Page 165 - DCOM507_STOCK_MARKET_OPERATIONS

Stock Market Operations

Notes The significance of the relationship can be determined using hypothesis testing
procedure.
(iii) Apply the relationship to estimate demand. If the degree of correlation between
purchases of a given product by present customers and their employment size is
considered significant, the demand estimation can be done as follows:

Computing the average number of items purchased per employee and applying
this ratio to total employment.

Formulating an estimating equation through regression method.
∑ y = Na + b ∑ x

x ∑ = a ∑ + b ∑ x
y
x
Where, a equals the number of products purchased when employment is zero and b
equals the amount of change in the number of products purchased with every change
in total employment.

!
Caution The latter method is more accurate because it is more sensitive to the influence of
independent variable on dependent variable.

Multiple regression analysis facilitates the study of impact of more than one
independent variable on the dependent variable.
Y = a + bx + cx + dx + ex + fx
1 2 3 4 5
Where Y = Yearly sales in lakhs of rupees.
x = yearly sales (lagged one year) in lakhs of rupees
1
x = yearly advertising expenditure in lakhs of rupees
2
x = a dummy variable
3
x = year
4
x = disposable personal income in lakhs of current rupees
5
3. Time series analysis: Time series analysis consists of decomposing the original sales series
over a period of time. The elements derived are:
Trend (T): It is the result of basic developments in population, capital formation, and
technology. It is found by fitting a straight or curved line through past sales.
Cycle (C): It captures the wave-like movement of sales. Many sales are affected by swings
in general economic activity, which tends to be somewhat periodic. The cyclical component
can be useful in intermediate range forecasting.
Season (S): It refers to a consistent pattern of sales movements within the year. The term
season describes any recurrent sales pattern. The seasonal component may be related to
weather factors, holidays, and trade customs. The seasonal pattern provides a norm for
forecasting short-range sales.
Erratic Events (E): It refers to the unpredictable sales caused by unforeseen events like
strikes, riots, war scares, floods, and other disturbances.

Another time series technique is exponential smoothing. For industries with several items in
product line, this technique is useful to produce efficient and economical short-run forecasts. It
requires only three pieces of information.

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