A look at the objectives of financial models and how they are developed and distributed.

1. Published by Moneycation™
Newsletter: June 2013
Innovation in financial modeling
Financial models have existed since the beginning of
finance. They are designed to evaluate, assess and
quantify financial methods and scenarios. Many known
financial models have had varying degrees of success
in terms of accuracy, utility and applicability.
Examples of financial models include the FICO credit score and the Black Scholes Model of price
variance. In many cases, financial models serve specific functions. However, since the variables in
these model are often limited, it only produces a result in terms of those factors. This presents a
problem when financial anomalies, or unmeasured influences affect the outcome of a financial
circumstance independent of the financial model. Innovation in financial modeling attempts to
resolve these quantitative and qualitative shortcomings.
What is financial modeling?
The Nasdaq stock exchange defines a financial model as reflection or quantification of business
events, indicators and accounts within the objectives of that business. Their definition is quoted
verbatim as follows:
“A model that represents the financial operations or financial statements of a company in
terms of its business parameters and forecasts future financial performance. Models are
used for risk management by examining different economic scenarios for the future.
Financial models are also used to provide valuations of individual assets that might not be
actively traded in a secondary market.”
The above definition uses broad enough parameters to leave room for innovation in design. In
other words, if the meaning of financial operations changes, and models are designed using those
meanings, they are still financial models according to the above definition. To illustrate further,
when contrasted with shareholder and profit driven corporations, a business with corporate social
responsibility high on its list of priorities is likely to have a different operating model in terms of
how it understands and manages cash flow and assets.
The following two models illustrate the diversity of graphic representation. Both these models use
illustration to describe a process. In the first case, two valuation methods are compared side by
side, and in the second, an investment decision making process is described. Attributes of financial
models include several techniques including concept maps, mathematical formulas, software
algorithms and various forms of additional graphical representations such as graphs, tables and

2. charts.
Example #1: Valuation models
Image license: Arnaud2311; CC0 1.0
Traditional models
The word “traditional” means many things, so perhaps it is best to clarify for the purposes of this
newsletter. In this sense, traditional financial models refer to models aimed at optimizing profit
objectives using well-established formulas, graphics, software applications and the like. An
example of such a model is the Capital Asset Pricing Model or CAPM, which weighs risk and
return to calculate yield using 'Beta', which is a correlation and volatility metric.
Existing models such as the CAPM are time tested to be accurate in so far as they are able to be. In
other words, the mathematical result is sound, but its validity depends on the accuracy of the risk
correlation and expected rate of return. Naturally, a competing or alternate model would attempt to
improve upon the input variables for the outcome of greater model accuracy. For instance, if the
CAPM correctly forecasts yield 90 percent of the time, what other model can do better?
Another type of model used in finance is the leveraged buyout overview or LBO model. These
models are often single-purposed and evaluate the financial viability of buying assets in terms of
cash flow and rate of return within a certain amount of time following purchase. Key input
variables for this kind of model include interest rate and percent of equity ownership. These
variables help determine an acceptable acquisition cost in order to yield an equity stake that is
greater than the cost of that equity.
Innovation in modeling
Improvements to models are sometimes necessary to optimize or increase profit margin, or shorten
the amount of time to acquire a majority stake in a corporation. Models implemented by financial
advisory service firms illustrate this point. Moreover, according to Wall Street Oasis, the models
created or implemented by these organizations, in particular “Financial Institution Groups”, are
called FIG models and sometimes rely on book value and book value per share metrics. These
groups are also capable of innovating models through the financial modeling process as illustrated
as follows:

3. THE MODEL TRANSFER AND IMPLEMENTATION PROCESS
Step 1:
Sources
Academia
I.T./Computing
Industry
Business
Competitions
Step 2:
Delivery
}
Step 3:
Results
Courses
Workshops
Degree programs
Job training
Self-education
Analysis
Valuation
Re-design
}
Benefits
}
Performance
Innovation
Positioning
Profit
Accuracy
Evaluation of effectiveness
The accuracy of financial models varies based on factors such as statistical significance, margin of
error, quality of the model itself, and suitability of population sample. For instance, if a financial
model is designed to forecast the effectiveness of the 4 percent rule of retirement spending, but the
model uses an annual growth rate that exceeds the average 10-20 year growth rate of retirement
portfolios, then the final results may be too optimistic. A strong model effectively reflects reality
under a number of circumstances and not just under ideal conditions. How many models actually
do this?
When designing new and innovative financial models, the most influential variables on the control
data should be included. To explain, if the model is used to assess the impact of layoff on
retirement savings, the duration of unemployment in reference to the probability of the statistical
trends within the demographic losing the job should be more accurate. To illustrate via example, if
it is a woman losing job, or teenager, is it more likely they will get a new job faster or slower than
a man? Using total population averages, or only using a population sample of males does not
specifically account for strong sub-trends in the population.
In addition to the most influential variables being included in financial models, the models should
be free from bias to be objective. In a PBS interview with Economist Ed Leamer, models used in
econometrics are “rife with biasing judgement calls about how to input and manipulate the 'data'.”
Confirmation bias, theoretical assumptions and flaws in the data collection process itself are
additional weaknesses of some models cited by Leamer.
How innovation occurs
In the model transfer and implementation process, re-design occurs in step three of the above flow
chart. However, it can also take place in step one provided sufficient knowledge for innovating

4. models is there. Innovation in financial modeling also depends on the area of finance in which the
innovation is to occur. Of the many branches of finance, the end objective of models includes
improving sales, capital acquisition, accounting purposes, regulatory requirements and even better
public relations or marketing methodology.
As shown in the following simple but illustrative model, an investment that is cheap, fast and good
is ideal and rare. Perhaps this rareness is due to the value of such models in the marketplace.
Existing models can, and do allow for affordability, profitability and functionality. However, they
are not necessarily easy to obtain. This is sometimes due to prohibitive costs, complexity of design
or difficulty such as ineffective or inefficient project management in the implementation process.
Such rarity, or high cost associated with lucrative financial models increases the potential benefits
of insourcing the modeling process or hiring lower cost consultants to design new models.
Example #2: Financial decision making model
Image attribution: Saruiter; GFDL, CC BY-SA 3.0
Models designed for a particular reason are not necessarily objective, but can be disguised to by
combining logical, statistically accurate and mathematically correct data into formulas and other
such models to support a given position. Instances of this type of model are selective surveying of
demographic samples, or the adjusting of metrics such as similar numerators that provide better
ratios.
Competitions such as the Financial Modeling World Championships pit modelers against each
other and encourage innovation. Also, corporations, academia and even the government
implement and innovate models to better optimize and fine tune existing models for accuracy,
improved measurement and better results. This is done either through re-design of existing models
or the creation of new models altogether.
If objectivity is a requirement for innovation, then new and alternate financial models should focus

5. on scientific precision as a goal just as much. Without accuracy and scope, the intended effect of
models is subject to a potentially higher level of risk. Breadth and depth of input variables are also
important in representing the financial reality properly. For instance, the Federal Reserve Bank
defines low-income individuals as having annual earnings below $75,000. This definition can
distort perception of a model's results if it replaces a far more common meaning of the phrase lowincome such as the metric used by the official poverty threshold of the U.S. Census; in 2012 this
number was $11,945 for individuals under age 65.
Goals and objectives
New financial models are created all the time. The Journal of Financial Modeling and Educational
Technology is one such publication that specializes in the subject. Moreover, in the May 2013 issue
of the journal, an alternate financial software model is proposed to improve upon the use of the
same beta utilized within the CAPM. A more accurate beta, it is thought, produces a better model
output.
The goals and objectives of adjustments to financial models and news models are sometimes more
technical than systemic. This is because they attempt to solve an issue for a smaller entity
regardless of impacts on the broader industry or economy; these kinds of models include goals
such as forecasting, valuation, market analysis, and return on investment. Without a broader vision
or purpose, such models are underutilized, ineffective or have a negative impact in the market.
Examples of risky financial models are those used by banks when assessing mortgage applications
prior to the housing bubble burst. Moreover, in the second model presented in the May 2013 issue
of the Journal of Financial Modeling and Educational Technology, a researched modeling solution
to this business level problem is presented. More specifically, the model seeks to improve
measurement of the probability an asset will lose money or fail.
Vision
The vision of financial models anticipates the functional outcome of that model and its impact on
an organization, institution, industry etc. Without a clear vision, a model is just a way of analyzing
financial data, organizing financial resources and categorizing assets. Even if a model is effective
at examining a financial event or scenario, knowing how to leverage the model and its results for
the 'better' requires vision or leadership all the way from the design phase to its implementation.
Otherwise would quite possibly prove less fruitful or underutilize an organization's financial
capacity.
Anytime a financial model is not 100 percent accurate, unsustainable, yields too little and so forth,
there is room for improvement. Vision sees the end goal or objective and innovation carries that
vision out. For example, technical analysis of stock prices is often used by day traders, capital
management firms and market observers. However, existing technical models such as candlestick
analysis is not necessarily sufficient to accurately base large and risky trading decisions on. In such
case the analyst has the option to seek confirmation of the model's indicators from within the
model, and also from outside the model via additional models or improvements to that model.

6. Example model innovations:
• Neural network redesign
An example of a financial model that improves upon existing principles are design changes in
technical analysis. Moreover, a company called Deep Insight uses neural networks to more
accurately optimize stock price correlations with influencing factors such as market psychology,
economic events and corporate data. The operation of these neural networks are themselves based
on upgraded programming of analysis models.
• Shadow banking modeling
As new financial transactions outside of the banking industry increase, the shadow banking
industry has taken on more relevance to main stream macro-economics. This is even more the case
in countries where gray economies have a wider scope and economic hold of a country. In a
January issue of the “Journal of Economics”, a model of shadow banking is presented; this model
aims to assess important factors affecting the industry such as the impact of changes in systemic
liquidity or less available funds within the primary banking system.
• Pricing and hedging methods
In a report published by an organization sponsored by the Federal Reserve Bank of St. Louis, the
evidence for improvement in financial models is clear. Moreover, in the beginning of the paper, the
authors state the following:
“Despite numerous papers, there is still a need to develop new pricing and hedging methods and
derive prices and hedging strategies in realistic financial and actuarial models.”
A keyword here is 'realistic', and this word highlights the problem with quasi-scientific approaches
to financial modeling. In other words, due to changes in the economic environment, where no fixed
laws apply, accurately accounting for variance is necessary for precise asset valuation.
This study proposes an alternative model that adjusts for changes in market and economic
conditions by changing model inputs when factors such as risk fluctuate. It attempts to do this by
instantaneously recalibrating variables to reflect up-to-date risk premiums and deviation in return
on investment.
Many new financial models are created all the time. Even if these new models provide additional
certainty of success, and higher performance probability, they may require expensive proprietary
research by a team of developers who incorporate quantitative finance, software coding and
graphic design into their model. However, what if the vision also requires a simple, affordable and
easy to use model in addition to improving model benefits? In such case, additional innovation is
required.
Ideally models operate like a cog in a wheel, serving a functional and profitable purpose within a
greater economic machine. As prior modeling errors have shown, problems in quantitative finance

7. have the potential for far reaching consequences. A financial model does not necessarily have to be
complicated and highly mathematical either. What is more important is that the input variables are
accurate, and the output variables have net positive benefit for local, regional and national
economies via model innovators such as consultants, analysts, capital management firms and
municipalities.
Sources:
1. “Path”; Sure Start project: Innovative financial models.
2. “ESFIM”; Innovative Financial Models.
3. “Emphasis”; The Last Word: Innovation in Financial Modeling; Peter D. Needleman; 2006, Issue 3.
4. “First Southwest Company”; Innovative Financial Modeling; Alaska Propane Initiatives Conference Power Point
presentation; September 24, 2009.
5. “New York University”; A Dynamic model of Financial Innovation and Investor Sophistication; Bruce Ian Carlin
and Gustavo Manso; October 20, 2008.
6. “National Bureau of Economic Research”; Financial Innovation and Endogenous Growth; Working paper 15356;
September, 2009.
7. The Financing of Innovation; Bronwyn H. Hall; December, 2005.
8. “Inspire”; The Economics of Innovation: Breakthrough Financial Modeling; Ward M. Peterson, Ph.D; Inspire
Pharmaceuticals, Inc. Power Point presentation.
9. “New York Federal Reserve Bank”; Are Stocks Cheap? A Review of the Evidence; Liberty Street Economics;
Fernando Duarte and Carlo Rosa.
10. “CNN Money”; Payroll tax hike prompts spending cuts”; Tami Luhby; May 15, 2013.
11. “NASDAQ”; Definition of Financial model; 2013.
12. “The Journal of Financial Modeling and Educational Technology”; Efficiently Calculating Betas for Individual
Stocks; R. Brian Balyeat and Julie A.B. Cagle; May, 2013.
13. “Moneycation”; Pros and cons of Constant Proportion Portfolio Analysis; A.W. Berry; October 27, 2011.
14. “U.S. Census”; “Poverty: Poverty thresholds; 2012.
15. “PBS”; A Libertarian Take on Economic Faith, 'Facts' and Follies; John Papola.
16. “Wall Street Oasis”; What is Financial Institutions Group (FIG).
17. “Landing Electronic Corp.”; New Approach of Technical Analysis; Deep Insight.
18. “The Journal of Finance”; A Model of Shadow Banking; Nicola Gennaioli, Andrei Shleifer, and Robert W. Vishny.
19. “Repec Project”; Instantaneous mean-variance hedging and instantaneous Sharpe ratio pricing in a regime
switching financial model, with applications to equity-linked claims; Federal Reserve Bank of St. Louis; Łukasz
Delong and Antoon Pelsser' March 19, 2013.
20. “Journal of Financial Modeling and Educational Technology”; Simulating an Effective and Tractable Measure of
Potential Portfolio Losses Beyond Traditional VaR; Kwamie Dunbar; May 2013.