Transforming Intangible Assets into Tangible Value
The evolution of the global economy to one that is increasingly services-driven has been accompanied by an increase in the proportion of many firms’ value that is dependent on hard-to-measure intangible assets. This is particularly prevalent within the Information Technology and Health Care sectors, where the long-term value of new technology and early-stage biotech can be difficult to assess and account for. Our work indicates that a focus on the quality of intangibles is key to creating a reasonable assessment of value.
The global economy has been shifting from manufacturing to services for over 50 years, with an ever-increasing number of firms deriving their value from intangible assets. These range from technological patents developed by software providers to drug development undertaken by pharmaceutical companies.1 Increasingly, these intangible assets are the key to determining industry winners and losers. So it is important for investors to understand how they contribute to a company’s long-term growth trajectory.
Unfortunately, intangible assets by their nature can be hard to value. Uncertainty as to their future economic benefits and outdated accounting rules can result in financial statements understating the book value of intangibles and the income they could generate. Attempting to measure this intangible value can help active stock-pickers price opportunities more effectively and ultimately lead to better risk-adjusted returns for investors.
Intangible Investment Overtakes Tangible
Investments in intangible assets in the US (see Figure 1), the UK and Europe have been on the rise for decades, overtaking that of tangible investment in the 1990s – a trend that has continued since then and looks set to continue.
Measuring Intangible Value
Measuring the intangible value that arises from such investment is not easy. Not only may it be overlooked by financial accounts but its characteristics may differ across sectors. However, as active quantitative managers, we already have a framework in place to help us address these kinds of problems. We start by putting forward a hypothesis based on economic rationale. We then undertake rigorous research and testing of that hypothesis before we consider it for inclusion in our investment models.
Our research around intangibles began with the following questions:
- Are intangibles fully captured in stock prices by market participants?
- How can we measure the value or quality of intangibles?
- In which industries are intangibles more significant?
We identified two main drivers of the possible mispricing of intangibles: the distortion of financial reporting based on antiquated accounting rules and behavioral biases. Behavioral biases can lead to the market missing essential information about a company’s assets and finding reasons to over or underpay for its shares. Examples of behavioral biases include:
- Limited attention (people can focus only on a limited number of things at once)
- Availability bias (the tendency to focus on what is top of mind), e.g., short-term profits
- Hyperbolic discounting (putting too high a value on the present and too low a value on the future)
- The “lottery effect” (the tendency to overpay for lottery-like payoffs)
If we account for these biases, we should be able to develop a greater understanding of the long-term value of intangibles. Our thesis is that firms with a higher value and quality of intangibles should outperform over the long term. In this instance, we looked specifically at examples within the Information Technology (IT) and Health Care (HC) sectors, where intangibles are most significant, to test our ideas to see if they offered insights into the true value of intangibles. We then designed a range of metrics, using proxies for value and quality, with the goal of predicting the relative performance of stock returns more accurately.
Behavioral biases can lead to the market missing essential information about a company’s assets and finding reasons to over or underpay for its shares.
How to Treat R&D Investment
Research and development (R&D) activities are crucial to understanding how IT and pharma/biotech firms initiate and grow intangible assets. These investments, alongside human capital, are major drivers of technological innovation. They typically lead to products that drive future profits, possibly for many years, or help the company maintain persistently high margins.
Current accounting principles direct a company to report expenses in the same period as the related revenues. Under the US Generally Accepted Accounting Principles (GAAP) system (and voluntarily in Europe and elsewhere), R&D expenditure is treated as an operating expense because of the uncertainty around the future benefits it may provide. But revenue from R&D may not occur in the same period as it is expensed. This can lead to an understatement of the book value of the intangible asset and short-term earnings. In our view, statements of accounts should instead capitalize R&D and amortize it over its estimated life (e.g., 10 years for pharma/biotech and five years for IT). This would bring it closer to the “capitalize and depreciate” model used for tangible assets.
Based on our analysis of the impact of the immediate expensing of R&D and of the biases mentioned above on valuations, we hypothesize that a) firms with higher R&D capital relative to their own market value should outperform (and vice versa); and b) value metrics adjusted for R&D capitalization better predict company returns than those without adjustment.
Finally, we also incorporated analysts’ longer-term forecasts into our assessment. While analyst forecasts are notoriously unreliable, especially the further into the future they go, they offer directional trends that may go unheeded by the market, which tends to focus on the next two fiscal years. We think that longer-term five-year forecasts can help predict the future conversion of intangible assets into firm sales (and profitability), as they account for extended development cycles.2 So our final hypotheses are that firms with higher long-term forward sales relative to market value outperform (and vice versa) and that this measure predicts returns more accurately than corresponding short-term valuation metrics.
Why Quality Matters
Investors focus on quality when considering both tangible and intangible assets, but arguably quality matters more to assessments of intangible value. This is because of the inherent uncertainty within intangible assets, which makes quality an important indicator of whether their value will ever be realized. Quality assessments can help determine the efficiency of R&D expenditure made by IT and pharma firms, and the quality of drug pipelines in pharma and biotech. Not all R&D leads to revenue, and some of it can take time to appear. Contrary to what one might intuit, abnormal R&D spending patterns may actually signal R&D effectiveness and link to the quality of intangibles generated by the R&D activity.3 This is because limited investor attention can often lead to under-reaction to abnormal R&D investments. So our hypothesis here is that firms with higher abnormal R&D investments will outperform (and vice versa).
De-risking Drug Pipelines
Drug development cycles are long and expensive. They last at least a decade with 6-7 years spent on clinical trials and cost on average US$2.7 billion4 with only a small subset of these drugs ever getting approved and commercialized. As drugs advance through the various clinical stages (pre-clinical, then stage I, stage II, stage III and finally new drug application [NDA]5), the lower the likelihood of failure and the greater the chance of conversion to realized revenue. The diversification of a drug pipeline can also impact its quality from a risk perspective and the lottery effect can come into play.6
There is no better example of a lottery stock than a small biotech firm with a single early-stage clinical trial. Investors can get very excited at the potential, however small, of discovering a cure for a major disease and the stock price reflects that, despite such a single-drug pipeline being riskier than that of more established firms with multiple trials underway.7
On the other hand, limited investor attention can lead to an initial under-reaction to clinical stage trial results, even for less-risky pipelines, as outcomes are often binary and information takes time to filter through to share prices. So we surmise that firms with riskier drug pipelines underperform (and vice versa) and this should offer us opportunities to exploit within our portfolios.
To address these issues, we have developed a unique and proprietary measure to de-risk drug pipelines for investment purposes by establishing both the concentration and likelihood of drug approval using pipeline information we have for hundreds of companies. Measures of pipeline concentration and likelihood of approval are based on the relative contribution to the risk-adjusted net present value of each drug. We use data on the number of drugs at each US Food and Drug Administration (US FDA) stage to calculate the probability of success that is conditional on the clinical stage.
Investors focus on quality when considering both tangible and intangible assets, but arguably quality matters more to assessments of intangible value.
Impact on Returns
This data analysis alongside our value and quality measures of intangibles offers a more rigorous approach, in our view, to selecting IT and Health Care companies, improving future returns and meaningfully reducing their variance. When compared with value and quality measures that do not use intangible adjustments, all the hypotheses laid out above have passed our testing hurdles. Moreover, the metrics we use to deal with intangible assets hardly correlate with each other or with other metrics we use to assess IT and Health Care companies. This adds new dimensions to our return forecasts.
When we back-test some of these measures with historical data, we can see how their introduction into our measures of value and quality for the IT and Health Care sectors leads to better results. Figure 2 compares our bottom-up stock selection model with and without intangible adjustments in terms of quintile spreads (top minus bottom) for mean, volatility (standard deviation) and information ratio. It shows how the intangible adjustments have improved our back-tested average spreads by around one-third, decreased the spread volatility (by one-seventh) and increased the information ratio by more than half.
For us, this analysis presents some exciting opportunities to enhance the way we think about such companies. The trend toward ever-greater intangible investment is set to continue and we are exploring further ways in which we can capture the contribution of intangibles to future returns, both within and beyond the IT and Health Care sectors. Using intangible analysis to make value more concrete and improve our forecasting should ultimately have a positive impact on investors’ risk-adjusted returns.