Temporal optimisation of signals emitted automatically by securities exchange indicators
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Published
27-11-2020
Rodrigo Martín-García
Enrique Ventura Pérez Raquel Arguedas-Sanz
https://orcid.org/0000-0002-9065-7481
Enrique Ventura Pérez Raquel Arguedas-Sanz
Abstract
Stock exchange indicators deliver buy/sell signals that enable analysts to improve the results of a strategy based strictly on fundamental analysis. Nonetheless, since the automatic implementation of signals as they appear may not yield optimal returns, the present paper analysed the suitability of using a series of technical indicators as guidance for portfolio results. A second aim pursued was to study how delaying the implementation of indicator signals may enhance profitability.A simulation was performed for the years 2005-2016 using the most representative index for the Spanish stock exchange, the IBEX35 and all its constituent securities, along with seven indicators (RoC, RSI, SMA, EMA, MACD, Bollinger bands and Stochastic Oscillator) and a total of 81 combinations of buy/sell lag times. The definition of three non-overlapping sub-periods to guarantee the reliability of the findings yielded a total of 61 236 simulated portfolios.The conclusion drawn from the results was that for certain combinations of indicators, delaying the implementation of buy/sell signals improves returns. More specifically, optimal lag times identified for RSI and EMA signals were shown to deliver statistically significant improvements in portfolio returns, irrespective of the period studied.Those findings were consistent the results of an alternative simulation in which the five securities that were both the most liquid and had the greatest impact on the index were not considered, to rule out the possible effect of the relative weight of securities on either portfolio returns or their normalisation.
How to Cite
Martín-García, R., Ventura Pérez, E., & Arguedas-Sanz, R. (2020). Temporal optimisation of signals emitted automatically by securities exchange indicators. Cuadernos De Gestión, 20(3), 61–71. https://doi.org/10.5295/cdg.170851rm
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Keywords
technical analysis, trading strategy, stock market, optimal lags, RSI, EMA
References
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Serbera, J.P. and Paumard, P., 2016. The fall of high-frequency trading: A survey of competition and profits. Research in International Business and Finance, 36, 271-287.
Taylor, M.P. and Allen, H., 1992. The use of technical analysis in the foreign exchange market. Journal of international Money and Finance, 11 (3), 304-314.
Taylor, N., 2014. The rise and fall of technical trading rule success. Journal of Banking & Finance, 40, 286-302.
Teixeira, L.A. and De Oliveira, A.L.I., 2010. A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert systems with applications, 37 (10), 6885-6890.
Wang, J.L. and Chan, S.H., 2007. Stock market trading rule discovery using pattern recognition and technical analysis. Expert Systems with Applications, 33 (2), 304-315.
Wang, J.Z., Wang, J.J., Zhang, Z.G. and Guo, S.P., 2011. Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38 (11), 14346-14355. doi:10.1016/j.eswa.2011.04.222.
Wang, L., An, H. and Liu, X., 2015. A PSO Approach to Search for Adaptive Trading Rules in the EUA Futures Market. Energy Procedia, 75, 2504-2509.
Yang, J., Zhou, Y. and Wang, Z., 2009. The stock–bond correlation and macroeconomic conditions: One and a half centuries of evidence. Journal of Banking & Finance, 33 (4), 670-680.
Agudelo D.A. and Uribe J.H., 2009. ¿Realidad o sofisma? Poniendo a prueba el análisis técnico en las acciones colombianas. Cuadernos de Administración, 22 (38), 189-217.
Australian Securities and Investments Commission, 2015. Review of high-frequency trading and dark liquidity. ASIC, 452.
Bessembinder, H. and Chan, K., 1998. Market efficiency and the returns to technical analysis. Financial management, 27, 5-17.
Brock, W., Lakonishok, J. and LeBaron, B., 1992. Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Brown, D.P. and Jennings, R.H., 1989. On technical analysis. Review of Financial Studies, 2 (4), 527-551.
Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P. and Oliveira, A.L., 2016. Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194-211.
Cervelló-Royo, R., Guijarro, F. and Michniuk, K., 2015. Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data. Expert systems with Applications, 42 (14), 5963-5975.
Chaboud, A.P., Chiquoine, B., Hjalmarsson, E. and Vega, C., 2014. Rise of the machines: Algorithmic trading in the foreign exchange market. The Journal of Finance, 69 (5), 2045-2084.
Chang, P.C., Wang, Y.W. and Yang, W.N., 2004. An investigation of the hybrid forecasting models for stock price variation in Taiwan. Journal of the Chinese Institute of Industrial Engineers, 21 (4), 358-368.
Chong, T.T.L. and Ng, W.K., 2008. Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30. Applied Economics Letters, 15 (14), 1111-1114.
Day, T.E. and Wang, P., 2002. Dividends, nonsynchronous prices, and the returns from trading the Dow Jones Industrial Average. Journal of Empirical Finance, 9 (4), 431-454.
European Securities and Markets Authority, 2015. Automated Trading Guidelines ESMA peer review among National Competent Authorities. Paris: ESMA/2015/592.
Fama, E.F. and Blume, M.E., 1966. Filter rules and stock-market trading. The Journal of Business, 39 (1), 226-241.
Fernandes, M., Hamberger, P. and do Valle, A., 2015. Technical analysis and financial market efficiency: an evaluation of the prediction powers of candlestick patterns. Revista evidenciãçao, contábil and finanças, 3 (3), 35-54.
Gerig, A., 2015. High-frequency trading synchronizes prices in financial markets. Available at SSRN 2173247.
Hudson, R., Dempsey, M. and Keasey, K., 1996. A note on the weak form efficiency of capital markets: The application of simple technical trading rules to UK stock prices-1935 to 1994. Journal of Banking & Finance, 20 (6), 1121-1132.
Ito, A., 1999. Profits on technical trading rules and time-varying expected returns: evidence from Pacific-Basin equity markets. Pacific-Basin Finance Journal, 7 (3), 283-330.
Jensen, M. and Bennington, G., 1970. Random walks and technical theories: Some additional evidences. Journal of Finance, 25 (2), 469-482.
Kara, Y., Boyacioglu, M.A. and Baykan, Ö.K., 2011. Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38 (5), 5311-5319.
Kim, H.J. and Shin, K.S., 2007. A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets. Applied Soft Computing, 7 (2), 569-576.
Leigh, W., Modani, N., Purvis, R. and Roberts, T., 2002. Stock market trading rule discovery using technical charting heuristics. Expert Systems with Applications, 23 (2), 155-159
Lewis, M. and Baker, D., 2014. Flash boys: A Wall Street Revolt. New York: WW Norton.
Lim, M.A., 2015. The Handbook of Technical Analysis+ Test Bank: The Practitioner’s Comprehensive Guide to Technical Analysis. John Wiley & Sons.
Mills, T.C., 1997. Technical analysis and the London Stock Exchange: testing trading rules using the FT30. International Journal of Finance and Economics, 2 (4), 319-331.
Olson, D., 2004. Have trading rule profits in the currency markets declined over time? Journal of Banking and Finance 28 (1), 85-105.
Park, C.H. and Irwin, S.H., 2007. What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21 (4), 786-826.
Ready, M.J., 2002. Profits from technical trading rules. Financial Management, 43-61.
Rodriguez-Gonzalez, A., Garcia-Crespo, A. and Colomo-Palacios, R., 2011. Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator. Expert Systems with Applications, 38 (9), 11489-11500.
Rosillo, R., De la Fuente, D. and Brugos, J.A.L., 2013. Technical analysis and the Spanish stock exchange: testing the RSI, MACD, momentum and stochastic rules using Spanish market companies. Applied Economics, 45 (12), 1541-1550.
Rosillo, R., Giner, J. and De la Fuente, D., 2014. Stock Market simulation using support vector machines, Journal of Forecasting, 33 (6), 488-500.
Serbera, J.P. and Paumard, P., 2016. The fall of high-frequency trading: A survey of competition and profits. Research in International Business and Finance, 36, 271-287.
Taylor, M.P. and Allen, H., 1992. The use of technical analysis in the foreign exchange market. Journal of international Money and Finance, 11 (3), 304-314.
Taylor, N., 2014. The rise and fall of technical trading rule success. Journal of Banking & Finance, 40, 286-302.
Teixeira, L.A. and De Oliveira, A.L.I., 2010. A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert systems with applications, 37 (10), 6885-6890.
Wang, J.L. and Chan, S.H., 2007. Stock market trading rule discovery using pattern recognition and technical analysis. Expert Systems with Applications, 33 (2), 304-315.
Wang, J.Z., Wang, J.J., Zhang, Z.G. and Guo, S.P., 2011. Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38 (11), 14346-14355. doi:10.1016/j.eswa.2011.04.222.
Wang, L., An, H. and Liu, X., 2015. A PSO Approach to Search for Adaptive Trading Rules in the EUA Futures Market. Energy Procedia, 75, 2504-2509.
Yang, J., Zhou, Y. and Wang, Z., 2009. The stock–bond correlation and macroeconomic conditions: One and a half centuries of evidence. Journal of Banking & Finance, 33 (4), 670-680.
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