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The Informational Role of Market Volatility in Technical Analysis
|Issue Date: ||2014-05-01 15:20:02 (UTC+8)|
|Abstract: ||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.
|Reference: ||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.
Hansen, P. R. (2005), “A Test for Superior Predictive Ability,” Journal of Business and Economic Statistics, 23, 365-380.
Hardouvelis, G. A. and P. Theodossiou (2002), “The Asymmetric Relation Between Initial Margin Requirements and Stock Market Volatility Across Bull and Bear Markets,” Review of Financial Studies, 15(5), 1525-1559.
Hellwig, M. (1982), “Rational Expectations Equilibrium with Conditioning on Past Prices: a Mean-Variance Example,” Journal of Economic Theory, 26, 279-312.
Henriksson, R. D. (1984), “Market Timing and Mutual Fund Performance: An Empirical Investigation,” Journal of Business, 57, 73-96.
Hsu, P. H. and C. M. Kuan (2005), “Reexamining the Profitability of Technical Analysis with Data Snooping Checks,” Journal of Financial Econometrics, 3(4), 606-628.
Hutson, J. K. (1984), “Filter Price Data: Moving Averages versus Exponential Moving Averages,” Technical Analysis of Stocks & Commodities, 2(3), 102-103.
Jansen, D. W. and C. L. Tsai (2010), “Monetary Policy and Stock Returns: Financing Constraints and Asymmetries in Bull and Bear Markets,” Journal of Empirical Finance, 17(5), 981-990.
Kavajecz, K. A. and E. R. Odders-White (2004), “Technical Analysis and Liquidity Provision,” Review of Financial Studies, 17, 1043-1071.
Kidd, W. V. and B. W. Brorsen (2004), “Why Have the Returns to Technical Analysis Decreased?,” Journal of Economics and Business, 56, 159-176.
Kim, C. J. (1994), “Dynamic Linear Models with Markov-Switching,” Journal of Econometrics, 60(1-2), 1-22.
Kho, B. C. (1996), “Time-varying Risk Premia, Volatility, and Technical Trading Rule Profits: Evidence from Foreign Currency Futures Markets,” Journal of Financial Economics, 41, 249-290.
Kleiman, R. T., A. P. Sahu and J. H. Callaghan (1996), “The Risk-adjusted Performance of Investment Advisors: Empirical Evidence on Selectivity and Timing Abilities,” Journal of Economics and Finance, 20, 87-98.
Lakonishok, J. and S. Smidt (1988), “Are Seasonal Anomalies Real? A Ninety-Year Perspective,” Review of Finance Studies, 1, 403-425.
LeBaron, B. (1999), “Technical Trading Rule Profitability and Foreign Exchange Intervention,” Journal of International Economics, 49, 125-143.
Lee, C. and S. Rahman (1990), “Market Timing, Selectivity and Mutual Fund Performance: An Empirical Investigation,” Journal of Business, 63, 261-278.
Lo, A. W. and A. C. MacKinlay (1990), “Data-Snooping Biases in Tests of Financial Asset Pricing Models,” Review of Financial Studies, 3, 431-467.
Lo, A. W. and J. Hasanhodzic (2009), “The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis,” Bloomberg Press, New York.
Lui, Y. and D. Mole (1998), “The Use of Fundamental and Technical Analysis by Foreign Exchange Dealers: Hong Kong Evidence,” Journal of International Money and Finance, 17, 535-545.
Lukac, L. P. and B. W. Brorsen (1990), “A Comprehensive Test of Futures Market Disequilibrium,” Financial Review, 25, 593-622.
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.
Neely, C. J. and P. A.Weller (2001), “Technical Analysis and Central Bank Intervention,” Journal of International Money and Finance, 20, 949-970.
Neely, C. J., P. A. Weller and R. Dittmar (1997), “Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach,” Journal of Financial and Quantitative Analysis, 32, 405-426.
Neely, C. J. and P. A. Weller (1999), “Technical Trading Rules in the European Monetary System,” Journal of International Money and Finance, 18, 429-458.
Newey, W. K. and K. D. West (1987), “A Simple, Positive Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica, 55(3), 703-708.
Oberlechner, T. (2001), “Importance of Technical and Fundamental Analysis in the European Foreign Exchange Market,” International Journal of Finance and Economics, 6, 81-93.
Okunev, J. and D. White (2003), “Do Momentum-based Strategies Still Work in Foreign Currency Markets?,” Journal of Financial and Quantitative Analysis, 38, 425-447.
Osler, C. L. (2003), “Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis,” Journal of Finance, 58, 1791-1819.
Owen, A. L. and B. Palmer (2012), “Macroeconomic Conditions and Technical Trading Profitability in Foreign Exchange Markets,” Applied Economics Letters, 19, 1107-1110.
Pagan, A.R. and K.A. Sossounov (2003), “A Simple Framework for Analyzing Bull and Bear Markets,” Journal of Applied Econometrics, 18, 23-46.
Park, C.H. and S.H. Irwin (2007), “What Do We Know About the Profitability of Technical Analysis?,” Journal of Economic Surveys, 21(4),786-826.
Perez-Quiros, G. and A. Timmermann (2000), “Firm Size and Cyclical Variations in Stock Returns,” Journal of Finance, 55(3), 1229-1262.
Perez-Quiros, G. and A. Timmermann (2001), “Business Cycle Asymmetries in Stock Returns: Evidence from Higher Order Moments and Conditional Densities,” Journal of Econometrics, 103(1-2), 259-306.
Qi, M. and Y. Wu (2006), “Technical Trading-rule Profitability, Data Snooping, and Reality Check: Evidence from the Foreign Exchange Market,” Journal of Money, Credit and Banking, 38, 2135-2158.
Rouwenhorst, K. G. (1999), “Local Return Factors and Turnover in Emerging Stock Markets,” Journal of Finance, 54, 1439-1464.
Saacke, P. (2002), “Technical Analysis and the Effectiveness of Central Bank Intervention,” Journal of International Money and Finance, 21, 459-479.
Sapp, S. (2004), “Are All Central Bank Interventions Created Equal? An Empirical Investigation,” Journal of Banking and Finance, 28, 443-474.
Schmidt, A. B. (2002), “Why Technical Trading May Be Successful? A Lesson from the Agent based Modeling,” Physica A, 303, 185-188.
Schulmeister, S. (2008), “Profitability of Technical Stock Trading: Has It Moved from Daily to Intraday data?,” Review of Financial Economics, 1-12.
Shleifer, A. and L. H. Summers (1990), “The Noise Trader Approach to Finance,” Journal of Economic Perspectives, 4, 19-33.
Smidt, S. (1965), “Amateur Speculators,” Ithaca, NY: Graduate School of. Business and Public Administration, Cornell University.
Stengos, T. (1996), “Nonparametric Forecasts of Gold Rates of Return,” In W.A. Barnett, A.P. Kirman and M. Salmon (eds), Nonlinear Dynamics and Economics: Proceedings of the Tenth International Symposium on Economic Theory and Econometrics, 393-406, Cambridge: Cambridge University Press.
Sullivan, R., A. Timmermann and H. White (1999), “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap,” Journal of Finance, 54, 1647-1691.
Sweeny, R. J. (1986), “Beating the Foreign Exchange Market,” Journal of Finance, 41, 163-182.
Timmermann, A. and C. W. J. Granger (2004), “Efficient Market Hypothesis and Forecasting,” International Journal of Forecasting, 20, 15-27.
Turner, C. M., R. Startz and C. R. Nelso (1989), “A Markov Model of Heteroskedasticity, Risk, and Learning in the Stock Market,” Journal of Financial Economics, 25(1), 3-22.
White, H. (2000), “Reality Check for Data Snooping,” Econometrica, 68, 1097-1126.
Yamamoto, R. (2012), “Intraday Technical Analysis of Individual Stocks on the Tokyo Stock Exchange,” Journal of Banking & Finance, 36, 3033-3047.
|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0095351503|
|Data Type: ||thesis|
|Appears in Collections:||[國際經營與貿易學系 ] 學位論文|
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