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Innovation Brief
Portfolio Selection of the NASDAQ and NYSE Energy Sectors using Genetic
Optimization
By Evan, L, Hjelmstad
University of Advancing Technology
ABSTRACT
With the currently unstable economy and the upcoming transfer of our dependency on oil
to renewable and cleaner energy, it is often hard to create a portfolio of energy stocks
with solid financials, especially if the investor is inexperienced. A program that utilizes
genetic algorithms to analyze and compare stocks in the NASDAQ and NYSE energy
sectors could possibly aid an investor in creating such a portfolio. It is the intent of this
project to create such a program that utilizes a genetic algorithm to quickly and
efficiently compile a portfolio that is the best possible for the energy sector. It will
determine this by taking into account the fundamental analysis and dividends of its
individual stocks and attempting to find a balance between them.
TABLE OF CONTENTS
1. ABOUT THE INNOVATION………………………………………
Innovation
Today’s Situation about the Innovation
Innovation Timeline
Innovation Inquiry
2. REVIEW OF RELATED MATERIALS……………………………..
3. LEARNING PROCESS………………………………………………
4. RESULTS…………………………………………………………….
5. REFERENCES……………………………………………………….
6. APPENDIX A. Portfolio Analysis Results Excel Insert……………...
7. APPENDIX B. Adjusted Scoring Ranges…..………………………...
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1
ABOUT THE INNOVATION
Innovation
Due to the unstable US economy, reduction of dependence on foreign oil and
transferring that dependence to renewable energy, it is more difficult than ever for an
individual to make a balanced portfolio of energy stocks. A computer program that
utilizes genetic algorithms can help to aid an individual investor’s decision by giving
unbiased stock picks that are based on solid financials. The purpose of this project is to
see if the use of a genetic algorithm to determine and sort through the most financially
healthy energy stocks and then compiling these into a portfolio will be more profitable
than a randomly compiled energy portfolio. For the scope of this project it will only look
at the NASDAQ and NYSE, specifically their energy sectors. However, if using a
genetic algorithm can assist an individual on the NASDAQ and NYSE energy sectors it is
reasonable to assume that it would work for the energy sectors of other exchanges with
minimal changes.
This project will be done in several stages. The first stage will select the most
secure small, medium and large cap stocks off the NASDAQ and NYSE energy indexes
using the fundamental analysis methods explained later. The next stage will generate a
random population of portfolios using the previously selected small, medium and large
cap stocks as its population. The last stage will use a genetic algorithm which would
recombine and evolve the population of portfolios until it found the final portfolio that
best fit the criteria.
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Today’s Situation about the Innovation
The NYSE was created in 1792 and remains one of the most respected stock
exchanges in the world. However, it also remains a purely broker to broker floor traded
system where individual investors cannot execute trades themselves. When the NASDAQ
was established in 1971 it was the first implementation of electronic stock trading and
ever since then it has been easy for individuals to manage their own investments. One
thing that individual investors learn early on is how easy it is to get emotionally involved
while trading stocks. Remaining emotionless during trades is a critical aspect of
investing. (Paulos, 2003). This is where automated investment and analysis programs
come into play.
Automated investment and analysis computer programs have existed since the
1980’s but until recently were only used by large banks and investment companies as
these were the only entities that could afford to have such programs made. (Khan &
Sharma, 2008). Recently however, the amount of individual investors has increased, thus
increasing the appeal for the mass market of investor aiding programs. These programs
analyze stocks from a purely statistical and analytical point of view, only reacting to
changes in data. They are not susceptible to outside influences such as emotions, rumors
or stress. According to The Korea Times, “A third of all EU and U.S. stock trades in
2006 were driven by such automatic programs, or algorithms, and the figure will reach 50
percent by 2010, according to Boston-based consulting firm Aite Group.” (Jin-seo, 2008,
koreatimes.co.kr). Taking this into account, it is easy to see how the market for programs
that aid investor decisions will only increase over the years.
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Innovation Timeline
This project is broken into several key sections. The first is acquiring the skills necessary
to complete the project as well as doing the initial research and designs. The second is
creating the program by implementing the designs. Finally the third and last section is
testing the genetic portfolio against a multitude of random portfolios and then analyzing
the results.
The following is a week by week breakdown of what will be accomplished.
Weeks 1 – 12 Learn basics of C#, experiment with Genetic Algorithms
Weeks 12 – 15 Design graphical user interface
Weeks 15 – 18 Code program to gather needed information off of the web
Weeks 18 – 20 Code program to take user input
Weeks 20 – 26 Code Genetic Algorithm
Weeks 26 – 30 Test, debug and determine most efficient criteria for genetic
algorithm
Week 30 Run program to create genetic portfolio
Week 30 Compile random portfolios
Weeks 30 – 34 Track portfolios progress
Weeks 34 – 40 Analyze and document results
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Innovation Inquiry
The purpose of this project is to see if the use of a genetic algorithm to determine and sort
through the most financially healthy energy stocks and then compiling these into a
portfolio will be more profitable than a randomly compiled energy portfolio. For the
scope of this project it will only look at the NASDAQ and NYSE, specifically their
energy sectors. At the conclusion of this project, through the analysis methods described
below, it will be determined if using a genetic algorithm is a viable solution to aiding
investors in compiling a balanced and solid portfolio of stocks. This will be determined
during the analysis stage of this project and will involve competing one genetic energy
portfolio that the program generates against 100 randomly compiled energy portfolios.
The analysis will take place over four stages and if the genetic portfolio outperforms over
half of the random portfolios in over half of the time periods it will be considered
successful. The time periods will be as follows, one week, two weeks, one month, and
one year (using 11 months of prior data if available).
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REVIEW OF RELATED MATERIAL
Introduction
The idea of being able to outsmart the stock market or create a strategy that
predicts which direction it will move has been the fantasy of professional and private
investors alike. This is shown through the creation of such fictional movies like Pi
(1998) and many books such as The Five Greatest Stock Traders of All Time that depict
stories of people that do manage to outsmart the market for at least short while and the
strategies they used. Regardless of the success that a strategy may meet, the drive for
more efficient strategies that use increasingly complex techniques will never stop.
Techniques to Analyze a Stock
In the beginning the techniques used to analyze a stock were simple. Many times
investors would look at the fundamentals of a company such as what industry they were
in, what direction they were taking, who ran the company and what their near term goals
were. Then the techniques grew more complicated as the stocks began to be analyzed
quantitatively and numerous ratios were used to judge the safety and value of a stock.
Collectively these techniques are known as fundamental analysis. After fundamental
analysis came technical analysis which involves using a series of techniques to analyze a
company’s stock chart. From this an investor can attempt to determine a variety of things
including which direction the stock will move. (Motley Fool Staff, 2008).
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Genetic Algorithms
While the above methods help an investor analyze a stock, it does not provide any
conclusive evidence as to what separates a successful stock from an unsuccessful one.
With ever increasing amounts of processing power and more advanced algorithms being
produced and improved every day, the idea of being able rationalize the movement of
certain stocks to specific variables and causes is becoming more of a reality. One way
that this is being achieved is through the use of genetic algorithms.
Genetic Algorithms are a subset of evolutionary algorithms that are based on
inheritance, mutation, selection and crossover. They work by essentially sorting through
a population using a variety of parameters until they have a new population. Just as the
theory of evolution works in real life, in each generation the most fit genes are kept and
the rest are thrown out. (Shapcott, 1992). They then repeat this process narrowing down
the results more and more each time until they have the correct or most correct answer.
This can be helpful in creating an efficient and balanced portfolio by sorting through a
selection of stocks until it has the very best (defined by its parameters) of each industry or
sector. These can then be compiled into a balanced portfolio. Each stock is assigned a
score based on different criteria and then randomly assigned to a portfolio. This is then
repeated until a specified number of random portfolios are created. The genetic
algorithm would then proceed through a specified number of generations, keeping the
most fit portfolios and randomly mutating these with a selected list of stocks until a prime
portfolio was discovered. (Thatcher, 2004; Orito & Yamazaki, 2001).
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Other Variations involving Genetic Algorithms
From the base idea of using genetic algorithms to create a portfolio of stocks,
many variations have been created. One idea consists of implementing a neural network
into the genetic optimization. Neural networks are closely related to genetic algorithms
in that they use a biological process to analyze and sort data to solve a specific problem.
Neural networks are structured similar to a human brain in that they are made up of
neurons which essentially implement the basic structure of how the human brain makes
connections. Thus a web of connections is created throughout a data source that can be
explored for a solution. By implementing this functionality into a genetic algorithm it
would allow the program to actually learn as it creates portfolios and theoretically
improve its methods each time it executes. This offers additional ideas for expanding the
project upon its completion. (Khan & Sharma, 2008).
Another variation improves upon the initial idea by using two separate genetic
algorithms in a two step process to create the portfolio. The first step consists of
selecting only the most fit stocks to begin with and compiling these into a list. This list is
then used in the creation of the random portfolios. This is more efficient as any
particularly weak stocks have already been eliminated. Essentially, the random portfolios
consist of only the best, resulting in the final portfolio theoretically containing the best of
the best. It is an adaptation of this method which I plan to employ to analyze the energy
sector of the NASDAQ and NYSE, but rather than using a two stage genetic algorithm, I
will be using a more direct analysis and sorting algorithm before I deploy the genetic
algorithm. (Keung, Yu, Wang, & Zhou, 2006).
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In addition to genetic algorithms being utilized to compile portfolio of stocks,
they have been used with much success in comparing and contrasting trading strategies to
determine the best combination of strategies and the optimum situations in which to
deploy them. This was accomplished by using the behavior of groups of stock traders
that was determined by variables such as amount of daily trades, the volume of these
trades, current price of the specific stocks and whether they took a profit or loss on these
trades and then using this information as the data set. The population for the algorithm
was compiled of objects that represented these traders via rule sets which live stock data
was fed into each day. The rule sets determined if that specific trader bought or sold a
specific stock and recorded if they made a profit. What was discovered was that the rule
sets that were output by this algorithm far exceeded any individual rule sets profitability.
While it is not the purpose of this project to create such an algorithm it does offer many
ideas for expanding this project upon its successful completion. (Eiben and Smith 2007).
Problems Faced
One problem faced with the ever increasing complexity of algorithms in the
investment arena is that as one algorithm reaches the efficiency of another algorithm the
competition between the two decreases their profits. Thus the developers for the
algorithms are stuck in a never ending battle for creativity, complexity and efficiency.
(Duhigg, 2006).
Another problem that is often faced is the dataset for the genetic algorithm is
incomplete. The algorithm relies entirely on the data for generating great results and
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without a complete dataset, which is made difficult by the massive amount of companies
that are listed on various exchanges, it cannot give optimum output.
Conclusion
Based on the research done on the application of genetic algorithms in regards to
stock portfolio selection, an opportunity arises to see if this method can be successfully
applied to a new and especially volatile sector such as that of the NASDAQ and NYSE
energy sectors. If this is proven to be true then more research is merited to find out in
what other volatile sectors genetic algorithms may prove useful and in what other ways
they can be applied to stock market analysis.
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LEARNING PROCESS
In order to prove that genetic optimization can indeed create a more profitable
portfolio of energy stocks than one that is randomly selected, the program must be
designed, created and tested. The program will be created in the C# programming
language and will contain the following characteristics.
Upon running the user will be required to input the number of stocks
desired in the portfolio.
The overall portfolio score will be out of 300 and will be comprised of
several individual scores including how close the portfolio is to the
dividend and fundamental analysis.
The user will not input a desired fundamental analysis score as it will
always be at a desired score of 100. The fundamental analysis score of an
entire portfolio will be the average of all the fundamental analysis scores
of its individual stocks.
The user will also not be required to input a dividend score as the best
possible balance between dividend and fundamental analysis will be
sought. The default dividend will be at $1.00 per share but the total
dividends can exceed this.
If an internet connection is available, it will navigate to NASDAQ.com and will
read information about all of the companies that are listed under the energy sector. This
information includes stocks on both the NASDAQ and NYSE. If an internet connection is
not available, it will use the most recent data contained in a database. From this it will
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assign points to each company based on the fundamental analysis of its stock and will
determine if the stock is a small, medium or large cap stock. For each of these categories
the most financially secure stocks will be selected based on their fundamental analysis
score. The collection of stocks that make up all three portfolios will make up a new
population. From this new population the genetic algorithm will randomly create a
population of portfolios which will be slightly and randomly adjusted (mutated) each
generation until a portfolio is created that meets all of the user’s criteria.
Fundamental analysis involves an analysis of a company’s fundamentals such as
its true value, assets owned, growth over the past quarters and debt status. The
fundamental analysis score will be comprised of ten sub scores which will add up to its
total score of 100. This score will then be doubled to be out of 200 to give fundamental
analysis double the weight of the dividend score which will be out of 100. Together, the
fundamental analysis and dividend scores will be out of 300 which will provide direct
comparisons between portfolios. The individual methods of analysis which are described
below were chosen as they represent some of the most common methods for determining
the health of a stock. If the stock receives great results across all of the categories it is a
huge indicator that this stock is likely to do better in the long term than other stocks with
less impressive results. Please note however that while the scales of each method are
described as being between 1 and 10 there are exceptions where they can receive a
negative score for abnormally bad results or extra points for abnormally great results in a
specific category, with the max score at either end being -5 and 15. The 1 through 10
score is used to total to 100 and most scores fall within this range. The exact process was
determined through a series of trials which can be found in Appendix B.
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Trailing P/E – The trailing P/E of a stock will be scored on a scale of 1 – 10. The price to
earnings of a company is often considered the king of fundamental analysis measures.
Since this is the trailing price to earnings it compares the current price of a stock with its
past earnings. A P/E ratio gives the buyer an idea of how much earnings power they are
buying and is a standard way to compare two companies’ earnings that have different
stock prices. However, more than the P/E ratio of a company must be taken into account,
as in the past companies have been able to manipulate their earnings in such a way that
they appear much more solvent than they really are. Two historical examples of this are
Enron and WorldCom. More recent examples include Lehman Brothers and Merrill
Lynch. (Kelly, 2003).
P/B – The P/B ratio of a stock will be scored on a scale of 1 – 10. The price to book ratio
compares a stock’s price with the total value of the company. This helps an investor
judge if the stock they are buying is under or overvalued. If price to book ratio is one
then the stock is selling for exactly what it is worth. If it sells for more than it is worth
than its ratio will be above one. However, being above one is not always a concern.
Some companies have intrinsic value that is not reflected in their book value. (Kelly,
2003).
P/S - The P/S ratio of a stock will be scored on a scale of 1-10. The price to sales value
of a stock compares its stock price with its total sales for the last four quarters. It is
advantageous to include a price to sales ratio in your analysis as while companies can
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manipulate earnings to their liking, it is practically impossible to manipulate your sales.
(Kelly, 2003).
Current Ratio – The current ratio of a company will be scored on a scale of 1 – 10. The
current ratio gives you an idea of how solvent a company is. It is simply an assets
divided by liabilities ratio. Ideally you would like to see a company have at least a 2 to 1
current ratio. (Kelly, 2003).
Quick Ratio –The quick ratio of a company will be scored on a scale of 1 – 10. The
quick ratio is similar to the current ratio but gives a more accurate reading of how well a
company can deal with unseen expenses or opportunities as it is only the cash on hand
divided by the current liabilities. Ideally you would like to see a quick ratio of at least .5.
(Kelly, 2003).
Net Profit Margin – The net profit margin of a company will be scored on a scale of 1 –
10. The net profit margin of a company is found by dividing the money it has left after
paying expenses by the money it had before paying expenses. This gives you an idea of
how much money a company keeps from its revenue. This is also a great way to compare
companies within the same industry. If two companies are of similar size, create similar
products, have similar stock prices and other similar fundamental analysis scores it may
be difficult to tell which company is healthiest. However, this will be clear when you
look at the net profit margin as the company with the higher margin has found how to
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squeeze more profit out of their sales. This means if business becomes challenging they
will be more likely to survive and adapt. (Kelly, 2003).
Cash Flow per Share – The cash flow per share will be scored on a scale of 1 – 10. The
cash flow per share is simply the company’s total cash flow divided by the total number
of shares. A company’s cash flow tells you how well that company manages its money
and how efficiently it reinvests it. By dividing it by the total number of shares you can
see how much you must pay for a share of the company’s cash flow. (Kelly, 2003).
Beta – The beta will be scored on a scale of 1 – 10. The beta of a company compares
how volatile its stock is with the rest of the market. The NASDAQ is measured by the
NASDAQ 500 which is comprised of the five hundred most influential stocks on the
exchange. The NASDAQ 500 has a beta of one. Every single stocks beta in the
NASDAQ is in relationship to the NASDAQ 500’s beta. The NYSE is measured by the
S&P 500 which again is comprised of the five hundred most influential stocks on the
exchange. The S&P 500 also has a beta of one, thus no changes need to be made when
analyzing the beta of a stock on either exchange. If a stock has a beta higher than one it
can either mean that the stock has been more successful than the market, or it has not
been as successful. If its beta is less than one that could mean that the market has been
successful but the stock has generally stayed the same. It could also mean that the market
is doing worse and that stock is not going down but again staying the same. Either way a
good solid stock should have a beta slightly greater than one. (Kelly, 2003).
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ROE – The ROE of a stock will be scored on a scale of 1 – 10. The return on equity of a
company is what many people believe to be the greatest measure of a stock’s success.
This tells how the stock has done in the past by dividing the net income by the total
shareholders’ equity. This is essentially determining how much money a company has
made from the investments it made with shareholders money. (Kelly, 2003).
EPS – The EPS of a company will be scored on a scale of 1 – 10. The earnings per share
of a company is a standard way to measure the growth of a company. However, like with
the P/E ratio it can be easily manipulated. The earnings per share is simply the total
earnings for that quarter or year divided by the total amount of shares outstanding.
Ideally you want this number to be as large as possible. (Kelly, 2003).
A dividend is a way that a company shares profit with its shareholders and is a specific
amount that is paid each quarter for each share of stock an investor owns of that
company. Many people wish to incorporate dividend paying stocks into their portfolio as
it offers a guaranteed income even if the current markets are volatile. The target amount
is $1.00 of dividends per share. It is assumed that it is alright if the portfolio exceeds that
amount of dividends but it is not alright if it falls short and thus for every one cent that
the total dividend amount falls short of the requested amount, one point will be subtracted
from the total score of 100. The portfolio dividend score will be an average of all the
dividends of its stock resulting in the dividends per share of that portfolio.
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Upon completion of the program running, the test phase will begin and the price of each
stock will be noted. For the sake of testing, an even amount of virtual money will be
invested in each stock. Then, another program will randomly create fifty portfolios of the
same number and ratio of small, medium and large cap stocks but drawing from the entire
energy sector rather than the genetically optimized one. In addition, five people will each
be asked to create ten random portfolios again based on the entire energy sector but again
keeping the same ratio of small, medium and large cap stocks in each one. This will
ensure that neither one method is biased in its randomization and will result in exactly
one hundred randomly created portfolios and one genetically created portfolio. Again,
the price of each stock will be noted and an even amount of virtual money invested. Due
to the constant fluctuation of the market, the portfolios will be compared and ranked after
one week, two weeks, one month, and one year (using prior data if available). If the
genetically created portfolio has outperformed a majority of the randomly created
portfolios in at least two of these time frames then the project will be deemed successful.
If not, an attempt will be made to isolate the variables that led to it being outperformed
for future correction.
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RESULTS
Thus far the project has turned out better than expected as it exceeded each of the
criteria’s for success by outperforming a majority of the random portfolios in three of the
time slots and tying in one. Development of the program was completed in late May but
many additional features have been added to it since. On June 8th, 2009, the analysis
stage began which involved competing the genetic portfolio the program created against
100 random portfolios. As stated earlier, the analysis was performed in four stages across
different time periods: one week, two weeks, one month and using 11 months of prior
data (if available), one year. The following is a breakdown of how the genetic portfolio
did during each stage and how it compared to the random portfolios. While the random
portfolios exact percentage gains and losses are too numerous to state in this report, you
can view all the data gathered during the analysis stage in Appendix A.
Analysis Period One (One Week)
2.35% overall genetic portfolio gain
50 random portfolios outperformed / 50 random portfolios outperformed by
Analysis Period Two (Two Weeks)
-5.55% overall genetic portfolio loss
93 random portfolios outperformed / 7 random portfolios outperformed by
Analysis Period Three (One Month)
-9.51% overall genetic portfolio loss
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74 random portfolios outperformed / 26 random portfolios outperformed by
Analysis Period Four (One Year, using 11 Months of Prior Data)
-43.85% overall genetic portfolio loss
71 random portfolios outperformed / 28 random portfolios outperformed by / 1 tied
Although at first glance it may seem as if the algorithm was unsuccessful as there was
only one time period when the portfolio was profitable, the goal of this project was to
create a program that would consistently outperform a random portfolio and thus aid an
investor in being profitable. Anyone who has invested in the stock market before
understands that you cannot always be profitable as there are times when all stocks go
down. During times like these it is your goal to lose as little money as possible which is
exactly what the genetic portfolio accomplished. Even while the genetic portfolio lost
value, when compared to the random portfolios it was doing extremely well. In fact, the
genetic portfolio only started excelling against the random portfolios when the energy
sectors of the NASDAQ and NYSE took a turn for the worse. This indicates that this
portfolio is not nearly as volatile and is much more financially stable than the random
portfolios. During the first analysis period there were exactly fifty portfolios that
outperformed the genetic portfolio. However, these portfolios quickly lost their value as
soon as bad news hit the sector, whereas the genetic portfolio was not hit nearly as hard.
During the next two analysis periods the genetic portfolio outperformed an incredible 93
and 74 random portfolios respectively. Even when using 11 months of prior data
combined with the one month of current data during the fourth analysis stage, the genetic
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portfolio outperformed an amazing 71 random portfolios. Although the portfolio posted
a loss during this time period of 43.85%, this is due to the horrendous market conditions
caused by the failures of many prominent banks and auto companies during the last
months of 2008. It is believed that it will be several years before the markets fully
recover from such a hard hit. The data stated above is more easily seen in the graph
below.
Thus this project can be deemed successful by the criteria stated earlier which stated that
the genetic portfolio must outperform at least half of the random portfolios in at least half
of the analysis periods. The genetic portfolio beat a majority of the portfolios in three of
the time periods and tied in one.
As it has been proven that the current fundamental analysis methods when combined with
a genetic algorithm can produce an above average portfolio in the energy sector, there is
still much to be explored. These same techniques can be applied to other sectors and
even exchanges as well. Furthermore, the fundamental analysis methods can be used
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separate from the genetic algorithm to rank and display the top stocks from each sector
and exchange, further helping an investor make educated decisions. Regardless of the
direction this project runs, the first successful stepping stone is in place.
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APPENDIX B. Adjusted Scoring Ranges
Upon completion of the initial rating and sorting system a series of test were
begun to optimize and tweak this process before feeding the resulting data into the
genetic algorithm. When the top companies were looked over by hand it was discovered
that the previously determined rating system was inefficient at properly sorting great
companies out from the phenomenal. As the energy sector tends to be abnormally
volatile when compared to other sectors and as small cap stocks are generally more
volatile than medium and large cap stocks, small cap stocks were used as an indicator of
how balanced the rating system was. Below are the top five companies from the small
cap subcategory and the corresponding data for each company.
1: 91.847 GulfMark Offshore, Inc.2: 90.261 EV Energy Partners, L.P.3: 87.664 Contango Oil & Gas Company4: 86.793 Goodrich Petroleum Corporation5: 84.799 Clayton Williams Energy, Inc.
1: GulfMark Offshore, Inc.Total FA: 91.847Score: 1.43 Trailing P/E: 3.43Score: 10 P/B: 0.73Score: 1.54 P/S: 1.54Score: 8.843 Current Ratio: 8.843Score: 10 Quick Ratio: 2Score: 12 Net Profit Margin: 44.64Score: 9.97 Cash Flow Per Share: 3.97Score: 10 Beta: 1.25Score: 9.604 ROE: 24.01Score: 10 EPS: 7.56
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2: EV Energy Partners, L.P.Total FA: 90.261Score: 0 Trailing P/E: 1.5Score: 10 P/B: 0.59Score: 1.31 P/S: 1.31Score: 10 Current Ratio: 10Score: 10 Quick Ratio: 2Score: 12 Net Profit Margin: 109.61Score: 8.581 Cash Flow Per Share: 2.581Score: 7.68 Beta: 0.96Score: 12 ROE: 60.9Score: 10 EPS: 11.14
3: Contango Oil & Gas CompanyTotal FA: 87.664Score: 1.74 Trailing P/E: 3.74Score: 10 P/B: 1.84Score: 3.17 P/S: 3.17Score: 5.024 Current Ratio: 5.024Score: 9.4 Quick Ratio: 1.2Score: 12 Net Profit Margin: 90.34Score: 10 Cash Flow Per Share: 4.722Score: 9.84 Beta: 1.27Score: 12 ROE: 42.13Score: 10 EPS: 11
4: Goodrich Petroleum CorporationTotal FA: 86.793Score: 4.31 Trailing P/E: 6.31Score: 10 P/B: 1.18Score: 3.58 P/S: 3.58Score: 7.708 Current Ratio: 7.708Score: 9.6 Quick Ratio: 1.3Score: 12 Net Profit Margin: 63.06Score: 9.921 Cash Flow Per Share: 3.921Score: 6.08 Beta: 0.76Score: 10.854 ROE: 29.27Score: 9.48 EPS: 3.48
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5: Clayton Williams Energy, Inc.Total FA: 84.799Score: 0.76 Trailing P/E: 2.76Score: 10 P/B: 1.19Score: 0.72 P/S: 0.72Score: 4.084 Current Ratio: 4.084Score: 8 Quick Ratio: 0.5Score: 12 Net Profit Margin: 26.97Score: 9.395 Cash Flow Per Share: 3.395Score: 8.56 Beta: 1.43Score: 12 ROE: 59.11Score: 10 EPS: 11.67
The next trial used a completely unrestricted rating system where there were no minimum
or maximum caps. While this did increase the gaps between the stocks, it resulted in
unrealistic differences that would only serve to skew the output of the genetic algorithm.
Below are the top five small cap stocks and the corresponding data for each company.
1: 3952.39 North European Oil Royality Trust2: 144.18 New Concept Energy, Inc3: 122.73 Permian Basin Royalty Trust4: 102.986 EV Energy Partners, L.P.5: 99.373 Contango Oil & Gas Company
1: North European Oil Royality TrustTotal FA: 3952.39Score: 4.96 Trailing P/E: 6.96Score: 10 P/B: 1Score: 6.66 P/S: 6.66Score: 4.068 Current Ratio: 4.068Score: 0 Quick Ratio: 0Score: 18.213 Net Profit Margin: 97.13Score: 4.312 Cash Flow Per Share: 1.078Score: 1.84 Beta: 0.23Score: 3898.342 ROE: 38908.42Score: 9.975 EPS: 3.975
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2: New Concept Energy, IncTotal FA: 144.18Score: -1.65 Trailing P/E: 0.35Score: 10 P/B: 0.34Score: 2.31 P/S: 2.31Score: 8.34 Current Ratio: 8.34Score: 8.6 Quick Ratio: 3.5Score: 61.272 Net Profit Margin: 527.72Score: 1.4 Cash Flow Per Share: 0.35Score: 8.64 Beta: 1.42Score: 21.94 ROE: 144.4Score: 16.258 EPS: 10.258
3: Permian Basin Royalty TrustTotal FA: 122.73Score: 2.41 Trailing P/E: 4.41Score: -832.4 P/B: 423.2Score: 4.39 P/S: 4.39Score: 4 Current Ratio: 4Score: 0 Quick Ratio: 0Score: 18.421 Net Profit Margin: 99.21Score: 0.44 Cash Flow Per Share: 0.11Score: 4.32 Beta: 0.54Score: 911.928 ROE: 9044.28Score: 8.391 EPS: 2.391
4: EV Energy Partners, L.P.Total FA: 102.986Score: -0.5 Trailing P/E: 1.5Score: 10 P/B: 0.59Score: 1.31 P/S: 1.31Score: 8.734 Current Ratio: 8.734Score: 8.3 Quick Ratio: 2Score: 19.461 Net Profit Margin: 109.61Score: 8.581 Cash Flow Per Share: 2.581Score: 7.68 Beta: 0.96Score: 13.59 ROE: 60.9Score: 17.14 EPS: 11.14
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5: Contango Oil & Gas CompanyTotal FA: 99.373Score: 1.74 Trailing P/E: 3.74Score: 10 P/B: 1.84Score: 3.17 P/S: 3.17Score: 5.024 Current Ratio: 5.024Score: 8.14 Quick Ratio: 1.2Score: 17.534 Net Profit Margin: 90.34Score: 10.722 Cash Flow Per Share: 4.722Score: 9.84 Beta: 1.27Score: 11.713 ROE: 42.13Score: 17 EPS: 11
The next and final trial involved using an adjusted rating system similar to the one
above but with minimum and maximum restrictions. After a company’s financials
surpass the 1 through 10 rating scale on either end, a new scale kicks in which adds
points to the rating scale much less willingly and subtracts points slightly less willingly.
This properly allowed the exceeding companies to stand out and helped to separate the
companies that had abnormally bad ratings but were not distinguished before as their
financials capped the rating scale. Overall, this creates a rating scale where it is much
easier to lose points then it is to gain them, again aiding in selecting only the most
financially secure stocks. Below are the top five small cap stocks and the corresponding
data for each company.
1: 96.385 EV Energy Partners, L.P.2: 94.839 Contango Oil & Gas Company3: 93.997 GulfMark Offshore, Inc.4: 90.407 Clayton Williams Energy, Inc.5: 89.71 New Concept Energy, Inc
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1: EV Energy Partners, L.P.Total FA: 96.385Score: -0.5 Trailing P/E: 1.5Score: 10 P/B: 0.59Score: 1.31 P/S: 1.31Score: 8.734 Current Ratio: 8.734Score: 8.3 Quick Ratio: 2Score: 15 Net Profit Margin: 109.61Score: 8.581 Cash Flow Per Share: 2.581Score: 7.68 Beta: 0.96Score: 13.59 ROE: 60.9Score: 15 EPS: 11.14
2: Contango Oil & Gas CompanyTotal FA: 94.839Score: 1.74 Trailing P/E: 3.74Score: 10 P/B: 1.84Score: 3.17 P/S: 3.17Score: 5.024 Current Ratio: 5.024Score: 8.14 Quick Ratio: 1.2Score: 15 Net Profit Margin: 90.34Score: 10.722 Cash Flow Per Share: 4.722Score: 9.84 Beta: 1.27Score: 11.713 ROE: 42.13Score: 15 EPS: 11
3: GulfMark Offshore, Inc.Total FA: 93.997Score: 1.43 Trailing P/E: 3.43Score: 10 P/B: 0.73Score: 1.54 P/S: 1.54Score: 8.169 Current Ratio: 8.169Score: 8.3 Quick Ratio: 2Score: 12.964 Net Profit Margin: 44.64Score: 9.97 Cash Flow Per Share: 3.97Score: 10 Beta: 1.25Score: 9.604 ROE: 24.01Score: 13.56 EPS: 7.56
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4: Clayton Williams Energy, Inc.Total FA: 90.407Score: 0.76 Trailing P/E: 2.76Score: 10 P/B: 1.19Score: 0.72 P/S: 0.72Score: 4.084 Current Ratio: 4.084Score: 8 Quick Ratio: 0.5Score: 11.197 Net Profit Margin: 26.97Score: 9.395 Cash Flow Per Share: 3.395Score: 8.56 Beta: 1.43Score: 13.411 ROE: 59.11Score: 15 EPS: 11.67
5: New Concept Energy, IncTotal FA: 89.71Score: -1.65 Trailing P/E: 0.35Score: 10 P/B: 0.34Score: 2.31 P/S: 2.31Score: 8.34 Current Ratio: 8.34Score: 8.6 Quick Ratio: 3.5Score: 15 Net Profit Margin: 527.72Score: 1.4 Cash Flow Per Share: 0.35Score: 8.64 Beta: 1.42Score: 15 ROE: 144.4Score: 15 EPS: 10.258
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