Ontonix Complexity & Cross Sectional Returns Academic Research

Complexity and Cross-Sectional Returns
Student: Di Chen
Company: Ontonix
FE supervisor: Semyon Malamud
Company supervisor: Jacek Marczyk
August 2013

Complexity and Cross-Sectional Returns
Di Chen
Abstract
The paper shows that complexity influences stock returns. By establishing
the complexity and resilience measure of the common stock and analyzing
the relationship between return, momentum, size, complexity, book-to-
market ratio and resilience, three measures (size, complexity and
momentum) stand out as the factors that can influence stock returns. Using
data from 2009 to 2013, complexity has negative relationship with stock
returns. Complexity-based trading strategies can provide more stable
returns than size-based and momentum-based strategies. These findings
might be exploited in factor investing.
1. Introduction
There are many factors that can influence the cross section of stock returns. Among
them, the state-of-the-art ones are size, book-to-market ratio and momentum. Many
studies support such factor premiums. Based on these, factor investing is getting more
and more popular. People are not satisfied with investing in the whole index or the
single stock and begin to invest based on these factor measures. From the financial
crisis, we all know that financial systems are threatened by excessive complexity.
Excessive complexity can lead to many disasters like plane crash(Zolli 2012),
financial crisis and the bankruptcy of a company. Those complex structure financial
products and "too big to fail" institutions make the systems vulnerable to external
shocks. In this paper, two complexity-related measures are introduced as the new risk
factors. The famous heuristic Occam’s razor states that given two equally options, the
one with the lowest complexity should be chosen. This is contrast to the basis behind
capital asset pricing model. In CAPM, given two portfolios with the same expected
returns, the one with the lower risk should be chosen.
There are few studies about the relationship between complexity and stock
returns. The reason is that the definition of complexity is difficult. Through Ontonix,

it is possible to measure the complexity of a system. High complexity implies a
capacity to deliver surprising behavior. Stocks with higher complexity might be more
fragile and offer lower returns. This implies a negative relationship between
complexity and stock returns. According to Marczyk(2011), ‘‘highly complex
systems are difficult to manage and can quickly run out of hand. ’’ (p. 29) A highly
complex system is also less efficient than simple ones. Consequently, complexity is a
crucial parameter for every system, including the common stock. Another risk factor
that is introduced into the study is resilience. Resilience is the measure of the system’s
ability to resist extreme shocks.
This paper provides a systematic study of the relationship between complexity
and equity return. Using the CRSP/Compustat Merged Database, complexity is
measured for the components of S&P 100 index. There exists significant cross-
sectional variation in complexity across different stocks. Three quantitative criteria
are used to compare complexity factor with other factors which can influence equity
returns. The three criteria are correlation between the factor and the stock return, t
statistics of the factor in the multiple regression and the performance of factor-based
strategy.
Different factor investing strategies are also compared for the whole market and
for the factor-based submarket. In the large cap subgroup and high complexity
subgroup, the complexity-based trading strategy can offer superior returns. This
suggests that highly complex companies are fragile and vulnerable to external shocks.
Then, returns on different factor-based trading strategies are calculated in the
test period. Complexity-based trading strategy performs best by considering both
return and standard deviation.
Some limitations of the study need to be highlighted. Due to the difficulty of
measuring the complexity of the common stock, we only test the cross-sectional data
in a single period from 2012 to 2013. It’s still unknown whether this relationship still
exists in different time periods, different assets as well as different countries.
The remainder of the paper is organized as follows. Section 2 will discuss related
literature. Section 3 will be devoted to measuring the complexity of the common stock.
Section 4 provides the theoretical basis for the relationship between complexity and

stock returns. Section 5, 6 and 7 provide the holistic comparison of different factors
that can influence equity returns. Section 8 concludes.
2. Related literature
Only few studies exist on the role complexity plays in the financial market.
Marczyk(2011) introduces complexity-based rating method, which is different from
traditional default-based rating method and is more adapted to the globalized and
chaotic economy. Mariotti(2008) shows that complexity adds costs and kills profits in
the business world. Meucci(2010) proposes an entropy pooling approach for trading
and portfolio management.
However, empirical asset pricing is a central problem in finance. Three factors
are identified by Fama and French(1993) on the return of stocks. Besides a market
factor, firm size and book-to-market equity are discovered by them as two factors
which play an important role in determining asset returns. Blitz, Falkenstein and
Vilet(2013) summaries the cause and effect of ‘volatility effect’, which is that
investing in low-volatility stocks can achieve higher risk-adjusted returns.
Goyal(2011) provides a survey on the huge amount of evidence in understanding the
cross-sectional difference in asset returns. During the survey, there is no complexity-
related evidence. Recently, there are some innovative studies on asset market. Preis,
Moat, and Stanley(2013) analyzes the relationship between Google trends data and
the return of Dow Jones Industrial Average Index(DJIA). Besides that DeMiguel,
Garlappi and Uppal(2009) analyzes the financial data and concludes there is no
significant gain out of sample by optimal diversification(like CAPM). It shows that
the traditional asset pricing model is under attack and new paradigm is needed.
3. Measuring the complexity of the common stock
1) Dataset
Through Wharton Research Data Services(WRDS), it is possible to use the
CRSP/Compustat database. This database provides the fundmental data as well as
market data for 27,000 stocks from NASDAQ, New York Stock Exchange(NYSE)
and NYSE MKT. It covers all S&P 500 components and most stocks listed in the US
security market. The financial statement data as well as the market price data are all

available from the database. The accuracy of the data is crucial for estimating the
complexity of the common stock.
Five risk factors are analyzed in this paper: size, book-to-market ratio,
momentum, complexity and resilience. The measure of size, book-to-market ratio and
momentum are obtained directly from the database. The measure of complexity and
resilience is calculated by a model-free algorithm provided by Ontonix using the data
from the database. The universe of stocks used in this study is all S&P 100
components since this is a preliminary analysis.
2) Complexity measures
The definition of complexity is presented in this part. Complexity is not a well-
defined concept. There are lots of discussions about complexity and its role in
financial crisis, system design and corporate management. But it’s hard to see a
quantitative measure of the complexity. Ontonix is the world’s first company to
provide the measure of complexity rationally.
Marczyk(2013) provides a detailed definition of the complexity.
Complexity is a natural property of every system. Its importance is similar to that
of energy. The complexity of a system described by a vector {x} of N
components is defined as a function of structure and entropy.
C = f(S ○ E)
Where S represents an N × N adjacency matrix, E is an N × N entropy matrix, ○
is the Hadamard matrix product operator and f is a norm operator. The adjacency
matrix indicates the correlations between the components of {x}. The adjacency
matrix is determined via a multi-dimensional algorithm which determines if
entry Sij is 0 or 1. This establishes the structure of the system. The intensity of
the dependencies, the so-called generalized correlation, is computed based on
entropy. Entropy measures how crisp (or fuzzy) the dependencies between the
elements of {x} are. In essence, it quantifies the amount of disorder within the
system. The huge advantage of this model-free approach is that it is independent
of numerical conditioning of the data and its ability to identify the existence of
structures where conventional methods fail. Once the entropy matrix and the
adjacency matrix have been obtained, one may compute the complexity of a
given system as the following matrix norm:
C = ║S ○ E║
A fundamental property of systems related to complexity is the so-called critical
complexity, CU, which corresponds to the upper bound of the complexity metric.
Critical complexity may be defined formally using the above expression,
CU = ║S ○ Emax║

where Emax is the entropy matrix in which the entries correspond to a situation of
maximum sustainable disorder within the system. In a similar fashion, the lower
bound of complexity, CL, may be computed as CL = ║S ○ Emin║. In proximity
of the lower complexity bound, a given system functions in a deterministic
structure-dominated fashion. In proximity of the upper complexity bound the
system in question is uncertainty-dominated and relationships between the
various components of {x} are fuzzy and therefore characterized by very low
generalized correlations.
A measure of system resilience may now be defined as follows:
R =f(CL; C; CU)
where CL, C and CU represent, respectively, the lower complexity bound, the
current system complexity and the upper complexity bound. The function f in the
above equation is a second-order polynomial function such that:
if C = CL → R = 100%, if C = CU → R = 0%
(Marczyk, 2013, p3-4)
For measuring the complexity of a common stock, the key issue is to describe a
common stock as a system of parameters. 32 parameters are selected to cover market
data, index data, corresponding risk-free rate, dividend data, balance sheet data and
income statement data. 12 quarters’ data of these 32 parameters are used for
calculating the complexity of a common stock. This measure of complexity is suitable
for the long term. But by using intraday data, high frequency measure can also be
obtained using different parameters.
4. Complexity of the common stock
The complexity of the common stock for 99 of the S&P 100 components is
measured using 12 quarters’ data from 2009 Q1 to 2011 Q4. Only one stock
Mondelez International is excluded due to spin-off. The measuring process is done
through the website www.rate-a-business.com. The common stock template in the
website is used for the analysis. We can also get interactive business structure maps
after performing the analysis. Some examples of the stock data used as the input for
the analysis can be seen in the appendix. Table 1 shows the mean, standard deviation
and other summary statistics for the complexity measure.

Table 1: Descriptive statistics of the complexity measure for S&P 100 components
Comparing complexity estimates across stocks, Target Corporation(TGT) has
the lowest complexity, which is consistent with its business of operating mainstream
discount stores across the United States. The company with the highest complexity is
EMC corporation. It can be explained by its business of data storage, virtualization
and cloud computing. Besides that the top three companies(EMC, Apple and Amazon)
with the highest complexity are all technology companies. This is a really volative
industry filled with opportunities as well as risks. Many of the high-tech companies in
1980 disappear today. The standard deviation of 3.57 also reflects the cross-sectional
difference in common stock complexities. It’s nonnegligible but not very significant
when this value is compared with the mean complexity of 15.35 and median
complexity of 15.31.
5. Comparison of different risk factors for the whole sample
1) Background information for three tests
Due to the high uncertainty and the danger in the prediction of equity returns, we
don’t intend to explain the equity return by one or a combination of several variables.
It’s well known that psychological effect must be taken into consideration for
analyzing the market. Instead, we’d like to see which variables can help provide
information in the understanding of the cross-sectional difference in common stock
returns. Three different kinds of criteria will be used to evaluate the effectiveness and
relative importance of different risk factors. The three criteria: correlation between the
Complexity
Mean 15.35
Standard Error 0.36
Median 15.31
Mode 15.49
Standard Deviation 3.57
Sample Variance 12.74
Kurtosis -0.06
Skewness 0.39
Range 17.4
Minimum 9.32
Maximum 26.72
Count 99

factor and the stock return, t statistics of the factor in the multiple regression and the
performance of factor-based strategy. Based on the three criteria, we can establish a
thorough evaluation of the importance and effectiveness of different risk factors in the
specific time period.
We analyze five risk factors. Size and book-to-market measures are identified by
FAMA and FRENCH(1992). Momentum measure is offered by JEGADEESH and
TITMAN(1993). The importance of the price information is also confirmed in the
field of experimental finance. The complexity and resilience measure are new
potential risk factors introduced in this article.
We explain the measure of momentum, complexity and resilience. Momentum is
defined as one year past return. For measuring the complexity and resilience, we use
32 components to describe the common stock(as provided by the common stock
template in the appendix). The past three years’ data are used to describe every
component. Using these data, we can measure complexity and resilience using
OntoSpace™. OntoSpace™ is a quantitative complexity management system
developed by Ontonix. The test period is from 02.04.2012 to 01.04.2013. It needs to
be noticed that there are no major shocks like Lehman Brothers’ bankruptcy in this
period. The stock return is calculated based on the adjusted closed price of the stock
in 02.04.2012 and 01.04.2013. The bid-ask spread and the problem of liquidity can be
neglected due to the return period of 1 year. During this period, the stock with the
highest return in the sample is Gilead Sciences (GILD) with the return of 96.72%. The
stock with the lowest return in the sample is Apple (AAPL) with the return of -
29.64%. By comparison, S&P 500 goes up 10.09% during this period. Besides that,
Target Corporation (TGT) has the return of 20.5% while EMC Corporation (EMC)
has the return of -20.67%. If you long the least complexity stock and short the highest
complexity stock, a significant return can be obtained. This also reflects the role
complexity plays in determining stock returns. We use the data of 02.04.2012 to
measure size, book-to-market ratio, and momentum and determine the components of
the test stocks. Due to the gap between accounting data and market data, we use the
12 quarterly data(from 2009 Q1 to 2011 Q4) to measure complexity and resilience.
And the test sample is 99 components of the S&P 100 index. Mondelez International
is excluded because the spin-off from Kraft Foods makes the data uncomparable. Due

to the restriction of the computing power, we use only 99 stocks for one single period
to perform the test. It can be seen like the first phase in the FDA drug approval.
2) Test 1
In table 2, five risk factors are compared via their correlation with stock return.
Two measures (book-to-market ratio and resilience) have almost no correlation with
stock returns. Size and complexity have negative relationship with stock return. And
momentum has positive correlation with the stock return. The ranking of the relative
importance of the three nontrivial risk factors are size, complexity and momentum.
In figure 1, we can see more clearly the relationship between complexity and 1
year return. The slightly negative trend can be seen clearly and the negative
relationship is more significant in the right end of the horizontal axis.
Table 2: Correlation between risk factors and stock return
Figure 1: Scatter plot for complexity of the common stock and 1 year return of the
stock
Correlation between size and 1 year return -0.18301
Correlation between book-to-market and 1 year return 0.004104
Correlation between complexity and 1 year return -0.18105
Correlation between resilience and 1 year return -0.01487
Correlation between momentum and 1 year retrun 0.112042
y = -0.0102x + 0.3116
R²= 0.0328
-40.00%
-20.00%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
0 5 10 15 20 25 30
1yearreturn
Complexity of the common stock
Complexity and 1-year return(2012-2013)

3) Test 2
Next, we evaluate different risk factors via multiple regression. The return
variable is regressed against different risk factors for several models and the one with
the highest adjusted R-square value is chosen. As can be seen in table 3, in the
regression with all five risk factors, only three factors(size, complexity and
momentum) have the absolue value of t statistic larger than 1. This suggests
attempting regression with these three risk factors (size, complexity and momentum)
Table 3: t-statistic in the multiple regression including all five risk factors
The result of the regression is presented in table 4. All three factors have t
statistics with absolute value larger than 1.5. In particular the t statistic of complexity
doesn’t change much and is significantly negative, supporting the negative
relationship between complexity and cross-sectional returns. Repeating the regression
for other combinations of factors also supports the important role of these three
factors. In table 5, the multiple regression with these three factors is the best model
according to the comparison of the adjusted r-square value. The low value of adjusted
r-square value confirms the unpredictability of stock returns. Investors should not
make investment decisions only based on several risk factors.
Table 4: t-statistic in the multiple regression including three risk factors
Risk factors t Stat
Intercept 0.557
Size -1.942
book-to-market 0.119
Complexity -1.448
Resilience 0.778
Momentum 1.578
Risk factors t Stat
Intercept 3.586
Size -1.905
Momentum 1.825
Complexity -1.516

Table 5: Adjusted r-square value for different regression models
Finally we find size, momentum and complexity have significant importance in
determining the cross-sectional returns. This finding is robust to the specification of
the regression model. The relative importance of the three factors according to the
ranking of the absolute value of the t statistic in the three factor regression model is
size, momentum and complexity.
6. Comparison of different risk factors for the high complexity group
The reason of comparing different risk factors for the high complexity
group(with the complexity value no less than the median complexity value) is that the
complexity measure is significantly important as an early warning for turbulent
companies. This can be seen from figure 2. The strength of relationship can be
compared with another two important measures(size and book-to-market ratio) by
comparing figure 2 with figure 3 and figure 4.
Figure 2: Relationship between complexity and 1-year return by different complexity
groups
Factors in the model Adjusted R Square
All factors 0.044
Size, Momentum, Complexity and Resilience 0.054
Size, Momentum and Complexity 0.057
Size, Momentum 0.044
Momentum, Complexity 0.031
Size, Complexity 0.034
-40.00%
-20.00%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
0 5 10 15 20 25 30
Return
Complexity
Complexity and Return by complexity type
High Complexity
Low Complexity

Figure 3: Relationship between size and 1-year return by different size groups
Figure 4: Relationship between size and book-to-market ratio by different book-to-
market ratio groups
-40.00%
-20.00%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
0 100'000 200'000 300'000 400'000 500'000 600'000 700'000
Return
Market equity, ME, in millions
Size and Return
large size
small size
-40.00%
-20.00%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
0 0.5 1 1.5 2 2.5
Return
Ratio of book equity to market equity
Book-To-Market Ratio and Return
Value Stock
Growth Stock

For the common stock with the low complexity value or simple operations, the
measure is not so important. And the complexity measure is very important for
complex and fragile companies. This can be confirmed by the table 6 and figure 5. In
table 6, five factors’ correlations with the return are compared. We can see that
complexity ranks first and the ranking of the relative importance of the three
nontrivial risk factors are complexity, size and resilience. We can also see the increase
of correlation between complexity and 1 year return. By contrast the change of sign
for the book-to-market ratio shows the instability of this measure.
Table 6: Correlation between risk factors and stock return for the high complexity
group
Figure 5: Scatter plot for complexity of the common stock and 1 year return of the
stock for the high complexity group
Correlation between size and 1 year return -0.24868
Correlation between book-to-market and 1 year return -0.08589
Correlation between complexity and 1 year return -0.28101
Correlation between resilience and 1 year return -0.12086
Correlation between momentum and 1 year retrun 0.023852
y = -0.025x + 0.5884
R²= 0.079
-40.00%
-20.00%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
15 20 25 30
1yearreturn
Complexity of the common stock
Complexity and 1-year Return

Results from multiple regression also confirm this. From Table 7, the ranking of
the factors has changed from size, momentum and complexity to complexity, book-to-
market ratio and momentum. These results are consistent with the role complexity
plays as an early warning for fragility of the business, which is finally reflected as the
highly negative return of the corresponding stock. For instance, the increase of 1 for
the complexity value leads to 2 percentage drop in 1 year return on average.
Table 7: Coefficient and t-statistics in the multiple regression including all risk
factors for the high complexity group
7. Comparison of different factor-based trading strategies
Finally we compare the five factors based on the performance of factor-based
strategy. This analysis is more relevant because the comparison basis is tradable
returns. In constructing the portfolio, we divide all stocks into two subgroups with
same number of stocks based on the ranking of the risk factor. Then we calculate the
difference between the return of the group with large values in the risk factor and the
return of the group with small values in the risk factor. To decide the weighting of
each stock in the subgroup, we choose the naive 1/N portfolio strategy. This strategy
is validated by DeMiguel, Garlappi and Uppal(2009). They find that among 14
models examined (including CAPM and CAPM-derived model), no one performs
better than 1/N rule out of sample consistently. Table 8 shows the performance of
these subgroups. We can rank these factors based on the difference of return between
the two factor-based subgroups. The ranking result is momentum, complexity,
resilience, size and book-to-market ratio.
Risk factors Coefficient t Stat
Intercept 0.580 1.123
Size 0.002 0.009
book-to-market 0.000 -1.033
Complexity -0.021 -1.444
Resilience 0.008 0.011
Momentum -0.132 -0.745

Table 8: Average return for different subgroups
Based on these three tests, we can see that size, complexity and momentum stand
out in this period. We would like to explore better the interaction of different factors.
We divide the components into different subgroups. Then within each subgroup we
compare the average return for different subgroups based on different risk factors.
From table 9, we can firstly see the large uncertainty in stock returns. Then we
can see in the large cap group and high complexity group, complexity-based portfolio
performs well. This is consistent with the idea that the fragility of highly complex and
large business can lead to low returns. It also shows complexity is an early warning
sign for the crisis. The measure and monitor of complexity is especially important for
those large cap and high complexity companies. For investors, complexity-based
trading strategy performs much better in these groups. And avoid investing in those
highly complex and fragile companies is a good advice. These companies are very
vulnerable to external shocks. From table 10, we can do a summary of the return
difference of different factors in different subgroups. We can see if we only consider
average return, momentum factor performs best. If we consider both average return
and standard deviation, then the complexity factor performs best. In summary,
complexity is an important factor for stock returns during the sample period.
Subgroup Type Return
Large Cap Subgroup 14.35%
Small Cap Subgroup 16.53%
Difference between the two size-based subgroup -2.18%
Value Stock Subgroup 14.66%
Growth Stock Subgroup 16.14%
Difference between the two book-to-market ratio based subgroup -1.48%
High Complexity Subgroup 13.59%
Low Complexity Subgroup 17.82%
Difference between the two complexity-based subgroup -4.22%
High Resilience Subgroup 14.08%
Low Resilience Subgroup 16.81%
Difference between the two resilience-based subgroup -2.74%
High Momentum Subgroup 19.63%
Low Momentum Subgroup 11.13%
Difference between the two momentum-based subgroup 8.50%

Table 9: Average return for different subgroups within the specific subgroup
Table 10: Summary of the performance of size-based strategy (the return is 1 year
return)
8. Conclusions
Our results show that among five plausible measures, three measures (size,
complexity and momentum) stand out as the factors that can influence stock returns.
Using data from 2009 to 2013, we find that the rise of complexity has negative
relationship with stock returns. We also find the complexity-based trading strategy
can provide more stable returns than size-based strategy and momentum-based
strategy. That means investors may require a premium for high complexity stocks.
However, it remains to be investigated whether these findings are specific to the
sample period. Meanwhile the unpredictability of stock returns is also significant
based on the study. Investors should be cautious about making investment decisions
based on several risk factors.
Excessive complexity implies fragility. That might be the reason that complexity
has negative relationship with equity returns. This is confirmed by the fact that the
complexity-based strategy performs better in the large cap group and the high
complexity group.
Average Return Standard deviation
Size -2.69% 6.72%
Complexity -4.38% 4.39%
Momentum 7.78% 12.97%

In this work, we find that besides two well-known measures (size and
momentum), complexity provides significant information relating to the stock returns.
The study opens up new doors into factor investing as well as the measure of
complexity in the bond and foreign exchange market.

References
1. Blitz, David, Falkenstein, Eric G. and Van Vliet, Pim. Explanations for the
Volatility Effect: An Overview Based on the CAPM Assumptions (May 28, 2013).
Available at SSRN: http://ssrn.com/abstract=2270973
2. DeMiguel, V., Garlappi, L., Uppal,R., 2009. Optimal Versus Naive Diversification:
How Inefficient is the 1/N portfolio strategy. The Review of Financial Studies, 22(5),
pp. 1915-1953.
3. Fama, E., French, K., 1992. “The Cross-Section of Expected Stock Returns“, The
Journal of Finance, 47, pp. 427-465.
4. Fama, E., French, K., 1993. Common risk factors in the returns on stocks and
bonds. Journal of Financial Economics 33, pp. 3-56.
5. Goyal, A, 2012. Empirical cross-sectional asset pricing: a survey. Financial
Markets and Portfolio Management, Volume 26, Issue 1, pp. 3-38.
6. JEGADEESH, N. , SHERIDAN,T., 1993. Returns to buying winners and selling
losers: Implications for stock market efficiency. The Journal of Finance, 48, pp. 65-91.
7. Marczyk, J., 2011. A new theory of risk and rating. UNI Service, Trento, pp. 29.
8. Marczyk, J., 2013. On the resilience and stability of banks and systems of banks: a
quantitative approach to systemic risks. Technical Note, Ontonix.
http://www.ontonix.com/ardocCM/files/3/file/Systemic_Risks_Resilience.pdf
9. Mariotti, J., 2008. The complexity crisis: Why too many products, markets, and
customers are crippling your company—and what to do about it. Adams Media, Avon,
MA
10. Meucci, A., 2008. Fully Flexible Views: Theory and Practice. Risk, Vol. 21, No.
10, pp. 97-102.
11. Preis, T., Moat, H., Stanley, H., 2013. Quantifying Trading Behavior in Financial
Markets Using Google Trends. Scientific Reports 3, Article number: 1684.
12. Zolli, A., 2012. Want to build resilience? Kill the complexity(blog post).
http://blogs.hbr.org/cs/2012/09/want_to_build_resilience_kill_the_complexity.html,

Appendix 1. 32 parameters for the Common Stock Template
Market Capitalization
P/E Ratio
Return on S&P 500
Return on Nasdaq
Return on FTSE 100
Inflation
Treasury return
Return on stock
Average Trading Volume
Close price
High price
Low price
Dividend
Assets
Cash and cash equivalents
Short-term investments
Total Inventories
Accounts receivable
Total current assets
Total Intangible assets
Debt in current liabilities
Goodwill
Income taxes
Total preferred stock
Total cost of goods sold
Total depreciation and amortization
Research and development expense
Selling, General and Administrative expenses
Foreign exchange income(Loss)
Non-operating income(expense)
Net income
Total long term investments

Appendix 2. Examples of stock data used for measuring complexity at
www.rate-a-business.com
Table 11: Data of Google used for measuring the complexity
PARAMETER 2009Q1 2009Q2 2009Q3 2009Q4 2010Q1 2010Q2 2010Q3 2010Q4 2011Q1 2011Q2 2011Q3 2011Q4
MarketCapitalization 109903.77 133400.35 157152.22 197012.28 180499.55 141766.85 167850.04 190843.16 189011.24 163392.62 166747.81 209849.68
P/ERatio 25.44 29.24 32.03 30.38 25.80 19.32 21.35 22.58 22.78 18.26 17.55 21.70
ReturnonS&P500 5.68% 13.14% 4.93% 3.64% 10.51% -7.17% 7.41% 8.69% 6.03% -5.23% -3.02% 4.72%
ReturnonNasdaq 16.32% 15.21% 3.37% 5.00% 14.62% -8.39% 11.21% 7.68% 6.42% -4.08% -2.61% 4.82%
ReturnonFTSE100 2.27% 8.59% 9.46% 2.85% 7.03% -5.32% 7.93% 3.31% 3.53% -4.20% -4.66% 2.48%
Inflation 0.40% 0.75% 0.76% 0.45% 0.14% 0.32% 0.54% 0.80% 0.88% 0.58% 0.26% 0.62%
Treasuryreturn 0.05% 0.05% 0.03% 0.01% 0.04% 0.03% 0.04% 0.04% 0.03% 0.01% 0.00% 0.00%
Returnonstock 13.14% 21.13% 17.61% 25.03% -8.53% -21.54% 18.17% 12.97% -1.21% -13.70% 1.71% 25.41%
AverageTradingVolume 5744167 3564867 2814967 2596567 3842633 3567533 2963600 3000233 2722000 2935900 3980200 3179333
Closeprice 348.06 421.59 495.85 619.98 567.12 444.95 525.79 593.97 586.76 506.38 515.04 645.9
Highprice 381 447.34 507 625.99 629.51 597.84 536.849 630.85 642.96 595.19 627.4992 646.76
Lowprice 282.75 340.61 395.9801 482.6 520 444.72 433.63 518.8501 551.2801 473.02 490.86 480.596
Dividend 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Assets 33513.029 35158.76 37702.845 40496.778 42871 48045 53342 57851 59960 64861 69088 72574
Cashandcashequivalents 10426.291 11911.351 12087.115 10197.588 9192 10713 11257 13630 12415 10320 10630 9983
Short-terminvestments 7358.642 7432.655 9907.276 14287.187 17322 19346 22998 21345 24260 28798 31930 34643
TotalInventories 0 0 0 0 0 0 0 0 0 0 0 0
Accountsreceivable 2543.105 2847.755 2879.604 3201.715 3084 3617 3844 5002 4818 5843 5861 6172
Totalcurrentassets 22080.802 23834.853 26353.544 29166.958 31132 35161 39447 41562 43310 46442 50042 52758
TotalIntangibleassets 5740.659 5693.299 5672.465 5677.503 5912 6692 7092 7300 7324 8058 8463 8924
Debtincurrentliabilities 0 0 0 0 0 0 2122 3465 3216 1217 1218 1218
Goodwill 4830.315 4836.913 4849.217 4902.565 5122 5788 6099 6256 6308 6677 6989 7346
Incometaxes 466.973 371.631 427.566 594.571 551 594 547 599 594 580 630 785
Totalpreferredstock 0 0 0 0 0 0 0 0 0 0 0 0
Totalcostofgoodssold 1702.175 1732.489 1851.838 2051.383 2125 2131 2209 2571 2540 2718 2889 3204
Totaldepreciationandamortization 399.329 375.482 374.402 357.017 327 336 343 375 396 453 489 499
Researchanddevelopmentexpense 641.643 707.626 757.524 736.234 818 898 995 1051 1226 1234 1404 1298
Selling,GeneralandAdministrativeexpenses 1497.595 1541.032 1644.893 1784.442 1835 1988 2188 2512 2843 2974 3285 3373
Foreignexchangeincome(Loss) -90.92 -83.701 -73.856 -11.301 -105 -106 -69 -75 -128 -72 -24 -155
Nonoperatingincome(expense) 6.21 -17.718 -7.177 87.688 18 69 168 160 100 217 323 2
Netincome 1422.828 1484.545 1638.975 1974.1 1955 1840 2167 2543 1798 2506 2729 2704
Totallongterminvestments 100.999 93.925 110.372 128.977 154 377 485 523 666 893 891 790

Table 12: Data of Visa Inc. used for measuring the complexity
P/ERatio 36.10 31.77 22.29 25.88 25.01 19.38 18.52 16.68 16.43 17.09 16.61 18.73
ReturnonS&P500 5.68% 13.14% 4.93% 3.64% 10.51% -7.17% 7.41% 8.69% 6.03% -5.23% -3.02% 4.72%
ReturnonNasdaq 16.32% 15.21% 3.37% 5.00% 14.62% -8.39% 11.21% 7.68% 6.42% -4.08% -2.61% 4.82%
ReturnonFTSE100 2.27% 8.59% 9.46% 2.85% 7.03% -5.32% 7.93% 3.31% 3.53% -4.20% -4.66% 2.48%
Inflation 0.40% 0.75% 0.76% 0.45% 0.14% 0.32% 0.54% 0.80% 0.88% 0.58% 0.26% 0.62%
Treasuryreturn 0.05% 0.05% 0.03% 0.01% 0.04% 0.03% 0.04% 0.04% 0.03% 0.01% 0.00% 0.00%
Returnonstock 6.21% 12.17% 11.16% 26.75% 4.24% -22.16% 5.14% -5.04% 4.81% 14.67% 1.90% 18.72%
Closeprice 55.6 62.26 69.11 87.46 91.03 70.75 74.26 70.38 73.62 84.26 85.72 101.53
Highprice 57.98 71.24 74.99 89.69 93.63 97.192 77.8 81.75 77.08 87.36 94.75 103.45
Lowprice 41.78 53.63 58 66.5452 80.54 68.29 64.9 66.5 67.51 73.11 76.11 81.71
Dividend 0.11 0.11 0.11 0.13 0.13 0.13 0.13 0.15 0.15 0.15 0.15 0.22
Assets 31964 32624 32281 31715 32068 32696 33408 33989 34422 34447 34760 35768
Cashandcashequivalents 3363 4200 4617 4160 4560 5205 3867 3684 3512 3600 2127 1942
Short-terminvestments 1783 1341 2361 2323 2292 2819 2949 3750 4064 4044 5059 6059
Accountsreceivable 1386 2441 1184 1272 1168 917 1018 1033 935 956 1084 1138
Totalcurrentassets 8225 9562 9241 8753 9115 9850 8734 9355 9606 9516 9190 9978
TotalIntangibleassets 21096 21096 21091 21091 21091 21091 22925 22910 23052 23121 23104 23122
Goodwill 10213 10213 10208 10208 10208 10208 11447 11447 11588 11668 11668 11668
Incometaxes 352 568 349 445 401 406 422 498 497 539 476 590
Totalcostofgoodssold 706 763 916 724 773 829 917 805 786 903 943 849
Totaldepreciationandamortization 56 57 61 62 62 63 78 67 70 74 77 80
Researchanddevelopmentexpense 0 0 0 0 0 0 0 0 0 0 0 0
Selling,GeneralandAdministrativeexpenses 0 0 0 0 0 0 0 0 0 0 0 0
Foreignexchangeincome(Loss) 0 0 0 0 0 0 0 0 0 0 0 0
Nonoperatingincome(expense) 36 32 19 7 19 -2 18 16 -6 113 -8 9
Netincome 536 729 514 763 713 716 774 884 881 1005 880 1029
Totallongterminvestments 856 231 270 260 252 233 138 126 100 108 811 899

Table 13: Data of Times Warner used for measuring the complexity
P/ERatio 12.37 16.79 17.55 16.75 15.87 13.77 14.74 14.30 16.08 15.68 11.40 13.34
ReturnonS&P500 5.68% 13.14% 4.93% 3.64% 10.51% -7.17% 7.41% 8.69% 6.03% -5.23% -3.02% 4.72%
ReturnonNasdaq 16.32% 15.21% 3.37% 5.00% 14.62% -8.39% 11.21% 7.68% 6.42% -4.08% -2.61% 4.82%
ReturnonFTSE100 2.27% 8.59% 9.46% 2.85% 7.03% -5.32% 7.93% 3.31% 3.53% -4.20% -4.66% 2.48%
Inflation 0.40% 0.75% 0.76% 0.45% 0.14% 0.32% 0.54% 0.80% 0.88% 0.58% 0.26% 0.62%
Treasuryreturn 0.05% 0.05% 0.03% 0.01% 0.04% 0.03% 0.04% 0.04% 0.03% 0.01% 0.00% 0.00%
Returnonstock -12.76% 31.56% 15.03% 1.83% 8.14% -6.90% 6.78% 5.72% 11.68% 2.54% -16.95% 21.48%
Closeprice 19.3 25.19 28.78 29.14 31.27 28.91 30.65 32.17 35.7 36.37 29.97 36.14
Highprice 33.4503 26.83 30.485 32.85 31.75 34.07 33.23 32.9 38.34 38.62 37.26 36.69
Lowprice 17.81 18.98 23.34 28.42 26.43 28.43 27.99 29.42 31.44 34.23 27.62 28.43
Dividend 0.19 0.19 0.19 0.19 0.21 0.21 0.21 0.21 0.24 0.24 0.24 0.24
Assets 70829 70920 71266 65730 66066 65030 65220 66524 65742 66628 66890 67801
Cashandcashequivalents 7115 7009 7126 4800 5167 4238 4009 3663 3029 3520 3245 3476
Short-terminvestments 0 0 0 0 0 0 0 0 0 0 0 0
Accountsreceivable 4674 4847 4833 5111 5143 5328 5309 6413 5854 5902 5742 6922
Totalcurrentassets 15287 15243 15252 13007 13393 12631 12334 13138 11903 12439 12082 13432
TotalIntangibleassets 43572 43390 43279 40699 40213 40068 40101 40313 40265 40302 40241 40066
Goodwill 32357 32064 31978 29795 29758 29697 29826 29994 30012 30080 30095 30029
Incometaxes 227 299 320 307 389 317 221 421 331 313 431 409
Totalcostofgoodssold 3193 3169 3250 3955 3189 3429 3361 4370 3564 3880 3648 4566
Totaldepreciationandamortization 242 231 240 235 232 236 222 248 231 230 228 233
Researchanddevelopmentexpense 0 0 0 0 0 0 0 0 0 0 0 0
Selling,GeneralandAdministrativeexpenses 1494 1452 1444 1653 1477 1504 1465 1716 1583 1617 1557 1660
Foreignexchangeincome(Loss) 0 0 0 0 0 0 0 0 0 0 0 0
Nonoperatingincome(expense) 18 36 -64 -36 30 27 0 61 19 6 0 -182
Netincome 660 524 662 622 725 562 522 769 653 638 822 773
Totallongterminvestments 1900 2127 2081 2032 1743 1679 1656 1796 1983 1998 1925 1820

Table 14: Data of EMC Corp used for measuring the complexity
P/ERatio 19.32 25.19 36.26 32.96 30.07 26.14 26.04 26.02 28.87 28.70 20.78 19.58
ReturnonS&P500 5.68% 13.14% 4.93% 3.64% 10.51% -7.17% 7.41% 8.69% 6.03% -5.23% -3.02% 4.72%
ReturnonNasdaq 16.32% 15.21% 3.37% 5.00% 14.62% -8.39% 11.21% 7.68% 6.42% -4.08% -2.61% 4.82%
ReturnonFTSE100 2.27% 8.59% 9.46% 2.85% 7.03% -5.32% 7.93% 3.31% 3.53% -4.20% -4.66% 2.48%
Inflation 0.40% 0.75% 0.76% 0.45% 0.14% 0.32% 0.54% 0.80% 0.88% 0.58% 0.26% 0.62%
Treasuryreturn 0.05% 0.05% 0.03% 0.01% 0.04% 0.03% 0.04% 0.04% 0.03% 0.01% 0.00% 0.00%
Returnonstock 8.88% 14.91% 30.08% 2.52% 3.26% 1.44% 10.98% 12.75% 15.98% 3.73% -23.81% 2.62%
Closeprice 11.4 13.1 17.04 17.47 18.04 18.3 20.31 22.9 26.56 27.55 20.99 21.54
Highprice 12.59 13.735 17.48 18.44 19.04 20 21.83 23.2 27.5901 28.73 28.25 25.13
Lowprice 9.61 10.91 12.31 16.12 16.45 17.1 17.87 19.39 22.84 25.39 19.84 19.99
Dividend 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Assets 23902.099 24487.332 25551.744 26812.003 27467.963 28011.451 28926.908 30833.284 31187.432 32331.862 32430.671 34268.179
Cashandcashequivalents 6323.452 6422.76 5518.359 6302.499 6256.626 5784.812 4940.592 4119.138 4102.332 3896.369 3125.61 4531.036
Short-terminvestments 928.277 835.49 617.535 392.839 641.576 1015.143 1712.41 1256.175 1491.454 1436.29 1635.533 1786.987
TotalInventories 820.504 773.528 810.925 886.289 851.501 813.756 838.622 856.405 921.004 1005.69 1104.143 1009.968
Accountsreceivable 1700.096 1855.986 1864.121 2108.575 1735.043 1976.311 2120.679 2589.423 2379.175 2620.448 2650.392 2964.499
Totalcurrentassets 10596.161 10686.262 9628.565 10538.302 10375.125 10546.303 10596.233 9783.322 10172.458 10233.41 9947.723 11582.683
TotalIntangibleassets 7042.473 7109.565 9222.725 10376.175 9402.851 9434.792 9787.321 13962.917 11790.048 12108.156 12153.651 14586.485
Debtincurrentliabilities 0 0 0 0 0 0 0 3214.771 3244.134 3271.865 3280.957 3304.974
Goodwill 7042.473 7109.565 9222.725 9210.376 9402.851 9434.792 9787.321 11772.65 11790.048 12108.156 12153.651 12154.97
Incometaxes 37.815 38.045 20.602 156.313 95.653 136.976 148.663 257.005 121.639 172.731 171.086 174.929
Totalpreferredstock 0 0 0 0 0 0 0 235.229 205.866 175.907 144.862 119.325
Totalcostofgoodssold 1210.813 1258.964 1282.162 1440.258 1390.005 1378.88 1428.236 1617.199 1569.295 1548.948 1554.86 1677.645
Totaldepreciationandamortization 255.798 253.268 281.977 282.092 281.58 284.859 296.525 304.586 339.272 349.803 359.076 373.447
Researchanddevelopmentexpense 383.293 397.881 422.092 424.243 434.933 477.725 483.264 492.093 502.108 538.891 548.021 560.767
Selling,GeneralandAdministrativeexpenses 1399.307 1441.518 1583.883 1689.452 1682.094 1751.357 1819.313 1975.641 1998.039 2114.58 2160.935 2355.615
Foreignexchangeincome(Loss) -28.4 -28.4 -28.4 -21.2 -21.2 -21.2 -21.2 -4.5 -4.5 -4.5 -4.5 -12.8
Nonoperatingincome(expense) 29.086 31.36 40.306 16.226 22.511 34.233 34.74 11.558 -4.947 10.343 -27.506 -56.593
Netincome 194.069 205.232 298.18 390.596 372.704 426.216 472.516 628.559 477.148 546.494 605.649 832.046
Totallongterminvestments 2533.237 2765.946 2291.672 2692.323 3254.641 3539.361 3860.062 4285.199 3876.569 4180.205 4499.373 4889.614

Appendix 3. Examples of complexity maps and complexity profiles for the
common stock
Figure 6: Complexity map of the EMC Corporation

Figure 7: Complexity profile of the EMC Corporation

Figure 8: Complexity map of Google

Figure 9: Complexity profile of Google

Ontonix Complexity & Cross Sectional Returns Academic Research

More Related Content

Viewers also liked (7)

Similar to Ontonix Complexity & Cross Sectional Returns Academic Research (20)

More from Datonix.it (19)

Recently uploaded (20)

Ontonix Complexity & Cross Sectional Returns Academic Research