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Monthly Moving Averages—An Effective Investment Tool?**

  • F. E. James


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.



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1 The expressions “predict the future” and “forecast” are used in this paper because they have come to be accepted terminology in the random walk literature. However, they are actually somewhat misleading; after long observation, a bystander might be able to accurately “forecast” the probability of the event: “black on the next ball” occurring on a roulette wheel. This would be true even though successive spins of the wheel are independent.

What is required as proof of dependence, for either a roulette wheel and a change in security price, is to demonstrate that the conditional probability of a future event, given information about past events, is not the same as the unconditional probability of the same future event, given no previous knowledge.

2 This fact is not strictly true; the investor who sells at price Xo and repurchases the same security later at price Xo + e or Xo − e, with equal probability of either event, will improve his position. However, the “loss” of dividends and the drift of prices upward offset this tendency (reduce the probability of repurchasing at Xo − e) to the extent that it is doubtful that the investor will do better than buying and holding.

3 I should like to express my appreciation to Dean Karger and to the faculty of the Rensselaer School of Management for their outstanding support throughout this project.

4 Lorie, James and Fisher, Lawrence, “Rates of Return on Common Stock,” The Journal of Business, Vol. XXXVIII, No. 1 (January 1964), p. 10.

7 Tomlinson, Lucille, Practical Formulas for Successful Investing (New York: William Funk, 1955), p. 243.

8 Ibid, p. 246.

9 Ibid., p. 248.

10 Merritt, Robert E., Financial Independence through Common Stock (New York: Simon and Schuster, Inc., 1957), p. 264.

11 Dahl, Curtiss, Consistent Profits in the Stock Market (Cincinnati, Ohio: Tri-State Offset Co., 1960), p. 155.

12 Granville, Joseph E., A Strategy of daily Stock Market Timing for Maximum Profit (Englewood Cliffs, N. J.: Prentice-Hall, 1960), p. 155.

13 Merritt, Robert D., Financial Independence through Common Stocks (New York: Simon and Schuster, 1957), p. 156.

14 Barnes, Leo, Your Investments (Englewood Cliffs, N. J.: American Research Council, 1967 Edition), p. 107.

15 It is instructive to note that the simple moving average is nothing more than an intercept fitted by the method of least square to successive time periods. See Yamane, Taro, Statistics, An Introductory Analysis (New York, N. Y.: Harper and Row, 1967), p. 860.

16 Some authorities who use or present information relative to 200-day Moving Averages are Granville, op. cit., p. 237, and Barnes, op. cit., p. 182.

17 Barnes, op. cit., p. 108. Note that although the average age of the two methods is identical, they may yield somewhat different answers.

18 James, F. E. Jr., The Implications of Trend Persistency in Portfolio Management, a thesis submitted to the Faculty of the School of Management at the Rensselaer Polytechnic Institute, Troy, New York in June 1967.

19 Where N(0,1) represents the standardized Normal variate. The size of the sample required to make the approximation adequate for tests of significance depends on the nature of the underlying population. As Miller and Freund state: “In practice, the normal distribution provides an excellent approximation to the sampling distribution of x for n as small as 25 or 30, with hardly any restrictions on the shape of the population.” Miller, I. and Freund, J., Probability and Statistics for Engineers (Englewood Cliffs, New Jersey: Prentice-Hall, Inc., 1965), p. 134. In our examples n is never less than 200.

20 The substitution is not direct, of course. The random variable formed by the ratio of a normalized random variable over the quantity obtained by taking the square root of an independent chi-square random variable divided by its degrees of freedom follows the “t” distribution with the degrees of freedom associated with the chi-square variate. Some algebraic manipulation of this quantity will yield a statistic in the form presented above. See Bowker, Albert and Lieberman, Gerald, Engineering Statistics (Englewood Cliffs, New Jersey: Prentice-Hall, Inc., 1964), p. 79.

** The views expressed herein are those of the author and do not necessarily reflect the views of the U. S. Air Force or the Department of Defense.

* Graduate School of Systems and Logistics, Wright-Patterson AFB.

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Journal of Financial and Quantitative Analysis
  • ISSN: 0022-1090
  • EISSN: 1756-6916
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