Setting the Stage
Quant models that are tasked to pick winning stocks are not uncommon in the industry. In fact most reputable sell-sides have their unique models. While their actual methodologies may differ, almost all are heuristic- and/or optimization-based - a set of ratios which are known to have explanatory power to price performance are first selected before an optimal weights are assigned to them.
Such a priori approach almost always assures a back-tested result that beats benchmark. Nevertheless the long-term sustainability of these models remains questionable simply because there is lack of genuine novelty involved. If everyone works with the set of ratios that are no different from the rest through similar process, there is practically little chance of finding something sustainable in the competitive equity market. It follows then that the right way is about identifying the fundamental needs of the industry which is, somehow, still missing.
So, what is it?
It starts with the determination of the intrinsic value of a company, which can theoretically be done via a discount model. Specifically, the model sums up discounted future earnings till either infinity or a pre-specified time frame. However, here is where the issue starts. On one hand, projection into infinity makes the intrinsic value highly sensitive to variables of the model. The concept of 'infinity', in the first place, is illusive. On the other hand, projection into a pre-specified time frame does not solve the issue either. It merely breaks infinity with an assumed exit multiple - something which is equally illusive.
To compound the problem, building discount models for all companies in this world and maintaining them is a daunting task. The fact that there are more than 1,600 companies in the MSCI World, which excludes small caps, illustrates the need for a financial ratio to execute effective cross-sectional comparison.
But, what is such a ratio that is able to concurrently solve illusiveness and calculate intrinsic value without the need of a discount model?
A discount model describes the intrinsic value of a company through discount rate, initial earnings, and future growth rate. It follows that a prerequisite of this ratio is to consume these arguments. The last piece of puzzle is a framework to link intrinsic value to stock price. Along this perspective, the Black-Scholes option pricing model offers an inspiration.
Conceptually there are many similarities between option/underlying and stock/company. First, just as an option should mirror its underlying asset in terms of price behavior under the arbitrage-free assumption, the same also applies between stock and company. Second, the strike of an option controls its price just as the growth rate of a company controls its valuation. In essence these similarities lay the near perfect groundwork for the Black-Scholes model to be modified to price stock. What aids the process further is the eradication of stochastic approach of the model since growth rate of a company is assumed to be deterministic.
Based on the above a new ratio has been derived. For reference purpose it is called Growth-Adjusted Price to Earnings ratio, or GAPE in short.
GAPE makes mathematical sense. However, sensibility is a necessary but not sufficient condition to make any new valuation ratio useful for practical purpose. To ascertain whether GAPE fits the latter condition, extensive back-testing has been carried out using data as early as Dec 31, 1989 covering stocks in all major regions: U.S., Developed Europe, Developed Asia, and Emerging Asia plus Latin America.
Specifically, for each of the region non-financial companies were first ranked according to GAPE. A fixed percentile of them was bought at equal weight to form a portfolio. To compliment the effectiveness of GAPE, companies with relatively high leverage were excluded. The portfolio was then rebalanced after six months.
The results are remarkable. Not only that the magnitudes of the returns are significantly higher than the relevant benchmarks, their consistency over time and across different geographical areas are unprecedented by any single valuation metric known today.
Exhibit 1 below shows the index performance of the strategy in the U.S. The cumulative return of the strategy since Dec 1989 is almost 4,500% while the corresponding benchmark returns 1,000%. Comparison with performance of mutual funds is even more dazzling. According to the Morningstar database, none of the large-cap U.S. funds has a 10-year return greater than 13%. In contrast, GAPE returns 22% during the same period of time.
Exhibit 3 and 4 chart the performances of GAPE in developed Europe. For the last two decades, the strategy has on average returned almost 17% annually in the region while the corresponding benchmark has yielded only 11%. Using the Morningstar database as reference, it again outperforms all large cap funds in the region in terms of 10-year return.
Similar results are observed in both Developed Asia and Emerging Asia plus Latin America region (Exhibit 5 - Exhibit 8). Specifically, GAPE selection has resulted compounded average return of 14% annually in the last 20 years in Developed Asia. In contrast, the corresponding benchmark has returned only 6% per year. The emerging markets have shorter back-testing results due to data limitation, but the application of GAPE in the region still has had significant results. The best emerging market fund in the Morningstar database has 10-year average return of 20% while GAPE has almost 30%.
GAPE results were not just compared to the corresponding benchmark returns. They were also compared to those of comparable funds and of other quant strategies from sell-sides. It is found that GAPE beats almost all of them in terms of results magnitude and consistency.
What are the fundamental drivers behind this?
By design, size plays no part in explaining the performance since only large/mid cap stocks were involved. On the other hand, further analysis revealed that exposure to value helps as GAPE portfolios have the propensity to tilt more to that than to growth. Nevertheless, common sense suggests that it takes more than that to explain such huge outperformance.
It is postulated that the ratio, believed to be the first to definitively connect company value to stock price, manages to identify mispricing opportunity. The lapse of such mispricing within holding period then drives the outperformance, repeatedly over time and around the world.
Inevitably there were times when GAPE underperformed, arising possibly due to two reasons. First, overwhelming investor sentiment prolonged the underperformance of mispriced stocks during the holding period. Second, rapid deterioration of company growth failed to be acknowledged as rigid back-testing procedure hindered flexible portfolio rebalancing timing.
It follows then that there are practical rooms for GAPE performance to be improved even further. As a start, flexible rebalancing timing should help. Specifically, augmenting GAPE stock selection with trading rules, which capture sentiments via signals such as momentum or breakouts, should yield positive marginal results. Timely order execution in response to change of company prospect is another aspect.
GAPE possesses the element of novelty. It is the only known valuation ratio which, unlike PEG ratio, is capable of concretely linking company value to stock price via a complete mathematical framework. In the last 20 years it has proven to be a consistently effective and efficient way to identify stocks with great price appreciation potential.
So far, there is lack of evidence suggesting otherwise trend going forward. This lays a good prospect for GAPE to continue creating wealth among investors over long-term.
In the second part, stocks that have been identified by GAPE as 'cheap' will be published. Stay tuned!