Professor Anne Marie Knott has made substantial contributions on a variety of fronts in this terrific read.
Her meticulously developed and researched concept of the Research Quotient (RQ) for quantifying and optimizing companies' R&D efforts - and, crucially, for estimating the impact on the top and bottom lines, margins and market values resulting from adjustments to R&D allocation - ought to be critically relevant to a variety of audiences.
These will include, most notably, public companies engaging in R&D, acquisitions specialists at both investment banks and private equity shops, equity research analysts on the sell and buy sides, institutional and activist investors, retail investors, academics and policymakers.
The quality and potential immense implications of this effort – and the fact of it also being a delightful reading experience – emanate from a number of strengths:
1. Most importantly, in linking the expected value of R&D to classic economic theory rooted in the production function and elasticity, Professor Knott is able to provide a rock solid theoretical foundation for the book's approach to the valuation and optimization of R&D. She is, to my knowledge, the first to build out the production function so as to fully capture R&D in context and to develop and test a production function equation that accounts for what Professor Knott demonstrates is clearly true: elasticities differ across companies. This key step is contrary to traditional economic assumptions but its validation permits a precise definition of RQ as the “company-specific output elasticity of R&D.” The takeaway: this Is the first true measure of R&D that has been validated in examining 45 years’ worth of data and that validation probably has a great deal to do with RQ also being the first metric of its kind to have its entire basis in economic theory.
2. Professor Knott has developed the concept and methodology supporting RQ such that it can be put to immediate use - the metric is observable (estimated entirely on the basis of information from companies' public filings); it can be distilled into a single number; consistently scaled across and within industries, allowing for a comparison of any two public companies; it has been subject to exhaustive testing , accounting for every year of every publicly traded firm in the U.S. doing R&D since 1973; and, helpfully, the metric appears to be normally distributed. The takeaway: RQ is observable, universal, uniform and – crucially – reliable, i.e. it measures what it aims to and produces consistent results across experiments.
3. Via an in-depth investigation of the properties of RQ, how it has changed over time and how - looking back via numbers and looking forward through an extraordinarily efficient and effective strategy rooted in an understanding of how RQ works- it appears that certain companies have been systematically more successful than others in maintaining high RQs or improving RQs, Professor Knott is able to present a robust quantitative refutation of a number of egregious misconceptions about R&D - misconceptions that have contributed to companies’ leaving hundreds of millions of dollars on the table annually; to market participants, ranging from institutional to retail investors, too frequently relying on measures of R&D that have no correlation with market value; and, indeed, to certain of the world's most prestigious economists putting forth grim scenarios under which global economic growth is finite, doomed to eventual decline. While cogently presenting the cases for these dire economic scenarios, in calling upon the theory of endogenous economic growth, Professor Knott puts forth a compelling empirical notion that, so long as there is R&D, the global economy will continue to expand.
4. While improving RQ is presented as the solution to the problems of accurately valuing, predicting and right-sizing R&D, Professor Knott’s bona fides truly shine in presenting impeccable research that supports a specific set of best practices in respect of doing R&D well, and in respect of a company improving its RQ. The coverage is panoramic and comprehensive, addressing challenges ranging from determining optimal R&D spending, to the question of whether radical innovation is more optimal innovation to choosing between centralized and decentralized R&D structures to managing R&D in uncontested markets. Seamlessly interweaving theory, data and case studies, Professor Knott offers a clear methodology for rightsizing R&D via the application of scientific management First, to improve its RQ, a firm must know its score. Second, firms ought to establish three RQ benchmarks – firm RQ should be compared to the RQ of competitors; current RQ should be compared to past RQ; and RQs across business units should be compared. Each benchmark, as the book shows, offers firms distinctive and important insights as to how their RQs can be improved. Overarching this process is the notion of right-sizing – that is, as It turns out, many firms would do better to actually cut R&D spending. Professor Knott adroitly shows that downsizing R&D – if it optimizes RQ – leads to more valuable innovation.
5. The Professor does not hide the ball re: how RQ is estimated. Now, to actually do it, one would need to spend the time gathering years of financial data for all publicly traded companies and then perform a series of second-order multivariate regression analyzes. In other words, if it’s not your full time job to do so, you’re not likely going to be estimating RQ at night. But doing the estimation wouldn’t be impossible – this isn’t a black box. Professor Knott methodically derives the equation by which a firm's RQ can be estimated (and a bonus point for the statistical integrity of being forthright that the metric must be estimated via a series of multivariate regression analyzes).
6. Saved this for last, as much of my own interest is investing implications and, while this is not a book primarily about investment management, it most certainly lays out the tantalizing possibility of an investment strategy based on buying shares of those firms whose R&D efforts are most optimized – those firms with the highest RQs. The results are no doubt top-line impressive, with backtests appearing to show that if beginning in 1973, one had invested an equal amount in the 50 companies with the highest RQs, re-balancing annually through the present, as opposed to investing an equal initial amount in the S&P 500 index at the same point, the "High RQ" strategy would have outperformed the index by some 9000% cumulatively.
**
As I conclude, a bit more on the investing aspect of the book. Lord knows, as I've watched the concepts of smart beta and factor investing explode, there's also been an explosion in the number of apparently wildly market-beating factors discovered. These are honest efforts, but they usually fall short for any number of reasons (the proposed factor is noise, not the signal; the proposed factor has historically been so volatile that even the proponent's own backtesting reveals inferior historical Sharpe Ratios; the proposed factor is not practically investable; execution – read trading cost – wipes out any excess return, etc.).
And indeed, even those factors that have, over large swathes of time -- most notably momentum, value, size and "quality" (arguably) – been subjected to the most rigorous backtesting, out-of-sample analysis, and painstakingly controlled for survivorship bias have all experienced significant stretches of underperformance. There are great debates about the lasting excess returns that may be earned by going only long a given factor, without also going short; and equally fascinating debates about whether, when and how fast, certain factors are arbitraged away and no longer deliver excess returns.
I will be clear in saying that I have no visibility into the backtesting done in respect of high RQ stocks. Nor do I have any visibility into how such a portfolio would have performed over segmented periods, e.g. three, five and ten year rolling periods. And I have no insight at all into excess (risk-adjusted) returns, as opposed to absolute, without any data on the volatility of the high RQ portfolio (I could go on - percentage of winning periods, impact of trading costs, max drawdowns, Sharpe and Sortino ratios, etc.).
That said, I'm impressed very much by the length and breadth of the data analyzed here. And it was important for me to learn, in the book, that high RQ, in multivariate regression analyses of returns apparently came out not only as a statistically significant predictor of returns, but as the most significant, just ahead of momentum. I look forward to what I imagine will be new rounds of backtesting and attribution analyzes. It is worth noting that the 50 stocks with the highest RQ for 2014 come from a wide range of market sectors. And that batch of 50 from 2014, incidentally, has significantly outperformed the market in terms of excess returns, based on my own (admittedly rudimentary) analysis.
To be very clear, I don’t think that Professor Knott set out to write a book about investment strategy, but I for one hope that in addition to her expertise in strategy and innovation, she might well contribute with great value to the investment management field. I would also expect and hope that in the near future, a firm's RQ score will become a fundamental metric made available by free providers of financial data such as Yahoo! Finance and my own favorite, the fabulous finviz.com. Certainly services that can offer me all the detail I might ever hope to know about each step of the cash conversion cycle going back several years and years of data on earnings revisions can show me a firm's RQ
The magnificent Professor Aswath Damodaran of NYU, in the first pages of his classic valuation books, and early on in his courses, introduces his concept of the financial balance sheet, in which a firm has two kinds of assets: assets in place and growth assets, which are those assets yet to be acquired. For those firms with a high proportion of growth assets, valuation requires, in many cases, estimating and discounting cash flows from product lines not even conceived as of yet. RQ, when thought of as a valuation tool, for me, holds out tremendous promise in increasing the accuracy of those estimates.
And yet the most significant impact of RQ – and Professor Knott’s book - is likely not immediately on the investment community - but rather on whether companies will show themselves capable of right-sizing R&D. Executives don't lack awareness that R&D is, in so many cases, the whole long term ballgame). But in some cases they lack the incentives, initiative, structure, insight, intuition and access to data that need to be in place in order for optimal R&D decision-making.
Whether RQ is a panacea is a premature determination. But I wouldn't bet against it being a true game changer and I hope that scaling network effects kick in sooner rather than later, creating a scenario where the metric is routinely employed by companies and reported to the financial community (including retail investors) and to policy planners, who have a vital interest in knowing the quality of the R&D being done within the private sector.
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