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Intuitive Analytics frees public finance analysts and decision makers from the limitations of available software.

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Financial Software: The Good, the Bad, the Ineffective


"The dividend of the computer revolution to us did not come in the flooding of self-perpetuating email messages and access to chat rooms; it was in the sudden availability of fast processors capable of generating a million sample paths per minute."

- Nassim Taleb, Fooled by Randomness

Investment banking, and perhaps finance generally is no stranger to the giant ego. I must admit sometimes I judge Street pros with my own unspoken assessment of their "ego/ability ratio". Given Wall Street's current condition, many might reasonably assume that the numerator has proven to be, beyond a doubt, wildly out of whack with the denominator.

Perhaps because they've worked in finance for a number of years or because they can put together a spreadsheet with formulas referencing more than three tabs, one byproduct of the high ego/ability financier is the assumption that s/he can create good financial models and software. In response, below I detail a few of the considerations involved in building what we consider to be powerful, robust, well-designed, intuitive, serviceable financial software. In some ways, it would be nice to say it is pure science, but as any good pure scientist will tell you…it's always partly art. The topics below are presented separately but in practice are inextricably interconnected.

The People

Many technophobes may think of software development as a remote and inscrutable process that only specialists with spinning propeller-heads understand. Admittedly at some level, this may be true. But in the end, software is built by people for people. And those are exactly the two groups that are essential to a successful analytic: those who can build great financial software and those users/consumers who will benefit from its results. First the developers…

You want your financial software developers to be both outstanding software engineers and financial engineers, ideally with decent commercial intuition. In my experience, this is an extremely rare combination. Most professionals in the field are of one of two types: finance people who, due to the aforementioned ego, get bold enough to become reasonably dangerous hacking out (sometimes) functional code; or they're software engineers who happen to like the paycheck that finance-related jobs provide and as such, have picked up what present value means and perhaps the Black-Scholes-Merton formula. I wholeheartedly admit I'm in the former category (I try and control the ego), though at least I was prudent enough to fire myself from the development team once we'd experienced sufficient growth. It also doesn't hurt if they're personable, can speak with clients, gauge what's working well for users and what's not, and modify the software accordingly. As one consumer products company put it when describing their own quant group, "PhD's with personality." I've come across people from each end of the spectrum and everywhere in between, but the professional who offers all of these attributes is rare indeed. We're extremely fortunate to have found a few of those here at IA.

The next and all-important group of people you need to understand fully is the audience. How do they view their environment? What factors affect their financial decisions? How sophisticated are they? What features are required? Which would be nice? Which would be "over-the-top" and likely confuse as often as illuminate? I think far too many quants, who frequently find themselves in the role of developer, expect far too little of their user/audience and thus leave many financial black box people under-equipped. This information about the user/audience must be gathered in order to gain the background data required to thoughtfully respond to the questions that follow.

The Medium

Selecting the medium in which to develop software is an essential decision that drives many other design considerations and features. Far and away the most common tool among finance practitioners for financial model building is the spreadsheet. Any Street banking analyst on the job more than a year has ultimately either heard or made the joke about staying up all night with a model…of the Excel™ variety. Spreadsheets are fantastic for certain types of analyses; for doing basic financial statement projections, nothing beats a spreadsheet. However, for performing multi-factor, multi-period simulations or solving complicated optimization problems, it's a medium that suffers major drawbacks.

One important question is whether or not the software will be deployed locally using only the compute resources on the desktop, or will it be deployed on an enterprise server, grid, or in the "cloud" as a web-based application. At IA, we allow users to choose whether or not they want to calc locally or in a remote environment, a software-plus-services approach.

Languages like C, C++ or FORTRAN offer speed but require more software engineering skills: ideally a thorough understanding of memory management, object oriented programming techniques and more generally, development best practices. Developing in (relatively) lower level languages like this can also require a great deal of time spent on the user interface, though this will rightfully occur in varying degrees in any development environment.

Scripting languages like JavaScript, Python or Perl and RAD frameworks like Ruby on Rails have become popular in a Web 2.0 world. These tools usually excel at rapid prototyping, especially for specific tasks such as web application development. However, despite extensions designed for number-crunching such as PDL for Perl, like spreadsheets scripting languages are largely ill-suited to implementing industrial strength computational algorithms. Furthermore, scripting languages even when compiled (often to some intermediate state) can't offer the speed of a truly compiled language like C++.

Java and .NET (i.e. C#) one might say function as safer versions of C++ with vast libraries. Safer, however, does mean slower. Specifically they deliberately attempt to lower the bar for entry therefore ultimately compromising on speed and flexibility.

Computational languages environments like Mathematica and MATLAB (or its open-source imitator, Octave) provide many features and offer a range of built-in functions and sit on top of powerful libraries such as LAPACK. I believe MATLAB now has over 90,000 built-in functions. Like Java and .NET these environments obscure certain lower level functions like memory management. Some may complain about the sub-optimal memory management (automatic "garbage collection") features. Others may say these are difficult to deploy without expensive licenses.

In the end, pick a development medium ill-suited to what you want to accomplish (for today or tomorrow), and you'll find the project more expensive to create, less adaptable to changing objectives, and more onerous to maintain.

The Features

Once you've selected the medium, you've got to decide what features to include in the model. This obviously depends upon the goals and objectives of the application through the eyes of your typical user, whom you hopefully understand intimately. You want to model the problem completely enough to provide structure, but provide enough flexibility to capture the widest range of circumstances users are likely to face. If it's a risk analysis, what is the purpose? What horizon? What factors will be included? How will they be modeled? What types of distributions will be used? Will you try and capture kurtosis (fat tails)? Will the factors be correlated? What is the source of correlation? Will covariance be stable through time? If it's a pricing model, what model will be used? Will the application require data feeds? How will they be integrated? What will the interface look like? Will output integrate with other desktop applications? How will results be stored and shared? What visualization features can be employed to illuminate features of the problem or solution?

If you're a software provider, how will the licensing mechanism work? How will you implement protection? What if your servers are breached? Will you house data? Will you use encryption? How much will this effect performance?

This is a small sampling of the types of questions that are likely to deserve appropriate consideration as it relates to features, which ultimately inform overall design.

The Risks

In the age of information, intellectual property is a hot topic. That means that whatever you build may have been thought of by someone else, and further, may be protected by their copy or patent rights. Ignorance is no excuse. Stepping on someone else's patent can wind you and/or your firm up in serious trouble. Be found guilty of intentional infringement and the infringed can claim treble damages against you. Work for a large firm with deep pockets, and you are that much more attractive to IP lawyers looking to get payout on contingency. If your clients find themselves getting unfriendly legal letters because of something you designed, you're likely to find yourself with fewer and certainly less happy clients as well.

All together

What IA Does?There's certainly plenty I've left out but in the end, designing and creating good financial software is just the first step. Automated test procedures, informative, context sensitive help, tutorials, and web based training ultimately increases the length of the technology lever that the underlying algorithms themselves represent. But it's all irrelevant if you don't also provide a clear path towards value that your customers/clients/users can successfully take. In my experience, competing on analytics is not necessarily a natural activity for financial firms, at least not across all lines of business; but it's an incredibly natural and inevitable fit. I expect books such as Competing on Analytics and Super Crunchers will increasingly reflect common wisdom; the financial services industry has only begun to really use technology to better and more efficiently service clients. But to fully maximize return on the investment, think carefully about whether you have the in-house expertise to fully execute a strategy end-to-end.

Finance Professionals: Black Box or Glass Box?


"Appreciation is a wonderful thing: it makes what is excellent in others belong to us as well."

- Voltaire

Many very talented finance professionals are out looking for work these days. If they want to stay in finance they may need to move more towards seeing the box as glass instead of black. Let me explain…

A long way back a friend of mine told me that finance is really just the marriage of economics and math. In that vein, I sometimes think of finance professionals in two flavors: black box people and glass box people. This often informs my sense of their ego/ability ratio. Some practitioners don't have the desire or background to ever look behind the curtain and see the inner workings of the math inside the "box". These are black box people. Most finance professionals are black box people and they don't need to have built a model or application from scratch in order to use it effectively. For example, only a small percentage of people in the field can derive the Black-Scholes-Merton formulas from first principles. That said, many, many more can use them profitably without this detailed knowledge.

To be truly effective as bankers/advisors dealing with clients, however, these black box people need to be skilled enough to convey insight about what's going on inside the box and why. If it's a well designed box relevant to its audience, it can shed unique light on the risks faced by institutions or individuals. Good black box people are efficient translators, educators, and consultants. Training black box people requires developing interesting, relevant, digestible materials for a non-quant audience. To put it gently, this is often not a quant's strong suit.

Glass box people on the other hand can get their hands dirty building powerful, relevant models that are intuitively easy to use. They must know or extract from users (usually black box people) the cares and concerns that would drive high quality, high impact, and accessible analytics. Further, a good glass box person can see when additional features will add another two weeks to development and only 1% more utility. Those types of bells/whistles don't survive a good glass box person's planning process. Perhaps most importantly, a good glass box person knows a bit of the perspective of the black box thinker – and it is this skill that makes her/him most valuable.

Good glass box people are rare, though their numbers are growing. Many black box people would aspire to become glass box people if the managers to whom they reported cultivated the development of the requisite glass box skills. Unfortunately, most senior finance professionals were never glass box people themselves and as such don't really know how to manage or encourage the growth of the glass box group. Look around as the financial services industry necessarily reinvents itself – more and more of the ones who survive and thrive will be of the glass box variety.

My Disagreement with Fischer Black


"What you risk reveals what you value."

- Jeanette Winterson Fischer Black

I never had the opportunity to meet Fischer Black, the canonical first "quant" (though he was far more than that), but we did exchange emails in July, 1995. It started when I was supposed to be studying for a course in measure/probability at the University of Minnesota library. However in the frequent and sometimes fervent desire to avoid what I should've been doing, I found myself reading what I recall was a Journal of Derivatives article by Dr. Black. In this post, I describe a regrettable shortcoming among finance practitioners today related to that email exchange.

In the article, he was mentioning something about an adjustable rate mortgage pool with a more stable value, and hence, lower risk than a fixed rate pool. At the time I was also working as a financial advisor to states and local governments and from that experience, I knew that there were at least certain economic actors that didn't view variable or adjustable rate instruments as less risky than fixed rate versions. This was particularly so when those securities were liabilities. So I emailed Dr. Black and as cogently as possible tried to ask my clarifying question about risk. I wondered whether or not a homeowner would think of an adjustable rate mortgage in that pool as less risky than a fixed rate alternative, particularly in an inflationary economy.

He gave a fairly lengthy response which, to be frank, didn't fully compute at the time. But towards the end he said, "I think in purely present value terms." That got me wondering, should we all be thinking in purely present value terms, all the time? Is the variability in present value the only *right* way to think about risk? I'm guessing many of you might wholeheartedly agree, to the point that even asking the question is heretical.

Risk is undoubtedly a curious animal. Any creature that exhibits non-linearity the way risk does, where 1+1 might equal anything between 0 and 2, is naturally unsettling to the uninitiated. But isn't risk, akin to beauty, appropriately viewed in the eye of the beholder? The trivial example of variable rate bonds I think illustrates the point.

The value of (theoretically continuously resetting) variable rate bonds, no matter the interest rate environment, is par. To most quants, if there's no price movement in a security or portfolio than they must be riskless. That is, quants traditionally if not religiously tie the concept of risk to the potential price variation of an instrument portfolio. Certain GAAP/accounting guidelines reinforce this perspective which happens to be a hotly debated topic these days. The folks at RiskMetrics call the places where price volatility reign "financial environments." These are places like banks, mutual funds, insurance companies; not coincidentally where lots of quants have jobs.

Contrast this view with those of a company CFO or treasurer who's managing one or more series of variable rate bonds or other floating rate securities. In this context the bonds are liabilities (or assets with a minus sign). That the prices of these securities are stable to the investor is little consolation to the CFO whose bonds are resetting at 15%. In this financial economy, that particular CFO is likely very happy with her/his fixed-rate obligations despite the fact that the quant would label the fixed-rate bonds as the risky securities. The CFO has been trained cash is king; that the value of your variable rate bonds stayed at par from time of issuance all the way to today matters little in front of the bankruptcy judge. And bankruptcy is unfortunately what happens if nominal interest payments to debt holders could no longer be satisfied. These settings are corporate environments, where earnings and cash flow are driving considerations.

In short, I'd have to respectfully disagree with Dr. Black if he intimated that we all should think about risk only in present value terms. In our political economy, nominal cash flow variability often drives real risk that should be understood and considered. The unfortunate truth is that much analytic focus has been placed on problems that exist only in financial environments, largely because of the widespread adherence among quants to the same view expressed by Dr. Black. Not nearly as many tools are available to those trying to navigate corporate environments.

Does this mean that corporate environments don't have challenging cash flow or earnings problems to solve? Are these problems somehow unworthy of high quality, data-driven analytics? I think this question is answered well in a recent Sep08 Harvard Business Review article. Its contents are ones with which I totally agree. What do you think?

Are Financial Models Worthless? Risk and Uncertainty…


"Risk comes from not knowing what you're doing."

- Warren Buffet

Are Financial Models Worthless? Risk and Uncertainty…In his 1921 dissertation, University of Chicago economist Frank Knight made an important distinction between risk and uncertainty. He maintained that risk and uncertainty are different in one critically important respect: risk involves events whose odds of occurrence can be known with precision or, at minimum, are based upon some statistical evidence or scientific methodology producing estimable likelihoods. The probability distribution of the event in a risky situation can be known. Uncertainty on the other hand is associated with those events whose odds or probability distributions are not known or not reliably quantifiable.

For example, there is risk inherent in the roll of a die. However, we know with certainty that the result will be an integer number with a minimum of 1 and a maximum of 6. If we bet on a 3 turning up prior to the roll, we have assumed some amount of risk. However, there is no uncertainty as to the possible outcomes or the odds of those outcomes; this event can be modeled quantitatively with absolute confidence.

Contrast this simple die roll with the infinitely more complex sets of possible outcomes that can occur on any day in the capital markets. Risk is certainly present, as in the roll of a die, but uncertainty also abounds. Events that are nearly if not entirely impossible to predict happen with some frequency, and as such, no amount of fancy number crunching (even from our applications!) is going to remove the danger inherent in financial exposure management. This lesson has been taught repeatedly throughout the history of finance, perhaps most notably with the portfolio insurance providers during the 1987 crash, the fall of Long Term Capital Management, and even today's, umm…challenging, credit market.

It may seem curious to find a cautionary warning about the limited power of quantitative finance tools in a blog post by a company that, in part, creates financial models. Nevertheless we cannot overemphasize the importance of humility in the face of capital market uncertainty. Market movements have been and will likely remain impossible to forecast with any meaningful degree of accuracy – that is, until we can with confidence statistically model the behavior of crowds and foresee the bouts of excessive optimism and fear that are characteristic of capitalist societies. Some practitioners within the burgeoning field of behavioral finance are attempting to do just that with encouraging results.

"Doubt is not a pleasant condition, but certainty is absurd."

- Voltaire

Since we know we need to consider both the knowable and unknowable range of future outcomes, are the risks alone worth quantifying? Do they only inject a false accuracy or worse, false security into the decision-making process? The unfortunate truth is that even in the face of looming and unknowable uncertainty (black swans?), we are compelled to make financial decisions. We unavoidably find ourselves in situations where we are forced to manage exposures as borrowers, as investors, or both. We don't have the luxury of "doing nothing." Doing nothing means we have either not removed or not assumed exposure to something; in both cases we are inevitably exposed to risk and uncertainty. Since we are forced to play our hand, I believe it is essential to have tools that at least give us a glimpse into the potential magnitude of the changes we might face.

Welcome to (Un)Calculated Risk


"Knowing is not enough; we must apply. Willing is not enough; we must do."

- Peter WeddingGoethe 

I have to admit, I've been wondering about doing this blog thing for quite some time, kind of avoiding it frankly. Not sure why. In my clearly unbiased opinion I've done some pretty interesting stuff. I've surfed, translated Virgil, played semi-pro soccer, grappled with Carlson Gracie (he won, don't think he was trying), high jumped 7 feet (well, 6' 11¼"), solved a stochastic differential equation or two. I even got married last month (see evidence at right – yup, that's me). I've also done some work in finance – been an institutional financial advisor, trader, investment banker, and derivatives marketer.

So, Intuitive Analytics along with doing some financial consulting/advisory work is in the business of making financial models. As you'll see from this blog, I believe that this model-building business is inherently doomed to fail to meet the expectations of so many, many people who deeply want to believe SOMEONE knows what the heck is going on. Unfortunately, I'd argue that most models are nearly fatally flawed from the outset, particularly ones that attempt to capture the dynamics of markets made up of us human types. Anyone that tells you different is trying to sell you something. But what do you think? This is a pretty important/relevant question right now given the likely and impending overhaul of the financial regulatory system.

"You must do the things you cannot do."

- Eleanor Roosevelt

Given my varied and humble background, I'll use this bully pulpit to make some observations which I hope you find occasionally insightful, sometimes controversial, and perhaps even valuable! The subject of (Un)Calculated Risk will primarily be the more scintillating sides of financial economic topics like the future of financial modeling, derivatives, finance professionals, consultative selling, maybe the economy, perhaps rarely and occasionally a little jiu-jitsu…

Enjoy and please let me know your thoughts about this or any post, SmartModels™ applications, or other topics on your mind here or at peterorr(AT)

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*Various products, solutions, methodologies, processes and techniques presented and/or described on this website are proprietary to Intuitive Analytics LLC, and are multiple patents pending.