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Thematic Alpha-Beta Screening via Applied Indexation


Ahead of Q3 earnings, opportunity presents to review core equity and corporate credit portfolio strategies. Varying cyclically rich versus cyclically advantaged sectors prompt drag-and-drop product development as well and an effort to build a better Beta, both factor- and valuation-based. In a period of divergent views, requisite first and second order investment screens challenge institutional managers to lever the qualitative assessments associated with quantitative analysis, rules-based discretion and the nature of performance dispersion.

Lost in translation perhaps is a thematic multi-factor approach to screening which combines the successful tenets of index-plus methodologies, existing Beta-Beta and Alpha-Beta baskets, in parallel with prospective allocated Alpha capture. Proprietary research realizations identify trading pairs (e.g., momentum + growth / low beta + value) and tactical technical partners against a defined motif to help translate specific factor metrics into preferred fundamental corporate characteristics based on, for example, the business segment operations delineated within a company’s revenue line (dtd 091520).

Given this year’s capital market volatility, investors may question the need for additional analytical tools to isolate Alpha against a dispersed Beta. However, as determined progress matches the uncertainty of COVID-19, newfound catalysts mark investable motifs and build points of inflection into productive Alpha-Beta screens.


Thematically, for instance, independent megatrends endure despite dislocations in present valuations. These transformative movements add breadth to an investment universe, amplifying total return potential relative to performance baselines and institutional benchmarks. Here, niche proxies serve as clarifying portable benchmarks lending context to performance differentials in tandem with conventional indexes.


For example, charted megatrend ETF proxies in the overlay below — Renewables (PBW - Invesco WilderHill Clean Energy), Biotechnology (IDNA - iShares Genomics Immunology and Healthcare), Robotics (BOTZ - Global X Robotics & Artificial Intelligence) — together form the coveted V-shaped performance recovery similarly to technology benchmark Nasdaq-100 (QQQ) as of 063020:

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Events may still effect the current steepness of V’s right arm slope to result in an alternate letter or shape (U, W, L, square root, reverse square root or swoosh) and subsequent total return proposition. Irrespective, in addition to the long view, megatrend ETFs frame aggregated portfolio performance in time series and importantly provide an accounting of absolute component member performance dispersion: PBW one-year total return +39.1% (ranged returns of US exchange-listed component members +841.7%, -42.8%), IDNA +41.2% (+798.3%, -59.5%), BOTZ +15.5% (+137.3%, -60.3%) and QQQ +33.7% (+375.3%, -40.2%). Correlated returns reflect common drivers of valuation at the same time persistent performance differentials create opportunities to complement existing Alpha-Beta portfolio strategies.


Clearly portfolio strategies in active markets are tactically enhanced by Beta-boosters and informed factor tilts. Unfortunately though, despite incremental gains, oblique broad market assumptions often mask Alpha-specific drivers due to a predominantly lagged approach. Contrasting unique company characteristics consistent with elements of the traditional nine-grid style box (Large-, Mid-, Small-cap; Growth, Value, Blend) to principles of Smart Beta and factor investing (growth, value, quality, size, momentum, volatility, equal weighting, rebalancing) recognizes an inherent cyclicality, and subcyclicality, to requisite modeled corporate valuations and relative value assessments. Proprietary research suggests the Alpha-Beta trade-off is best mitigated from the bottom-up.


Conceptually, reconstructing megatrend ETFs and benchmark proxies via Applied Indexation starts at the revenue line by delineating differentiated growth rates of respective business segment operations. Alpha-capturing exercises link ETF component member corporate business profiles tangentially to form distinctive peer groups based on refined sector/industry/subindustry nomenclature assignments. Comparatively, the resultant variances of component member portfolio position weight classifications versus market capitalized position weights reveal meaningful performance causality between structurally embedded ETF research (security selection, size) and implicit tenets of Smart Beta and factor strategies.


The empirical premise of designing portfolio management strategies based on business segment operations lends advantage to index applications by limiting inefficiencies in third party data that predictably skew peer group analytics and valuation. Properties of business segment operations presentation are three-fold: 1) multinational and Large-cap companies function as benchmark proxies based on scale, 2) Small- and Mid-cap companies compete as peers by defined business segment operations and 3) subsets of 1) and 2) provision multiple economic sectors, asset classes and geographies.


To illustrate, Top/Bottom-5 component member equity performers of Renewables megatrend (PBW) are exhibited in the proceeding table listed with their corresponding prior five-period data set of past-positive forward looking inverse indicators (variance of portfolio weight to market capitalization per position per classification). Observable are the quarterly variances derived from business segment operations which produce directional valuation trends in both long and short positions:

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Matrix EPV 063020 Beta_2 (crop).png
Matrix EPV 063020 Beta_3 (crop).png

The prevalence and depth of Top/Bottom-5 negative differentials are representative of position weighted trends, effectively profiling relative strength indicators with sufficient specificity to portend and attribute performance. Small-cap companies generally display the greatest sensitivity to price and demand changes, hence the absolute range of outperformance/underperformance. Large-caps maintain positive indicators reflecting the dominance of capitalization within a classification, Mid-caps alternately lead and lag. Applied Indexation offers a capability to accommodate component member corporate actions, the fluidity of new segment or classification introductions, portfolio management and research evolutions.


For reference, performance dispersion in the (A) Automotive segment is evident above in review of Top/Bottom-5 listed (A) Automotive classifications: (A) Manufactures, (A) Fuel Cells – Lithium, (A) Fuel Cells – Hydrogen, (A) Components – Composites, (A) Components – Conversion and (A) Interiors. Tesla’s performance exemplifies the dynamic of innovation and scope of global operations. Interestingly, a competitive peer in classification — (A) Automotive (A) Manufactures — Workhorse Group (then a Small-cap) outperforms by demonstrating more immediate exposure to demand-driven scaling of electric commercial delivery trucks. SpaceX notably represents yet another prevailing megatrend although its motif is beyond the parameters of Renewables megatrend (PBW) construct and, separately, also excluded from Tesla’s currently filed SEC documents.


Successful trading strategies — thematic, systematic, momentum — isolate Value in Growth (and Growth in Value) by incorporating prospective Alpha drivers directly associated with ecosystem and supply chain verticals. Predictive value in tiered portfolios is established only by the degree objective research measures increase total return performance above a designated benchmark (i.e., Alpha equals excess return). Standard benchmark analysis begins an iterative process to include computed variances as a means to articulate relationships among investment themes, portfolio structure and security selection. Borne from a fundamental posture, derivative analytics via Applied Indexation help manage the complexity and nuance in barbell portfolio strategies—a bridge from allocated Beta to Alpha capture.

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