The Immediate Impact of SEC Filings on Volume
and Volatility
David Grossman 1
1
David Grossman is a member of the Class of 2017 at the University of Chicago.
UChicago Undergraduate Business Journal
Abstract
In this paper, I use a stratified single variable regression analysis to explore the causal impact of the
publishing of financial filings with the Securities and Exchange Commission (SEC) on real time market prices
and volume. The intended purpose of SEC filing requirements is to encourage transparent dissemination of
relevant information to shareholders. My work aims to evaluate whether entities in the market actively monitor
the SEC in order to be the first to trade on recent filing information. I found that the average filing is associated
with a 0.0091% change in stock price in the first five minutes after its posting but that this relationship is not
statistically significant. More importantly, I found a highly statistically significant relationship (p<0.001) between
filings and volume. On average, I found that a filing results in a 36.7% increase in volume traded in the first five
minutes after its posting. My findings indicate that markets are more responsive to filings which contain new
information about the company than they are to notifications of trades made by other investors. In addition,
markets are more sensitive to disclosure of trading by company insiders than to disclosure of large acquisitions
by institutions. Lastly, I tested for the existence of insider trading and information leakage: the market reaction
to SEC filings beginning just prior to their actual publication. While I did find weak cumulative evidence of
insider trading based on the direction and magnitude of the observed effect, that effect was not statistically
significant.
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Introduction and Literature Review
There has been a plethora of research done on what moves markets. This includes research focused on
the SEC. Specifically, there have been studies conducted on the impact of SEC-mandated market risk
disclosures, market reactions to SEC filings correcting previous statements, and the benefits of SEC accounting
standards requirements (Feroz et al., 2008; Roulstone, 1999; Linsmeier, 2002). Feroz et al. found that outcomes
of SEC investigations into accounting fraud have significant impact on market prices (Feroz et. al 2008). While it
is possible that traders only pay attention to SEC filings when it turns out that they have been falsified, these
findings suggest that traders are likely make financial bets based on SEC filings, the thesis of which no longer
holds when the numbers are revised – resulting in a reversal of that bet and an associated market fluctuation.
Because this requires traders to pay attention to SEC filings in the first place, there is reason to examine the realtime impact of SEC filings when they first become available in the marketplace. An observed decrease in longterm volatility following detailed discussion of risk in published 10-K (comprehensive annual report) filings
provides additional evidence that markets pay attention to SEC filings (Linsmeier, 2002).
However, no one has yet researched whether SEC filings have real-time implications for stock volume
and volatility (prices) because the data was not available. The SEC does not provide a timestamp for when a
filing is made public, only for when it is electronically filed with the SEC, so previous researchers did not have
access to the actual timestamp of when the information contained in SEC filings became available to the
markets.
Therefore, to facilitate this research, I coded a scanner of the SEC website which constantly monitored
the Edgar database for updates, and collected the data myself.
Due to the infrequency of tangible new market information, I am confident that given a short enough
time window around the publishing of a filing, any non-random relative price movements or increases in volume
traded could be directly attributed to the filing. Conservatively, I picked an observation time frame of ten
minutes, comparing the five minutes of stock market data after a filing’s posting to the five minutes prior.
Lastly, I attempted to determine if there was any insider trading happening through the SEC. Given the
value of having early access to market information, there is an incentive for someone at the SEC to distribute the
filing to interested parties before posting it publicly. If a bribe is acted upon then there could be a non-random
relationship between how long it takes the SEC to post a filing after receiving it and the observed impact of the
filing. Presumably, the longer it takes to post the greater the chance information will be leaked early and the
more time insider traders have to act on that information. The more the information contained in the filing is
priced into the market already at the time of ‘official’ posting, the less of an observed impact it would have. So in
the case of insider trading, I would expect a small (insider traders constitute a small fraction of the market)
negative relationship between how long it took to post a filing after receiving it and the magnitude of volume
fluctuations associated with that filing.
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The Data Set
To determine the impact of publicizing SEC filings on the real-time state of the financial markets, I
needed data from the SEC and historical stock market data to compare it against.
The SEC Data:
The SEC publishes filings through their Edgar search engine and their RSS Feed. I wrote code which
copied and parsed the contents of the SEC list of the latest 2,000 filings every second. For each filing this
information contained the name of the company, the type of the filing and the time the filing was submitted to
the SEC. By comparing the differences between the contents of different scans which were a second or fraction
of a second apart, I got a second-resolution timestamp for when the filing was actually posted on the SEC
website; if a certain filing is present in the list of most recent 2,000 filings at time T but wasn’t at time T-1 where
T and T-1 are one second apart, then there is a one-second window during which the filing might have been
uploaded. I collected data on filings from February 2, 2014 to May 8, 2014. I ended up with data on a total of
285,713 filings.
The Market Data:
From the website Kibot.com, I got ticker data aggregated to one-second intervals for each of the S&P
500 companies. For each interval the data consisted of open, close, high and low values for price and volume.
Throughout the rest of this study, I use the open price and the volume quantity to conduct my analysis.
Cross-referencing the data
Because the SEC filings only had the name of the filer, not a unique identifier of the company to which
the filing pertained, I had to make the determination of which company’s stock price and volume movements to
analyze around the time of the filing’s posting. Using a conservative word-matching algorithm, I isolated filings
whose names contained the name of an S&P 500 company. For example, a Blackrock Inc. filing would be
matched to the company Blackrock as would a filing by BlackRock Long-Term Municipal Advantage Trust.
Since information about a company’s subsidiaries and branches is directly relevant to the financial value of the
parent company’s stock value, I feel that such a matching was reasonable.
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Types of Filings
Of the 285,713 filings analyzed, 2,364 were filed by entities matched to S&P 500 companies. Those
2,364 filings contained a total variety of 77 different types with varying frequencies. The full distribution can be
seen in Table 1. Due to time constraints, and out of concern for a sufficiently large sample size, the only types of
filings which were analyzed were those whose frequency was greater than 50.
Briefly described, they were:
1. 10-K : Comprehensive Annual Report of Company performance
2. 4, 4A : Statement of Changes in Beneficial Ownership of Securities
3. SC 13G/A, SC 13G : Schedule filed to report acquisition of beneficial ownership of 5% or more of a
class of equity securities by passive investors and certain institutions
4. 8-K: ‘current report’ of any material current event that happened used to notify investors of any material
event that is important to shareholders
The 10-K, or Annual Report, contains the most information of any filing. Often spanning hundreds of pages, it
contains everything from a letter to shareholders from the board detailing company progress to detailed accounts
of the company’s accounting records, including distribution of assets and liabilities, revenues and profits. While it
is reasonable to expect that a 10-K would have, by far, the largest impact on prices and volume, this expectation
should be tempered by the idea that only new information impacts markets and much of the information in the
actual 10-K is made public long before the full 10-K is. In fact, since the 10-K is so long, it is reasonable to
expect that it would take more than 5 minutes to analyze it, so there is also reason to expect that there would not
be any observable market response to the 10-K within the observed time window, not because 10-Ks are
unimportant but because the time window is too short.
The 4 or 4A is a notification of legal insider trading. Legally, they are only allowed to trade on public
information and so this type of filing should, in theory, contain no relevant information. However, statistically
speaking, some of these people might in fact have insider information relevant to the company they work for,
and the contents of such a filing might contain important signaling information. An example of a 4A filing can
be found in Figure 1.
The SC 13G or SC 13G/A is a notification that someone with a large amount of money is interested in
purchasing a large percentage of the shares. These filings have a significant implication for market momentum,
not to mention the signaling aspect: presumably, if an entity is pouring millions or billions into a financial asset, it
has done its research on that asset and has a well-informed understanding of that asset’s true underlying value is
relative to the market’s. An example of a SC 13G filing can be found in Figure 2.
An 8-K is a catch-all filing for information that the company feels shareholders should know about. An
example of an 8-K can be found in Figure 3. However, this is just one example and is not representative of the
full range of information that could be contained in an 8-K.
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While the four types of filings which occurred with a high frequency were highly likely to impact the
financial market, the one type of filing which was unfortunately infrequent was the 10-Q: quarterly report of a
similar nature to the 10-K but covering a shorter time span and doing so in less detail. I only observed a very
small number of 10-Qs (only four, see Table 1) because I collected data over the span of two months, and very
few S&P 500 companies defined their company calendar year in a way which resulted in a quarterly report release
date during those two months. However, this characteristic of S&P 500 companies is not a major issue because
the content of 10-Qs is explicitly a part of the content of 10-Ks.
Restricting our data to filings of these types for S&P 500 companies, I was left with a data pool of 1179
4(/A)’s, 446 SC13G(/A)’s, 133 8-K’s, and 51 10-K’s, for a total of 1,809 filings.
Mechanism and Time Frame
Since equity prices are tied to the real value of the underlying market asset, there are four conditions that
must be met for the publishing of SEC filings to have a significant impact on market prices:
1. SEC filings must contain relevant information.
I expect this to be the case because the whole point of publishing filings is to make shareholders aware of
information they need to know to make informed decisions about the stock. For example, a release of an
earnings report which differs from analyst expectations directly affects the implied value of the company; it has
less money and will likely make less money in the future.
2. SEC filings must contain new information.
Much of the information disclosed in SEC filings is not really new information, at least not to the entire market.
For example, the outcome of an election of a new board of directors would be filed with the SEC, but many of
the traders who are interested in the outcome of the election would not wait for the outcome to be published on
the SEC website and would find out some other way.
However, a significant amount of the information in SEC filings is new information to most of the
market, like the full contents of an annual report, and could potentially have a significant impact on market
prices and volume traded. Additionally, even for information that was likely known, SEC filings provide official
confirmation.
3. SEC filings must actually be made public when we think they are.
There is the logical possibility that there is insider trading happening on the basis of SEC filings. Potentially,
someone in the SEC might be releasing the filing’s contents to certain traders early, before it gets posted on the
website, so that they have additional time to react and be the first to trade on that information. If this is really
happening, then the observed market reaction to SEC filings would be understated because some or all of the
new information would be already priced into the stock’s price when the filing becomes officially posted.
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4. Someone must actually be paying attention to the contents of SEC filings and have the capability to
process them within the five-minute observation time frame.
To isolate the impact of SEC filings from any other new market information, I had to look at an arbitrarily short
time window after the filing’s posting to increase the chances that the non-random market changes were related
only to SEC filings. I settled on a time window of five minutes.
By decreasing our time window to such a small amount, I increased internal validity with respect to
which effect I was measuring but greatly compromised our ability to measure its magnitude. Presumably, the
entire market doesn’t have an algorithm which monitors the SEC website, quickly scans the content of filings,
and makes trades based on the processed information, so the period over which the entire market receives and
prices in the contents of a SEC filing could be hours or even days.
I was more interested in determining whether SEC filings have an impact, not just in isolating the
magnitude of that impact, so I picked five minutes as the length of our post-filing posting time as our
observation window because I felt it was a reasonable balance between being long enough for it to be possible
for someone to scan through a new SEC filing and make a trade on it and making it short enough that there
would likely be no new (or at the very least no non-random) market data relevant to the company associated with
any given SEC filing, even if the SEC filing times themselves are non-random. I therefore expect that our
findings are a significant understatement of the full impact of the information contained in the SEC filing.
Calculating the Variables
Having isolated the 1809 S&P 500-affiliated filings of type 4(/A), SC13G(/A), 8-K or 10-K to use in our
research, I was ready to match them against historical data. However, the full data set was not usable because the
stock exchanges and the SEC’s electronic filing room operate during different hours. The markets are open 9:30
AM to 4:00 PM EST, and the SEC posts filings either at around midnight or from 9:30 AM to 10:00 PM EST.
Since it is not possible to observe the effect of a filing’s publishing if the markets are not open when it is
published, I was unable to use the majority of our SEC-side data set. I also could not use filings published in the
first or last five minutes of the trading day, because it would be impossible to record the market state either
before or after the filing.
In the end, I was left with 261 filings uploaded within the overlapping 6+2/6th hour time window from
9:35 AM to 3:55 PM EST.
This set was further limited by our inability to collect market data on several companies that had only
been recently added to the S&P 500. Namely: Facebook, Allegion, Transocean, Grahm Holding Company,
Zoetis and Michael Kors (the effect of which was the difference between the frequency values before and after
the !).
Their breakdown was as follows:
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•
(All): 261 ! 255
•
4: 140 ! 135
•
SC 13G(/A): 99 ! 98
•
8-K: 11 ! 11
•
10-K: 11 ! 11
For the filings that were published inside market hours, I used second-resolution aggregated market data
to, for each filing, calculate the difference of differences in average stock price and volume change in the five
minutes before and after the posting of every filing.
For every filing, I then calculated volatility of prices [P] and volume – changes in the quantities traded [V].
To calculate the [P] of any given filing, I took the differences between the stock price when the filing
was first posted and the prices five minutes before and after the filing was posted. I then took the absolute value
of the difference of the percent change relative to the stock price at posting time. Consider the hypothetical that
stocks normally go up +/- 2 percent at equal frequency and after filings they go up +/- 10 percent. In both cases
it would average out to a zero percent change, even though filings had a strong impact on volatility. Since I was
interested in determining whether filings have an impact on stock prices, not in whether filings generally contain
‘good news’ or ‘bad news’ for the stock, I worked with absolute values. It is important to confirm that I only
took the absolute value of the final value. If the stock price went down 5 percent before the filing and went up 5
percent after the filing, that registered as volatility staying flat.
To calculate the [V] of any given filing, I calculated the ratio of the total volume in the five minutes after
the posting to the total volume in the five minutes prior to the posting, and converted to the relative percent
change by subtracting one and then multiplying by 100.
Having calculated the [P] and [V] for every filing, I then did the same for semi-random 10-minute
snapshots in time. These times were randomly selected within the time constraints of the filings I was using in
our data, so they were random 10-minute periods between when I started collecting data in February to when I
stopped in May, and were between 9:25 and 3:55. I collected data on 746 such random samples, bringing our
total data set up to 1001.
I also calculated the time difference between filing time and posting time to determine if this difference
has any impact on prices or volume. As discussed, if there is no insider trading and the sample size is sufficiently
large, then there should be no observable effect. The distribution of filing-posting time gaps can be found in
Figure 6. It was a coincidence that the largest filing-posting gap was just under five minutes, the time interval
within which I arbitrarily decided to limit our observations.
The Data
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As a reminder, the breakdown of the filing types can be found in Table 3:
Type
Frequency
Percent
Cum
No Filing
746
74.53
74.53
4(/A)
135
13.49
88.01
SC 13G(/A)
98
9.79
97.80
8-K
11
1,1
98.90
10-K
11
1.1
100.00
A general variable summary can be found in Table 4:
Var
Obs
Mean
Std. Dev.
Min
Max
type
1001
0.408
0.797
0
4
pricediff
1001
0.123
0.122
0
0.946
volumediff
1001
31.39
116.5
-93.5
2404.7
postinggap
1001
10.66034
23.21165
0
294
posted
1001
0.255
0.436
0
1
The distribution of price changes was, as might be expected, the right half of a normal distribution of double
height, since I was dealing with absolute values. Histograms of price changes can be found in Figure 4 and Figure
5 on the following page:
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0
2
Density
4
6
Figure 4: Price Changes for time intervals including filings
0
.2
.4
.6
.8
Difference in price changes in the five minutes after and before filing
4
0
2
Density
6
8
Figure 5: Price Changes for time intervals not including filings
0
.2
.4
.6
.8
Differences in price changes between the last and first halves of random 10-minute snapshots.
1
The distributions of volume were right-skewed, as expected, since volume can only go down by 100
percent but can go up a potentially infinite amount. They can be found in Figure 6 and Figure 7, also shown on
the following page. If you note the different x-axis for Figure 6 and Figure 7, you notice the massive increase in
volume change around the time a filing gets posted.
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0
.002
Density
.004
.006
.008
Figure 6: Percent changes for intervals including filings
0
500
1000
1500
2000
2500
Percent change in volume five minutes after and before posting
0
.002
Density
.004
.006
.008
Figure 7: Percent changes in volume for intervals not including filings
-200
0
200
400
600
Differences in volume changes between the last and first halves of random 10-minte snapshots
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.04
0
.02
Density
.06
.08
Figure 8: Distribution of filing-posting gaps, in seconds.
0
100
postingGap
200
300
I then regressed both the [P] and [V] on the 0 or 1 posting dummy variable representing whether or not
the data came from a 10-minute time window whose five-minute mark was associated with the posting of a
filing. Stratified by filing type and then consolidated, our output can be found in Table 5.
Table 5: Single Variable Regression, Stratified by Filing Type and Consolidated. Significance level of [P]
and [V] labeled [P]*,**, [V]**,***,****.
Filing Type
[P]
[P]*,**
[V]
[V]**,***,****
Consolidated (All)
0.0091
0.302
36.70
0.000****
4(/A)
(0.0094)
0.405
39.01
0.000****
SC 13G(/A)
0.0289
0.030**
37.01
0.003***
8-K
0.0074
0.841
57.22
0.028**
10-K
0.0610
0.099*
(14.89)
0.558
To clarify, the values in the table are the coefficients and significance of the coefficients of the 0/1 posted
dummy variable, where P denotes that volatility of prices [P] was the dependent variable and V denotes that
volatility of volume [V] was the dependent variable, with a confidence interval of 95 percent.
0.01:*, 0.05:**, 0.01:***, 0.001****
Lastly, I ran the same regressions as in Table 5, but included the number of seconds between the filing
time and posting time as a control for the time associated with a filing. For the random time snapshots, I treated
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it as missing. Again, a statistically significant non-zero coefficient value for the control would be evidence of
insider trading. The results can be found in Table 6.
Table 6: Regression using time to post filing as explanatory variable, consolidated.
Filing Type
[P]
[P]*,**
[V]
[V]**,***,****
Consolidated
0.0018
0.502
(0.3863)
0.326
The significance level of [P] and [V] are labeled [P]*,** and [V]**,***,****. To clarify, the values in the
table are the regression coefficients and significance levels of two of the independent variables: the posting gap
(number of seconds between posting and filing) P and the volume (V). The regressions in this table do not
include the randomly sampled 10-minute segments which were not associated with a timestamp.
Discussion and Interpretation
The objectives of this study were threefold:
1. Determine if SEC filings are noticed in the market and, if so, what types?
2. Measure the extent to which they have a real-time impact.
3. Determine whether the early dissemination of SEC filing data is a mechanism for insider trading.
Are SEC filings noticed by the market?
Yes. The key underlying assumption that we use to eliminate the need for controls is that, given a small
enough time frame and a potentially market-impacting event in the middle of that time frame, any non-random
fluctuations in price or volume in the period after that event relative to the period before can be causally
attributed to that event. Since I limited my observation window to five minutes before and after the event, a
filing’s posting, I am reasonably confident that there is sufficient internal validity to conclude that the variations
we observed are directly attributable to the SEC filing. Therefore, since there is an observed increase in volume,
significant to p<0.001, I conclude that SEC filings are monitored by trading entities in the market who respond
to their contents within the first five minutes of their publication.
How impactful are SEC filings, really?
Unknown. While the five-minute post-filing posting observation window increases internal validity, it
greatly limits the potential to observe the full effect of the SEC filings because not everyone in the market is
responding to SEC filings within five minutes.
One reason that this is the case is that some types of filings take much longer than five minutes to
analyze. For example, 10-Ks are hundreds of pages long, and can take days or even weeks to interpret. This is
likely why, when stratified, 10-Ks were the only filing type which did not have a statistically significant impact on
volume.
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For this reason, the price data is unable to accurately represent the financial implication of a SEC filing
because the full effect of the filing’s posting takes place over a long period of time, and initially there are always
actors willing to provide liquidity in the market, essentially creating a buffer zone. Thus the initial trades based on
SEC filings are unlikely to move the price, and only after many people make trades based on the data will the
market actually meet the demand by increasing price. Of course, active market makers significantly increase their
bid-ask spread when they notice a change in demand. The actual buffer zone is created by inactive customers
through standing limit orders, a type of order which is placed and waits until prices sufficiently move (Sarkar,
Locke 1999). If there are enough standing limit orders to purchase a penny above the market price, the price
can’t move more than a penny until all those orders are satisfied. Finally, price is an insufficient indicator of
market response to filings because the contents of SEC filings are not unambiguously good or bad. They may
contain a mixed bag of information that results in lots of trades one way or the other which average out to zero
and are not reflected in a price change.
On the other hand, because volume has no such drawbacks and is generally low absent new market
information, it is much easier to observe the impact of SEC filings through fluctuations in volume. The increase
in volume of 36.70 percent in the first five minutes after a filing might intuitively seem large, but there’s no way
to know whether that is because there’s little activity absent information or because the new information
contained in SEC filings is particularly interesting to the market.
The best way to determine the impact of SEC filings would be to quantify it in dollars by multiplying the
change in market price by the number of shares outstanding. Again though, that would need to be calculated
over the full period during which people make trades based on the SEC filings. Past the time frame of fiveminutes, it becomes harder to attribute market price fluctuations solely to filing-related new information, so
without actually controlling for other types of new information, a data set in general is difficult to obtain and
certainly inaccessible to me. Thus it is not possible to quantify the financial impact of an SEC filing at this time.
However, based on the relative volume fluctuations observed through stratifying by filing type, we can
make a relative observation about which filings tend to be more important. The 4(/A) and SC 13G(/A) type
filings resulted in a volume increase of 35-40 percent, but the 8-Ks resulted in a relatively massive increase of
almost 60 percent, suggesting that the information contained in the 8-Ks is likely to have a significantly greater
impact on the market. This relationship provides interesting insight into what type of information the market
considers important. As a reminder, 4(/A) type filings publicize the fact that someone inside the company
purchased or sold some of the company’s stock, and SC 13G(/A) type filings publicize that someone outside the
company made a move to acquire five percent or more of the entire stock. So these filings provide a strong
signal about the changing opinions of individual actors in the market, but no new information. However, 8-Ks
are material notices to shareholders about recent developments and do contain new information. The relatively
higher increase in volume associated with 8-Ks is an indication that entities in the markets are more interested in
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tangible new information than in signals provided by other entities making their decision based on already
existing information. This interpretation is also consistent with the fact that 4(/A) filings have a slightly higher
impact on volume than SC 13G(/A)’s. Large institutions purchasing more than five percent of the stock do a lot
of research into the equity, so a SC 13G(/A) represents a lot of research based on current market knowledge. On
the other hand, a 4(/A) represents potentially no research but may represent a small amount of insider
knowledge since it is a trade made by someone inside the company. It is quite interesting that the markets
consider a potential unknowable quantity of insider knowledge more important than the fruits of significant due
diligence.
Is early dissemination of SEC filings resulting in insider trading?
Maybe. Insider trading is likely being done by a relatively small portion of the market, so the effect of
insider trading, or early market reaction to an SEC filing, would have a much smaller magnitude than the rest of
the market responding to the SEC filing. Also, the direction of the impact of insider trading would be opposite
the direction of the response of the rest of the market, because the market response after the filing’s posting was
measured relative to that of the market state before the posting, and if the real market response actually began
earlier then it would partially cancel itself out and the observed effect would be weakened.
Both of these conditions were observed when using volume as the dependent variable. It was of small
magnitude and negative, both of which would be predicted if insider trading was occurring.
Using the dependent variable, the price change was almost zero but actually positive. This cannot be
clearly interpreted as evidence for or against insider trading because there are two effects going on that determine
the sign of the relative absolute volatility. First, insider trading would work to cancel out the later market
response, increasing the chance of a decrease in observed price fluctuations after posting. This would result in a
negative sign. Second, insider trading would break down the liquidity buffer in advance of the posting, increasing
volatility and making prices more sensitive to the rest of the market response to follow. This would result in a
positive sign. The observed price magnitude fluctuation was 0.0018 percent which is so close to zero that, even if
we assume insider trading, we cannot determine which of these two effects are stronger.
So the volume-regression is the only information we can use to determine whether there is insider
trading. While both the sign and magnitude of the observed fluctuations are indicative of insider trading, these
were found to be statistically significant only to the p<35 percent level. Absent the sign and magnitude
coincidence, this would be no evidence at all, but in conjunction I would argue that there is weak evidence that
insider trading is happening.
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Discussion of Limitations and Potential Sources of Error
There are several potential sources of error that may have compromised either internal or external
validity:
1. Accuracy of mapping filing to companies
2. Representativeness of the counterfactual sample
3. Insufficiently small time window of observation around filing time
4. Insufficiently large time window of filing data set collection time
5. Lack of extrapolation across filing types and companies
Accuracy of mapping filing to companies
As explained earlier, there was no direct one-to-one mapping from filing name to company stock
symbol, so I used a word matching algorithm which produced the aggregate contents of Table 2. The actual
implementation of the algorithm can be found in the code in the appendix, but the basic procedure was to strip
the filing name of punctuation and find a company whose name was contained in the filing name and then to
match the name to the stock symbol of that company. Subjectively, both the algorithm and the output seem
reasonable, but it is possible that this procedure could result in an underestimate of the magnitude of the filing’s
impact. If the algorithm were less than perfect, then some filings would be matched with companies they were
unrelated to and would barely, if at all, affect the real-time price or volume traded of the company’s stock.
Representativeness of the counterfactual sample
To determine the ‘normal’ price and volume fluctuation of a typical stock in our sample, we used
random 10-minute snapshots which were unrelated to a filing. I could not rely entirely on the data obtained in
the five minutes before the filing as the counterfactual because, while it would provide a great deal of useful
information about the time and date of the observed filings the data could be affected by insider trading in a way
that would be difficult to later decompose.
Still, to be a counterfactual, the random sample needed to be constrained in such a way that the sample
of random times would be representative of the times in which we observed filings and the companies against
which we matched them.
To provide a counterfactual with respect to the time window, we randomly sampled 10-minute time
segments within the set of dates during which we were collecting data and within market hours, in a manner
similar to how we obtained our primary data set. There are several potential issues with this method. First, the
code occasionally threw an error and stopped the data collection (in a way that did not impact the accuracy of
already-collected data), causing there to be intermittent gaps of hours or in some cases even several days during
which data collection stopped. Second, SEC filing posting times are non-random; they are posted soon after they
are filed, and filing times are non-random. However, this is not a large issue of concern because market trends
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that might be true during some parts of the day but not others are tiny compared to the patterns we observed in
other data. Still, this was a variable that was not fully controlled for.
For a counterfactual with respect to the companies against whose stock we matched the random time
sampled (which was random, and not associated with a filing, and so had no obvious company to match it to),
we picked a randomly representative sample of the set of companies whose filings constituted the original data
set. That is, we only matched the random timestamp against a company to which we matched at least one filing,
and the likelihood of picking that company was directly proportional to the fraction of filings in the total data set
matched to that company; if company A was matched to by eight out of the 255 filings, it had an eight in 255
chance of having its stock’s historical data be examined around the time of any random counterfactual
timestamp.
Insufficiently small time window of observation around filing time.
As already mentioned, the length of the time-window of historical market data around a filing’s posting
time had to be kept small to avoid the need for controlling for the introduction of new market information.
Given a small enough time window, the non-random difference in the likelihood of there being new market data
unrelated to the SEC filing immediately before and after its posting would be 0, even if the times when SEC
filings are filed are non-random. I believe that our five-minute window before and after, or 10-minutes total, is
sufficiently small to ensure that this is the case. This is an assumption that if false would be the source of error.
Still, it is not a major concern for the same reason that the random time sample not being a perfect
counterfactual with respect to time isn’t a major concern: market fluctuations correlated with the date are much
smaller than those observed in our data.
Insufficiently large time window of filing data set collection time.
Since I only collected data on filings for several months, there is insufficient data to observe seasonal
patterns or relationships between many types of filings. For example, if 4(/A)s have more of an impact during
the first and third fiscal quarters, but 8-Ks are more meaningful in the second and fourth (for whatever reason),
that and other potentially interesting patterns have gone unobserved. More importantly, a larger data set would
result in a more statistically significant, and thus more definitive, answer to the question of whether or not there
are bad actors within the SEC who facilitate insider trading.
Lack of generalizability across filing types and companies
Given the limited scope and size of the data set, I am unable to apply my claims about the market
reaction to certain types of filings by S&P 500 companies to other filing types, time periods, or companies. The
S&P 500 index was chosen because it was an arbitrary set of large, recognizable companies and was thus easier to
work with. However, since it is a non-random set, it is quite plausible that the markets largely ignore SEC filings
for smaller companies, or even that markets pay even more attention to filings for smaller companies because
that’s the main source of information about the company. I can read a press release about Microsoft’s earnings,
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but if I want any important information about the other, smaller tech company Miosoft’s earnings, I would have
to actually read their 10-K. Similar concerns extend to filing types, which again were selectively picked by
frequency, familiarity and import, all factors which affect the extent to which the market pays attention to them.
Conclusion
The primary objective of this paper was to establish that SEC filings impact stock prices or volume
immediately after publication, a claim which is well supported by our evidence. With a confidence level of p <
0.001, SEC filings increase volume within five minutes of publication. For two types of filings, SC 13G(/A) and
10-K, I also found statistically significant movements in price within the same time frame. These findings show
both that SEC filings contain new, market-relevant information and that there are actors in the market actively
monitoring the SEC and making trades based on that information within a short time frame.
The secondary objectives were to quantify the magnitude of the financial impact of SEC filings and to
discover which types of filings have the greatest impact. However, to isolate the impact of SEC filings from
other potential sources of market information, I had to restrict my observations to a time window of five
minutes, much shorter than the period of time over which the markets respond to the contents of SEC filings.
As a result, I was unable to quantify to cumulative impact of SEC filings. By stratifying my regressions by filing
type, I did find an interesting pattern: the filing types which resulted in the greatest volume increases were those
based on new market information, rather than those based on signals from large players in the market acting on
previously current market information.
A tertiary but important objective was to determine if there was any evidence that insider trading might
be facilitated through the early release of SEC filings. I predicted that if insider trading was occurring, I would
observe a small negative coefficient when regressing the time it takes the SEC filings on the observed change in
volume. This follows from the theory that insider trading would shift the market response to the filing
backwards in time before the official posting of the filings, reducing the observed magnitude of the market’s
response. While this is exactly what was found in the data, the predicted coefficient was not statistically
significant, so at best I found weak evidence of insider trading.
Given the relative dearth of academic research and data collection on SEC filings in comparison to other
areas of trading and finance, there is still substantial work to be done in this area. Specifically, a larger data set
should be collected and analyzed so that patterns related to more filing types, or to alternative types of
stratifications such as by industry sector, could be observed.
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Appendix
1. Tables
Table 1: Filing Types, sorted by frequency
Filing Type
4
SC 13G/A
8-K
SC 13G
497K
10-K
497
10-D
3
13F-NT
485BPOS
FWP
DEFA14A
DEF 14A
424B5
N-CSR
S-8 POS
N-Q
5
ARS
4/A
NSAR-B
Freq. (Max-11)
1167
346
133
100
53
51
43
34
33
31
28
22
19
18
16
15
15
14
13
13
12
11
Filing Type
8-K/A
13F-HR
(Filer) 13F-NT
NT 10-K
D/A
EFFECT
PRE 14A
424B3
ABS-15G
SC 13D/A
144
NO ACT
424B2
SC TO-I
40-6B/A
485APOS
N-1A/A
10-K/A
(Filer) D/A
POS 8C
SC TO-I/A
10-Q
D
S-3ASR
N-CSRS
8-A12B
497J
SC 13D
NSAR-A
Freq. (10-3)
10
10
9
9
7
7
6
6
5
5
5
5
5
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
Filing Type
POSASR
CERTNYS
TA-2
24F-2NT
40-APP
10-Q/A
ABS-15G/A
(Filer) 13F-HR
13F-HR/A
PREM14A
15-15D
15-12G
CERTNAS
DEFR14A
IRANNOTICE
PRER14A
3/A
NCSR/A
(Filer) 40-6B/A
25-NSE
40-17G
S-8
CT ORDER
POS AM
N-8F
10-D
Freq. (2-1)
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
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Table 2: Mappings from Filing Company Name to Stock Symbol
Filing Name
Stock
Mapping
Frequency
Allegion plc
ALLE
1
Avery Dennison Corp
AVY
3
BlackRock Alternatives Allocation Master Portfolio LLC
BLK
1
BlackRock EcoSolutions Investment Trust
BLK
1
BlackRock Energy & Resources Trust
BLK
3
BlackRock Enhanced Equity Dividend Trust
BLK
2
BlackRock Enhanced Government Fund, Inc.
BLK
1
BlackRock Fund Advisors
BLK
1
BlackRock Health Sciences Trust
BLK
3
BlackRock Inc.
BLK
25
BlackRock International Growth & Income Trust
BLK
2
BlackRock Long-Term Municipal Advantage Trust
BLK
1
BlackRock Preferred Partners LLC
BLK
1
BlackRock Real Asset Equity Trust
BLK
1
Delphi Automotive PLC
DLPH
Discover Financial Services
DFS
1
Discovery Communications, Inc.
DISCA
1
Dr Pepper Snapple Group, Inc.
DPS
1
Duke Energy CORP
DUK
1
Express Scripts Holding Co.
ESRX
1
Fidelity National Information Services, Inc.
FIS
3
Fixed Income Trust for Prudential Financial, Inc. Notes, Series 2012-1
PRU
1
GameStop Corp.
GME
1
General Mills Group Trust
GIS
1
Google Inc.
GOOG
8
Graham Holdings Co
GHC
1
Invesco Ltd.
IVZ
38
Legg Mason Investment Counsel, LLC
LM
1
LyondellBasell Industries N.V.
LYB
1
Marathon Petroleum Corp
MPC
13
Mead Johnson Nutrition Co
MJN
11
Merck & Co. Inc.
MRK
13
Michael Kors Holdings Ltd
KORS
1
Monster Beverage Corp
MNST
2
20
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Filing Name (cont’d)
Stock
Mapping
Frequency
Morgan Stanley Bank of America Merrill Lynch Trust 2012-C6
MS
1
Morgan Stanley Bank of America Merrill Lynch Trust 2013-C10
MS
1
Morgan Stanley Bank of America Merrill Lynch Trust 2013-C11
MS
1
Morgan Stanley Bank of America Merrill Lynch Trust 2013-C12
MS
1
Morgan Stanley Bank of America Merrill Lynch Trust 2013-C13
MS
1
Morgan Stanley Bank of America Merrill Lynch Trust 2013-C9
MS
1
Morgan Stanley Capital I Trust 2012-C4
MS
1
Noble Corp plc
NE
5
Nordstrom James F JR
JWN
1
Philip Morris International Inc.
PM
11
Public Storage
PSA
9
Sabra Health Care REIT, Inc.
HCN
5
Spectra Energy Corp.
SE
11
TE Connectivity Ltd.
TEL
23
Transocean Ltd.
RIG
4
TripAdvisor, Inc.
TRIP
1
Unum Group
UNM
1
Viacom Inc.
VIAB
2
Vulcan Materials CO
VMC
3
Wells Fargo Commercial Mortgage Trust 2013-LC12
WFC
1
Xylem Inc.
XYL
9
Zoetis Inc.
ZTS
2
Table 3: Breakdown of the filing types
Type
No Filing
4(/A)
SC 13G(/A)
8-K
10-K
Frequency
746
135
98
11
11
Percent
74.53
13.49
9.79
1,1
1.1
Cum
74.53
88.01
97.80
98.90
100.00
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Table 4: Variable Summary
Var
type
pricediff
volumediff
postinggap
posted
Obs
1001
1001
1001
1001
1001
Mean
0.408
0.123
31.39
10.66034
0.255
Std. Dev.
0.797
0.122
116.5
23.21165
0.436
Min
0
0
-93.5
0
0
Max
4
0.946
2404.7
294
1
Table 5: Single Variable Regression, Stratified by Filing Type and Consolidated.
Filing Type
Consolidated (All)
4(/A)
SC 13G(/A)
8-K
10-K
[P]
0.0091
(0.0094)
0.0289
0.0074
0.0610
[P]*,**
0.302
0.405
0.030**
0.841
0.099*
[V]
36.70
39.01
37.01
57.22
(14.89)
[V]**,***,****
0.000****
0.000****
0.003***
0.028**
0.558
Significance level of price [P] and volume [V] labeled [P]*,**, [V]**,***,****.
All regressions have a confidence interval of 95%.
0.01:*, 0.05:**, 0.01:***, 0.001****
Table 6: Regression using time to post filing as explanatory variable, consolidated.
Filing Type
Consolidated
[P]
0.0018
[P]*,**
0.502
[V]
(0.3863)
[V]**,***,****
0.326
The significance level of [P] and [V] are labeled [P]*,** and [V]**,***,****. To clarify, the values in the
table are the regression coefficients and significance levels of two of the independent variables: the posting gap
(number of seconds between posting and filing) P and the volume (V). The regressions in this table do not
include the randomly sampled 10-minute segments which were not associated with a timestamp.
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2. Figures
Figure 1.
Example of SEC Form 4
SEC Form 4
FORM 4
OMB APPROVAL
UNITED STATES SECURITIES AND EXCHANGE COMMISSION
OMB
Washington, D.C. 20549
Number:
3235-0287
Check this box if no
December
longer subject to Section
16. Form 4 or Form 5
Expires:
STATEMENT OF CHANGES IN BENEFICIAL OWNERSHIP
31, 2014
obligations may
Estimated average
continue. See Instruction
Filed pursuant to Section 16(a) of the Securities Exchange Act of 1934
1(b).
or Section 30(h) of the Investment Company Act of 1940
burden
hours per
0.5
response:
1. Name and Address of Reporting Person*
2. Issuer Name and Ticker or Trading Symbol
5. Relationship of Reporting Person(s) to
Lawson Douglas A.
AXCELIS TECHNOLOGIES INC [ ACLS ]
Issuer
(Check all applicable)
3. Date of Earliest Transaction
(Last)
(First)
(Middle)
C/O AXCELIS TECHNOLOGIES, INC.
Director
(Month/Day/Year)
Other
05/09/2014
Officer (give
X
108 CHERRY HILL DRIVE
10% Owner
(specify
title below)
below)
EVP, Corporate Mktg & Strategy
(Street)
BEVERLY
(City)
MA
(State)
01915
(Zip)
4. If Amendment, Date of Original Filed
6. Individual or Joint/Group Filing (Check
(Month/Day/Year)
Applicable Line)
05/12/2014
Form filed by One Reporting
X
Person
Form filed by More than One
Reporting Person
Table I - Non-Derivative Securities Acquired, Disposed of, or Beneficially Owned
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1. Title of 2. Transaction
2A. Deemed
3.
Security
Execution Date, if
Transaction (A) or Disposed Of
Date
(Instr. 3) (Month/Day/Year) any
(Month/Day/Year)
4. Securities Acquired 5. Amount of
6.
7. Nature of
Securities
Ownership
Indirect
Code (Instr. (D) (Instr. 3, 4 and 5)
Beneficially
Form:
Beneficial
8)
Owned
Direct (D)
Ownership
Following
or Indirect
(Instr. 4)
Reported
(I) (Instr. 4)
Transaction(s)
(Instr. 3 and 4)
(A)
Code
V
Amount or
Price
(D)
Common
05/09/2014
P
528
A
$1.58 10,626
D
05/09/2014
P
4,472
A
$1.59 15,098
D
05/09/2014
P
5,000
A
$1.59 12,500
I
Stock
Common
Stock
Common
By spouse
Stock
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UChicago Undergraduate Business Journal
Figure 2.
Example of a SC 13G filing (Parts without relevant information have been omitted).
SC 13G/A 1 americanpubliceduinc04302014.htm AMERICAN PUBLIC EDUCATION INC.
SECURITIES AND EXCHANGE COMMISSION
Washington, D.C. 20549
SCHEDULE 13G/A
(Rule 13d-102)
INFORMATION TO BE INCLUDED IN STATEMENTS FILED PURSUANT TO RULES 13D-1(b) AND
AMENDMENTS FILED THERETO FILED PURSUANT TO RULE 13D-2(b)
Under the Securities Exchange Act of 1934
(Amendment No. 2)*
American Public Education Inc.
(Name of Issuer)
Common Stock
(Title of Classes of Securities)
02913V103
(CUSIP Number)
April 30, 2014
(Date of Event Which Requires Filing of this Statement)
CUSIP No.:02913V103
1
NAME OF REPORTING PERSON
I.R.S. IDENTIFICATION NO. OF ABOVE PERSON (ENTITIES ONLY)
Invesco Ltd.
IRS # 980557567
4
CITIZENSHIP OR PLACE OF ORGANIZATION
Invesco Ltd. – Bermuda
NUMBER OF
SHARES
BENEFICIALLY
OWNED BY
EACH
REPORTING
PERSON
WITH
9
5
SOLE VOTING POWER – 1,771,324
6
SHARED VOTING POWER – 0
7
SOLE DISPOSITIVE POWER – 1,771,324
8
SHARED DISPOSITIVE POWER – 0
AGGREGATE AMOUNT BENEFICIALLY OWNED BY EACH REPORTING PERSON
1,771,324
Item 1(a). Name of Issuer:
American Public Education Inc.
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Figure 2, cont’d
(b). Address of Issuer’s Principal Executive Offices:
111 West Congress Street; Charles Town, WV 25414;United States
Item 2(a). Name of Person Filing:
Invesco Ltd.
(b). Address of Principal Business Office or, if none, residence of filing person:
1555 Peachtree Street NE; Atlanta, GA 30309; United States
(c). Citizenship of filing person:
Bermuda
(d). Title of Classes of Securities:
Common Stock
(e). CUSIP Number:
02913V103
Item 3. If this statement is filed pursuant to ss240.13d-1(b) or 240.13d-2(b) or (c), check whether the person filing is a:
(e) [x] An investment adviser in accordance with section 240.13d-1(b)(1)(ii)(E)
(g) [x] A parent holding company or control person in accordance with section 240.13d-1(b)(1)(ii)(G)
Item 6. Ownership of More than Five Percent on Behalf of Another Person:
Invesco Canada Ltd. is subsidiary of Invesco Ltd. and it advises the Invesco Select Companies Fund which owns 8.14% of the security reported here
in. However no one individual has greater than 5% economic ownership. The shareholders of the Fund have the right to receive or the power to direct
the receipt of dividends and proceeds from the sale of securities listed above.
Item 7. Identification and Classification of the Subsidiary which Acquired the Security Being Reported on by the Parent Holding Company:
The following subsidiaries of Invesco Ltd. are investment advisers which hold shares of the security being reported:
Invesco Canada Ltd.
Invesco Advisers, Inc.
Invesco PowerShares Capital Management
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UChicago Undergraduate Business Journal
Figure 3.
Example of n 8-K filing (Parts without relevant information have been omitted).
CURRENT REPORT
Pursuant to Section 13 or 15(d)
of the Securities Exchange Act of 1934
Date of Report (Date of Earliest Event Reported): May 7, 2014
Aqua America, Inc.
(Exact name of registrant as specified in its charter)
Pennsylvania
001-06659
23-1702594
(State or other jurisdiction
(Commission
(I.R.S. Employer
of incorporation)
File Number)
Identification No.)
762 West Lancaster Avenue,
Bryn Mawr, Pennsylvania
19010-3489
(Address of principal executive offices)
(Zip Code)
Item 5.07 Submission of Matters to a Vote of Security Holders
The 2014 Annual Meeting of Shareholders of Aqua America, Inc. (the “Company”) was held on May 7, 2014 at the Drexelbrook Banquet Facility &
Corporate Events Center, 4700 Drexelbrook Drive, Drexel Hill, Pennsylvania 19026, pursuant to the Notice sent, beginning on March 27, 2014, to all
shareholders of record at the close of business on March 10, 2014. At the annual meeting:
1. The following nominees were elected as directors of Aqua America, Inc. to serve for one-year terms and until their successors are elected and
qualified. The votes received are set forth adjacent to the names below:
Name of Nominee
Nicholas DeBenedictis
For
Withheld
97,519,584
6,675,058
Michael L. Browne
102,251,073
1,943,569
Richard H. Glanton
101,168,228
3,026,414
97,227,613
6,967,029
William P. Hankowsky
102,464,622
1,730,020
Wendell F. Holland
102,799,101
1,395,541
Ellen T. Ruff
102,659,968
1,534,674
Andrew J. Sordoni, III
102,482,214
1,712,428
Lon R. Greenberg
There were a total of 41,134,185 broker non-votes for the election of directors.
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Figure 3, cont’d
2. The appointment of PricewaterhouseCoopers LLP as the independent registered public accounting firm for the Company for the year ending
December 31, 2014, was ratified by the following vote of shareholders:
For
Against
Abstain
142,897,940
1,694,427
736,460
3. The advisory vote to approve the Company’s executive compensation as disclosed in the Company’s Proxy Statement for the 2014 Annual Meeting
of Shareholders was approved by the following vote of shareholders:
For
Against
Abstain
Broker Non-Votes
96,232,744
5,629,928
2,331,970
41,134,185
4. The Amended Aqua America, Inc. 2009 Omnibus Compensation Plan was approved by the following vote of shareholders:
For
Against
Abstain
Broker Non-Votes
97,668,672
4,830,738
1,695,232
41,134,185
5. The shareholder proposal requesting that the Board of Directors create a comprehensive policy articulating the Company’s respect for and
commitment to the human right to water was not approved by the shareholders and received the following vote:
For
Against
Abstain
Broker Non-Votes
11,177,742
88,408,849
4,608,051
41,134,185
6. The shareholder proposal requesting that the Board of Directors create a policy in which the Board’s Chairman is an independent director who has
not previously serves as an executive officer of the Company was not approved by the shareholders and received the following vote:
For
Against
Abstain
Broker Non-Votes
36,366,308
66,289,197
1,539,137
41,134,185
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UChicago Undergraduate Business Journal
0
2
Density
4
6
Figure 4: Price Changes for time intervals including filings
0
.2
.4
.6
.8
Difference in price changes in the five minutes after and before filing
Figure 5: Price Changes for time intervals not
Figure 6: Percent changes for intervals including Percent change in volume five minutes after and before posting
2000
1500
1000
500
0
Density
.004
.006
.008
.002
0
filings
0
.2
.4
.6
.8
1
Differences in price changes between the last and first halves of random 10-minute snapshots.
2500
including filings
0
2
4
Density
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seconds.
200
400
200
postingGap
100
0
-200
0
300
not including filings
600
Figure 8: Distribution of filing-posting gaps, in
Differences in volume changes between the last and first halves of random 10-minte snapshots
Figure 7: Percent changes in volume for intervals
0
.02
.04
Density
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.06
.08
0
.002
Density
.004
.006
.008
UChicago Undergraduate Business Journal
Works Cited
Feroz, Ehsan H. and Park, Kyung Joo and Pastena, Victor, The Financial and Market Effects of the SEC's
Accounting and Auditing Enforcement Releases (July 24, 2008). Journal of Accounting Research, Vol. 29, pp.
107-142, 1991. Available at SSRN: http://ssrn.com/abstract=1175102
Darren T. Roulstone (1999) Effect of SEC Financial Reporting Release No. 48 on Derivative and Market Risk
Disclosures. Accounting Horizons: December 1999, Vol. 13, No. 4, pp. 343-363.
doi: http://dx.doi.org/10.2308/acch.1999.13.4.343
Thomas J. Linsmeier, Daniel B. Thornton, Mohan Venkatachalam, and Michael Welker (2002) The Effect of
Mandated Market Risk Disclosures on Trading Volume Sensitivity to Interest Rate, Exchange Rate, and
Commodity Price Movements. The Accounting Review: April 2002, Vol. 77, No. 2, pp. 343-377.
doi: http://dx.doi.org/10.2308/accr.2002.77.2.343
Peter R. Locke (1999), Commodity Futures Trading Commission
George Washington University; Liquidity Supply and Volatility: Futures Market Evidence
http://www.newyorkfed.org/research/economists/sarkar/sarkar_locke.pdf
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