Designing Automatic Re-balancing Model Using Technical Analysis Concept of Divergence

Subject Areas : Financial Knowledge of Securities Analysis

S. M.  Lale Sajjadi ¹ (Msc Financial Engineering of Khaje Nasirodin Toosi University.)
S. Hojjat  Vakili ² (Msc Financial Engineering of Khaje Nasirodin Toosi University.)
S. Babak  Ebrahimi ³ (Faculty member of Khaje Nasirodin Toosi University.)

Received: 2017-03-07 Accepted : 2017-07-06 Published : 2019-03-21

Keywords: Automated re-balancing model, Divergence, Meta-heuristic algorithms, portfolio, Technical Analysis,

Abstract :

The classical efficient market hypothesis states that it is not possible to beat the market by developing a strategy based on historical price series. In this paper we propose a profitable automatic trading system based on the divergence definition in relative strength index and using other technical analysis tools which presents empirical evidence confronting the classical efficient market hypothesis. In order to validate the developed solution an extensive evaluation was performed, comparing the designed strategy against the market itself and several other investment methodologies. An intraday database comprised of 59 symbols from NYSE in The time span 2010 to 2016 was employed. The whole sample is categorized over two sub-periods, training and widening its validity. By enjoying Meta-heuristic algorithms the rules in the first sub-period was improved. Then, in the second division the improved model was evaluated. The results indicates that this model improved predictability power and its performance is better than buy and hold and random strategies

References:

*      Salmani, S (1389). Study of Heterogeneity in Tehran Stock Exchange Concerning Technical Analysis Criteria. Financial Engineering and Portfolio Management, 6(2), 139:165 (in Persian)

*      Alexander, S.S. (1961) Price movements in speculative markets: Trends or random walks. Industrial Management Review (pre-1986), 2(2), p.7.

*      Bagheri, A., Peyhani, H. M., & Akbari, M. (2014). Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Systems with Applications, 41(14), 6235-6250.

*      Baumol, W. J., Heim, P., Malkiel, B. G., & Quandt, R. E. (1973). Efficiency of corporate investment: reply. The Review of Economics and Statistics, 128-131.

*      Blume, L., Easley, D., & O'hara, M. (1994). Market statistics and technical analysis: The role of volume. The Journal of Finance, 49(1), 153-181.

*      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), pp.1731-1764.

*      Cervelló-Royo, R., Guijarro, F., & 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.

*      Coakley, J., Marzano, M., & Nankervis, J. (2016). How profitable are FX technical trading rules?. International Review of Financial Analysis, 45, 273-282.

*      Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.

*      Fama, E.F. and Blume, M.E., (1966) Filter rules and stock-market trading.The Journal of Business, 39(1), pp.226-241.

*      Gorgulho, A., Neves, R., & Horta, N. (2011). Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition. Expert systems with Applications, 38(11), 14072-14085.

*      Heidorn, T., Kaiser, D. G., & Roder, C. (2009). The risk of funds of hedge funds: An empirical analysis of the maximum drawdown. The Journal of Wealth Management, 12(2), 89-100.

*      Lin, X., Yang, Z. and Song, Y. (2011) Intelligent stock trading system based on improved technical analysis and Echo State Network. Expert Systems with Applications, 38(9), pp.11347-11354.

*      Mills, T. C. (1997). Technical analysis and the London Stock Exchange: Testing trading rules using the FT30. International Journal of Finance & Economics, 2(4), 319-331.

*      O'Neil, W. J., & Ryan, C. (1988). How to make money in stocks (Vol. 10). New York: McGraw-Hill.

*      Park, C. H., & Irwin, S. H. (2004). The profitability of technical analysis: A review.

*      Papadamou, S. and Stephanides, G. (2007) Improving technical trading systems by using a new MATLAB-based genetic algorithm procedure.Mathematical and Computer Modelling, 46(1), pp.189-197.

*      Radeerom, M. (2014) April. Building a Trade System by Genetic Algorithm and Technical Analysis for Thai Stock Index. In Asian Conference on Intelligent Information and Database Systems (pp. 414-423). Springer International Publishing.

*      Silva, A., Neves, R., & Horta, N. (2015). A hybrid approach to portfolio composition based on fundamental and technical indicators. Expert Systems with Applications, 42(4), 2036-2048.

*      Sullivan, R., Timmermann, A., & White, H. (1999). Data‐snooping, technical trading rule performance, and the bootstrap. The journal of Finance, 54(5), 1647-1691.