Mini Case 1
Apollo Investments Research
Machine-gradable questions
1. Based on the data in Dashboard 1, which of the following best describes
the relationship between
growth in the parking lot fill rate (Delta Parking Rate) and growth in same-
store sales revenue (Delta
Comp Sales)?
A. There is a positive relationship between growth in the parking lot fill rate
and growth in same-
store sales revenue. As growth in the parking lot fill rate increases, growth in
same-store sales
revenue also increases.
B. There is a negative relationship between growth in the parking lot fill rate
and growth in same-
store sales revenue. As growth in the parking lot fill rate increases, growth in
same-store sales
revenue decreases.
C. There is no discernible relationship between growth in the parking lot fill
rate and growth in same-
store sales revenue.
D. There is a negative relationship between growth in the parking lot fill rate
and growth in same-
store sales revenue. As growth in the parking lot fill rate decreases, growth
in same-store sales
revenue increases.
2. Based on the trend line in Dashboard 1, what is the predicted change in
same-store sales revenue
(Delta Comp Sales) for a 100% increase in the parking lot fill rate (i.e., a one-
unit increase in the
Delta Parking Rate)? State your answer as a percentage and round to the
nearest percent. For
example, if the change is 0.5425, state your answer as 54%.
11%
3. Based on the data in Dashboard 2, for which company is the relationship
between growth in the
parking lot fill rate (Delta Parking Rate) and growth in same-store sales
revenue (Delta Comp
Sales) the strongest? In other words, which company’s growth in the parking
lot fill rate is most
predictive of same-store sales growth?
A. Home Depot (ticker = HD)
B. Lowe’s (ticker = LOW)
C. Target (ticker = TGT)
D. Walmart (ticker = WMT)
Mini Case 1: Case studies – Apollo Investments Research 6
4. Based on the trend lines in Dashboard 2, for which company is the
relationship between growth in
the parking lot fill rate (Delta Parking Rate) and growth in same-store sales
revenue (Delta Comp
Sales) the lowest?
A. Home Depot (ticker = HD)
B. Lowe’s (ticker = LOW)
C. Target (ticker = TGT)
D. Walmart (ticker = WMT)
5. Based on the data in Dashboard 3, for which season of the year is the
relationship between growth
in the parking lot fill rate (Delta Parking Rate) and growth in same-store sales
revenue (Delta Comp
Sales) the strongest? In other words, is growth in the parking lot fill rate most
predictive of same-
store sales growth during holiday months or non-holiday months?
A. Holiday months (orange)
B. Non-holiday months (blue)
6. Based on the trend lines in Dashboard 3, what is the predicted change in
same-store sales revenue
(Delta Comp Sales) for a 100% increase in the parking lot fill rate (i.e., a one-
unit increase in the
Delta Parking Rate) for non-holiday months? State your answer as a
percentage and round to the
nearest percent. For example, if the change is 0.5425, state your answer as
54%.
7. Based on the data in all three dashboards, which of the following is the
most reasonable conclusion
from the data?
A. Growth in the parking lot fill rates can be used to predict growth in same-
store sales revenue.
This is especially true for Lowe’s and non-holiday months.
B. Growth in the parking lot fill rates cannot be used to predict growth in
same-store sales revenue.
C. Growth in the parking lot fill rates can be used to predict growth in same-
store sales revenue. The
relationship between these variables does not vary across companies or
seasons of the year.
Mini Case 1: Case studies – Apollo Investments Research 7
Mini Case 1
Apollo Investments Research
Discussion questions
1. Why do you think analysts and investors care about predicting accounting
metrics before those
metrics are announced to the public?
Analysts and investors want to predict numbers before they’re officially
announced because it helps them get ahead. If they can guess how a
company is doing before everyone else knows, they can make smarter
choices and adjust their investments early. This gives them an edge over
others who have to wait for the official reports to come out.
2. Why would investment companies go to the trouble of launching
nanosatellites into space to improve
their predictions? Why wouldn’t they just rely on traditional sources of data
to make these
predictions?
Investment companies might use nanosatellites because they give them
real-time information that regular data sources can’t provide. These
satellites give frequent updates and more accurate data, which is way better
than the reports and updates that come out at set times. This helps
companies make quicker, smarter decisions based on up-to-date information.
3. Why do you think the relationship between growth in the parking lot fill
rate (Delta Parking Rate) and
growth in same-store sales revenue (Delta Comp Sales) was strongest for
Lowe’s?
Lowe’s might have the strongest relationship because its customers and
sales model could directly match parking lot trends. This is especially true if
Lowe’s has a lot of DIY customers, since their store visits often show a clear
link to actual sales. When more people are in the parking lot, it probably
means more people are shopping and buying.
4. Why do you think the relationship between growth in the parking lot fill
rate (Delta Parking Rate) and
growth in same-store sales revenue (Delta Comp Sales) was strongest during
non-holiday months?
Was this consistent with your expectations?
Non-holiday months might show a stronger and more stable relationship
because they aren't affected by big shopping spikes or holiday events like
sales and promotions. These things can mess with the usual connection
between parking lot activity and sales. Non-holiday months tend to have
more predictable shopping patterns, so it's easier to see a clearer link
between the parking lot and store sales.
5. The dashboards cover the time period from 2012–18. How do you think
the relationships examined
Since satellite data has been getting more accurate over time, if this trend
continued past 2018, we would likely see an even stronger connection
between parking lot data and sales. As algorithms and data processing
improve, these predictions will become more reliable. However, changes in
how people shop and shifts in the retail industry could affect how useful this
data is in the future.
