NEW TECHNIQUES
I Remember VOMOMA
Adding Volume To The Move-Adjusted Moving Average
by Stephan Bisse
In this second article of the series, we look at how adding volume
can help identify large moves in one direction.
In "Visiting MOMA," my previous Technical
Analysis of STOCKS & COMMODITIES article, a simple moving average
(SMA) was adjusted by the relative magnitude of the change between closes
to create a move-adjusted moving average (MOMA). The reasoning behind this
adjustment was that moving averages on their own can, by definition, never
say anything about the future direction of a time series, only give a view
of where a time series has been. This holds true regardless of the lookback
period used or any weightings applied to the datapoints, be it a linear
weighting such as in a weighted moving average (WMA) or an exponential
moving average (EMA), which uses an exponent to determine the rate at which
the significance of older datapoint decays. It is no coincidence that moving
averages often form the basis of trend-following trading systems.
FUTURE DIRECTION
Logically, the only way that a moving average can act as a predictor
of the prices is if additional information is incorporated into the calculation.
This additional information has to be some sort of leading indicator for
the time series in question.
Two well-known variations on moving averages already introduce additional
information into the calculation: the volatility index dynamic average
(VIDYA) developed by Tushar Chande and the volume-adjusted moving average
(VAMA) developed by Richard Arms. The VIDYA uses a volatility index for
weighting the datapoints, while the VAMA weights the datapoints in the
lookback period based on their corresponding relative volume.
In both moving averages the adjustment is done primarily to improve
their responsiveness in times of heightened volatility or increased volume
with the aim of cutting down on lag. However, if either volatility or volume
is a leading indicator, then in theory VIDYA or VAMA can have predictive
power and go beyond showing where a time series has been.
FIGURE 1: VOMOMA VS. MOMA. VOMOMA turns up before MOMA after
a trough and turns down before MOMA after a peak. VOMOMA smoothes out the
data better than MOMA or the simple moving average.
...Continued in the March issue of Technical Analysis
of STOCKS & COMMODITIES
Excerpted from an article originally published in the March 2005
issue of Technical Analysis of STOCKS & COMMODITIES magazine. All rights
reserved. © Copyright 2005, Technical Analysis, Inc.
Return to March 2005 Contents
