Skip to main content
SpringerLink
Log in

A Flexible Approach Towards Multi-frequency Re-engineering of the Moving Average Convergence Divergence Indicator

  • Original Article
  • Published:
Global Journal of Flexible Systems Management Aims and scope Submit manuscript

Abstract

The study develops an innovative and flexible methodology for re-defining the traditional convergence–divergence indicators in the light of multi frequency trading behaviour of the heterogeneous agents. The developed indicator is labelled as multi-resolution convergence divergence indicator (MRCD). In contrast to the traditional moving average convergence divergence (MACD), the MRCD is “flexible” as it reacts to fluctuations arising at any frequency interval and is thereby capable of adapting to a wide variety of future possibilities. The “innovative dimension” underpinning this methodology is the replacement of the traditional trend extractor (moving-average) with a more novel methodology—the multi-resolution analysis. The forecasting ability of this newly engineered indicator is examined by structuring a neural network based MRCD–NARX model. The performance of this model is bench-marked against that of a similar model developed using the traditional MACD indicator. Out-of-the sample mean square error and the Diebold–Mariano test are used to examine the statistical accuracy of the forecasts. The profitability of the indicator is ascertained using the correlation measure and the hit ratio. A “long-short trading rule” is developed and back-tested on the testing data-sample to validate the practical applicability and “reproducibility” of the methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abbey, B. S., & Doukas, J. A. (2012). Is technical analysis profitable for individual currency traders? Journal of Portfolio Management, 39(1), 142.

    Google Scholar 

  • Anderson, J. A., & Faff, R. W. (2008). Point and figure charting: A computational methodology and trading rule performance in the S&P 500 futures market. International Review of Financial Analysis, 17(1), 198–217.

    Google Scholar 

  • Appel, G. (2003). Become your own technical analyst: How to identify significant market turning points using the moving average convergence–divergence indicator or MACD. The Journal of Wealth Management, 6(1), 27–36.

    Google Scholar 

  • Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932–5941.

    Google Scholar 

  • Beneish, M. D., Lee, C. M., & Tarpley, R. L. (2001). Contextual fundamental analysis through the prediction of extreme returns. Review of Accounting Studies, 6(2–3), 165–189.

    Google Scholar 

  • Caginalp, G., & Constantine, G. (1995). Statistical inference and modeling of momentum in stock prices. Applied Mathematical Finance, 2(4), 225–242.

    Google Scholar 

  • Caginalp, G., & Laurent, H. (1998). The predictive power of price patterns. Applied Mathematical Finance, 5(3–4), 181–205.

    Google Scholar 

  • Candelon, B., Piplack, J., & Straetmans, S. (2008). On measuring synchronization of bulls and bears: The case of East Asia. Journal of Banking & Finance, 32(6), 1022–1035.

    Google Scholar 

  • Cao, Q., Leggio, K. B., & Schniederjans, M. J. (2005). A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32(10), 2499–2512.

    Google Scholar 

  • Cawley, G. C., & Talbot, N. L. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. The Journal of Machine Learning Research, 99, 2079–2107.

    Google Scholar 

  • Chang, P. C., & Liu, C. H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34(1), 135–144.

    Google Scholar 

  • Chang, P. C., Liu, C. H., Lin, J. L., Fan, C. Y., & Ng, C. S. (2009). A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications, 36(3), 6889–6898.

    Google Scholar 

  • Chavarnakul, T., & Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34(2), 1004–1017.

    Google Scholar 

  • Chen, Y., Yang, B., & Abraham, A. (2007). Flexible neural trees ensemble for stock index modeling. Neurocomputing, 70(4), 697–703.

    Google Scholar 

  • Chong, T. T. L., & Ip, H. T. S. (2009). Do momentum-based strategies work in emerging currency markets? Pacific-Basin Finance Journal, 17(4), 479–493.

    Google Scholar 

  • Chong, T. T. L., & Lam, T. H. (2010). Predictability of nonlinear trading rules in the US stock market. Quantitative Finance, 10(9), 1067–1076.

    Google Scholar 

  • Chong, T. T. L., Lam, T. H., & Yan, I. K. M. (2012). Is the Chinese stock market really inefficient? China Economic Review, 23(1), 122–137.

    Google Scholar 

  • Chong, T. T. L., & 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.

    Google Scholar 

  • Constantinou, E., Georgiades, R., Kazandjian, A., & Kouretas, G. P. (2006). Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns. International Journal of Finance & Economics, 11(4), 371–383.

    Google Scholar 

  • Costantini, M., & Kunst, R. M. (2011). On the usefulness of the Diebold-Mariano test in the selection of prediction models: some Monte Carlo evidence (No. 276). Economics Series, Institute for Advanced Studies.

  • Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4), 303–314.

    Google Scholar 

  • Darrat, A. F., & Zhong, M. (2000). On testing the random-walk hypothesis: A model-comparison approach. Financial Review, 35(3), 105–124.

    Google Scholar 

  • Dechow, P. M., Hutton, A. P., Meulbroek, L., & Sloan, R. G. (2001). Short-sellers, fundamental analysis, and stock returns. Journal of Financial Economics, 61(1), 77–106.

    Google Scholar 

  • Demuth, H., Beale, M., & Hagan, M. (2008). Neural network toolbox™ 6. User’s guide.

  • Desai, V. S., & Bharati, R. (1998). A comparison of linear regression and neural network methods for predicting excess returns on large stocks. Annals of Operations Research, 78, 127–163.

    Google Scholar 

  • Diaconescu, E. (2008). The use of NARX neural networks to predict chaotic time series. WSEAS Transactions on Computer Research, 3(3), 182–191.

    Google Scholar 

  • Diebold, F. X. (2012). Comparing predictive accuracy, twenty years later: A personal perspective on the use and abuse of Diebold-Mariano tests (No. w18391). National Bureau of Economic Research.

  • Diebold, F. X., & Mariano, R. S. (2002). Comparing predictive accuracy. Journal of Business & Economic Statistics, 20(1), 134–144.

    Google Scholar 

  • El-Hammady, A. I., & Abo-Rizka, M. (2011). Neural network based stock market forecasting. IJCSNS, 11(8), 204.

    Google Scholar 

  • Emin, A. V. C. I. (2011). Stock market forecasting with artificial neural network models: An analysis of literature and an application on ISE-30 index. İktisat Fakültesi Mecmuası, 59(1), 55.

    Google Scholar 

  • Fama, E. F. (1965). Random walks in stock market prices. Financial Analysts Journal, 21, 55–59.

    Google Scholar 

  • Fernandez-Rodrıguez, F., Gonzalez-Martel, C., & Sosvilla-Rivero, S. (2000). On the profitability of technical trading rules based on artificial neural networks: Evidence from the Madrid stock market. Economics letters, 69(1), 89–94.

    Google Scholar 

  • Gencay, R. (1998). Optimization of technical trading strategies and the profitability in security markets. Economics Letters, 59(2), 249–254.

    Google Scholar 

  • Gencay, R. (1999). Linear, non-linear and essential foreign exchange rate prediction with some simple technical trading rules. Journal of International Economics, 47(1), 91–107.

    Google Scholar 

  • Grossman, S. (1976). On the efficiency of competitive stock markets where trades have diverse information. The Journal of Finance, 31(2), 573–585.

    Google Scholar 

  • Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American Economic Review, 70, 393–408.

    Google Scholar 

  • Hasan, M. H., Al Hazza, M., ALGrafi, M. W., & Syed, Z. I. (2014). ANN modeling of nickel base super alloys for time dependent deformation. Journal of Automation and Control Engineering, 2(4), 353–356.

    Google Scholar 

  • Hassibi, B., Stork, D. G., & Wolff, G. J. (1993). Optimal brain surgeon and general network pruning. In IEEE International Conference on Neural Networks, 1993., (pp. 293–299).

  • Hill, T., Marquez, L., O’Connor, M., & Remus, W. (1994). Artificial neural network models for forecasting and decision making. International Journal of Forecasting, 10(1), 5–15.

    Google Scholar 

  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximator. Neural Networks, 2(5), 359–366.

    Google Scholar 

  • Hornik, K., Stinchcombe, M., & White, H. (1990). Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks, 3(5), 551–560.

    Google Scholar 

  • Hsieh, D. A. (1991). Chaos and nonlinear dynamics: Application to financial markets. The Journal of Finance, 46(5), 1839–1877.

    Google Scholar 

  • Hsieh, T. J., Hsiao, H. F., & Yeh, W. C. (2011). Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Applied Soft Computing, 11(2), 2510–2525.

    Google Scholar 

  • Hu, M. Y., Zhang, G. P., Jiang, C. X., & Patuwo, B. E. (1999). A cross-validation analysis of neural network out-of-sample performance in exchange rate forecasting. Decision Sciences, 30(1), 197–216.

    Google Scholar 

  • Ing, C. K. (2007). Accumulated prediction errors, information criteria and optimal forecasting for autoregressive time series. The Annals of Statistics, 35(3), 1238–1277.

    Google Scholar 

  • Inoue, A., & Kilian, L. (2006). On the selection of forecasting models. Journal of Econometrics, 130(2), 273–306.

    Google Scholar 

  • Kanas, A. (2001). Neural network linear forecasts for stock returns. International Journal of Finance & Economics, 6(3), 245–254.

    Google Scholar 

  • Kanas, A., & Yannopoulos, A. (2001). Comparing linear and nonlinear forecasts for stock returns. International Review of Economics & Finance, 10(4), 383–398.

    Google Scholar 

  • Kirkpatrick, C. D., & Dahlquist, J. (2010). Technical analysis: The complete resource for financial market technicians. Upper Saddle River: FT press.

    Google Scholar 

  • Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), 169–181.

    Google Scholar 

  • Lam, M. (2004). Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis. Decision Support Systems, 37(4), 567–581.

    Google Scholar 

  • Larose, D. T. (2005). K‐Nearest neighbor algorithm. Discovering knowledge. Data: An introduction to data mining (pp. 90–106). Hoboken: Wiley.

    Google Scholar 

  • LeBaron, B. (2000). The stability of moving average technical trading rules on the Dow Jones Index. Derivatives Use, Trading and Regulation, 5(4), 324–338.

    Google Scholar 

  • Ledoit, O., Santa-Clara, P., & Wolf, M. (2003). Flexible multivariate GARCH modeling with an application to international stock markets. Review of Economics and Statistics, 85(3), 735–747.

    Google Scholar 

  • Lee, C. F., Wu, C., & Wei, K. C. (1990). The heterogeneous investment horizon and the capital asset pricing model: Theory and implications. Journal of Financial and Quantitative Analysis, 25(03), 361–376.

    Google Scholar 

  • Leigh, W., Purvis, R., & Ragusa, J. M. (2002). Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: A case study in romantic decision support. Decision Support Systems, 32(4), 361–377.

    Google Scholar 

  • Leitch, G., & Tanner, J. E. (1991). Economic forecast evaluation: Profits versus the conventional error measures. The American Economic Review, 81(3), 580–590.

    Google Scholar 

  • Lento, C., & Gradojevic, N. (2011). The profitability of technical trading rules: A combined signal approach. Journal of Applied Business Research (JABR), 23(1), 13–27.

    Google Scholar 

  • Lento, C., Gradojevic, N., & Wright, C. S. (2007). Investment information content in Bollinger Bands? Applied Financial Economics Letters, 3(4), 263–267.

    Google Scholar 

  • Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies, 1(1), 41–66.

    Google Scholar 

  • Lo, A. W., & MacKinlay, A. C. (2011). A non-random walk down Wall Street. Princeton: Princeton University Press.

    Google Scholar 

  • Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705–1770.

    Google Scholar 

  • MacKay, D. J. (1992). Bayesian interpolation. Neural Computation, 4(3), 415–447.

    Google Scholar 

  • Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.

    Google Scholar 

  • Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.

    Google Scholar 

  • Marcellino, M., Stock, J. H., & Watson, M. W. (2006). A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. Journal of Econometrics, 135(1), 499–526.

    Google Scholar 

  • Marshall, B. R., Young, M. R., & Cahan, R. (2008). Are candlestick technical trading strategies profitable in the Japanese equity market? Review of Quantitative Finance and Accounting, 31(2), 191–207.

    Google Scholar 

  • Marshall, B. R., Young, M. R., & Rose, L. C. (2006). Candlestick technical trading strategies: Can they create value for investors? Journal of Banking & Finance, 30(8), 2303–2323.

    Google Scholar 

  • Matilla-García, M., & Argüello, C. (2005). A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the Spanish Stock Market. Applied Economics Letters, 12(5), 303–308.

    Google Scholar 

  • Medsker, L., Turban, E., & Trippi, R. R. (1993). Neural network fundamentals for financial analysts. The Journal of Investing, 2(1), 59–68.

    Google Scholar 

  • Menezes, J. M. P, Jr, & Barreto, G. A. (2008). Long-term time series prediction with the NARX network: An empirical evaluation. Neurocomputing, 71(16), 3335–3343.

    Google Scholar 

  • Merkhofer, M. W. (1977). The value of information given decision flexibility. Management Science, 23(7), 716–727.

    Google Scholar 

  • 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.

    Google Scholar 

  • Mizuno, H., Kosaka, M., Yajima, H., & Komoda, N. (1998). Application of neural network to technical analysis of stock market prediction. Studies in Informatics and control, 7(3), 111–120.

    Google Scholar 

  • Murphy, C. M., Koehler, G. J., & Fogler, H. R. (1997). Artificial stupidity. The Journal of Portfolio Management, 23(2), 24–29.

    Google Scholar 

  • Ngia, L. S., & Sjoberg, J. (2000). Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg–Marquardt algorithm. Signal Processing, IEEE Transactions on, 48(7), 1915–1927.

    Google Scholar 

  • Osler, C. L. (2003). Currency orders and exchange rate dynamics: An explanation for the predictive success of technical analysis. The Journal of Finance, 58(5), 1791–1820.

    Google Scholar 

  • Ou, J. A., & Penman, S. H. (1989). Financial statement analysis and the prediction of stock returns. Journal of Accounting and Economics, 11(4), 295–329.

    Google Scholar 

  • Ozun, A., & Cifter, A. (2010). A wavelet network model for analyzing exchange rate effects on interest rates. Journal of Economic Studies, 37(4), 405–418.

    Google Scholar 

  • Park, C. H., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21(4), 786–826.

    Google Scholar 

  • Percival, D. B., & Walden, A. T. (2000). Cambridge series in statistical and probabilistic mathematics. Wavelet methods for time series analysis. Cambridge: Cambridge University Press.

    Google Scholar 

  • Pesaran, M. H., & Timmermann, A. (1995). Predictability of stock returns: Robustness and economic significance. The Journal of Finance, 50(4), 1201–1228.

    Google Scholar 

  • Peterson, G. E., St Clair, D. C., Aylward, S. R., & Bond, W. E. (1995). Using Taguchi’s method of experimental design to control errors in layered perceptrons. IEEE Transactions on Neural Networks, 6(4), 949–961.

    Google Scholar 

  • Prechelt, L. (1998a). Automatic early stopping using cross validation: Quantifying the criteria. Neural Networks, 11(4), 761–767.

    Google Scholar 

  • Prechelt, L. (1998b). Early stopping-but when? Neural networks: Tricks of the trade. Lecture Notes in Computer Science (Vol. 1524, pp. 55–69). Berlin: Springer.

    Google Scholar 

  • Ramsey, J. B. (2002). Wavelets in economics and finance: Past and future. Studies in Nonlinear Dynamics & Econometrics, 6(3), 1558–3708.

    Google Scholar 

  • Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6(2), 41–49.

    Google Scholar 

  • Sarle, W. S. (1995). Stopped training and other remedies for overfitting. In Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics (pp. 352–360). Interface Foundation of North America.

  • Saunders, M. N., Saunders, M., Lewis, P., & Thornhill, A. (2011). Research methods for business students, 5/e. Delhi: Pearson Education India.

    Google Scholar 

  • Sharma, M. K., Sushil, & Jain, P. K. (2010). Revisiting flexibility in organizations: Exploring its impact on performance. Global Journal of Flexible Systems Management, 11(3), 51–68.

    Google Scholar 

  • Shi, F., Liu, Y. Y., Kong, X., & Chen, Y. (2013). Artificial Neural Network for search for metal poor galaxies. arXiv preprint. http://arxiv.org/abs/1312.1779.

  • Smith, G., & Ryoo, H. J. (2003). Variance ratio tests of the random walk hypothesis for European emerging stock markets. The European Journal of Finance, 9(3), 290–300.

    Google Scholar 

  • Smith, B. L., Williams, B. M., & Keith Oswald, R. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C, 10(4), 303–321.

    Google Scholar 

  • Sushil, (2000). Systemic flexibility. Global Journal of Flexible Systems Management, 1(1), 77–80.

    Google Scholar 

  • Sushil, (2001). SAP–LAP framework. Global Journal of Flexible Systems Management, 2(1), 51–55.

    Google Scholar 

  • Sushil, (2005). A flexible strategy framework for managing continuity and change. International Journal of Global Business and Competitiveness, 1(1), 22–32.

    Google Scholar 

  • Sushil, (2007). Principles of flowing stream strategy. Global Journal of Flexible Systems Management, 8(3), iii–iv.

    Google Scholar 

  • Sushil, (2012). Multiple perspective of flexible system management. Global Journal of Flexible Systems Management, 13(1), 1–2.

    Google Scholar 

  • Sushil, (2013). Managing flexibility: Developing a framework of flexibility maturity model. Proceedings of GLOGIFT, 13, 1–15.

    Google Scholar 

  • Tanaka-Yamawaki, M., & Tokuoka, S. (2007). Adaptive use of technical indicators for the prediction of intra-day stock prices. Physica A, 383(1), 125–133.

    Google Scholar 

  • Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. Journal of International Money and Finance, 11(3), 304–314.

    Google Scholar 

  • Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.

    Google Scholar 

  • Upton, D. M. (1994). The management of manufacturing flexibility. California Management Review, 36(2), 72–89.

    Google Scholar 

  • Van Horne, J. C., & Parker, G. G. (1967). The random-walk theory: An empirical test. Financial Analysts Journal, 23(6), 87–92.

    Google Scholar 

  • Wei, C. Z. (1992). On predictive least squares principles. The Annals of Statistics, 20(1), 1–42.

    Google Scholar 

  • Weigend, A. S., Rumelhart, D. E., & Huberman, B. A. (1991, July). Generalization by weight-elimination applied to currency exchange rate prediction. In IJCNN-91-Seattle International Joint Conference on Neural Networks, 1991, 1 (pp. 837–841).

  • White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097–1126.

    Google Scholar 

  • Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543–551.

    Google Scholar 

  • Yao, Y., Rosasco, L., & Caponnetto, A. (2007). On early stopping in gradient descent learning. Constructive Approximation, 26(2), 289–315.

    Google Scholar 

  • Yao, J., & Tan, C. L. (2000). A case study on using neural networks to perform technical forecasting of forex. Neurocomputing, 34(1), 79–98.

    Google Scholar 

  • Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.

    Google Scholar 

  • Zhang, G. P. (2004). Business forecasting with artificial neural networks: An overview. Neural networks in business forecasting (pp. 1–22). Hershey: Idea Group Publishing.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Symbiosis Institute of Operation Management, Symbiosis International University, Nashik, 422008, India

    Anindya Chakrabarty & Rameshwar Dubey

  2. Department of Management Studies, National Institute of Technology, M. G. Avenue, Durgapur, 713209, India

    Anupam De

Authors
  1. Anindya Chakrabarty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anupam De
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rameshwar Dubey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rameshwar Dubey.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chakrabarty, A., De, A. & Dubey, R. A Flexible Approach Towards Multi-frequency Re-engineering of the Moving Average Convergence Divergence Indicator. Glob J Flex Syst Manag 15, 219–234 (2014). https://doi.org/10.1007/s40171-014-0068-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40171-014-0068-7

Keywords

Search

Navigation