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題名 探究市場波動度資訊在技術分析中的價值
The Informational Role of Market Volatility in Technical Analysis
作者 莊珮玲
貢獻者 郭炳伸<br>林信助
莊珮玲
關鍵詞 市場波動度
技術分析
Market Volatility
Technical Analysis
日期 2012
上傳時間 1-May-2014 15:20:02 (UTC+8)
摘要 The theme of this thesis seeks to explore the value of information of market volatility in technical analysis. In the literature, the technical analysis primarily involves the use of the information of past prices and/or volumes to predict future price movements in financial assets, yet little is known about whether there exists other information that is valuable to improve the predictability of technical analysis. The possible relation between volatility and profitability of technical analysis mentioned in some studies drives us to investigate whether the information of market volatility within the framework of the technical analysis can improve our understanding toward the market price movements.

1.Does Market Volatility Improve Profitability of Technical Analysis?

This chapter first studies whether the information of market volatility is capable of yielding higher profitability. Specifically, we compare the performance of a Variable Moving Average (VMA) rule, in which market volatility plays an important role, with five other popular trading rules. When applied to the Dow Jones Industrial Average index, the Superior Predictive Ability test by Hansen (2005) shows that the VMA rule outperforms other rules with higher profitability. Second, to further investigate the origin of superior profitability, we conduct the test of Cumby and Modest (1987), and find that the VMA rule does enjoy better market timing ability. Third, we explore whether the VMA rule has differential performance in different market conditions. The results show that the market timing ability of the best VMA rule is asymmetric in bull and bear markets, and the best VMA rule outperforms the Moving Average (MA) rule and the Momentum Strategies in Volume (MSV) rule both in bull and bear markets, particular in bear markets.

2.Exploring the Information Content of Market Volatility in Technical Analysis

In this chapter, we study how market volatility information affects trading signals generated from the technical analysis. Through the use of the time-varying-transition-probability (TVTP) Markov-switching model, we find that the increase of market volatility leads to a higher probability of signals generated from the VMA rule. Moreover, such an effect is asymmetric in bull and bear markets. This chapter also reexamines the value of market volatility in the simple MA rule by comparing the trading signals produced from the Fixed-transition-probability (FTP) and the TVTP Markov-switching model. Our results show that the time to enter or exit the market affected by market volatility information will benefit investors with higher profit.
參考文獻 Alexander, S. S. (1961), “Price Movements in Speculative Market: Trends or Random Walks,” Industrial Management Review, 2, 7-26.
Allen, H. and M. P. Taylor (1992), “The Use of Technical Analysis in the Foreign Exchange Market,” Journal of International Money and Finance, 113, 301-314.
Billingsley, R. and D. Chance (1996), “Benefits and Limitations of Diversification Among Commodity Trading Advisors,” Journal of Portfolio Management, 23, 65-80.
Blume, L., D. Easley and M. O`Hara (1994), “Market Statistics and Technical Analysis: the Role of Volume,” Journal of Finance, 49, 151-181.
Bollen, N. P. B. and J. A. Busse (2001), “On the Timing Ability of Mutual Fund Managers,” Journal of Finance, 56(3), 1075-1094.
Brock, W., J. Lakonishok and B. LeBaron (1992), “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” Journal of Finance, 47, 1731-1764.
Brown, D. P. and R. H. Jennings (1989), “On Technical Analysis,” Review of Financial Studies, 2, 527-551.
Chan, L. K. C., N. Jegadeesh and J. Lakonishok (1996), “Momentum Strategies,” Journal of Finance, 51, 1681-1713.
Chan, L. K. C., J. Karceski and J. Lakonishok (1998), “The Risk and Return from Factors,” Journal of Financial and Quantitative Analysis, 33, 159-188.
Chande, T. S. (1992), “Adapting Moving Averages To Market Volatility,” Stocks and Commodities,10(3), 108-114.
Chande, T. S. and S. Kroll (1994), “The New Technical Trader : Boost Your Profit by Plugging into the Latest Indicators,” New York : John Wiley & Sons.
Chang, P. H. K. and C.L. Osler (1999), “Methodical Madness: Technical Analysis and the Irrationality of Exchange-rate Forecasts,” Economic Journal, 109, 636-661.
Chen, S. S. (2007), “Does Monetary Policy Have Asymmetric Effects on Stock Returns?,” Journal of Money, Credit and Banking, 39(2-3), 667-688.
Clyde,W.C. and C.L. Osler (1997), “Charting: Chaos Theory in Disguise?,” Journal of Futures Markets, 17, 489-514.
Covel, M. W. (2005), “Trend Following: How Great Traders Make Millions in Up or Down Markets,” Prentice-Hall, New York, New York.
Cumby, R.E. and D.M. Modest (1987), “Testing for Market Timing Ability: A Framework for Forecast Evaluation,” Journal of Financial Economics, 19, 169-189.
Daniel, K., M. Grinblatt, S. Titman and R. Wermers (1997), “Measuring Mutual Fund Performance with Characteristic-based Benchmarks,” Journal of Finance, 52, 1035-1058.
Day, T. E. and P. Wang (2002), “Dividends, Nonsynchronous Prices, and the Returns from Trading the Dow Jones Industrial Average,” Journal of Empirical Finance, 9, 431-454.
De Long, J. B., A. Shleifer, L. H. Summers and R. J. Waldmann (1990a), “Noise Trader Risk in Financial Markets,” Journal of Political Economy, 98, 703-738.
De Long, J. B., A. Shleifer, L. H. Summers and R. J. Waldmann (1990b), “Positive Feedback Investment Strategies and Destabilizing Rational Speculation,” Journal of Finance, 45, 379-395.
Diebold, F. X., J.-H. Lee and G. Weinbach (1994), “Regime Switching with Time-Varying Transition Probabilities,” in C. Hargreaves (ed.), Nonstationary Time Series Analysis and Cointegration. (Advanced Texts in Econometrics, C.W.J. Granger and G. Mizon, eds.), 283-302. Oxford: Oxford University Press.
Dooley, M. P. and J. R. Shafer (1983), “Analysis of Short-run Exchange Rate Behavior: March 1973 to November 1981,” In D. Bigman and T. Taya (eds), Exchange Rate and Trade Instability: Causes, Consequences, and Remedies, 43-69, Cambridge, MA: Ballinger.
Fabozzi, F. J. and J. C. Francis (1979), “Mutual Fund Systematic Risk for Bull and Bear Markets: An Empirical Examination,” Journal of Finance, 34(5), 1243-1250.
Filardo, A. J. (1994), “Business-Cycle Phases and Their Transitional Dynamics,” Journal of Business and Economic Statistics, 12(3), 299-308.
Froot, K. A., D. S. Scharfstein and J. C. Stein (1992), “Herd on the Street: Informational Inefficiencies in a Market with Short-term Speculation,” Journal of Finance, 47, 1461-1484.
Gehrig, T. and L. Menkhoff (2004), “The Use of Flow Analysis in Foreign Exchange: Exploratory Evidence,” Journal of International Money and Finance, 23, 573-594.
Gehrig, T. and L. Menkhoff (2006), “Extended Evidence on the Use of Technical Analysis in Foreign Exchange,” International Journal of Finance and Economics, 11(4), 327-338.
Gencay, R. (1998), “The Predictability of Security Returns with Simple Technical Trading Rules,” Journal of Empirical Finance, 5, 347-359.
Graham, J. R. and C. R. Harvey (1996), “Market Timing Ability and Volatility Implied in Investment Newsletters’ Asset Allocation Recommendations,” Journal of Financial Economics, 42, 397-421.
Greer, T. V., B. W. Brorsen and S. M. Liu (1992), “Slippage Costs in Order Execution for a Public Futures Fund,” Review of Agricultural Economics, 14, 281-288.
Grundy, B. D. and M. McNichols (1989), “Trade and the Revelation of Information Through Prices and Direct Disclosure,” Review of Financial Studies, 2, 495-526.
Guidolin, M. and A. Timmermann (2005), “Economic Implications of Bull and Bear Regimes in UK Stock and Bond Returns?,” The Economic Journal, 115(500), 111-143.
Hamilton, J. D. (1989), “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, 57, 357-384.
Hamilton, J. D. and G. Lin (1996), “Stock Market Volatility and the Business Cycle,” Journal of Applied Econometrics, 11(5), 573-593.
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Kidd, W. V. and B. W. Brorsen (2004), “Why Have the Returns to Technical Analysis Decreased?,” Journal of Economics and Business, 56, 159-176.
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描述 博士
國立政治大學
國際經營與貿易研究所
95351503
101
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0095351503
資料類型 thesis
dc.contributor.advisor 郭炳伸<br>林信助zh_TW
dc.contributor.author (Authors) 莊珮玲zh_TW
dc.creator (作者) 莊珮玲zh_TW
dc.date (日期) 2012en_US
dc.date.accessioned 1-May-2014 15:20:02 (UTC+8)-
dc.date.available 1-May-2014 15:20:02 (UTC+8)-
dc.date.issued (上傳時間) 1-May-2014 15:20:02 (UTC+8)-
dc.identifier (Other Identifiers) G0095351503en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/65767-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際經營與貿易研究所zh_TW
dc.description (描述) 95351503zh_TW
dc.description (描述) 101zh_TW
dc.description.abstract (摘要) The theme of this thesis seeks to explore the value of information of market volatility in technical analysis. In the literature, the technical analysis primarily involves the use of the information of past prices and/or volumes to predict future price movements in financial assets, yet little is known about whether there exists other information that is valuable to improve the predictability of technical analysis. The possible relation between volatility and profitability of technical analysis mentioned in some studies drives us to investigate whether the information of market volatility within the framework of the technical analysis can improve our understanding toward the market price movements.

1.Does Market Volatility Improve Profitability of Technical Analysis?

This chapter first studies whether the information of market volatility is capable of yielding higher profitability. Specifically, we compare the performance of a Variable Moving Average (VMA) rule, in which market volatility plays an important role, with five other popular trading rules. When applied to the Dow Jones Industrial Average index, the Superior Predictive Ability test by Hansen (2005) shows that the VMA rule outperforms other rules with higher profitability. Second, to further investigate the origin of superior profitability, we conduct the test of Cumby and Modest (1987), and find that the VMA rule does enjoy better market timing ability. Third, we explore whether the VMA rule has differential performance in different market conditions. The results show that the market timing ability of the best VMA rule is asymmetric in bull and bear markets, and the best VMA rule outperforms the Moving Average (MA) rule and the Momentum Strategies in Volume (MSV) rule both in bull and bear markets, particular in bear markets.

2.Exploring the Information Content of Market Volatility in Technical Analysis

In this chapter, we study how market volatility information affects trading signals generated from the technical analysis. Through the use of the time-varying-transition-probability (TVTP) Markov-switching model, we find that the increase of market volatility leads to a higher probability of signals generated from the VMA rule. Moreover, such an effect is asymmetric in bull and bear markets. This chapter also reexamines the value of market volatility in the simple MA rule by comparing the trading signals produced from the Fixed-transition-probability (FTP) and the TVTP Markov-switching model. Our results show that the time to enter or exit the market affected by market volatility information will benefit investors with higher profit.		zh_TW
dc.description.tableofcontents 1 Introduction 1

2 Does Market Volatility Improve Profitability of Technical Analysis? 3
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Technical Trading Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.1 Static Trading Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.2 Dynamic Trading Rule . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1 Data Snooping Bias and the SPA test . . . . . . . . . . . . . . . . . . 10
2.4 Empirical Results on the Trading Rule Profitability . . . . . . . . . . . . . . . 12
2.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4.2 SPA Test Results: Full Sample and Sub-samples . . . . . . . . . . . . 12
2.5 Market Timing Ability Test . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.6 Is the Profitability of the Trading Rule Asymmetric in Different Market Conditions?
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.6.1 Market Timing Ability in Bull and Bear Market . . . . . . . . . . . . . 24
2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3 Exploring the Information Content of Market Volatility in Technical Analysis 32
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2 Moving Average Trading Systems . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.1 Market Volatility in Moving Averages . . . . . . . . . . . . . . . . . . 35
3.3 Trading Signals and Market Volatility Ratio . . . . . . . . . . . . . . . . . . . 37
3.3.1 The TVTP Markov-Switching Model . . . . . . . . . . . . . . . . . . 38
3.3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.3 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.4 The Value of Market Volatility Ratio in Simple Moving Average Rule . . . . . 51
3.4.1 Data and Estimation Results . . . . . . . . . . . . . . . . . . . . . . . 52
3.4.2 The Profitability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.4.3 Simple Analysis for Trades . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5 The Future Way of Exploring Explanations for Higher Profits Gained from Market Volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68		zh_TW
dc.format.extent 605307 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0095351503en_US
dc.subject (關鍵詞) 市場波動度zh_TW
dc.subject (關鍵詞) 技術分析zh_TW
dc.subject (關鍵詞) Market Volatilityen_US
dc.subject (關鍵詞) Technical Analysisen_US
dc.title (題名) 探究市場波動度資訊在技術分析中的價值zh_TW
dc.title (題名) The Informational Role of Market Volatility in Technical Analysisen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Alexander, S. S. (1961), “Price Movements in Speculative Market: Trends or Random Walks,” Industrial Management Review, 2, 7-26.
Allen, H. and M. P. Taylor (1992), “The Use of Technical Analysis in the Foreign Exchange Market,” Journal of International Money and Finance, 113, 301-314.
Billingsley, R. and D. Chance (1996), “Benefits and Limitations of Diversification Among Commodity Trading Advisors,” Journal of Portfolio Management, 23, 65-80.
Blume, L., D. Easley and M. O`Hara (1994), “Market Statistics and Technical Analysis: the Role of Volume,” Journal of Finance, 49, 151-181.
Bollen, N. P. B. and J. A. Busse (2001), “On the Timing Ability of Mutual Fund Managers,” Journal of Finance, 56(3), 1075-1094.
Brock, W., J. Lakonishok and B. LeBaron (1992), “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” Journal of Finance, 47, 1731-1764.
Brown, D. P. and R. H. Jennings (1989), “On Technical Analysis,” Review of Financial Studies, 2, 527-551.
Chan, L. K. C., N. Jegadeesh and J. Lakonishok (1996), “Momentum Strategies,” Journal of Finance, 51, 1681-1713.
Chan, L. K. C., J. Karceski and J. Lakonishok (1998), “The Risk and Return from Factors,” Journal of Financial and Quantitative Analysis, 33, 159-188.
Chande, T. S. (1992), “Adapting Moving Averages To Market Volatility,” Stocks and Commodities,10(3), 108-114.
Chande, T. S. and S. Kroll (1994), “The New Technical Trader : Boost Your Profit by Plugging into the Latest Indicators,” New York : John Wiley & Sons.
Chang, P. H. K. and C.L. Osler (1999), “Methodical Madness: Technical Analysis and the Irrationality of Exchange-rate Forecasts,” Economic Journal, 109, 636-661.
Chen, S. S. (2007), “Does Monetary Policy Have Asymmetric Effects on Stock Returns?,” Journal of Money, Credit and Banking, 39(2-3), 667-688.
Clyde,W.C. and C.L. Osler (1997), “Charting: Chaos Theory in Disguise?,” Journal of Futures Markets, 17, 489-514.
Covel, M. W. (2005), “Trend Following: How Great Traders Make Millions in Up or Down Markets,” Prentice-Hall, New York, New York.
Cumby, R.E. and D.M. Modest (1987), “Testing for Market Timing Ability: A Framework for Forecast Evaluation,” Journal of Financial Economics, 19, 169-189.
Daniel, K., M. Grinblatt, S. Titman and R. Wermers (1997), “Measuring Mutual Fund Performance with Characteristic-based Benchmarks,” Journal of Finance, 52, 1035-1058.
Day, T. E. and P. Wang (2002), “Dividends, Nonsynchronous Prices, and the Returns from Trading the Dow Jones Industrial Average,” Journal of Empirical Finance, 9, 431-454.
De Long, J. B., A. Shleifer, L. H. Summers and R. J. Waldmann (1990a), “Noise Trader Risk in Financial Markets,” Journal of Political Economy, 98, 703-738.
De Long, J. B., A. Shleifer, L. H. Summers and R. J. Waldmann (1990b), “Positive Feedback Investment Strategies and Destabilizing Rational Speculation,” Journal of Finance, 45, 379-395.
Diebold, F. X., J.-H. Lee and G. Weinbach (1994), “Regime Switching with Time-Varying Transition Probabilities,” in C. Hargreaves (ed.), Nonstationary Time Series Analysis and Cointegration. (Advanced Texts in Econometrics, C.W.J. Granger and G. Mizon, eds.), 283-302. Oxford: Oxford University Press.
Dooley, M. P. and J. R. Shafer (1983), “Analysis of Short-run Exchange Rate Behavior: March 1973 to November 1981,” In D. Bigman and T. Taya (eds), Exchange Rate and Trade Instability: Causes, Consequences, and Remedies, 43-69, Cambridge, MA: Ballinger.
Fabozzi, F. J. and J. C. Francis (1979), “Mutual Fund Systematic Risk for Bull and Bear Markets: An Empirical Examination,” Journal of Finance, 34(5), 1243-1250.
Filardo, A. J. (1994), “Business-Cycle Phases and Their Transitional Dynamics,” Journal of Business and Economic Statistics, 12(3), 299-308.
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