CMSC734: Application Presentations and Web Page
Visualizing NASDAQ Daily Market Trading and Velocity
Robin Berthier (robinb@umd.edu)
Huyen Dao (daotueh@umd.edu)
Introduction
In traditional stock markets with trading floors, traders often used noise and physical
activity of other traders to determine changes in direction and momentum. A trader can
see where everyone is crowding and listen to who is selling what, who is buying what, what
prices people are shouting, and how frantically other traders are communicating. However,
the NASDAQ stock market is a purely electronic stock market (the world’s first to be in
fact). Therefore, NASDAQ has tried to develop numeric measures that will provide the same
information as the human buzz of a physical floor. These measures are the Market Velocity
and Market Forces. Market Velocity is a numeric equivalent to floor noise and activity. To
calculate velocity, NASDAQ takes the ratio of the actual volume of shares traded and the
expected volume of shares traded multiplied by a factor of 1000. The expected volume of
shares is a 21-day running average of trade volume for a particular time of day. Market
Forces is the ratio of buy orders to sell orders.
The NASDAQ Experimental Market Information site offers daily data including Market
Velocity and Market Forces along with buy and sell price and volume. The data is offered to
customers in relative real-time and customers can see the velocity information for particular
stocks or indices of interest. Market Forces data is offered similarly but not in as varied
time periods as velocity. The data is derived from a file that is essentially a log of all buy
orders and sell orders sent in the system. Each order is timestamped and a running velocity
and force value for that timestamp is calculated. The data analyzed in this report is the raw
log of recorded buys and sells. We should note that the buy and sell orders to do not
precisely correlate. Buy and sell orders are not always requests and response. Sometimes
a buyer will initiate and put an order in for a certain price and quantity and if the seller
agrees to the terms will place a corresponding sell order. However, a seller can also but up
a sell order with an asking price for a given quantity and if a buyer wishes to buy these
shares on these terms he or she will. The majority of buy and sell orders have a response
but there are significant numbers of buy and sell orders that go unanswered. Unfortunately,
the log does not identify corresponding buy and sell orders so we can only analyze sheer
number of buy versus sell orders. In fact, this is the Market Forces ratio, but in terms of
looking at which orders are actually answered, we were unable to determine this with this
data.
The following are the fields relevant to our analysis:
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Add Timestamp – time the order was added to velocity/forces ratios
Ask BBO – prevailing best ask price at the time of the order
Bid BBO – prevailing best bid price at the time of the order
Buy Price – if the order is a buy order, this is the price of the order
Buy Quantity – if the order is a buy order, this is the quantity of the order
ISI – issue symbol
Ratio (Forces) – ratio of buy orders to sell orders currently in the forces ratio
Sell Price – if the order is a sell order, this it the price of the order
Sell Quantity – if the order is a sell order, this is the quantity of the order
Total Actual – Total volume in the forces/velocity calculations
Robin Berthier
Huyen Tue Dao
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Total Expected – Expected volume in forces/velocity calculations based on a 21-day
running total
Timestamp – timestamp placed when record is created
Velocity – velocity ratio = (total actual / total expected) * 1000
The dataset we used contains buy and sell orders from Wednesday, February 7, 2007.
We initially thought that the data was a time series, but in fact, it is a log of sales, and so
we decided to use Spotfire, to help analyze relationships between fields. Another reason we
decided on Spotfire was that we felt it smoothly handled fairly large datasets. The full
dataset of over 3,000 NASDAQ stocks was over 9 million rows long. We selected a subset
of 10 issues to examine. Since NASDAQ is known for being a technology-heavy exchange,
we took 10 leading tech companies: : Google (GOOG), Yahoo! (YHOO), Microsoft (MSFT),
Apple (AAPL), Cisco (CSCO), Intel (INTC), Nvidia (NVDA), Dell (DELL), Ebay (EBAY), and
Amazon (AMZN). Our subset of 10 stocks (or issues as they are generally known and as will
hence be referred) generated a data set of approximately 200,000 rows for both buy and
sell orders. We wanted to make sure that we could analyze this vast number of rows
smoothly with as little crashing as possible. We had hoped to perform analysis over several
days but the size of the dataset and the fact that daily data is wiped at the close of the
market prohibited us from doing this.
We had to do significant parsing of the data. In particular, buy and sell orders are only
distinguishable by the fact that a buy order has zero-valued sell price and sell quantity and
sell order has zero-valued buy price and buy quantity. Also, as we mentioned we decided to
parse out only those orders concerned with the above companies in order to focus our
analysis and shrink our dataset size.
Analysis
Cisco Leading NASDAQ in Market Shares after Second-Quarter Earning Report
The graph below shows total buy and sell quantities for various issues. On February 7,
2007 NASDAQ recorded 101,158 buy orders and 105,549 sell orders. The orders here were
organized into 10-minute slices. It can be immediately seen that the orange and red lines
of Cisco sell and buy orders have the highest peaks on this day. In fact, on February 7,
2007 Cisco announced a 40% increase in profit for its second-quarter [1]. The news pushed
Cisco’s stock up 3% and the NASDAQ Composite Index up 0.8%.
The graph also demonstrates the typical pattern of a day of trades at the NASDAQ Stock
Market. Orders are recorded from 7am to 4:30pm, but share trading really picks up around
9:30am. The last spice on the right shows high trading activity right before the close of the
market. The last minute activity shows how quickly the NASDAQ Stock Market can change.
Visualizing NASDAQ Daily Market Trading and Velocity
Robin Berthier
Huyen Tue Dao
Google’s Sell Order Value Dwarfs Other Top Tech-Based Companies with Sky-High
Prices
The plot below shows the total dollar value of buy orders for each issue over the 10hour period from 7am to 4pm. The graph clearly shows a huge gap between the value of
Google shares traded on February 7 and all of the other 9 technology companies. While
most of the other companies have sell orders totaling less than $50 million, Google has a
staggering $400 million in sell orders.
Visualizing NASDAQ Daily Market Trading and Velocity
Robin Berthier
Huyen Tue Dao
Examining the expected volume of each issue per hour, the expected volume of trades for
Google for each hour is not maximal. In fact a major of the other companies trade more in
volume than Google. However, Google’s huge sell total can be attributed to its average sell
price, $471, much larger than any other company.
Apple and Microsoft Distinguish Themselves on a Day of High Activity
Apple and Microsoft exhibited noticeably different patterns of activity throughout
February 7, 2007. The general patterns of maximum issue velocity shared by the other 7
companies in the dataset showed high activity in the morning, possibly a show of the
bolstering effect that Cisco’s announcement had on most tech stocks, and a second peak
towards the end of the trading day. However, Apple, showed low to moderate activity, with
a few peaks, but for the most part, significantly less activity than the others. The velocity of
Apple shows that shares traded at about the same volume as over the past 3 weeks.
Microsoft on the other had a slow start, with little activity at the opening of the market but
increasing steadily throughout the day. The line for the Dell graph below is not clustered
with the other tech companies because of the fact that no sell orders were timestamped
before 8am. The reason for this cannot be conclusively determined. Perhaps, the sell
orders were timestamped late or perhaps there was no selling before 8am. However, it can
be seen that it is similar to the first cluster with a high activity in the morning, with an early
afternoon lull, and then a small peak again before the close of the market.
Visualizing NASDAQ Daily Market Trading and Velocity
Robin Berthier
Huyen Tue Dao
Spotfire Critique
Spotfire overall was very polished and versatile. It handled large data sets well
without a lot of deterioration in performance, although there was a definite limit to the
number of rows it could reasonably handle.
The controls and the design of the interface were consistent throughout, different
graphs having similar specifications and controls. There could have been more shortcuts for
various tasks like running through pages or adding common graphs. There was decent
feedback; however, there were some instances where the feedback did not change once a
new action was taken. For example, when creating a custom expression for a chart axis,
Spotfire will give feedback if an erroneous display name or expression was entered and
gives a description of the problem, but sometimes when the user changes the expression or
display name accordingly there is no more feedback to make sure that the new expression
is correct. It seems that Spotfire has a bug in re-checking the expression.
The few design dialogs do give the user closure; however, there are no dialogs for
some common tasks with which the user may want more assistance. There is not as much
error prevention but Spotfire does offer informative error messages, but does not prevent
errors from changing state. For example, if you specify erroneous columns in a graph, the
graph will go blank as opposed to just rejecting the column. Spotfire does have a great
undo feature that allows a user to easily remove mistakes. There is a decent degree of
control given to the user, although there are certain capabilities that are missing that a user
may want to accomplish. Spotfire lacks the capability to overlay graphs of different types,
which may be useful for directly comparing different types of data. Also, some graphs offer
relative scales but not all of the graphs do. We had wanted to do other types of
comparisons in relative growth of price and volume, but because the bar graphs and the
Visualizing NASDAQ Daily Market Trading and Velocity
Robin Berthier
Huyen Tue Dao
scatter plots cannot have relative scales, it was hard to see patterns in relative growth and
decrease since the price and volume magnitudes could vary greatly.
Also, it was helpful to have all of the visualizations linked. The filtering capabilities
were concise but powerful, although it would have been convenient to be able to specify
different filters for different pages, in case the user wanted to examine different portions of
the data simultaneously. Another thing that Spotfire does well is providing lots of
information to user with tooltips or secondary windows. Clicking on graph lines or plot
points immediately yields details and all of the graphs are linked meaning that further
information is demonstrated with highlighting rather than requiring the user to remember
the behavior of various rows across different visualizations.
Overall, Spotfire is well-done and extremely powerful, although there is still a lack of
versatility in small areas and the optimal dataset size could be increased.
References
[1] Russell, Frank M. "Today's Nasdaq, Tech-Stock Rally Powered by Cisco Systems." The
Mercury News 07 Feb. 2007. 18 Feb. 2007
<http://www.mercurynews.com/mld/mercurynews/business/columnists/
business_update/16645542.htm>
Visualizing NASDAQ Daily Market Trading and Velocity