THE ACCOUNTANT_AUTUMN_2018_VER-7-L

TECHNICAL
see if our returns on Apple Corp stock (notice that
I apply it on returns and not to prices) are roughly
shaped in this manner.
Using some Excel tricks that you will learn, you
can see that the chart above could be called a distant
cousin to the Normal Distribution (nothing will ever
be perfectly Normal in practice) and, for the sake
of this discussion, we can assume that our returns
follow a Bell-Shaped curve with the peak being close
to 0% daily returns (not a surprise here because in
practice you should not expect any money within
the very short period of a day). In practice there are
various statistical tests that one can do but they are
beyond the scope of this exercise.
Relationships
So now that we have assumed that our Apple returns
follow a normal distribution with average daily
return of 0% we can start thinking about doing some
forecasting. Can we create a tool which accepts an
input and gives us an output for expected return?
The answer to this is “yes!” and the technique (that
you will also learn in class) is called Regression.
Before going into the details of what Regression
is, I would like to discuss this question with you: Since
Apple is a company that is part of the Tech industry,
is it reasonable to think that what happens in the
tech industry in general can have a major effect on
Apple’s fortunes as a company? Intuitively we might
think that the answer is “yes”. This is because every
company out there is affected by both internal affairs
(profitability, efficiency of personnel, technological
breakthroughs, scandals and conflict etc) and also
system-wide affairs (economy, inflation, tourism,
media relations etc). It is, therefore, fair to ask by
“how much” the Tech industry effects the fortunes
of Apple. The Regression tool that you will learn in
this course tackles exactly this problem. If some
conditions on your data are satisfied (errors look like
a bell-shaped graph etc) you can safely apply this tool
and get some forecasts on future data points to a
reasonable degree of accuracy.
I will use this technique here and try to find
the strength of the relationship between the Tech
industry and Apple. To do this, I will use the NASDAQ
index, which is the Index following the top Tech
companies in the US, and measure the strength of
the linear relationship between NASDAQ index and
Apple stock. In a nutshell, what regression does is it
attempts to fit a straight line (called line of Best Fit)
to a scatter plot formed between Apple Returns and
NASDAQ Index Returns, just like the chart below:
Focusing only on the last 2 years of daily data and
applying the in-built Excel Regression function I get
the following relationship:
Apple Returns =
NASDAQ Returns * 1.05 + 0.000414
Notice that this has the classical Y = mX + C Straight
Line format that you learnt at school. This is because we are
fitting a line. The 1.05 is the gradient and the 0.000414 is the
y-intercept. Notice also that since NASDAQ returns are my
“X-Variable”, they are being used to predict the “Y-Variable”,
in this case Apple Returns.
The interpretation is that 1% increase in NASDAQ
returns leads to 1.05% increase in the returns of
Apple. It is to be kept in mind that this discussion
is very basic, and in practice things are more
complicated. However, this example should be a
good taste of the type of analytical tools that will
be applied in a finance degree to take a “Scientific
Approach” to the decision-making problem of
Investment. The Above indicates that if we could
find a trend for NASDAQ returns (being an index it is
easier to find such a trend) we could then apply the
above formula to forecast Apple Corp returns in the
next day. In practice you would want to apply such an
analysis to at least monthly or quarterly data to have
more predictive power.
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