臺灣股票市場技術指標之研究─不同頻率資料績效比較

The Study on the Technical Indicator of Taiwan Stock Market-Using Different Data Frequencies

10.29724/TMR.201107.0006

陳淑玲(Shu-Ling Chen)；吳安琪(An-Chi Wu)；費業勳(Yeh-Hsun Fey)

證券市場 ； 技術分析 ； 移動平均線 ； 隨機指標 ； 雙指標組合 ； 資料頻率 ； Stock Market ； Technical Analysis ； Moving Average ； Stochastic Oscillator ； Dual-Indicators ； Frequency

東海管理評論

12卷1S期（2011 / 07 / 01）

187 - 225

繁體中文

本研究針對日、週、月及日內頻率台灣加權股價指數資料，在考量交易成本下，利用不同長、短週期的移動平均線（Moving Average, MA）與隨機指標（Stochastic Oscillator, KD）配置，其包含短線操作（5日、5週）、季報效應（60日、13週、3月）、半年報效應（120日、26週、6月）和年報效應（240日、52週、12月），探討台股市場獲利績效表現。實證結果顯示，以移動平均線作為買賣交易策略時，不論於日、週及月頻資料，均以季線型移動平均線發出的買賣訊號獲得較高的投資報酬率。本研究進一步以MA及KD組成雙指標組合，投資報酬則相對為佳，其中又以60日MA與9日KD組合績效在所有技術分析指標中獲利績效最佳。同時考量市場歷年重大事件造成之多、空時期，60日MA與9日KD組合績效不論在多頭或空頭均可獲得穩健的獲利結果。最後，針對日內資料再作驗證，仍以60根分時MA與9根分時KD雙指標操作績效最佳，再次印證本文雙指標投資績效的優越性。總合本文結果，印證技術指標可獲超額報酬，運用雙指標判斷可獲得較佳績效，說明投資者可參考不同技術指標之頻率組合而獲得績效較佳之買賣時點。

In this paper, we employ daily, weekly, monthly and intra-day data of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) with application of the Moving Average Line (MA) and Stochastic Oscillator (KD) to analyze differences in investment performances under particular technical indicator. The transaction costs are taken into consideration in our research and the following Moving Average Lines are applied: 5 days (5 weeks)-MA, 60 days(13 weeks, 3 months)-MA, 120 days(26 weeks, 6 months)-MA, 240 days(52 weeks, 12 months)-MA. These MA Lines represent the short-term trading, quarterly report effect, semi-annual report effect, and annual report effect respectively. The results show that while using MA as short-term trading strategy, the quarterly characterized indicators cause better total and average returns for each data frequency. By combining both MA and KD as dual-indicators, we observe that the indicators composed of quarterly MA and KD with daily frequency obtain the highest return. Moreover, the result indicates that both quarterly MA and the dual-indicators perform well when taking bullish and bearish market conditions into account. We also find that the dual-indicators with intra-day frequency have the best market returns. Comparing with low-frequency data, the indicators with high frequency do achieve better annual return. In conclusion, we examine that trading strategies for getting excess returns can be implemented by using technical indicators; and superior investment performances are realizable when adopting dual-indicators as referrals. Our findings confirm that analysis on both technical indicators and data frequencies suggests investors in searching for better trading timing.

1. 林金賢、李淑惠(2006)。技術指標與股價漲跌幅非線性關係之獲利能力之探討。台灣管理學刊，6(1)，129-156。
   連結：
2. 許溪南、何怡滿、張瓊如(2012)。KD 與MA 技術指標在避險時機之應用：以台指選擇權為例。輔仁管理評論，19(1)，27-46。
   連結：
3. 許溪南、何怡滿、劉玉琦(2009)。權變避險模式在台灣股市之應用。台灣管理學刊，9(1)，23-46。
   連結：
4. 許溪南、何怡滿、劉泰山(2011)。KD 及MACD 在避險時機之應用：以台指期貨避險為􈓯。東吳經濟商學學報，72，109-138。
   連結：
5. Marshall, B.R., Cahan, R.C., and Cahan, J.M. (2010). Technical Analysis around The World. working paper, Massey University New Zealand.
6. Bessembinder, H.,Chan, K.(1995).The Profitability of Technical Trading Rules in the Asian Stock Markets.Pacific-Basin Finance Journal,257-284.
7. Brock, W.,Lakonishok, J.,LeBaron, B.(1992).Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.The Journal of Finance,47,1731-1764.
8. Dickey, D.A.,Fuller, W.A.(1981).Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root.Econometrica,49,1057-1072.
9. Edwards, R.D.,Magee, J.,Bassetti, W.H.C.(2001).Technical Analysis of Stock Trend.New York:St Lucie Press.
10. Eng, W.F.(1988).The Technical Analysis of Stock, Options and Futures: Advanced Trading Systems and Technioues.Chicago:Probus Publishing.
11. Engle, R.F.,Granger, C.W.J.(1987).Co-Integration and Error Correction: Representation, Estimation and Testing.Econometrica,55,1-87.
12. Fama, E.F.(1970).Efficient Capital Markets: A Review of Theory and Empirical Work.Journal of Finance,25,383-417.
13. Fifield, S.G. M.,Power, D.M.,Knipe, D.G.S.(2008).Performance of Moving Average Rules in Emerging Stock Markets.Applied Financial Economics,18(19),1515-1532.
14. Gencay, R.,Stengos, T.(1998).Moving Average Rules, Volume and Predictability of Security Return with Feed Forward Network.Journal of Forecasting,17(1),401-414.
15. Granger, C.W.J.,Newbold, P.(1974).Spurious Regressions in Econometrics.Journal of Econometrics,2(2),111-120.
16. Gunasekarage, A.,Power, D.M.(2001).The Profitability of Moving Average Trading Rules in South Asian Stock Markets.Emerging Markets Review,2,17-33.
17. Hsu, P.H.,Hsu, Y.C.,Kuan, C.H.(2010).Testing the Predictive Ability of Technical Analysis Using a New Stepwise Test without Data Snooping Bias.Journal of Empirical Finance,17(3),471-484.
18. Hsu, P.H.,Kuan, C.M.(2005).Reexamining the Profitability of Technical Analysis with Data Snooping Checks.Journal of Financial Econometrics,3(4),606-628.
19. Kendell, M.G.(1953).The Analysis of Economic Time-Series-Part I: Prices.Journal of the Royal Statistical Society. Series A (General),116(1),11-34.
20. Lai, M.M.,Lau, S.H.(2006).The Profitability of the Simple Moving Averages and Trading Range Breakout in the Asian Stock Markets.Journal of Asian Economics,17(1),144-170.
21. Lento, C.(2008).Forecasting Security Returns with Simple Moving Averages.International Business and Economics Research Journal,7(11)
22. Ling, P.W.(2011).Development of Stock Trading Optimization Analysis System Using Moving Average Method.Applied Mechanics and Materials,66-68,1703-1707.
23. Marshall, B.R.,Cahan, R.H.(2005).Is Technical Analysis Profitable on A Stock Market which has Characteristics that Suggest it may be Inefficient?.Research in International Business and Finance,19,384-398.
24. McKenzie, M.D.(2007).Technical Trading Rules in Emerging Markets and the 1997 Asian Currency Crises.Emerging Markets Finance and Trade,43(4),46-73.
25. Murphy, J.J.(1986).Technical Analysis of the Futures Markets. New York Institute of Finance.Englewood Cliffs:Prentice Hall.
26. Papathanasiou, S.,Samitas, A.(2010).Profits from Technical Trading Rules: The Case of Cyprus Stock Exchange.Journal of Money, Investment and Banking,13,35-43.
27. Ratner, M.,Leal, R.(1999).Tests of Technical Trading Strategies in the Emerging Equity markets of Latin America and Asia.Journal of Banking and Finance,23(12),1887-1905.
28. Wang, J.L.,Chan, S.H.(2009).Trading Rule Discovery in the US Stock Market: An Empirical Study.Expert Systems with Applications,36(3),5450-5455.
29. Zhu, Y.,Zhou, G.(2009).Technical analysis: An Asset Allocation Perspective on the Use of Moving Averages.Journal of Financial Economics,92,519-544.
30. 王邵佑(2000)。碩士論文(碩士論文)。國立台北大學企業管理研究所。
31. 林天運(2007)。碩士論文(碩士論文)。國立成功大學國際企業研究所。
32. 黃彥聖(1995)。移動平均法的投資績效。管理評論，40(1)，47-68。
33. 趙永昱(2002)。碩士論文(碩士論文)。國立中山大學財務管理研究所。
34. 樓禎祺、何培基(2003)。股價移動平均線之理論與實證─以台灣股市模擬投資操作為例。育達研究叢刊，5、6，27-52。

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