The investment management landscape has changed dramatically with the proliferation of passive investment vehicles and the genesis of machine learning. Active investors are increasingly challenged by mechanistic and non-idiosyncratic methodologies that result in low-fee, but broadly undifferentiated products. As long-term active investors in emerging markets (EM), we contend that there is still a bright future for strategies that employ creativity and imagination to discover and invest in the next generation of idiosyncratic world-class companies.
In this blog, we pinpoint a host of interlinked strategic issues, ranging from the existing institutional structure to human psychology, that underpin changes taking place in the investment industry. We also delve into our proposition that conducting truly differentiated and insightful research lays the foundation for sustaining a competitive edge in a changing EM world.
Figure 1: Passive strategies attract flows, but may offer disadvantages in EM
Cumulative flows into passive vs. active funds
Passive strategies often fail to capture the opportunities in EM
The most significant investment shift in recent years has been the seismic trend of money moving from active to passive strategies. With total assets of $4.305 trillion, passive US equity funds (low-cost alternatives that replicate benchmark performance) hold just $6 billion less than active US equity funds.1 Approximately 15% of the total S&P 500 float is now owned by passively managed funds.2 While passive strategies can be an effective way to access large-cap US equities, investing in EM is different.
Figure 2: Share of passive ownership has been rising since the global financial crisis
US-domiciled passive fund ownership of S&P 500 float
First, passive global equity products are underexposed to EM. China, for example, contributes 19% of global gross domestic product (GDP) but represents a mere 3.8% of the MSCI ACWI. Even within the EM world, China is underrepresented, contributing 45% of EM GDP, while making up just 33% of the MSCI EM benchmark. On the other hand, the more advanced EM economies, Korea and Taiwan, contribute a combined 7% of EM GDP but represent a quarter of the MSCI EM benchmark.3
Figure 3: Passive strategies often over- or under-represent EM relative to GDP
EM bench weights and percentage of EM GDP
Second, we would argue that investment opportunities in emerging markets are evolving faster and in a more non-linear fashion than in the developed world. Therefore, even if investors gained exposure through an EM-dedicated passive product, we feel the inefficiency of the benchmarks and their backward-looking nature make them a faulty roadmap for investors.
In our opinion, EM indices are overly exposed to sectors that reflect historical EM growth drivers rather than the dynamic drivers of future growth. The Energy and Materials sectors, for example, account for 15% of the MSCI EM Index, while Healthcare accounts for a mere 3%, despite some very striking new growth opportunities in the sector. In short, the benchmark is heavily skewed toward the traditional industries like banks (18%) and Telecoms (4%), while it assigns low weights to new economy sectors like tourism (0.8%) and education (0.6%) that are picking up momentum across EM.4
Third, since the benchmark is essentially market-cap-weighted, it mandates a significant exposure to large state-owned enterprises (SOEs), most of which have poor governance track records. By market cap, SOEs formed 42% of the weight in the MSCI China Index and 24% in the MSCI EM Index as of May 31, 2019.5
Figure 4: EM benchmarks are overexposed to historical EM growth drivers
MSCI industry group weights
Finally, the MSCI EM Index is essentially a large-cap index with 70% of its assets in companies with market caps larger than US$10 billion. Large-cap companies account for less than 10% of the investable opportunity in EM, but the index offers investors little exposure to the other 90% of the EM universe.6 For this reason, we believe active management is a must for investors who want to broaden their range of EM market-cap exposure.
Organizational intolerance for creativity inhibits investment success
The investment industry has developed a set of institutional governance principles that discourage truly imaginative active investors. This has had dire consequences for the industry. Active management fees have halved since 2000. Equity funds have seen the largest drop in expense ratios, with the average fee down 40 basis points between 2000 and 2017.7 While falling fees present a meaningful obstacle, we argue that the real threat to the industry is that most active managers are not truly active. Most compensation schemes are based on relative peer rankings, which in turn orients portfolio construction decisions more toward peer and benchmark performance than absolute returns. Many active investors have become like news reporters – reactive and struggling with short-term mandates and a limited appetite for differentiation.
This cultural shift has created a growing misalignment between the high-velocity, short-term orientation adopted by most equity portfolio managers and the long-term needs of their underlying clients, who look to their investments to pay school fees, large medical costs and retirement expenses. We believe this misalignment has fostered conflicts of interest and poor capital allocation decisions.
The rise of machines
The war on fundamental active investing has many fronts. Beyond the large-scale shift to passive management, the increasing application of machine learning to equity investing will likely present significant competitive disadvantages for non-idiosyncratic investors. Machine learning, which implements data-driven approaches to make portfolio recommendations and uses complex algorithms to test sophisticated investment ideas, has the potential to better exploit the richness in historical data compared with traditional forecasting approaches.
We’re seeing artificial intelligence (AI) applications sprout up in many contexts, sparking both hope and unease. AlphaGo, an AI system designed by a team of researchers at DeepMind, soundly defeated the world Go champion, Lee Sedol, in 2016. The defeat shocked the world as the ancient and enormously complex game of Go, which requires tremendous intuition and creativity, was until then believed to be beyond the capability of any machine.
In the investment industry, the rise of machine learning also forebodes a future in which most active managers could be rendered obsolete. Machines are inherently better than human investors at deep learning – including recognizing patterns, understanding correlations and forecasting mean reversions. As a result, machine learning can better exploit nuances in historical returns than traditional analysis, replacing investors who make judgment calls based purely on past experience. For example, machines will be far better than human intuition in forecasting mean reversions in credit cycles, inventory cycles and other cyclical trades. In fact, an estimated 2.5 quintillion bytes of data are being created daily.8 Active investors truly have their backs against the wall.
Figure 5: Machine learning threatens traditional fundamental approaches
Google search trends for “factor investing” vs. “fundamental investing”
What the machine cannot touch – imagination and differentiated research
The future for active managers does not reside in financial engineering or benchmark tracking tactics, which essentially extrapolate the past. Instead, we believe it lies in what machines are not particularly adept at doing (and what benchmarks can never do): imagining the future.
Kering (4.09% of Invesco Oppenheimer Developing Markets Fund assets as of March 31, 2019), the owner of Gucci, Saint Laurent, Balenciaga and other leading fashion houses, demonstrates the opportunities in transformation, which are blind to both passive investments and mean-reversion, pattern recognition-oriented algorithms. The company has made a transition over the past decade from a messy conglomerate with diverse business interests to a leading pure luxury group. Additionally, Kering has proven adept at managing and nurturing creative talent. Such management capacity has enabled the transformation of its largest brand, Gucci, into the first true Millennial atelier. These skills are now being applied with marked success to smaller brands in the portfolio, including Balenciaga and Bottega Veneta. Kering has navigated with unusual success the twin seismic shifts that have struck the relatively traditional luxury industry – digital communications and the emergence of new, younger consumers as the dominant customer cohort. These forces have enhanced the power of scale and unearthed potential long-term options for the consolidation of companies that have struggled to adapt to these structural changes. Kering is also increasingly running these brands with digital intelligence in marketing, logistics, merchandising, customer engagement and even product design.
Our investment in Kering demonstrates two things: First, the capacity to look beyond benchmarks for investment opportunities across the developing world. Kering, like almost half of the holdings in Invesco Oppenheimer Developing Markets Fund, is neither in the benchmark nor domiciled in the developing world. However, insatiable growth in the emerging world, particularly the rise of aspirational Chinese nouveau-riche population, clearly drives Kering’s business, Second, our investment in Kering highlights the capacity for active managers to unearth discoveries that would be nearly impossible for machine learning to extrapolate. The transformation of Kering could not be quantitatively predicted; it could only be thoughtfully imagined. Finally, the structural changes in the luxury industry, including products, communication, distribution channels, the underlying operations and the consequent revolutionary shifts in competitive advantages, could only be deduced by idiosyncratic and differentiated business analysis.
Disruptive innovation is another area where we think passive investing and machine-learning algorithms cannot hope to compete with truly active, fundamental investors. Again, a future that has not yet been seen cannot be extrapolated from the past. Meituan Dianping (1.79% of fund assets as of March 31, 2019), the dominant services platform in China, and Grab (0.99% of fund assets as of March 31, 2019), the dominant Southeast Asia ride-hailing giant, are two examples of this. Both are high-growth platform companies that have yet to experience profitability as they continue to invest aggressively in large pools of growth in both their core and ancillary markets. Both are becoming “super apps” in some of the world’s largest markets.
Both Meituan and Grab have analogues in the developed world – in food delivery, ride-hailing and financial technology. However, parallels in the developed world fail for a number of reasons, including significantly better economics and market sizes. Driver (and delivery) platform economics are superior in the developing world because EM have much broader pools of labor attracted to incomes meaningfully above the median level incomes achievable in the deeply informal labor markets of most developing economies. And both companies have significant real options that are inaccessible to the likes of Uber and Grub Hub (not fund holdings as of March 31, 2019) as a result of the highly penetrated and well-established legacy systems in the developed economies of the US and Europe.
Meituan, for example, has been able to vertically integrate all services embedded in apps such as Yelp (and other content platforms), Open Table (reservations) (not fund holdings as of March 31, 2019) and Seamless (deliveries). Its enormous consumer platform – approximately 400 million users – has also allowed Meituan to generate leads and transactions for other verticals as well, most notably travel, where it has emerged in just a few years as the largest online travel agency in China. Meituan is beginning a long journey into developing a loyalty system, analogous to Amazon Prime (not a fund holding as of March 31, 2019), which should permit greater frequency, better unit economics and significantly greater new vertical opportunities. It would be impossible for machines to extrapolate the promising future of Meituan in the absence of both historic profitability (margin expansion) and mathematical understandings of new, real business options.
Similarly, Grab is becoming the dominant super app in Southeast Asia. Grab has a core business in ride-hailing across eight countries in Southeast Asia with a passenger count of approximately 148 million.9 This business has significant profitable growth potential stemming from the enormous underserved markets in these developing countries that are the result of the high cost of transportation ownership relative to income and underdeveloped public transportation in very dense cities. As a two-sided market, Grab also is able to offer driver economics that are superior to those in the developed world as a result of wide pools of informal labor that are highly incentivized to drive. Unlike either the developed world – or even China –Grab has powerful adjacent market opportunities in food delivery, loyalty and fintech as a result of nascent competition. Thus, Grab has the potential to grow into a super app with large revenue pools and significant profitability at scale. Again, machines would be unlikely to extrapolate this future with the absence of imagination (and fundamental research) embedded in the algorithms.
Passive strategies and machine learning create opportunities for idiosyncratic investors in EM
Unfavorable industry trends have historically created opportunities for truly skilled, active managers. We believe that this peculiar environment, now signified by the rising ubiquity of machine learning and passive investing, favors those with an idiosyncratic approach and rewards genuine imagination and creativity, which has the potential to unearth the rare breed of extraordinary companies.
As long-term EM investors, company fundamentals are especially crucial to us. The essence of our investment method has always been an unwavering focus on unearthing high-quality compounders instead of constantly chasing after moonshots. We believe that a long-term investor will be best served by gaining exposure to truly innovative EM companies that take advantage of structural tailwinds such as rising affluence, the ubiquity of technology and the formalization of economies. In our view, to do so consistently over time takes a loner instinct, the discipline to resist reacting instantaneously to short-term stimulus, and more importantly, an insatiable curiosity.
Regarding the prospect of machine learning’s widespread application in investing, we argue that, in the absence of an appreciation for the nuances of imagination and creativity in fundamental investing, mean reversion investors who look for pattern recognition based merely on experience and datasets will be wiped out by machines.
On a more positive note, although machine learning can learn from countless games like Go and chess played by professional human players, and implement reinforcement learning (playing against itself), the future outcomes it can project are all based on past experiences or an approximation of human creativity in strategic thinking. As active investors, we take solace in the knowledge that the most profound investment opportunities lie in our ability to identify real options that have not yet been discovered. We envision that the role AI can play in creativity is perhaps to enhance human creativity and that there could be a partnership between humans and machines going forward.
1 Source: Morningstar, as of April 30, 2019.
2 Source: BoA Merrill Lynch, as of as of May 31, 2019.
3 Sources: MSCI, IMF. MSCI data as of April 2019. IMF data as of December 31, 2018.
4 Factset, as of 4/30/2019.
5 Source: HSBC Research. This considers A-shares at 5% inclusion factor. Data as of May 31, 2019.
6 Source: MSCI, as of May 31, 2019.
7 Source: BoA Merrill Lynch.
8 Source: DOMO, as of December 2017.
9 Source: Grab. Customer figure is based on number of downloads, as of June 30, 2019.
The MSCI ACWI is a market-capitalization weighted index designed to provide a broad measure of equity-market performance throughout the world. The MSCI ACWI is maintained by Morgan Stanley Capital International (MSCI) and is comprised of stocks from 23 developed countries and 24 emerging markets.
The MSCI Emerging Markets Index captures large- and mid-cap representation across 26 Emerging Markets (EM) countries. With 1,198 constituents, the index covers approximately 85% of the free-float-adjusted market capitalization in each country.
The MSCI China Index captures large- and mid-cap representation across China H shares, B shares, Red chips, P chips and foreign listings (e.g. ADRs). With 495 constituents, the index covers about 85% of this China equity universe. Currently, the index also includes Large Cap A shares represented at 10% of their free float adjusted market capitalization.
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