Modified moving-average crossover trading strategy: evidence in Malaysia equity market

This study examine the profitability of technical analysis using the most renowned trendfollowing tool, the original moving-average (MA) crossover strategy, to compare with the conventional simple buy-and-hold strategy, using the evidence from Malaysia equity market the FBMKLCI Index from 2000 to 2...

Full description

Saved in:

Bibliographic Details
Main Author:	Soh, Chuen Yean
Format:	Thesis
Language:	eng eng
Published:	2016
Subjects:	HG Finance
Online Access:	https://etd.uum.edu.my/6243/1/s817265_01.pdf https://etd.uum.edu.my/6243/2/s817265_02.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.6243
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Tapa, Afiruddin
topic	HG Finance
spellingShingle	HG Finance Soh, Chuen Yean Modified moving-average crossover trading strategy: evidence in Malaysia equity market
description	This study examine the profitability of technical analysis using the most renowned trendfollowing tool, the original moving-average (MA) crossover strategy, to compare with the conventional simple buy-and-hold strategy, using the evidence from Malaysia equity market the FBMKLCI Index from 2000 to 2014. Specifically, this study investigates the performance of the original moving-average strategy and a modified moving-average crossover strategy with additional trading rules such as entry rule, exit rule, holding rule, and stop-loss rule. The results in this study are consistent to past studies that stronglysupport moving-average crossover trading strategies. The result here suggests that all combinations of short-MA and long-MA periods of the original MA crossover strategy and majority combinations of short-MA and long-MA of the modified MA crossover strategy outperform market benchmark with higher risk-adjusted return. In addition, the 1-period short-MA demonstrates the best return in both original and modified moving-average crossover strategy; better still the modified strategy outperforms the original strategy with lower frequency of trades which could largely reduce transaction costs and with lower return distribution variability.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Soh, Chuen Yean
author_facet	Soh, Chuen Yean
author_sort	Soh, Chuen Yean
title	Modified moving-average crossover trading strategy: evidence in Malaysia equity market
title_short	Modified moving-average crossover trading strategy: evidence in Malaysia equity market
title_full	Modified moving-average crossover trading strategy: evidence in Malaysia equity market
title_fullStr	Modified moving-average crossover trading strategy: evidence in Malaysia equity market
title_full_unstemmed	Modified moving-average crossover trading strategy: evidence in Malaysia equity market
title_sort	modified moving-average crossover trading strategy: evidence in malaysia equity market
granting_institution	Universiti Utara Malaysia
granting_department	Othman Yeop Abdullah Graduate School of Business
publishDate	2016
url	https://etd.uum.edu.my/6243/1/s817265_01.pdf https://etd.uum.edu.my/6243/2/s817265_02.pdf
_version_	1776103684708499456
spelling	my-uum-etd.62432023-03-09T03:04:39Z Modified moving-average crossover trading strategy: evidence in Malaysia equity market 2016 Soh, Chuen Yean Tapa, Afiruddin Othman Yeop Abdullah Graduate School of Business Othman Yeop Abdullah Graduate School of Business HG Finance This study examine the profitability of technical analysis using the most renowned trendfollowing tool, the original moving-average (MA) crossover strategy, to compare with the conventional simple buy-and-hold strategy, using the evidence from Malaysia equity market the FBMKLCI Index from 2000 to 2014. Specifically, this study investigates the performance of the original moving-average strategy and a modified moving-average crossover strategy with additional trading rules such as entry rule, exit rule, holding rule, and stop-loss rule. The results in this study are consistent to past studies that stronglysupport moving-average crossover trading strategies. The result here suggests that all combinations of short-MA and long-MA periods of the original MA crossover strategy and majority combinations of short-MA and long-MA of the modified MA crossover strategy outperform market benchmark with higher risk-adjusted return. In addition, the 1-period short-MA demonstrates the best return in both original and modified moving-average crossover strategy; better still the modified strategy outperforms the original strategy with lower frequency of trades which could largely reduce transaction costs and with lower return distribution variability. 2016 Thesis https://etd.uum.edu.my/6243/ https://etd.uum.edu.my/6243/1/s817265_01.pdf text eng public https://etd.uum.edu.my/6243/2/s817265_02.pdf text eng public masters masters Universiti Utara Malaysia Admati, A. (1985). A Noisy Rational Expectations Equilibrium for Multi-Asset Securities Markets. Econometrica, 53(3), 629-657. Alexander, S. (1961). Price Movements in Speculative Markets: Trends or Random Walks. Industrial Management Review, 2, 7-26. Aronson, D. (2007). Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals. Hoboken, New Jersey: Wiley. Asness, C., Frazzini, A., & Pedersen, L. (2012). Leverage Aversion and Risk Parity. Financial Analysts Journal, 68(1). Bachelier, L. (1900). Théorie de la Spéculation. Doctoral Dissertation in Mathematics, University of Paris. Translated into English by Cootner, P.H. (1964). Barberis, N., Shleifer, A., & Vishny, R. (1998). A Model of Investor Sentiment. Journal of Financial Economics, 49, 307-343. Bauer, R., & Dahlquist, J. (2012). Market Timing and Roulette Wheels Revisited. Financial Anlayst Journal, 2012 (1). Bessembinder, H., & Chan, K. (1995). The Profitability of Technical Trading Rules in the Asian Stock Markets. Pacific-Basic Finance Journal, 3, 257-284. Bessembinder, H., & Chan, K. (1998). Market Efficiency and the Returns to Technical Analysis. Financial Management, 27, 5-17. Black, F. (1986). Noise. Journal of Finance, 41, 529-543. Blume, L., Easley, D., & O'Hara, M. (1994). Market Statistics and Technical Analysis: The Role of Volume. Journal of Finance, 49, 153-181. Bollinger, J. (2002). Bollinger on Bollinger Bands. McGraw Hill. Brock, W., Lakonishock, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance, 47, 1731-1764. Brorsen, B., & Townsend, J. (1998). Performance Persistence for Managed Futures. Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. Brown, D., & Jennings, R. (1989). On Technical Analysis. Review of Financial Studies, 2, 527-551. Campbell, J. (1987). Stock Returns and The Term Structure. Journal of Financial Economics, 18(2), 373-399. Campbell, J., & Kyle, A. (1993). Smart Money, Noise Trading and Stock Price Behavior. Review of Economic Studies, 60, 1-34. Campbell, J., & Thompson, S. (2008). Predicting Excess Stock Returns Out-of-Sample: Can Anything Beat The Historical Average? Review of Financial Studies, 21(4), 1509-1531. Chen, Q., Goldstein, I., & Jiang, W. (2007). Price Informativeness and Investment Sensitivity to Stock Price. Review of Financial Studies, 20 (3), 619-650. Cochrane, J. (2008). The Dog That Did Not Bark: A Defense of Return Predictability. Review of Financial Studies, 21(4), 1533-1575. Coutts, J. A., & Cheng, K. C. (2000). Trading rules and stock returns: some preliminary short-run evidence from Hang Seng 1985-1997. Applied Financial Economics 10, 579-586. Covel, M. (2011). Trend Commandments: Trading for Exceptional Returns. FT Press. Cowles, A. (1933). Can Stock Market Forecasters Forecast? Econometrica: Journal of the Econometric Society, 309-324. Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under- and Overreactions. Journal of Finance, 53(6), 1839–1885. Day, T., & Wang, P. (2002). Dividends, Nonsynchronous Prices, and The Returns From Trading The Dow Jones Industrial Average. Journal of Empirical Finance, 9(4), 431-454. Diamond, D., & Verrecchia, R. (1981). Information Aggregation in a Noisy Rational Expectations Economy. Journal of Financial Economics, 9, 221-235. Dybvig, P. (1988). Inefficient Dynamic Portfolio Strategies, or How to Throw Away a Million Dollars in the Stock Market. Review of Financial Studies, 1, 67-88. Edward, R., Magee, J., & Bassetti, W. (2007). Technical Analysis of Stock Trends (9 ed.). CRC Press. Elder, A. (1993). Trading for A Living. John Wiley & Sons, Inc. Faber, M. (2007). A Quantitative Approach to Tactical Asset Allocation. Journal of Investing, 16, 69-79. Fabozzi, F., & Markowitz, H. (2002). The Theory and Practice of Investment Management. John Wiley & Sons, Inc. Faith, C. (2007). Way of the Turtle: The Secret Methods that Turned Ordinary People into Legendary Traders. McGraw Hill. Fama, E. (1965). Random Walks in Stock Market Prices. Financial Analyst Journal, 21(5), 55-59. Fama, E. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25, 383-417. Fama, E., & Blume, M. (1966). Filter Rules and Stock Market Trading. Journal of Business, 39, 226-241. Fama, E., & Schwert, G. (1977). Asset Returns and Inflation. Journal of Financial Economics, 5(2), 115-146. Friesen, G., Weller, P., & & Dunham, L. (2009). Price Trends and Patterns in Technical Analysis: A Theoretical and Empirical Examination. Journal of Banking and Finance, 33(6), 1089-1100. Gandevani, H.M. (2001). Trading Systems As A Determining Factor In Increasing Financial Trading Performance (Doctoral dissertation). Retrieved from ProQuest Information and Learning. (UMI Number: 3015992). Gartley, H. (1930). Profits in the Stock Market. New York: H.M. Gartley Inc. Gehrig, T., & Menkhoff, L. (2006). Extended Evidence on The Use of Technical Analysis on Foreign Exchange. International Journal of Finance and Economics, 11(4). Gollier, C. (1997). On the Inefficiency of Bang-Bang and Stop-Loss Portfolio Strategies. Journal of Risk and Uncertainty, 14, 143-154. Grossman, S., & Stiglitz, J. (1980). On the Impossibility of Informationally Efficient Markets. American Economic Review, 70, 393-408. Gunasekarage, A., & Power, D. M. (2011). The profitability of moving average trading rules in South Asian stock markets. Emerging Markets Review 2(1), 17-33. Hirshleifer, D., & Luo, G. (2001). On the Survival of Overconfident Traders in a Competitive Securities Market. Journal of Financial Markets, 4, 73-84. Hong, H., & Stein, J. (1997). A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets. Unpublished working paper. Sloan School of Management, Massachusetts Institute of Technology. Hurst, B., Ooi, Y., & Pedersen, L. (2010). Understanding Managed Futures. AQR Working Paper. Illmanen, A. (2011). Expected Returns: An Investor's Guide to Harvesting Market Rewards. Chap. 14: Commodity Momentum and Trend Following. Wiley. Jegadeesh, N. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation - Discussion. Journal of Finance, 55, 1765-1770. Jensen, M. (1978). Some Anomalous Evidence Regarding Market Efficiency. Journal of Financial Economics, 6, 95-101. Kaminski, K., & Lo, A. (2008). When Do Stop-Loss Rules Stop Losses? SIFR Research Report Series, 63. Kaufman, P. (2013). Trading Systems and Methods (4 ed.). John Wiley & Sons. Kogan, L., Ross, S., Wang, J., & Westerfield, M. (2006). The Price Impact and Survival of Irrational Traders. Journal of Finance, 61, 195-229. Kong, A., Rapach, D., Strauss, J., & Zhou, G. (2011). Predicting Market Components Out-of-Sample: Asset Allocation Implications. The Journal of Portfolio Management, 37(4), 29-41. LeBaron, B. (1999). Technical Trading Rule Profitability and Foreign Exchange Intervention. Journal of International Economics, 49, 125-143. Levich, R., & Thomas, L. (1993). The Significance of Technical Trading Rule Profits in the Foreign Exchange Market: A Bootstrap Approach. Journal of International Money and Finance, 12, 451-474. Lo, A., & MacKinlay, A. (1990). Data Snooping Biases in Tests of Financial Asset Pricing Models. Review of Financial Studies, 3, 431-467. Lo, A., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. Journal of Finance, 55, 1705-1765. Lui, Y., & Mole, D. (1998). The Use of Fundamental and Technical Analyses by Foreign Exchange Dealers: Hong Kong Evidence. Journal of International Money and Finance, 17, 535-545. Luo, G. (2003). Evolution, Efficiency and Noise Traders in a One-Sided Auction Market. Journal of Financial Markets, 6, 163-197. Maillet, B., & Michel, T. (2000). Further insights on the puzzle of technical analysis profitability. The European Journal of Finance 6 (2), 196-224. Neely, C., Rapach, D., Tu, J., & Zhou, G. (2013). Forecasting the Equity Risk Premum: The Role of Technical Indicators. Working Paper: Federal Researve Bank of St. Louis. Neely, C., Weller, P., & Dittmar, R. (1997). Is Technical Analysis Profitable in the Foreign Exchange Market? A Genetic Programming Approach. Journal of Financial and Quantitative Analysis, 32, 405-426. Neely, C. (2002). The Temporal Pattern of Trading Rule Returns and Exchange Rate Intervention: Intervention Does Not Generate Technical Trading Profits. Journal of International Economics, 58, 211-232. Neftci, S. (1991). Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of Technical Analysis. Journal of Business, 64, 549-571. Olson, D. (2004). Have Trading Rule Profits in the Currency Markets Declined Over Time. Journal of Banking & Finance 28 (1), 85-105. Olszewski, E. (2001). A Strategy for Trading the S&P 500 Futures Market. Journal of Economics and Finance, 25(1), 62-79. Ostgaard, S. (2008). On the Nature of Trend Following. Last Atlantis Capital Management. Parisi, F., & Vasquez, A. (2000). Simple technical trading rules of stock returns: evidence from 1987 to 1998 in Chile. Emerging Market Review 1 (2), 152-164. Qi, M., & Wu, Y. (2006). Technical trading-rule profitability, data snooping, and reality check: evidence from the foreign exchange market. Journal of Money, Credit and Banking, 2135-2158. Ready, M. (1997). Profits from Technical Trading Rules. Working paper. University of Wisconsin-Madison. Roberts, H. (1967). Statistical versus Clinical Prediction of the Stock Market. Unpublished manuscript. Russel, P., & Torbey, V. (2002). The Efficient Market Hypothesis on Trial: A Survey. Business Quest Journal, 1-19. Samuelson, P. (1965). Proof That Properly Anticipated Prices Fluctuate Randomly. Industrial Management Review, 6, 41-49. Schwager, J. (1995). Futures: Fundamental Analysis. John Wiley & Sons, Inc. Shynkevich, A. (2012). Performance of Technical Analysis in Growth and Small Cap Segments of the US Equity Market. Journal of Banking and Finance, 36(1), 193-208. Sullivan, R., Timmermann, A., & White, H. (2003). Forecast Evaluation with Shared Data Sets. International Journal of Forecasting, 19, 217-227. Sweeney, R. (1988). Some New Filter Rule Tests: Methods and Results. Journal of Financial and Quantitative Analysis, 23, 285-300. Szakmary, A. C., & Mathur, I. (1997). Central bank intervention and trading rule profits in foreign exchange markets. Journal of International Money and Finance, 16 (4), 513-535. Szakmary, A., Shen, Q., & Sharma, S. (2010). Trend-Following Trading Strategies in Commodity Futures: A Re-Examination. Journal of Banking and Finance, 34, 409-426. Taylor, J. (2000). A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns. Journal of Forecasting, 299-311. Taylor, M., & Allen, H. (1992). The Use of Technical Analysis in the Foreign Exchange Market. Journal of International Money and Finance, 11, 304-314. Taylor, S. (1994). Trading Futures Using a Channel Rule: A Study of the Predictive Power of Technical Analysis with Currency Examples. Journal of Futures Markets, 14, 215-235. Tharp, V. (2009). Super Trader: Make Consistent Profits in Good and Bad Markets. McGraw Hill. Wilcox, C., & Crittenden, E. (2009). Does Trend Following Work on Stocks? Working Paper, Blackstar Funds, LLC. Zakamulin, V. (2014). The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules. Journal of Asset Management. Zhang, X. (2003). Information Uncertainty and Stock Returns. Journal of Finance, 61, 105-137. Zhu, Y., & Zhou, G. (2009). Technical Analysis: An Asset Allocation Perspective on the Use of Moving Averages. Journal of Financial Economics, 92(3), 519-544.

Modified moving-average crossover trading strategy: evidence in Malaysia equity market

Similar Items