This research analyzed securities that experienced large price jumps on opening to test market efficiency. Researchers looked at price movements of over 1,500 stocks the day after a 30-100% jump. Stocks were divided into small, medium, and large groups by trading volume. Results showed stocks in the smallest volume group had the largest average price movements after opening, suggesting markets are less efficient for less actively traded stocks. The findings support the theory that a stock's competitive status in the market, as measured by volume, influences how efficiently new information is incorporated into prices.

1. Gerstein Fisher Financial Fellowship Research Report
Research Team
Derek Mohr: Director of SUNY at Buffalo M.S. in Finance Program
John Spencer: Accounting and Finance Undergraduate Major
Purpose
The purpose of this research was to conduct a micro­test of market efficiency.
Method
Test Structure
How was this idea tested? We captured large percentage jumps on the opening price for select
securities and measured reactions the next day. For example, XYZ stock’s close on Monday
was $10 / share. On Tuesday, the opening price for XYZ is $15 / share. This 50% jump would
indicate some type of information event occurred after the close on Monday and before the
Open on Tuesday. Ultimately, this research measured the relationship between volume (our
selected metric for competitiveness) and market adjusted day one percentage changes (what
happened on Tuesday).
Sources
Research was conducted using WRDS, the Wharton Research Database Services databases.
The primary database we used was CRSP, the Center for Research in Securities Prices.
Tools
The programming languages used to extract the data were SQL and SAS along with SAS PC
Connect. The software used to pull the data from WRDS was SAS.
Controls
Jump Control ­ The jump on open had to be between 30% and 100%. For example, our queries
would select stock information if the initial jump was greater than 30% but less than 100%.

2. Volume Control ­ The volume on day one had to be greater than 1,000 shares.
FIX and Adjust ­ Volume on Day 0 > 1,000 shares while volume on day 1 > then 1,000 shares
Price Control ­ The price of the security had to be greater than $5 per share.
Security Control ­ If the name of the security contained the words “fund” or “trust”, these
securities were excluded from the dataset.
Time Control ­ Our data extends from 1984 to 2014.
Market Control ­ All percentage changes on Day One were adjusted for the market movement
on day one. For example, if security A’s movement on day one was 5% and the market’s
movement on day one was 1%, the day one market adjusted day one percentage change would
be 4%. We used the S&P 500 Index as the variable for the market.
Audit
The data was audited using a third party source to ensure data accuracy. We used Yahoo
Finance to double check random rows with yahoo finance data. Although risk exists regarding
the accuracy of all the data, we are fairly certain the majority of data is accurate.
Results
The queries resulted in 1510 observations. We divided the group by three to categorize the data
into small, medium, and large groups based on day 1 volume. Below are the expectations for
the two prevailing sides in modern finance.
○ Efficiency Expectation
■ Relatively flat next day price movement. Firm specific events may cause
large percentage changes but as a whole, we expect minimal price
movement after the open.
○ Behaviorist Expectation

3. ■ The behaviorist expectation would be either a large buy or sell off after
the open. This would indicate overreactions or under reactions by
investors to some type of information event, ie. a positive (or negative)
earnings release.
Results
Excel Dimensions for charts:
X Axis: 33000 MIN to 40,000 MAX
Y Axis: ­50 MIN to 50 MAX
Dimensions used for reducing effects outliers have on image of information.
Small Chart ­ (Smallest 3rd by Volume on Day One)
Medium Chart ­ (2nd by Volume on Day One)

4.
Large Chart ­ (Largest 3rd by Volume on Day One)
Conclusion
According to modern finance, market efficiency is driven by information. Professor Mohr
and I postulate the relative competitiveness of a security drives efficiency alongside public

5. information. For example, consider hypothetical security A and hypothetical security B. Security
A has 10 potential buyers and sellers. Security B has 100 potential buyers and sellers. Holding
all else constant, which security is valued with greater accuracy assuming all information is
available to the public? We postulate security A would priced with larger deviations from
underlying value relative to security B because the competitiveness surrounding the valuation of
the asset was less substantial than security B. Volume was used as a rough proxy for
competitiveness for this research.
The result of this research is markets appear to be less efficient on day one for
companies with smaller volume than larger volumes. The evidence for this can be found above.
The trend lines for the three categorical sizes of volume (which take outliers into effect) show
how markets tend to overprice securities with smaller volumes than larger volumes. This
evidence would support a theory of market efficiency which explains efficiency as a function of
the security’s competitive status in the market (we measured this by volume ). Financiers and
academics may want to consider the potential of theory which explains how the amount of
analysis and quality of analysis on a security, combined with information available drives
efficiency rather than public information alone. Public data does not make markets efficient.
Analysis of that data does. Companies with smaller volumes showed larger price movements
and tended to possess negative returns after the open. There appears to be no systematic trend
or behavior for companies which had large volumes after the open. By no means is this
research conclusive, exhaustive, or definitive of the preceding theory of efficiency. However, this
evidence provides an opportunity for researchers to discover functions of competitive demand
which together with public information, drive the efficiency in modern markets.
References
● Code used for Wharton Research Database Services available upon request
● Excel spreadsheet used to interpret data derived from code available upon request
● Contact John Spencer at ​johnspencer91@gmail.com​ for information regarding Excel/ SQL and SAS code / information
auditing