Abstract
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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hamilton, J.D.: Time series analysis, vol. 2. Princeton University Press, Princeton (1994)
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)
Perry, J.: Kaufman, New Trading Systems and Methods, 4th edn. John Wiley & Sons (2005)
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)
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)
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)
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)
John, E.: Cybernetic Analysis for Stocks and Futures, pp. 213–227. John Wiley & Sons (2004)
John, E.: Rocket Science for Traders, 245 pages. John Wiley & Sons (2001)
Kirkpatrick, C.D., Dahlquist, J.R.: The Complete Resource for Financial Market Technicians, pp. 39–50. Financial Times Press (2006)
Tillson, T.: Smoothing Techniques For More Accurate Signals. Stocks & Commodities 16, 33–37 (1998)
Hull, A.: Hull moving average, http://www.justdata.com.au/Journals/AlanHull/hull_ma.htm
John, E.: Cybernetic Analysis for Stocks and Futures, pp. 213–227. John Wiley & Sons (2004)
John, E.: Rocket Science for Traders. John Wiley & Sons (2001)
Person, P.-O., Strang, G.: Smoothing by Sawitzky-Golay and Legendre filters, http://persson.berkeley.edu/pub/persson03smoothing.pdf
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raudys, A., Lenčiauskas, V., Malčius, E. (2013). Moving Averages for Financial Data Smoothing. In: Skersys, T., Butleris, R., Butkiene, R. (eds) Information and Software Technologies. ICIST 2013. Communications in Computer and Information Science, vol 403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41947-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-41947-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41946-1
Online ISBN: 978-3-642-41947-8
eBook Packages: Computer ScienceComputer Science (R0)