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Optimising the smoothness and accuracy of moving average for stock price data

    Aistis Raudys Affiliation
    ; Židrina Pabarškaitė Affiliation

Abstract

Smoothing time series allows removing noise. Moving averages are used in finance to smooth stock price series and forecast trend direction. We propose optimised custom moving average that is the most suitable for stock time series smoothing. Suitability criteria are defined by smoothness and accuracy. Previous research focused only on one of the two criteria in isolation. We define this as multi-criteria Pareto optimisation problem and compare the proposed method to the five most popular moving average methods on synthetic and real world stock data. The comparison was performed using unseen data. The new method outperforms other methods in 99.5% of cases on synthetic and in 91% on real world data. The method allows better time series smoothing with the same level of accuracy as traditional methods, or better accuracy with the same smoothness. Weights optimised on one stock are very similar to weights optimised for any other stock and can be used interchangeably. Traders can use the new method to detect trends earlier and increase the profitability of their strategies. The concept is also applicable to sensors, weather forecasting, and traffic prediction where both the smoothness and accuracy of the filtered signal are important.

Keyword : moving average filter, smoothness and accuracy, weight optimisation, triple smoothed exponential moving average (TSEMA), custom moving average

How to Cite
Raudys, A., & Pabarškaitė, Židrina. (2018). Optimising the smoothness and accuracy of moving average for stock price data. Technological and Economic Development of Economy, 24(3), 984-1003. https://doi.org/10.3846/20294913.2016.1216906
Published
May 18, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Bury, T. 2014. Predicting trend reversals using market instantaneous state, Physica A: Statistical Mechanics and its Applications 404: 79–91. https://doi.org/10.1016/j.physa.2014.02.044

Chikhi, M.; Péguin-Feissolle, A.; Terraza, M. 2013. SEMIFARMA-HYGARCH modeling of Dow Jones return persistence, Computational Economics 41(2): 249–265. https://doi.org/10.1007/s10614-012-9328-9

Ehlers, J. 2001. Rocket science for traders. New York: JohnWiley.

Ellis, C. A.; Parbery, S. A. 2005. Is smarter better? A comparison of adaptive, and simple moving average trading strategies, Research in International Business and Finance 19(3): 399–411. https://doi.org/10.1016/j.ribaf.2004.12.009

Fiedor, P. 2014. Sector strength and efficiency on developed and emerging financial markets, Physica A: Statistical Mechanics and its Applications 413: 180–188. https://doi.org/10.1016/j.physa.2014.06.066

Grebenkov, D. S.; Serror, J. 2014. Following a trend with an exponential moving average: analytical results for a Gaussian model, Physica A: Statistical Mechanics and its Applications 394: 288–303. https://doi.org/10.1016/j.physa.2013.10.007

Hendrix, E. M. T.; Lancinskas, A. 2015. On benchmarking stochastic global optimization algorithms, INFORMATICA 26(4): 649–662. https://doi.org/10.15388/Informatica.2015.69

Hull, A. 2004. Hull moving average (HMA) [online], [cited 24 March 2011]. Available from Internet: http://www.justdata.com.au/Journals/AlanHull/hull_ma.htm

Kaufman, P. J. 2013. Trading systems and methods. John Wiley & Sons.

Kononovicius, A.; Gontis, V. 2013. Three-state herding model of the financial markets, EPL (Europhysics Letters) 101(2): 28001. https://doi.org/10.1209/0295-5075/101/28001

Kononovicius, A.; Gontis, V. 2015. Herding interactions as an opportunity to prevent extreme events in financial markets, The European Physical Journal B 88(7): 1–6. https://doi.org/10.1140/epjb/e2015-60160-0

Kriaučiūnienė, J.; Kovalenkovienė, M.; Meilutytė-Barauskienė, D. 2007. Changes of the Low Flow in Lithuanian Rivers, Environmental Research, Engineering & Management 42(4): 5–12.

Letchford, A.; Gao, J.; Zheng, L. 2011. Penalized least squares for smoothing financial time series, in D. Wang, M. Reynolds (Eds.). Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science. Vol 7106. Berlin, Heidelberg: Springer, 72–81.

Letchford, A.; Gao, J.; Zheng, L. 2012. Optimizing the moving average, in The 2012 International Joint Conference on Neural Networks (IJCNN), 10–15 June 2012, Brisbane, Australia, 1–8.

Letchford, A.; Gao, J.; Zheng, L. 2013. Filtering financial time series by least squares, International Journal of Machine Learning and Cybernetics 4(2): 149–154. https://doi.org/10.1007/s13042-012-0081-0

Makridakis, S.; Wheelwright, S. C. 1977. Adaptive filtering: an an integrated autoregressive/moving average filter for time series forecasting, Journal of the Operational Research Society 28(2): 425–437. https://doi.org/10.1057/jors.1977.76

Oppenheim, A. V.; Schafer, R. W.; Buck, J. R. 1989. Discrete-time signal processing. Prentice-Hall, Inc.

Orłowski, P. 2010. Simplified design of low-pass, linear parameter-varying, finite impulse response filters, Information Technology and Control 39(2): 130–137.

Pereira, C. M.; de Mello, R. F. 2014. TS-stream: clustering time series on data streams, Journal of Intelligent Information Systems 42(3): 531–566.

Raudys, A.; Lenčiauskas, V.; Malčius, E. 2013. Moving averages for financial data smoothing, in T. Skersys, R. Butleris, R. Butkiene (Eds.). Information and Software Technologies. ICIST 2013. Communications in Computer and Information Science. Vol 403. Berlin, Heidelberg: Springer, 34–45. https://doi.org/10.1007/978-3-642-41947-8_4

Raudys, A.; Matkenaite, S. 2016. Analysis of execution methods in US and European futures, The Journal of Trading 11(1): 38–52. https://doi.org/10.3905/jot.2016.11.1.038

Raudys, S.; Raudys, A. 2010. Pairwise costs in multiclass perceptrons, Pattern Analysis and Machine Intelligence, IEEE Transactions on 32(7): 1324–1328.

Ruseckas, J.; Gontis, V.; Kaulakys, B. 2012. Nonextensive statistical mechanics distributions and dynamics of financial observables from the nonlinear stochastic differential equations, Advances in Complex Systems 55(2): 161–167. https://doi.org/10.1142/s0219525912500737

Sakalauskienė, G. 2003. The Hurst phenomenon in hydrology, Environmental Research, Engineering and Management 3(25): 16–20.

Tillson, T. 1998. Smoothing techniques for more accurate signals, Technical Analysis of Stocks and Commodities 16: 57–59.

Ulubeyli, S.; Kazaz, A. 2016. Fuzzy multi-criteria decision making model for subcontractor selection in international construction projects, Technological and Economic Development of Economy 22(2): 210–234. https://doi.org/10.3846/20294913.2014.984363

Wang, Y.; Wu, C. 2013. Efficiency of crude oil futures markets: new evidence from multifractal detrending moving average analysis, Computational Economics 42(4): 393–414. https://doi.org/10.1007/s10614-012-9347-6

Welch, G.; Bishop, G. 1995. An introduction to the Kalman filter. Technical paper. Unpublished.

Yager, R. R. 2008. Time series smoothing and OWA aggregation, Fuzzy Systems, IEEE Transactions on Fuzzy Systems 16(4): 994–1007. https://doi.org/10.1109/TFUZZ.2008.917299

Yager, R. R. 2013. Exponential smoothing with credibility weighted observations, Information Sciences 252: 96–105. https://doi.org/10.1016/j.ins.2013.07.008

Zeng, S. Z.; Su, W. H.; Zhang, C. 2016. Intuitionistic fuzzy generalized probabilistic ordered weighted averaging operator and its application to group decision making, Technological and Economic Development of Economy 22(2): 177–193. https://doi.org/10.3846/20294913.2014.984253

Zhang, B.; Wei, Y.; Yu, J.; Lai, X.; Peng, Z. 2014. Forecasting VaR and ES of stock index portfolio: a Vine copula method, Physica A: Statistical Mechanics and its Applications 416: 112–124. https://doi.org/10.1016/j.physa.2014.08.043

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