2. Purpose:
•Amazon, is an American electronic commerce and
cloud computing company with headquarters in Seattle,
Washington. It is the largest Internet-based retailer in
the United States. It started out as an online book seller
and later diversified into a variety of consumer-related
products, including electronics, music, videos, toys and
much more.
•For online retailers, web searches for a company are
often equated with increased sales revenue, market
share, and profits.
•The purpose of this study was to predict Amazon stock
prices using the Google Trends searchs on
“Amazon.com.” Google Trends provides an index of the
popularity of particular web search terms.
3. Previous
Literature
• The Relevance of Web Traffic for Internet Stock Prices
by Shivaram Rajgopal, Suresh Kotha, and Mohan
Venkatachalam.
In their paper they mentioned that “without traffic and a
critical mass of visitors it is impossible to build customer
relationship.” Consumer relationship is extremely
important when determining if you are going to remain a
viable as a company.
The literature is fairly sparse given
that google trends has only been
around since 2004.
• A working paper entitled Predictability of stock market
activity using Google search queries by Pedro Latoeiro,
Sofia B. Ramos and Helena Veiga
There paper analyzes whether web search queries predict
stock market activity in a sample of the largest European
stocks. They suggest that an increase in web searches for
stocks on GoogleTrends is followed by a temporary
increase in volatility and volume and a drop in cumulative
returns.
4. My Data
I gathered from multiple sources:
• google trends on the search term “amazon.com” and other close
competitors both internet based and brick-and-mortar stores.
• Amazon.com stock price from yahoofinance.com
• I included the Russell 3000 index to control for the overall movements of
the stock market.
• I created a seasonal dummy variables (DEC) to check for seasonality since
Amazon is a retailer…you know…holiday shopping!
• I created a recession dummy for recession months during the timeframe of
my data.
• The inflation rate and unemployment rate was obtained from the Bureau of
the Census to control for overall economic conditions.
• Finally, I included a trend variable (T) and squared it (T2) to account for an
obvious upward-bending trend in the data.
5. Descriptive Statistics
Variable N Mean Std Dev Minimum Maximum
Date 144 17516.46 1691.35 14610 20423
T 144 96.5 55.569776 1 192
T2 144 12384.17 11073.08 1 36864
Dec 144 0.0833333 0.277108 0 1
AmazonAdjClose 144 134.225144 139.167847 5.97 675.890015
BookStoreSales 144 1233.15 458.122258 653 2423
RecessionMonth 144 0.1145833 0.3193512 0 1
amazon_com 144 42.6788194 14.3735827 17 85.75
BarnesNoble 144 2.7288194 1.0281625 1 6.25
ebay 144 70.3152778 11.5732983 43.8 94.25
BestBuy 144 9.1979167 3.1451977 6 19
Chegg 144 9.8690972 13.0629059 0 69
Russell3000 144 773.056407 202.123017 424.880005 1259.26
InflationRate 144 2.2370521 1.3530983 -1.959 5.501
UnemploymentRate 144 6.3036458 1.7752921 3.8 10
• Amazon.com’s adjusted close
ranges from a low of $5.97 to a
high of $675.89 with an average
value of $134.23
• Unemployment during this period
ranged from 3.8% to 10% for the
U.S. The average was 6.3%
• Monthly inflation rates range from
-2% to +5.5%, averaging 2.2% for
the nation
• The Russell3000 ranged from 424
to 1,259, averaging 773 over the
period.
7. On the left is the regression on the original
data. On the right is with the first
difference approach, used because the DW
indicated serial correlation.
Without using the first difference
approach, this model explains 94.25% of
variation in Amazon stock price, but R2 is
likely overstated.
When using the first difference approach,
this model successfully explains 25.27% of
variation in Amazon stock price and
potential for both serial correlation and
heteroscedasticity is reduced.
9. Conclusions
• For Amazon.com, the change in
searches on the term has no
significant impact on the change in
stock prices…doesn’t support
earlier research.
• Major competitors’ stock prices are
not good predictors of Amazon’s.
• The best predictors include the overall
movements in the stock market,
measured by the Russell3000 index
and the unemployment rate. Variables
depicting the economy overall
appears to be the best predictors.
• Amazon’s stock price apparently is
negatively affected during the
holidays…possibly due to the fact that
the last few holiday seasons have
resulted in lower than expected sales
overall.
