Rees, Principles of Financial Modelling

In the old days banks wanted applicants to be comfortable with Excel. Now they’re upping the ante. Citi, for instance, wants its incoming investment bank analysts to know Python. But Excel hasn’t gone the way of the dodo. It’s still incredibly useful for a range of financial tasks. The problem is that most Excel users have no idea how to go beyond basic formulas and engage the program as a high-level tool. In Principles of Financial Modelling: Model Design and Best Practices using Excel and VBA (Wiley, 2018) Michael Rees sets out to fill this void.

Rees’s more than 500-page book is divided into six parts: (1) introduction to modeling, core themes and best practices, (2) model design and planning, (3) model building, testing and auditing, (4) sensitivity and scenario analysis, simulation and optimization, (5) Excel functions and functionality, and (6) foundations of VBA and macros. As these part titles indicate, Rees first addresses financial modeling, from design to optimization, and then explains how to use Excel and VBA to implement the models. Complementary to this book is a website, which contains 237 Excel files.

Here, to give a sense of the book, I will summarize Rees’s distinction between database and formula-driven approaches to modeling.

Traditional models, for instance those used for cash flow valuation, are formula-focused. They “often have a small set of numerical assumptions, from which large tables of calculations are performed. Certainly, where a single value is used for an assumption across multiple time periods (such as a single growth rate in revenues that applies to all future time periods), arbitrarily large tables of calculations may be generated simply by extending the time axis sufficiently, even as the number of inputs remains fixed.”

Where a large volume of data is required, however, the appropriate model will use “database concepts, functionality or data-oriented architectures and modular structures. These include the structuring of data sets into (perhaps several) contiguous ranges, using a column (field)-based approach for the model’s variables (with well-structured field identifiers, disciplined naming conventions, and so on).”

Even though in practice these two approaches to modeling can sometimes overlap, with the modeler confronted with both large data sets and potentially many formulas, Rees contends that “at the design stage, the reflection on the appropriate approach is fundamental: an inappropriate choice can lead to a model that is inflexible, cumbersome and not fit for the purpose.”

Before they set out to build models in Excel, analysts would do well to read Rees’s book. With its help, they will avoid many pitfalls.

Hall, A Carnival of Losses

I’m getting to the age that I read things about getting old. Not the advice that AARP sends out but essays by writers who are comfortably ahead of me on the march to 100. Donald Hall, the former poet laureate, delivered two such volumes of late, Essays After Eighty and A Carnival of Losses (Houghton Mifflin Harcourt, 2018), the latter written as he was nearing 90. (Hall died last month, on June 23.)

Essays After Eighty addressed old age more expansively, and humorously, than A Carnival of Losses. The new book has qualities of leftover stew: warmed-up reminiscences and bits and pieces that were probably in the literary root cellar, such as his recollections of poets. It’s still a delightful read, but if I were to recommend only a single title, it would be Essays After Eighty.

Govindarajan & Ramamurti, Reverse Innovation in Health Care

I got an advance reader’s copy of Reverse Innovation in Health Care: How to Make Value-Based Delivery Work (Harvard Business Review Press, 2018) by Vijay Govindarajan and Ravi Ramamurti. I put it aside, thinking that it was not within the scope of this blog. But then came the tornadoes that struck Connecticut, one of which touched down far too close for comfort, on May 15. My property (fortunately not the house) was devastated, with large uprooted trees all around the house and the edges of the property and the top of one mighty oak pinning, and miraculously only denting, the car sitting in the driveway. Naturally, I had no power for days, and no Internet access for days more. And so, with my usual routine upended as well, I turned to this book.

The book’s premise is that U.S. healthcare providers can learn from models that have been successful in India. The authors are not, of course, touting Indian healthcare as a whole, which is sorely wanting. But one hospital system in particular, Narayana Health, could serve as an exemplar.

Founded by Dr. Devi Shetty in 2001 with a vision to treat all patients regardless of their ability to pay, Narayana Health is now a profitable company that offers, most notably, open-heart surgery (which would normally cost between $100,000 and $150,000 in the U.S.) to paying patients for $2,100 and to subsidized patients for $1,307. The hospital’s cost for each surgery is $1,100 to $1,200. Narayana is now doing about 14,700 cardiac surgeries a year. On average, in 2016-17 Narayana’s cardiac surgeons performed two to three times as many open-heart surgeries as their U.S. counterparts. And their outcome metrics rival those of the best hospitals in the world.

Shetty is a ruthless cost-cutter, as long as cutting costs doesn’t negatively impact quality of care. To construct Narayana’s no-frills hospitals, for example, costs about half that of its competitors. And when Shetty wanted to buy disposable surgery gowns and drapes from multinational suppliers who refused to budge on price, he had them stitched locally. Within four years, this firm became the largest manufacturer of disposable surgical gowns in India. The multinationals, unable to compete on price, left the market.

Narayana has innovated through task-shifting, allowing surgeons to do three operations in the time it takes other hospitals to do one. “[E]very motion in the operating cycle is choreographed to reduce turnaround time and optimize pay grades.” Senior surgeons do little or nothing that can be done by lower-paid, less-skilled staff.

In perhaps the most striking instance of task-shifting, in Narayana’s multispecialty hospital in Mysore, family members provide much of the post-ICU care. Since, in India, the entire family comes to the hospital with the patient and typically spends three days there, Narayana upgraded them from “underfoot” to caregivers. They get instruction from a four-hour video curriculum. “The practice of training families for in-hospital postoperative care not only frees up the nursing staff for other work but also eases the transition to reliable, high-quality home care, reducing readmissions by 30 percent.”

Narayana uses a hub-and-spoke model and, through farming cooperatives in Shetty’s home state, instituted an insurance plan to reach out to underserved villages. By 2017 the insurer had four million members who, for 22 cents a month, could get free treatment at 800 network hospitals across the state for any procedure whose cost did not exceed $2,200.

Shetty is also starting to pursue opportunities in telemedicine.

The authors highlight four new models in or near the United States that use some of the Indian tactics: Health City Cayman Islands (founded by Narayana Health), University of Mississippi Medical Center, Ascension, and Iora Health. All of these are making strides in trying to change the American healthcare system from the bottom up.

Reverse Innovation in Health Care offers ways for U.S. healthcare to save billions without compromising (indeed, perhaps with improving) quality. And it’s not simply on the back of low wages. The authors address a series of questions that skeptics raise to show that aspects of the model would be viable in the United States. As such, it’s an essential read for anyone who is prepared to tackle the change-resistant healthcare establishment.