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Monthly Moving Averages—An Effective Investment Tool?*

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  • James, F. E.

Abstract

Analysts and investment advisors have long searched for investment tools that would either furnish predictive probabilities for future security price movements, or would aid in minimizing losses. One such tool, often recommended by market practitioners, is the Moving Average. This article describes a series of experiments that were performed upon actual market data, using Moving Averages of different lengths and weights, and presents results of the experiments. Conclusions derived from these experiments are suggested.

Suggested Citation

  • James, F. E., 1968. "Monthly Moving Averages—An Effective Investment Tool?*," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 3(3), pages 315-326, September.
    • Handle: RePEc:cup:jfinqa:v:3:y:1968:i:03:p:315-326_01
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    Cited by:

    1. Gunasekarage, Abeyratna & Power, David M., 2001. "The profitability of moving average trading rules in South Asian stock markets," Emerging Markets Review, Elsevier, vol. 2(1), pages 17-33, March.
    2. Andreas Thomann, 2021. "Multi-asset scenario building for trend-following trading strategies," Annals of Operations Research, Springer, vol. 299(1), pages 293-315, April.
    3. Kung, James J., 2009. "Predictability of Technical Trading Rules: Evidence from the Taiwan Stock Market," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 5(1-2), pages 1-17, March.
    4. Urquhart, Andrew & Gebka, Bartosz & Hudson, Robert, 2015. "How exactly do markets adapt? Evidence from the moving average rule in three developed markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 127-147.
    5. Han, Yufeng & Hu, Ting & Yang, Jian, 2016. "Are there exploitable trends in commodity futures prices?," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 214-234.
    6. Jason F. Nicholls & Andries P. Engelbrecht, 2019. "Co‐evolved genetic programs for stock market trading," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(3), pages 117-136, July.
    7. Matthew Lorig & Zhou Zhou & Bin Zou, 2017. "A Mathematical Analysis of Technical Analysis," Papers 1710.09476, arXiv.org, revised Feb 2019.
    8. Kung, James J. & Wu, E-Ching, 2013. "An evaluation of some popular investment strategies under stochastic interest rates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 96-108.
    9. Marcus Davidsson, 2014. "Tactic Asset Allocation and Conditional Return Expectations," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 3(2), pages 1-1.
    10. Roy Hayes & Jingwei Wu & Ruijra Chaysiri & Jean Bae & Peter Beling & William Scherer, 2016. "Effects of time horizon and asset condition on the profitability of technical trading rules," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(1), pages 41-59, January.
    11. Yu, Hao & Nartea, Gilbert V. & Gan, Christopher & Yao, Lee J., 2013. "Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 356-371.
    12. Jaime Alberto Gómez Vilchis & Federico Hernández Álvarez & Luis Ignacio Román de la Sancha, 2021. "Autómata Evolutivo (AE) para el mercado accionario usando Martingalas y un Algoritmo Genético," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(4), pages 1-22, Octubre -.
    13. Karol Chojnacki & Robert Ślepaczuk, 2023. "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ense," Working Papers 2023-15, Faculty of Economic Sciences, University of Warsaw.

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