：不公開電子論文尚未授權公開，紙本請查館藏目錄

簡易檢索 / 詳目顯示

相關論文