Temporal optimisation of signals emitted automatically by securities exchange indicators
DOI:
https://doi.org/10.1229/tecempresarialjournal.v19i1.381Keywords:
Technical analysis, trading strategy, stock market, optimal lags, RSI, EMAAbstract
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.References
Achelis, S. B., 2001. Technical Analysis from A to Z. New York: McGraw
Hill.
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,
-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),
-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),
-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.
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