Moving Averages for Financial Data Smoothing

  • Aistis Raudys
  • Vaidotas Lenčiauskas
  • Edmundas Malčius
Part of the Communications in Computer and Information Science book series (CCIS, volume 403)


For a long time moving averages has been used for a financial data smoothing. It is one of the first indicators in technical analysis trading. Many traders debated that one moving average is better than other. As a result a lot of moving averages have been created. In this empirical study we overview 19 most popular moving averages, create a taxonomy and compare them using two most important factors – smoothness and lag. Smoothness indicates how much an indicator change (angle) and lag indicates how much moving average is lagging behind the current price. The aim is to have values as smooth as possible to avoid erroneous trades and with minimal lag – to increase trend detection speed. This large-scale empirical study performed on 1850 real-world time series including stocks, ETF, Forex and futures daily data demonstrate that the best smoothness/lag ratio is achieved by the Exponential Hull Moving Average (with price correction) and Triple Exponential Moving Average (without correction).


moving average smoothing filer time series smoothness lag hull exponential TRIX 


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  1. 1.
    Hamilton, J.D.: Time series analysis, vol. 2. Princeton University Press, Princeton (1994)Google Scholar
  2. 2.
    Tan, Z., Quek, C., Cheng, P.Y.K.: Stock trading with cycles: A financial application of ANFIS and reinforcement learning. Expert Systems with Applications 38(5) (2011)Google Scholar
  3. 3.
    Perry, J.: Kaufman, New Trading Systems and Methods, 4th edn. John Wiley & Sons (2005)Google Scholar
  4. 4.
    Ni, Y.-S., Lee, J.-T., Liao, Y.-C.: Do variable length moving average trading rules matter during a financial crisis period? Applied Economics Letters (2012)Google Scholar
  5. 5.
    Marques, N.C., Gomes, C.: Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy. In: Bi, Y., Williams, M.-A. (eds.) KSEM 2010. LNCS, vol. 6291, pp. 579–584. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Ruseckas, J., Gontis, V., Kaulakys, B.: Nonextensive Statistical Mechanics Distributions And Dynamics of Financial Observables From The Nonlinear Stochastic Differential Equations. Advances in Complex Systems 15(suppl. 1) (2012)Google Scholar
  7. 7.
    Jurgutis, A., Simutis, R.: An investor risk profiling using fuzzy logic-based approach in multi-agents decision support system. In: Proceedings of the 17th International Conference on Information and Software Technologies, Kaunas (2011)Google Scholar
  8. 8.
    John, E.: Cybernetic Analysis for Stocks and Futures, pp. 213–227. John Wiley & Sons (2004)Google Scholar
  9. 9.
    John, E.: Rocket Science for Traders, 245 pages. John Wiley & Sons (2001)Google Scholar
  10. 10.
    Kirkpatrick, C.D., Dahlquist, J.R.: The Complete Resource for Financial Market Technicians, pp. 39–50. Financial Times Press (2006)Google Scholar
  11. 11.
    Tillson, T.: Smoothing Techniques For More Accurate Signals. Stocks & Commodities 16, 33–37 (1998)Google Scholar
  12. 12.
  13. 13.
    John, E.: Cybernetic Analysis for Stocks and Futures, pp. 213–227. John Wiley & Sons (2004)Google Scholar
  14. 14.
    John, E.: Rocket Science for Traders. John Wiley & Sons (2001)Google Scholar
  15. 15.
    Person, P.-O., Strang, G.: Smoothing by Sawitzky-Golay and Legendre filters,
  16. 16.
    Ellis, C.A., Parbery, S.A.: Is smarter better? A comparison of adaptive, and simple moving average trading strategies. Research in International Business and Finance 19(3), 399–411 (2005)CrossRefGoogle Scholar
  17. 17.
    Skurichina, M.: Effect of the kernel functional form on the quality of nonparametic Parzen window classifier. In: Raudys, S. (ed.) Statistical Problems of Control, vol. 93, pp. 167–181. Institute Mathematics and Informatics, Vilnius (1991) (in Russian)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aistis Raudys
    • 1
  • Vaidotas Lenčiauskas
    • 1
  • Edmundas Malčius
    • 1
  1. 1.Faculty of Mathematics and InformaticsVilnius UniversityVilniusLithuania

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