Page 214 - DMGT404 RESEARCH_METHODOLOGY

Research Methodology

Notes Column (6) shows the seasonal effect of this decision variable—share trading on the Stock
Exchange. Regardless of heavy or light daily volume, the first hour volume is the heaviest by
far. It is 7.4% above what may be considered average trading volume for any given day. Keep in
mind that a very limited data set was used in this analysis and while the season, reaching its low
point between 1 and 2 p.m., is generally correctly depicted, individual index members may be
exaggerated. What managerial action programs would result from an analyses such as this?
Would traders go out for tea and samosas between 10-11? How about lunch between 1-2? When
would brokers call clients with hot or lukewarm tips? Assuming that a decrease in volume
means a decrease in prices in general during the trading day, when would a savvy trader buy?
When would he sell? Think of some other intervening variables and you have yourself a nice
little bull session in one of Dalal Street’s watering holes. If, in addition, you make money for
yourself or firm, then, you have got it.

Non-linear Analysis

Any number of different curves may be fitted to a data set. The most widely used program in
computer libraries, known as CURFIT, offers a minimum of 5 curves plus the straight line. The
curves may differ from program to program. So, which ones are the “best” ones? There is no
answer. Every forecaster has to decide individually about his pet forecasting tools. We will
discuss and apply three curves in this section. They appear to be promising decision tools
especially in problem situations that in some way incorporate the life cycle concept and the
range of such problems is vast, indeed.
If you take a look again at Figure 10.2, you see that three curves have been plotted. As we know
from many empirical studies, achievement is usually normally distributed. Growth, on the
other hand, seem to be exponentially distributed. The same holds true for decline. As the life
cycle moves from growth to maturity, a parabolic trend may often be used as the forecasting
tool. These are two of the curves that will be considered. The third one is related to the exponential
curve. As you look at the growth stage and mentally extrapolate the trend, your eyes will run off
the page. Now, we know—again from all sorts of empirical evidence—that trees don’t grow
into the high heavens. Even the most spectacular growth must come to an end. Therefore, when
using the exponential forecast, care must be taken that the eventual ceiling or floor ( in the case
of a decline) are not overlooked. The modified exponential trend has the ceiling or floor build
in. It is the third curve to be discussed.

One final piece of advice before we start fitting curves. If you can do it by straight line, do it. For
obvious reasons, just look at Figure 10.2, any possible error—and there is always a built-in five
percent chance—is worse when a curve is fitted. By extending the planning and forecasting
horizon over a reasonable shorter period rather than spectacular but dangerous longer period,
the straight line can serve as useful prediction tool.

The Parabola Fit

The parabola is defined by
y = a + bx + cx 2
c
Where a, b and c are constants a and b have been dealt. c can be treated as acceleration. The
normal equations are (method of least square).
Sy = na + bSx +cSx 2
2
Sxy = aSx + bSx + cSx 3
3
2
2
Sx y = aSx + bSx + cSx 4

208 LOVELY PROFESSIONAL UNIVERSITY