This document discusses analyzing the relationship between earnings reported in 10-Q financial filings and stock market value. It will use descriptive statistics, correlation analysis, and regression models to test the relationship between market value (stock price and returns) and earnings metrics reported in 10-Qs. The key variables examined are total receivables, total revenue, common equity, cash and cash equivalents, net income, and total assets from 10-Qs against subsequent stock price and returns. The regression models aim to see how stock price and returns are influenced by earnings figures disclosed in 10-Q filings.

1. Analysis of the Relationship between Earnings
from 10-Q and Stock Market Value
Acc 692Q Accounting Research Project
MAY 8, 2015
PACE UNIVERSITY
Xun Zhou

2. Analysis of the Relationship between Earnings
from 10-Q and Stock Market Value
Xun Zhou
Pace University
Abstract: During past decades, many researchers try to find one or more variable which can
predict the stock market. This paper intend to show the analysis between earnings from 10-Q and
stock market value. I try to find a possible relation during the analysis and find a significant
result. It is proved that Total receivable and Total Revenue act important role in the regression
model I built. This paper expect to provide a combined method of statistics and accounting, help
to provide solution for the situation in the market.
Purpose: To show analysis of the relation between the markets valuations of Earnings disclosed
on 10-Q by testing the relation between Market Value (stock price and stock returns) and
Earnings of financial reports 10-Q.
Design/methodology/approach: Use Descriptive Statistics test, Pearson Correlation and
Regression Model.
Key words: 10-Q Earnings; stock market valuation; person correlation; regression model
Data availability: data are available from sources identified in the paper.

3. 1. Introduction
This paper intend to find out what may the current period earnings of company financial
reports affect the stock price and stock return on next period. It’s not a prediction, but a
potential relation between Market Value and Earnings disclosed. Cause we all know the
stock market is hard to predict, based on many reasonable or unreasonable factors. But this
paper try to show during the past 10 years : 2003 – 2012, how the stock market react to
financial reports (especially the 10-Q) disclosed, and may inspire the investors and
companies for further research. I try to combine with the recent hot issue “big data”, so in
this paper I use many statistics method like person correlation, regression model, residual
plot and scatter plot. I expect to provide a combined method of statistics and accounting, help
to provide solution for situation in the market.
There are many papers in the past did the research on or related to financial reports filings
and stock markets. Since it is obvious daily problem for investors and really profitable. In the
paper Information about Corporate Restructurings (see Bens, 2001), the author mentioned
that “several empirical studies have analyzed the association between performance and
disclosure finds that firms with negative earnings surprises are more likely to provide
earnings forecasts than those with good news.” In the paper Accruals Management and
Equity Valuation (see Balsam, Bartov and Marquardt, 2002), the authors gave the opinions
that investor sophistication plays a role in determining the timing of the market reaction to

4. accruals management, with the price reaction of sophisticated investors preceding that of
unsophisticated ones. The results suggest that investors reassess reported quarterly earnings
figures using other financial statement information and that this reassessment is associated
with substantial stock price change. Also in the paper Does the disclosure of corporate
governance structures affect firms’ earnings quality? (see Chang and Sun, 2010), “it is
found that the market valuation of earnings surprise is significantly higher for firms which
disclose stronger corporate governance functions....also that the effectiveness of corporate
governance in monitoring earnings management is improved after the mandated disclosure.”
Based on their findings, in this paper I chose the 10-Q for financial statements resource. The
other reason is that, during my data collection, I find out that 10K has too many year-end
adjustments, which the information affection are lagging for the stock market. What’s more,
10-Q are more fit for the frequently change stock market.
The reason use earnings as the variables is that most investors will put their eyes on earnings
first during making investment decision. Also generally companies are more willing to
disclose the earnings situation. Besides Total Revenues, Total Assets, Common Equity, Net
Income, and Income before Extraordinary Item: the traditional important information in
reports. Refer to the paper Market Valuation of Accrual Components (see Francis, 2008), the
author gave evidence support that market valuations for the receivable accrual are greater
than the valuations for other current accruals, and market valuations for cash flows are not
monotonically greater than the valuations for accruals. Based on his judgments, I also include
Total Receivable and Cash and Cash equivalent as variables. So there are total 8 variables
will be tested in this paper.

5. For the test objects, I choose Stock Price and Holding Period Returns (shows as Stock
Returns in this paper) which are the most investor most concerned factors. The Holding
Period Return is the total return on an asset or portfolio over a period during which it was
held. It is one of the simplest and most important measures of investment performance. It can
be calculated as:
HPR = (End Value - Initial Value) / Initial Value
where the End Value includes income, such as dividends, earned on the investment.
2. Theoretical Measurement
In order to control the test efficiency, I will first implement the Descriptive Statistics Table.
The table is used to show the range of my observations, since during the analysis, I hope the
data I used can sit in a comfortable range. The numbers too high or too low will affect the
test accuracy, so I will eliminate the unique data during sample selection. The descriptive
table will include all the elements, 8 variables from 10-Q and 2 stock variables from CRSP:
total 10 variables. Also will show the mean, median, maximize and minimize sample, which
will give a visualize process of the data.
For the next step, I choose the Person Correlation method. Correlation between sets of data is
a measure of how well they are related and the Pearson Correlation is the most common
measure of correlation in stats. The full name is the Pearson Product Moment Correlation or

6. PPMC. It shows the linear relationship between two sets of data. Two letters are used to
represent the Pearson correlation: Greek letter rho (ρ) for a population and the letter “r” for a
sample, the formula shows below:
The result for r will be between -1 and 1. High correlation: .5 to 1.0 or -0.5 to 1.0; Medium
correlation: .3 to .5 or -0.3 to .5; Low correlation: .1 to .3 or -0.1 to -0.3. But the limitation of
PPMC is that not able to tell the difference between dependent and independent variables. It
means it cannot prove which variable pull the trigger, but can only tell you whether there is a
relationship. So that’s why I will implement the regression model next to make the analysis more
reliable.
Regression analysis is a statistical process for estimating the relationships among variables. It
includes many techniques for modeling and analyzing several variables, when the focus is on the
relationship between a dependent variable and one or more independent variables. More
specifically, regression analysis helps one understand how the typical value of the dependent
variable (or 'criterion variable') changes when any one of the independent variables is varied,
while the other independent variables are held fixed. Most commonly, regression analysis
estimates the conditional expectation of the dependent variable given the independent variables –
that is, the average value of the dependent variable when the independent variables are fixed.
Regression models involve the following variables:

7. (1)The unknown parameters, denoted as β, which may represent a scalar or a vector.
(2)The independent variables, X.
(3)The dependent variable, Y.
So in this paper, the X will be 8 variables from 10-Q, which are the inputs in regression model,
and the Y will be the 2 categories from stock markets, which are outputs. I will separately set the
8 inputs for stock price and stock returns. So my regression model will be insert as:
SPt+1 = β0 + β1ARt + β2REVt + β3CEt + β4CFt + β5IBEt + β6TAn + β7NIt +
β8WCt + ε t+1
SRt+1 = η0 + η 1ARt + η 2REVt + η 3CEt + η 4CFt + η 5IBEt + η 6TAt + η 7NIt + η
8WCn+ ε t+1
For here above: SP=Stock Price, SR=Stock Returns, AR=Total Receivable, REV=Total
Revenue, CE=Common Equity, CF=Cash and Cash Equivalent, IBE=Income before
Extraordinary Item, TA=Total Assets, NI=Net Income, WC=Working Capital.
Classical assumptions for regression analysis include:
 The sample is representative of the population for the inference prediction.
 The error is a random variable with a mean of zero conditional on the explanatory
variables.
 The independent variables are measured with no error. (Note: If this is not so,
modeling may be done instead using errors-in-variables model techniques).
 The predictors are linearly independent, i.e. it is not possible to express any
predictor as a linear combination of the others.

8.  The errors are uncorrelated, that is, the variance–covariance matrix of the errors is
diagonal and each non-zero element is the variance of the error.
 The variance of the error is constant across observations (homoscedasticity). If
not, weighted least squares or other methods might instead be used.
To provide the accuracy and effectiveness of the regression model, I will use the stepwise model
when implement the regression model. Stepwise regression includes regression models in which
the choice of predictive variables is carried out by an automatic procedure. Usually, this takes the
form of a sequence of F-tests or t-tests, but other techniques are possible, such as adjusted R-
square, Akaike information criterion, Bayesian information criterion, Mallows's Cp, PRESS, or
false discovery rate.
The frequent practice of fitting the final selected model followed by reporting estimates and
confidence intervals without adjusting them to take the model building process into account has
led to calls to stop using stepwise model building altogether or to at least make sure model
uncertainty is correctly reflected.
3. Sample Selection
The 10-Q sample are selected from Compustat (1), which is a database of financial,
statistical and market information on active and inactive global companies throughout the
world. This database provides a broad range of information products directed at
institutional investors, universities, bankers, advisors, analysts, and asset/portfolio
managers in corporate, M&A, private capital, equity, and fixed income markets. And the

9. stock market data are selected from CRSP (The Center for Research in Security Prices),
which is a provider of historical stock market data. For this paper, I chose from the North
American sector and monthly file for the stock market data. I pivot the monthly CRSP
data to quarterly first, and then joint it with Compustat by Tickers Symbol. The sample
must met the following criteria after joint:
(a) The company must have full 40 quarters (from October 2002 to September 2012)
records.
(b) The firm has a December fiscal year-end.
(c) The firm must have full records of the 8 variables including Total Assets, Common
Equity, Net Income, Total Receivable, Income before Extraordinary Item, Total
Revenue, Cash and Cash equivalent, and Working Capital.
(d) No consolidated filings.
(e) No firm with average stock price in the upper 10% or bottom 10%.
The reason I chose 40 quarters period from October 2002 to September 2012 is that the
affection to stock market is lagging to next period. Since the research period is from 2003
to 2012, the earning data need to be a quarter prior. In this paper, all the data are joint
lagging a quarter. I require a December fiscal year-end in (b) so that seasonal differences
across the calendar year are eliminated when I cross the data from earnings to stock
market. And the requirement for (d), no consolidated filings is considered that in CRSP,
parent company and subsidiary use different sticker symbols, which cannot be joint
appropriately with Compustat. So no consolidated filings firms can avoid this problem.
For criteria (e), too high or too low stock price will affect the accuracy during data

10. analysis, so I filter the firm with average stock price in the upper 10% range, or at the
bottom 10%.
After all the filters, the total observation company’s number is 769, each observation with
40 quarters data.
4. Hypothesis and Analysis
Since all the variables I choose are earnings, I make the hypothesis as:
H1: The Stock Price has positive relation with all the variables.
The first hypothesis are based on our common sense. Investors intend to buy the stocks
with high growth or potential growth. More earnings or more profit on the financial
reports naturally lead the investors willing to devote more on the stocks.
H2: The Stock Returns has negative relation with all the variables.
Since the Stock Returns based on (End Value - Initial Value) / Initial Value, higher the
original price, lower the Stock Return here. For example, company A’s initial price is 20
and company B’s initial price is 50, both of their price rise for 2 dollars in the same
period. Stock Return of company A equals to 0.1, but company B’s return is 0.04. So I
assume that company with strong price which may be more earnings, has a smaller
return. Furthermore, Balsam, Bartov and Marquardt had already prove that there is a
negative association between unexpected discretionary accruals and cumulative abnormal
returns around the 10-Q filing data in their paper.

11. H3: Total Receivable is the strongest variable that can affect Stock Price and Stock
Returns.
The idea choose Total Receivable as the leading variable is inspired by Francis I mention
above. He gave the idea that (2008,p.153) “Equity investors may find changes in account
payable to be less valuable that changes in accounts receivable due to the increased
persistence associated with revenue increases as compared to cost reductions.”
Furthermore, he also mentioned that(2008, p.153) “the good news associated with a
positive change in accounts receivable is more likely to exhibit future growth and
persistence than good news stemming from a cost-cutting strategy that is reflected in
accounts payable”. So I make the assumption that market valuation of the receivable is
greater than the market valuation for other earnings.
Table 1 is the Descriptive Statistics Test table for all 10 variables of 769 observations.
Observation period of Stock Price and Stock Returns are calendar year from 2003 to
2012. The other 8 variables from financial reports are from calendar year quarter 4 of
2002 to quarter 3 of 2012 due to the quarter lagging reflection need. Total 40 quarters
data.

13. Table 2 is the Pearson Correlation Matrix.

14. From table 2 we can see that:
All the variables are in the positive relation with Stock Price
All the variables are in the negative relation with Stock Returns.
The strongest 3 variables related to Stock Price are Total Revenue with R value 0.2023,
Income before Extraordinary Items with R value 0.1997, and Net Income with R value
0.1950.
The Strongest 3 variables related to Stock Returns are Total Assets with R value -0.0558,
Common Equity with R value -0.0533, and Total Receivables with R value -0.0527.
In the comprehensive observation, Receivables and Revenue are the two outstanding
variables with comparable strong relation to both Stock Price and Stock Returns.
Receivables has R value 0.1833 with Stock Price and -0.0527 with Stock Returns, and
Revenue has R value 0.2023 with Stock Price and -0.0462 with Stock Returns.
For further analysis, I insert the Liner Regression Model and implemented the stepwise
procedure. Stepwise regression includes regression models in which the choice of
predictive variables is carried out by an automatic procedure. It will automatically pick
out the suitable result, which means it can eliminate some no so reverent variables. So the
results of Stepwise Regression may not have total 8 variables.

15. Table 3 is the Regression Model of Stock Price after stepwise.
From table 3, we can see that the stepwise procedure automatically pick out the 7
variables. Among them, Common Equity, Income Before Extraordinary Items and
Revenue (2) these 3 variables have the smallest P value, which means these 3 variables
have the strongest relation during the total 8 variables this paper mention before. And the
Revenue and Income before Extraordinary Items are consistent with the Pearson
Correlation Matrix result.

16. Table 4 is the Regression Model of Stock Returns after stepwise.
The stepwise regression pick out only 5 significant variables in this model. Common
Equity, Net Income and Receivables (3) have the best performance. The results of
Common Equity and Receivables also consistent with the Correlation test.
To make sure the effective of the regression models, I insert an addition test, which is
the residual plot. Residual plot is a test assess the appropriateness of the model by
defining residuals and examining residual plots.

17. Here’s the Residual Plots Test for Stock Price Regression model:

18. Here’s the Residual Plots Test for Stock Returns Regression model:
Based on the Residual Plot test, I can make a conclusion that the Stock Price Regression
Model is more appropriate when dealing with the data sit in stock price range from 20 to
50, and the Stock Returns Regression Model is more appropriate when dealing with the
data sit in stock returns range from 120000 to 140000. Reflect to the Descriptive
Statistics table which is table 1, it shows that the Price Model will run better in the higher
half range while the Return Model will run better in the lower half range. It means, the
Price Model and the Return Model are suitable for the big companies. I assumed that the
result may cause for several reasons:

19. a. Large companies disclosed financial reports pattern are similar.
b. Most large companies with comparable long history and well-development stage,
and their stock market performance are tend to be more stable.
c. Most of large companies’ stock are holding by the fund, organization or financial
intermediate. Those shareholders are professional and sophisticated investor and
they have similar holding pattern.
Below are the graphic description of model results. I chose the Scatter Plot, which is a
type of mathematical diagram using Cartesian coordinates to display values for two
variables for a set of data. The data is displayed as a collection of points, each having the
value of one variable determining the position on the horizontal axis and the value of the
other variable determining the position on the vertical axis. A scatter plot is used when a
variable exists that is under the control of the experimenter. If a parameter exists that is
systematically incremented and/or decremented by the other, it is called the control
parameter or independent variable and is customarily plotted along the horizontal axis.
The measured or dependent variable is customarily plotted along the vertical axis. If no
dependent variable exists, either type of variable can be plotted on either axis or a scatter
plot will illustrate only the degree of correlation (not causation) between two variables.
(4)(5)

24. 5. Conclusion
Based on the test and results. I can conclude that: for hypothesis 1 is true, earning variables
are positive related to Stock Price, and the 3 most relevant variables are Common Equity,
Income before Extraordinary Items, and Total Revenue. The hypothesis 2 is also true,
earning variables are negative related to Stock Returns, and the 3 most relevant variables are
Common Equity, Net Income and Total Receivables. For hypothesis 3, I prove that Total
Receivables indeed is one of the most useful variables during my choice in this paper.
Besides, Total Revenue and Common Equity can also be used to evaluate the Stock Price and
Stock Returns in future research. These two new found significant variables show good
performance in both Correlation test and Regression Model test.
It is proved in the model that Total Receivable and Total Revenue acting important role. This
is predictable since most investor will focus on these two factors when making decision,
besides, the new surprising findings from the model is that Common Equity is also a
significant factor. Common Equity shows comparable strong effect in both analyze with
Stock Price and Stock Returns. In further research dealing with stock market, researchers can
focus more on the Common Equity.
There are still some limitation of the paper. For example, I only include earnings as variables,
but in reality, accounts payable, long-term debt and other debts also effect on Stock Price and
Stock Returns a lot. But debt information are not as available as earnings since company
generally are willing to disclose the earnings. This paper shows quarterly change for stock
market value, which is still not detail enough, since stock market value change is a time

25. sensitive factor. Stock market can change sharply within a month, quarterly show may not so
appropriate fit the actual situation. Price Model will run better in the higher half range while
the Return Model will run better in the lower half range. For further research, the better way
may be input different range sector of price and returns, the results will be more accuracy.
I expect the analysis and method described in this paper provide a possible way when dealing
with this kind of situation in the future, and inspire more accounting issue combined with
“big data”.

26. Notes
1. All the number data from Compustat are in million (1000000)
2. The revenues mention in the paper is refer to Total Revenues.
3. The receivable mention in the paper is refer to Total Receivable.
4. The price mention in the paper is refer to Stock Price
5. The returns mention in the paper is refer to Stock Returns.

27. References
Balsam, Steven, Eli Bartov, and Carol Marquardt. "Accruals Management, Investor
Sophistication, and Equity Valuation: Evidence from 10-Q Filings." Journal of Accounting
Research 40.4 (2002): 987-1012. Web.
Bens, Daniel A. "The Determinants of the Amount of Information Disclosed about Corporate
Restructurings." Journal of Accounting Research 40.1 (2002): 1-20. Web.
Chang, Jui-Chin, and Huey-Lian Sun. "Does the Disclosure of Corporate Governance Structures
Affect Firms' Earnings Quality?" Review of Accounting and Finance 9.3 (2010): 212-43. Web.
CORREL function, Microsoft Office support pages, retrieved 2015-02-22
David A. Freedman, Statistical Models: Theory and Practice, Cambridge University Press
(2005)
Dechow, Patricia M., and Ilia D. Dichev. "The Quality of Accruals and Earnings: The Role of
Accrual Estimation Errors." The Accounting Review 77.S-1 (2002): 35-59. Web.
Doyle, Jeffrey T., Weili Ge, and Sarah Mcvay. "Accruals Quality and Internal Control over
Financial Reporting." The Accounting Review 82.5 (2007): 1141-170. Web.
Francis, Rick. "Market Valuation of Accrual Components." Review of Accounting and Finance
7.2 (2008): 150-66. Web.

28. Jarrell, Stephen B. (1994). Basic Statistics (Special pre-publication ed.). Dubuque, Iowa: Wm. C.
Brown Pub. p. 492. ISBN 0-697-21595-4.
R. Dennis Cook; Sanford Weisberg Criticism and Influence Analysis in Regression, Sociological
Methodology, Vol. 13. (1982), pp. 313–361
Rodney Ramcharan. Regressions: Why Are Economists Obessessed with Them? March 2006.
Accessed 2011-12-03.
Ronald A. Fisher (1954). Statistical Methods for Research Workers (Twelfth ed.). Edinburgh:
Oliver and Boyd. ISBN 0-05-002170-2.
Schmid Jr., John (December 1947). "The Relationship between the Coefficient of Correlation
and the Angle Included between Regression Lines". The Journal of Educational Research 41 (4).
JSTOR 27528906
Utts, Jessica M. Seeing Through Statistics 3rd Edition, Thomson Brooks/Cole, 2005, pp 166-
167. ISBN 0-534-39402-7