Financial Decision Making: 3 Questions Every CFO Must Ask and Answer

Credit markets are certainly not “normal” (in any sense of the
word) but at least they’re stable enough for issuers to make some decisions. That said, keeping in mind the answers to three deceptively simple yet vitally important questions will always serve CFOs, governing boards, finance committees, and other financial decision makers very well.

Notice that these questions are not framed in terms of some specific risk metric or probability. That’s because they are intended to address decision making in a way that we (as a species) are best suited to understanding. Despite the fact that banking regulation has often focused on extremely remote events like 99.9% annualized confidence intervals i.e. events that happen every 1,000 years, it’s a well researched fact that we human types simply don’t do very well making decisions about such tiny likelihoods. We tend to overemphasize the dramatic remote risks (shark attacks and plane crashes) over the far more dangerous yet mundane occurrences (auto accidents and drowning).   

Can we make it through the worst plausible scenario?

The nature of risk management comes first in defining a plausible event or set of events to be concerned about. Without some sense for what that the downside concern is and how it will impact a corporation’s financial position, risk management doesn’t exist. Notice that this is where the entity’s level of risk aversion comes explicitly to the surface.

“Make it through” will mean different things to different entities since incentives and consequences, including political fallout, are obviously not uniform across institutions. For many, this concept is tied to liquidity access – a topic that’s found a great deal of interest over the last 18 months.

“Plausible” is also an important word here. An issuer I know, when answering this question for themselves, looked at the marks on their swaps if the entire yield curve moved to 0%. This is obviously a definable event and it gives one boundary value for their swaps; some people may consider it so implausible however that it should not be the focus in response to this question.  

How much might we gain in the best plausible scenario?

This is an important question in that if there’s very little gain expected relative to the “do nothing” scenario, absorbing the risk may not be worth it. This question wraps in it whether you want to evaluate the best scenario in terms of the individual transaction in isolation, or evaluate the overall impact against the backdrop of the entire portfolio (debt and/or investment).

The answer to this question in conjunction with the first helps determine the nature of the strategy’s distribution. A remote but large downside with a modest but likely upside is similar to a “sold option” situation. A fairly uniform upside and downside is a simple long position in some risk, etc. 

What is the breakeven?

How far do the factors that affect the performance of the instrument(s) need to move in order for the strategy to break even with the “do nothing scenario”? For instance, do you want exposure to SIFMA based variable rates as a tax-exempt borrower if you believe significant inflation will arrive eventually and you can lock in a rate at 3.75% fixed? How fast to floating rates need to rise for this strategy to break even (download model here)?

Understanding the break even helps us evaluate the likelihood that the transaction will work in your favor in a way that no other calculation really does. It allows us to directly assess a tangible, quantified event and the subjective probability that that event will occur. With that information in hand, evaluation of the best course of action is often much more clear.

For analytics that help answer each of these questions using rigorous, comprehensive decision frameworks see here.

Too Much Raw Data in Your Probabilistic Analysis? It’s Likely

Two people were examining the output of the new computer in their department. After an hour or so of analyzing the data, one of them remarked: "Do you realize it would take 400 men at least 250 years to make a mistake this big?"       Unknown  

I'm a big fan of Riccardo Rebonato. From the book on interest rate models, a required text in my grad school, to the papers he's done on interest rates measures in the "real-world", he's an extremely clear thinker on otherwise murky stuff. I can't recommend more highly his recent book, Plight of the Fortune Tellers. If you or your clients are in the business of making tough financial decisions, it's a must read and enjoyable to boot. Enough gushing (I need payment to go any further ...)

One extremely important concept woven throughout Plight is the difference between the traditional "probability as frequency" concept and the more general Bayesian or "subjective" probability. Probability as a pure frequentist concept is a special case of Bayesian/subjective probabilities that would be appropriate when looking at the likelihood of a head after a coin flip. Outside of a belief the coin is fair, no prior knowledge is necessary to reliably assess the likelihood of such an event. Contrast that with say, the probability that the Jets win the SuperBowl in 2011, or the Republicans retake the House in November, or even that gold goes over $1,500 an ounce by year end. These are all events to which we could also assign a probability, though analyzing purely historical data in a frequentist sort of way will yield few helpful results. We are much more inclined to include and use other relevant information such as the Jets strong defense going into the next season, the anti-incumbent mood of the electorate, and the growth of global money supplies.

What does this have to do with the use of raw historical data in financial decisions support analytics? A lot. Certain financial questions are better answered using frequentist concepts. Others are far more judgment-based relying on more subjective criteria and professional experience. But how do you know which situations are which? Though no hard and fast rules exist, there are basically four criteria:

Data frequency - The more relevant data you have, the more inclined towards a frequentist approach.

Time horizon - the longer the horizon of analysis, the more likely a subjective analysis will be more relevant.

Rarity of event - the more rare the event, the more the analysis calls for a Bayesian/subjective approach. 

Time homogeneity of data - Were there no regime changes or other tectonic shifts in the underlying phenomena from which data was gathered? If so, analysis will tend more towards frequentist methods.

So for long time horizons, a scarcity of data, significant changes through time in the realm in which the data lives, and highly improbable events, we land squarely in the realm of subjective probabilities. Though historical/frequentist data isn't ever completely irrelevant, in these circumstances professional judgment of the situation at hand trumps pure number crunching. Unfortunately, from rating agencies to regulators to a large swath of finance professionals, this is not well understood. Things are just much more clean and simple if we allow ourselves to believe that 100 data points and a fancy model will yield 99.97% confidence precision. This is a particularly dangerous type of belief in finance, as acutely borne out over the last 18 months.  

The good news is that whether frequentist or subjective, widely available probability-based models should always be used to capture risk metrics, evaluate best and worst outcomes, assess breakevens, and ultimately to avoid the ever pervasive flaw of averages.

Monte Carlo: Model, Method, or Nice Place to Visit?

"The first and most important thing to understand about Monte Carlo is that it is a numerical technique, not a model."

                   Ricardo Rebonato, Plight of the Fortune Tellers

 If you ever hear people talking authoritatively about their powerful "Monte Carlo model," be very suspicious of the message and the messenger.  The Monte Carlo numerical method (in contrast to the lovely place on the French Riviera) is no more a "model" than addition is a "model" for ascertaining that two plus two equals four.  It is simply a way to perform certain calculations.  For any lingering Pythagoreans out there, Monte Carlo is specifically a very efficient way to calculate integrals in high dimensional spaces.  In finance, Markov chain Monte Carlo is used for generating estimated distributions for things like interest rates, equity prices, investment returns, and exchange rates.  People who think the Monte Carlo technique is a "model" are confused.  My hope is this quick post clears that up and convinces you the distinction is important.     

The simple fact of the matter is that once we face a situation that involves more than about three risk factors, Monte Carlo methods are the best we've got for calculating statistics of interest.  Modern homo sapiens, with our flat screen TVs, computers, multi-tasking cell phones, ipads, and big brains have simply not invented anything better than Monte Carlo to evaluate these types of problems.  And the more complicated the analysis, the more factors to analyze, and the better Monte Carlo does relative to other approaches.  Without getting bogged down in only mildly relevant detail, this is a direct result of Monte Carlo's uniquely wonderful properties in the face of the curse of dimensionality. 

So what?  Why should you care?  If you're like me, you hear people periodically either dismissing outright the utility of "Monte Carlo models," or alternatively gushing about how amazingly well their "Monte Carlo model" predicts the future.  When you hear this now you can rest comfortably in your understanding of the much more moderate truth: neither the naysayers nor the chest thumpers are in a position to properly use Monte Carlo to help make better financial decisions.  And properly used, Monte Carlo can absolutely help inform difficult financial decisions.  To that end, I leave you with a quote from Mr. Black Swan himself.

"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