Though quantitative analysis is interesting in its own right, we expect its practice to become more widespread if we can make money doing it. In this post we identify some choices to be made when designing a process for quantitative analysis.
Picking up Nickels
Sustainable strategies for driving revenue include picking up nickels and bagging elephants. The former tack assumes high volumes and admits the following characteristics:
- Low per-transaction revenue
- Applicability to a wide population of clients
- Limited customization of the analysis parameters (for specifying client or environment details)
- Reuse of models and analysis techniques
Examples of this type of analysis include credit metrics, selection of debt tenor, liability management trades, and interest rate risk management.
We can generate volume while maintaining quality in one of two ways: hiring lots of bright, expensive analysts, or using automation heavily. Under the automation approach requires several prerequisites:
- A reliable source of data (or a less-than-reliable source paired with a mechanism for catching errors)
- Ability to model a new firm and the current environment rapidly
- A limited set of configuration switches to enable a minimal amount of customization
- Intelligent defaulting of parameters and projections; for example, where a company’s CAPEX plan is not otherwise specified, it could scale with growth in company revenues
- To as great an extent possible, soup-to-nuts computer automation of the analysis (including chart production and book assembly)
- Careful review of the results
- Experience with the types of mistakes that can slip through the process undetected
Bespoke analysis is slower and less reusable; combined with a lower likelihood of a revenue-generating event this suggests a higher required fee when the work is successful. The situation exhibits these characteristics:
- The required data may not reside in a public source, but may come from another analyst’s spreadsheet or be confidential in nature
- Company models require customization, for example including price*quantity models for commodity exposures
- The constraints and goals of optimization are idiosyncratic, requiring hand-coding
- Analysis is long-lived and iterative; early results may change the direction of analysis or set of proposed solutions
On a positive note, the analysis is likely to be closely scrutinized at all stages of the process, reducing the need for a data source that’s perfect.
Topics suitable for a deep dive include asset acquisition or disposition, deal-contingent hedging, and multicurrency analyses.
The Annual Physical
A third type of analysis sits somewhere between the nickels and elephants approaches. Sometimes referred to as a fishing expedition, for this post we’ll call it the annual physical: a periodic, thoughtful review of a company’s current situation and prospects. Topics may include capital allocation, capital return, credit rating, share repurchase tactics, pension plan asset allocation, peer-based “best practices”, and optimal cash balance or liquidity reserves.
There are a few tradeoffs to consider here:
- If we expect the client to request a follow-on analysis, beware that results may change simply as a result of using more or better data on the second iteration. It may be awkward at a second meeting to propose a strategy radically different from what you suggested at the previous meeting. This simple fact, compounded with a healthily skeptical attitude toward the limitations of analytic analysis even in the best of situations, suggests restraint when leaping from findings to recommendations. Conversely, if time is a constraint, and a follow-up is anticipated, try to analyze a small number of questions comprehensively rather than a large number of questions superficially.
- Among a firm’s peers, some may be better suited for a specific analysis. For example, a retail firm with a captive credit-card financing arm should compare its interest-rate hedging against peers with a similar asset. Capital return policies should be benchmarked against firms with similar investor clienteles. Exchange-rate hedging practice is comparable across firms with similar global footprints.
- If the annual physical analysis process includes a number of distinct modules (topics), understand the dependence of each module on the underlying data and its quality.
Other Types of Analysis
Choosing Your Path
Each of the approaches above suggests a different mix of personnel skills and systems support. For example, if you’re solely chasing elephants, you will probably lean less on automation and more on highly trained staff to conduct the analysis.