3.5 The Future Way of Exploring Explanations for Higher Profits Gained from Market Volatility
The question whether financial assets prices are predictable by technical analysis has its long history. Numerous empirical studies published in last 50 years investigate the profitability of technical trading strategies in terms of the different trading systems, treatment of transaction costs, risk consideration, parameter optimization, data snooping biases reduction, out-of-sample performance’s verification and statistical tests application. A bulk of evidences in past studies suggests that using technical analysis helps investors to forecast the market. In addition to exploring whether the technical analysis is profitable or not, another stream in the literature tries to explain why technical trading strategies may generate positive profits in certain speculative markets. Various theoretical and empirical explanations have been proposed.
A broad review of studies about the explanations for technical trading profits can be found in the paper by Park and Irwan (2007). The authors summaries that theoretical explanations for profits stem from market frictions such as noise in current equilibrium prices, trades’ sentiments, herding behavior and chaos, and they have been discussed based on four types of models as follows: noisy rational expectations models, behavioral models, herding models and Chaos Theory.17, 18, 19, 20 In terms of empirical explanations, central bank interventions, order flow, temporary market inefficiencies, risk premiums, market microstructure deficiencies and data snooping have been investigated.21, 22, 23, 24, 25, 26
17Noise in current equilibrium prices: Hellwig (1982)、Brown and Jeannings (1989)、Grundy and McNichols (1989)、Blume et al. (1994).
18Trades’ sentiments: De Long et al. (1990a)、De Long et al. (1990b)、Shleifer and Summers (1990).
19Herding behavior: Froot et al. (1992)、Schmidt (2002).
20Choas: Stengos (1996)、Clyde and Osler (1997).
21Central bank interventions: Dooley and Shafer (1983)、Lukac et al. (1988)、LeBaron (1999)、Neely and Weller (2001)、Saacke (2002)、Sapp (2004).
22Order flow: Olser (2003)、Kavajecz and Odders-White (2004)、Gehrig and Menkhoff (2004).
23Temporary market inefficiencies: Sweeney (1986)、Lukac et al. (1988)、Brock et al. (1992)、Sullivan et al.
(1999)、Kidd and Brorsen (2004).
24Risk premiums: Lukac and Brorsen (1990)、Kho (1996)、Chang and Osler (1999)、LeBaron (1999)、Sapp (2004).
25Market microstructure deficiencies: Greeer et al. (1992)、Day and Wang (2002).
26Data snooping: Brock et al. (1992)、Neely et al. (1997)、Sullivan et al. (1999)、White, (2000).
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In this study, how the information of market volatility affecting the profitability of technical analysis has been showed, but why the information of market volatility being able to improve technical analysis’ performance is not yet discussed. In our future work, we are interested in exploring the explanations for higher profits gained from market volatility. Among the existing literature, there might be two ways of dealing with the question of explanation in higher profits from market volatility.
First, whether the VMA rule has the self-destructive nature could be examined.27 The self-destructive nature of technical trading rules by the statement of Timmermann and Granger (2004) is that the gains the first users of new technical analysis get are likely short-lived. Once this new technical analysis becomes more widely known and used, its information may get in-corporated into prices and then the investors might not get significant profits from this trading rule. In the literature, several studies report that the profits of technical trading rules disap-pear after their publication (Sweeney, 1986; Lukac et al., 1988; Brock et al., 1992). The self-destructive explanation motives us to think that whether VMA rule’s higher profits might stem from its later publication. In order to examine our conjecture, we will conduct the following empirical analyses: 1. Examining whether the static trading rules possess the self-destructive nature. For example, if the MA rule is documented in the academic literature in 1980, we can compare its profitability before and after 1980; 2. Exploring whether the profitability of the VMA rule decline substantially after its publication in 1992; 3. Splitting the data into three parts: before 1980 period, the 1980 1992 period and after 1992 period and exploring whether the higher profitability of the VMA rule still holds in the 1980 1992 period and after 1992 period. If the results reveal that the VMA rule possesses better predictability no matter it is documented in the literature or not, the higher profits of the VMA rule resulting from its less widely using might be excluded.
The second way to figure out the possible explanation for VMA’s higher profits depends on risk premium. Technical trading rules getting positive profits might be due to its compensation
27The self-destructive nature of technical trading rules is one explanation for the temporary market inefficiencies.
Park and Irwin (2007) provide detailed discussion about it.
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for bearing risk. Several studies use the Sharpe ratio, the excess returns to standard deviation, as a risk-adjusted performance measure, and find that technical trading rules generate higher Sharp ratios than the buy-and-hold strategy (Chang and Osler, 1999; LeBaron, 1999). Some empirical papers (Kho, 1996; Sapp, 2004) argue that the technical trading returns can be explained by time-varying risk premiums but other studies (Okunev and White, 2003) get opposite results.
In our future work, we plan to examine whether the risk premium is the cause of VMA’s better predictability by two approaches. The first one is by investigating that whether the VMA rule generates higher Sharpe ratio than other rules by the SPA test. If the Sharpe ratio of the VMA rule is not significantly different from that of other rules, the higher profitability of the VMA rule might be explained as it bears higher risk. Secondly, we can also conduct regression analyses to study whether the return of the VMA rule and other rules can be explained by the risk, and test if the estimated coefficient for the VMA rule is different from that for other rules.
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3.6 Conclusion
This chapter, the extension of our study in understand the value of market volatility in technical analysis, seeks to examine empirically how the information of market volatility affects the gen-eration of trading signals. We have proposed a two-state Markov-switching model for the gap between the price line and the VMA line where the dynamics of the state are governed by the information of market volatility.
Using the TVTP Markov-switching model, this paper has presented a significant effect of the information of market volatility on trading signals. The higher the change in market volatility, the higher is the probability of generating a trading signal. Moreover, it has been shown that market volatility seems to have much larger effects on the generation of selling signals during bear-market periods than the effects during bull-market periods. While for the buying signals, its effect is stronger in bull markets than in bear markets. These results may provide evidence to support that the market volatility in technical analysis is designed for taking better advantage of price movements.
In addition, we have considered an investigation that how market volatility alters the signal time and whether the profit improves or deteriorates for a particular simple MA rule. Such in-vestigation enables us to clearly see the relationship between the information of market volatil-ity and the trading signals. Empirical results suggest the signal time changes, stemming from the earlier, later or new signals due to market volatility, will raise the probability in technical analysis.
The main contribution in this paper is that we conquer the difficulty in empirically measuring the nonlinear relationship between the information of market volatility and the trading signals by proposing the TVTP Markov-switching model.
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Appendix
The essential but critical part in Pagan and Sossounov (2003) identification for bull and bear markets is to find all turning points in the series, peaks and troughs. To determine turning points, they apply the dating algorithm of Bry and Boschan (BB), which is quite common in the business cycle literature, with two modifications. In Appendix B of their paper, the procedure for programmed determination of turning points are described as follows:
1. Determination of initial turning points in raw data:
The initial turning points in raw data are determined by choosing local peaks (troughs) as occurring when they are the highest (lowest) values in a window 8 months (168 days) on either side of the date. Then the alternation of turns by selecting highest of multiple peaks (or lowest of multiple troughs) should be enforced.
2. Censoring operations:
There are four elimination operations, which are built according to some characteristics of stock markets. First, we eliminate turning points within 6 months (126 days) of beginning and end of series. Then peaks (or troughs) at both ends of series which are lower or higher are dropped. Further, we eliminate cycles whose duration is less than 16 months (336 days). At last, phases whose duration is less than 4 months (84 days) should be eliminated unless the stock price falls(rises) exceeds 20
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References
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 Com-modity 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 Commodi-ties, 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.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
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 Perfor-mance 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 Feed-back 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 Insta-bility: 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 Mar-kets: An Empirical Examination,” Journal of Finance, 34(5), 1243−1250.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
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 Inef-ficiencies 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: Ex-ploratory 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 Invest-ment 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 Eco-nomic Statistics, 23, 365−380.
Hardouvelis, G. A. and P. Theodossiou (2002), “The Asymmetric Relation Between Initial Mar-gin Requirements and Stock Market Volatility Across Bull and Bear Markets,” Review of
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
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 In-vestigation,” 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 Aver-ages,” Technical Analysis of Stocks & Commodities, 2(3), 102−103.
Jansen, D. W. and C. L. Tsai (2010), “Monetary Policy and Stock Returns: Financing Con-straints 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 De-creased?,” 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 Perspec-tive,” Review of Finance Studies, 1, 403−425.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
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 Disequilib-rium,” 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,” Jour-nal of InternatioJour-nal Money and Finance, 20, 949−970.
Neely, C. J., P. A. Weller and R. Dittmar (1997), “Is Technical Analysis in the Foreign Ex-change 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 Sys-tem,” 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.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Okunev, J. and D. White (2003), “Do Momentum-based Strategies Still Work in Foreign Cur-rency 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 Prof-itability 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 Economet-rics, 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 Bank-ing, 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 Investiga-tion,” 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.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
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, Cam-bridge: 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.