Optimal Lengths of Moving Averages for the MACD Oscillator for Companies Listed on the Warsaw Stock Exchange
The aim of the paper is to find optimal exponential moving averages for the main technical analysis oscillator MACD (Moving Average Convergence Divergence) which trigger buy and sell signals for companies from the WIG20 index, the mWIG40 and the sWIG80 index, listed on the Warsaw Stock Exchange. The analysis was conducted on the basis of data collected from 17 November 2000 to 31 December 2018, i.e. from the day when the WARSET system was introduced. The optimization was done with the method based on rates of return because they influence the final investment result. It is the first research of this kind on the Polish financial market. It shows that each of the examined companies has its own lengths of moving averages which optimize rates of return, however, for some enterprises these values may be the same. Many research papers on technical analysis apply standard lengths for averages used in MACD. The authors of the present paper find out that investors should not follow them automatically, but rather look for optimal values for each market and each company. Such an opinion stands in sync with research literature which proves a varying efficiency of technical analysis tools, depending on the market development level, its transaction volume or its liquidity. (original abstract)
- Anghel G.D.I. (2015), Stock market efficiency and the MACD. Evidence from countries around the world, Emerging Markets Queries in Finance and Business, Procedia Economics and Finance, 32, 1414 -1431.
- Apirine V. (2017), Weekly & daily MACD, Technical Analysis of Stock & Commodities, 35(12), 10-15.
- Appel G. (1979), The Moving Average Convergence Divergence Method, Signalert.
- Appel G. (2005), Technical Analysis, Power Tool for Active Investors, Prentice Hall.
- Armour J., Lofton M., Oyenekan O., Metghalchi M. (2010), Efficient market hypothesis and technical analysis: the Irish stock index, Proceedings of Intellectbase International Consortium, 10.
- Biondo A.E., Pluchino A., Rapisarda A., Helbing D. (2013), Are random trading strategies more successful than technical ones?, PLoS ONE, 8(7), e68344, 1-17.
- Bodas-Sagi D.J., Fernandez P., Hidalgo J.I., Soltero F.J., Risco-Martin J.L. (2009), Multiobjective optimization of technical market indicators, GECCO '09 Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers.
- Borowski K. (2014), Miary efektywności zarządzania na rynkach finansowych, Difin.
- Brock W., Lakonishok J., LeBaron B. (1992), Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance, 47(5), 1731-1764.
- Chen Ch.P., Metghalchi M. (2012), Weak-form market efficiency: evidence from the Brazilian stock market, International Journal of Economics and Finance, 4(7), 22-32.
- Chen Ch.P. Metghalchi M., Garza-Gomez X. (2011), Technical analysis of the Danish stock market, Business Studies Journal, 3(2), 107-115.
- Chong T., Ng W. (2008), Technical analysis and the London Stock Exchange: testing the MACD and RSI rules using the FT30, Applied Economics Letters, 15(14), 1111-1114.
- Du Plessis A.W. (2012), The effectiveness of the technical analysis strategy versus a buy-and-hold strategy on the FTSE/JSE top 40 index shares of the JSE Ltd: the case of the Moving Average Convergence Divergence Indicator, University of Johannesburg.
- Erić D., Andjelic G.B., Redźepagić S. (2009), Application of MACD and RVI indicators as functions of investment strategy optimization on the financial market, Journal of Economics and Business, 27(1), 171-196.
- Fama E.F. (1970), Efficient capital markets: a review of theory and empirical work, The Journal of Finance, 25(2), 383-417.
- Fifield S., Power D., Sinclair C. (2005), An analysis of trading strategies in eleven European stock markets, European Journal of Finance, 11(6), 531-548.
- Hsu P., Hsu Y., Kuan Ch. (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.
- Irwing Ch., S. Park, (2007), What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21(4), 786-826.
- Kara Y., Boyacioglu M.A., Baykan O.K. (2011), Expert systems with applications, International Journal, 38(5), 5311-5319.
- Kaufman P. (2013), Trading Systems and Methods + Website, Wiley & Sons.
- Khatua A. (2016), An application of moving average convergence and divergence (MACD) indicator on selected stocks listed on National Stock Exchange (NSE), https://papers.ssrn.com/sol3/papers. cfm?abstract_id=2872665.
- Kulkarni A.D., More A. (2014), An application of moving average convergence divergence (MACD) indicator on selected stocks listed on Bombay Stock Exchange (BSE), Oriental Journal of Computer Science and Technology, 7(3), 396-400.
- McKenzie M. (2014), Technical trading rules in emerging markets and the 1997 Asian currency crisis, Emerging Markets Finance and Trade, 43(4), 46-73.
- Meissner G., Alex A., Nolte K. (2001), A refined MACD indicator - evidence against the random walk hypothesis?, ABAC Journal, 21(2), 1-17.
- Methalchi M., Marcucci J., Chang Y. (2012), Are moving average trading rules profitable? Evidence from the European stock markets, Applied Economics, 44(12), 1539-1559.
- Ming-Ming L., Siok-Hwa L. (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.
- Murphy J.J. (1999), Analiza techniczna rynków finansowych, WIG-PRESS.
- Neely Ch.J., Rapach D., Zhou J., Tu G. (2014), Forecasting the equity risk premium: the role of technical indicators, Management Science, 60(7), 1617-1859.
- Pauwels S., Inghelbrecht K., Heyman D., Marius P. (2011), Technical trading rules in emerging stock markets, Engineering and Technology, 59, 2241-2264.
- Predipbhai N.P. (2013), Comparison between exponential moving average based MACD with simple moving average based MACD of technical analysis, International Journal of Scientific Research, 2(12), 189 -197.
- Pruchnicka-Grabias I. (2018), Empirical study of the relative strength in the currency portfolio construction, Studia Ekonomiczne, 356, Uniwersytet Ekonomiczny w Katowicach.
- Rosillo R., Fuente D., Brugos J. (2013), Technical analysis and the Spanish stock exchange: testing the RSI, MACD, momentum and stochastic rules using Spanish market companies, Applied Economics, 45(2), 1541-1550.
- Seykota E. (1991), MACD, Sweet anticipation?, Modern Trader, 20(4), https://www.questia.com/ magazine/1G1-10490255/macd-sweet-anticipation.
- Stanković J., Marković I., Stojanović M. (2015), Investment strategy optimization using technical analysis and predictive modeling in emerging markets, Procedia Economics and Finance, 19, 51-62.
- Sullivan R., Timmermann A., White H. (1999), Data-snooping, technical trading rule performance, and the bootstrap, Journal of Finance, 54(5), 1647-1691.
- Taylor N. (2014), The rise and fall of technical trading rule success, Journal of Banking and Finance, 40(C), 286-302.
- Todea A., Zoicaş-Ienciu A., Filip A. (2009), Profitability of the moving average strategy and the episodic dependencies: empirical evidence from European stock, European Research Studies Journal, 12(1), 63-72.
- Todea A., Ulici M., Silaghi S. (2009), Adaptive markets hypothesis: evidence from Asia-Pacific financial markets, Review of Finance and Banking, 1(1), 7-13.
- Wang J., Kim J. (2018), Predicting stock price trend using MACD optimized by historical volatility, Mathematical Problems in Engineering, ID 9280590, 1-12.
- Wong W., Manzur M., Chew B. (2003), How rewarding is technical analysis? Evidence from Singapore stock market, Applied Financial Economics, 13(7), 543-551.
- Zoicaş-Ienciu A. (2015), The sensitivity of moving average trading rules performance with respect to methodological assumptions, Emerging Markets Queries in Finance and Business, Procedia Economics and Finance, 32, 1353-1361.