U/O Matrix via Applied Indexation – Establishing Predictive Value 2020

Key period takeaways: 1) bounded thematic PBW/PZD composite performance YTD 121120 (+162.4%/+40.2%) is ranged by constituent dispersion of +1,364.5% and -54.3%, 2) tech benchmark QQQ performance (+42.7%) is bested by niche proxies TAN (+172.1%), LIT (+98.7%), FAN (+44.3%) and CARZ (+46.5%), and 3) with the exception of MTUM (+25.8%), SPY (+15.5%) outperformed Smart Beta (PRF, PRFZ, EUSA) and factor-based (LRGF, SMLF, VLUE, QUAL, SIZE, USMV) strategies though trend is reversed on a recent one- and three-month basis (absent USMV).

Alternative Energy Subindustry Benchmark ETFs PBW/PZD are publicly-sourced to create a Renewables/Diversified Industrials/Technology sector-themed overlay based on component member business segment operations (10 segments, 55 classifications, 76 single and multi-listed component members). Proprietary research suggests empirical past-positive forward looking variance inversions of portfolio position weights versus market capitalization indicate directional valuations in select companies.

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Appendix:

To advance analysis, needed is a reconciliation of specific business segment operations among representative component members apart from the general sector designations presented in standard fund reporting.

At Venn’s intersection, sets and subsets of competing interests endure. Proprietary research suggests its dynamic principle in relation to business segment operations is three-fold: 1) multinational and Large-cap companies function as benchmark sector/industry/subindustry proxies based on scale and business segment operations, 2) Small- and Mid-cap companies compete as peers and are delineated by business segment operations and 3) among subsets of 1) and 2) are vendors provisioning multiple economic sectors, asset classes and geographies. Successful trading strategies (systematic, momentum, thematic) isolate Value in Growth by not overlooking the prospective Alpha drivers directly associated with ecosystem and supply chain verticals or profiles of Small- and Mid-cap companies functioning as competitive peers and, hence, acquisition candidates.

From a portfolio management perspective, designing strategies based on business segment operations lends the advantage of iterative index applications by exploiting the inefficiencies in third party data nomenclature assignments which inevitability skew peer group analytics and valuation. While means and methods vary of course, a latticed framework—gleaned from competitive market information, built by segments/classifications, interpolated for integrity—exhibits the proportionality revealed by business segment operation considerations and distinguishes differentiated growth rates beyond simple revenue line aggregation. Additionally, a developed thesis for security selection in the Energy complex incorporates: 1) a barbell to CAPE as an extrapolation, 2) a barbell of Cleantech to Diversified Industrials as a foundation and 3) a barbell of Value to Growth implicit in corporate anatomy.

Common portfolio position weight allocations (0.5% < x < 4.0%) may be aligned consistent with long/short peak-to-trough cyclical/counter-cyclical exposures and emerging technologies among individual and multi-listed component members. Importantly, often discarded negative PE companies are included to capture points of inflection for cash flow growth and forward earnings momentum. In the end, a structure of analysis in the Energy complex is borne from the examination of business segment operations within diverse companies across economic sectors and asset classes plus, on a standalone basis, competitive peers—by definition, Alpha is singular.

Forward looking statements, estimates and certain information contained herein are based upon proprietary and non-proprietary research and other sources. Information contained herein has been obtained from sources believed to be reliable but are not assured as to accuracy. Past performance is not indicative of future results. There is neither representation nor warranty as to the current accuracy of, nor liability for, decisions based on such information. This content is distributed for informational purposes only and should not be considered as investing advice or a recommendation of any particular security, strategy or investment product. The author's opinions are subject to change without notice. No part of this product, related articles, publications or web-based content may be reproduced in any form or referred to in any other publication without express written permission of Universal Orbit © 2020 and David B. Kleinberg.

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:

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:

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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<U/O> Universal Orbit © 2020

U/O Matrix via Applied Indexation – Establishing Predictive Value 2019

U/O Matrix via Applied Indexation – Establishing Predictive Value 2019 exercise demonstrates the capability to confer the directional value of select securities utilizing benchmark applications of peer group analytics and valuation.

Exhibited in this Establishing Predictive Value series is 1) the importance of both standardized specific and descriptive nomenclature in fundamental benchmark index component member assignments, 2) period outperformance based on differentiated growth rates delineated by business segment operations (BSOs) within proprietary segment verticals (primary/secondary/tertiary classifications as determined), 3) capacity to profile M&A employing common BSOs and 4) development of portfolio strategies including clinical applications for long/short active/passive managers.

In a combined Renewables/Diversified Industrials/Technology sector-themed overlay based on publicly-sourced Alternative Energy Subindustry Benchmark ETFs PBW/PZD, aggregated are top and bottom constituent equity performers (YTD as of 112919) displayed by refined sector/industry/subindustry nomenclature (BSOs) in tandem with a five-period sequential quarterly data set of forward-looking inverse indicators derived from variances between respective market capitalization and position weightings across proprietary segments (Wind, Solar, Fuel Cells, Smart Grid, Water, LED, Biofuel, Automotive, Natural Gas) and within segment verticals (53 classifications, including 184 single and multi-listed component members).

Interesting to note during general examination of niche ETF strategies (thematic, Smart Beta and factor-based) against conventional benchmarks, actual historic performance suggests only an intermittency of outperformance amidst observed economic cycles and subcycles. However thematic capture, coupled with a fundamental bias towards Smart Beta and factor-based attributes (plus period recognition), lends opportunity as a tactical and strategic complement to equity and corporate credit portfolio strategies (Alpha, Alpha-Beta). The robustness of generated data patterns below corresponds to absolute performance dispersion, arithmetic to mean proxy total returns (scroll down and right to center).


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      #AppliedIndexation  #ETFs  #BSOs
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Appendix:

To advance analysis, needed is a reconciliation of specific business segment operations among representative component members apart from the general sector designations presented in standard fund reporting.

At Venn’s intersection, sets and subsets of competing interests endure. Proprietary research suggests its dynamic principle in relation to business segment operations is three-fold: 1) multinational and Large-cap companies function as benchmark sector/industry/subindustry proxies based on scale and business segment operations, 2) Small- and Mid-cap companies compete as peers and are delineated by business segment operations and 3) among subsets of 1) and 2) are vendors provisioning multiple economic sectors, asset classes and geographies. Successful trading strategies (systematic, momentum, thematic) isolate Value in Growth by not overlooking the prospective Alpha drivers directly associated with ecosystem and supply chain verticals or profiles of Small- and Mid-cap companies functioning as competitive peers and, hence, acquisition candidates.

From a portfolio management perspective, designing strategies based on business segment operations lends the advantage of iterative index applications by exploiting the inefficiencies in third party data nomenclature assignments which inevitability skew peer group analytics and valuation. While means and methods vary of course, a latticed framework—gleaned from competitive market information, built by segments/classifications, interpolated for integrity—exhibits the proportionality revealed by business segment operation considerations and distinguishes differentiated growth rates beyond simple revenue line aggregation. Additionally, a developed thesis for security selection in the Energy complex incorporates: 1) a barbell to CAPE as an extrapolation, 2) a barbell of Cleantech to Diversified Industrials as a foundation and 3) a barbell of Value to Growth implicit in corporate anatomy.

Common portfolio position weight allocations (0.5% < x < 4.0%) may be aligned consistent with long/short peak-to-trough cyclical/counter-cyclical exposures and emerging technologies among individual and multi-listed component members. Importantly, often discarded negative PE companies are included to capture points of inflection for cash flow growth and forward earnings momentum. In the end, a structure of analysis in the Energy complex is borne from the examination of business segment operations within diverse companies across economic sectors and asset classes plus, on a standalone basis, competitive peers—by definition, Alpha is singular.

​

Forward looking statements, estimates and certain information contained herein are based upon proprietary and non-proprietary research and other sources. Information contained herein has been obtained from sources believed to be reliable but are not assured as to accuracy. Past performance is not indicative of future results. There is neither representation nor warranty as to the current accuracy of, nor liability for, decisions based on such information. This content is distributed for informational purposes only and should not be considered as investing advice or a recommendation of any particular security, strategy or investment product. The author's opinions are subject to change without notice. No part of this product, related articles, publications or web-based content may be reproduced in any form or referred to in any other publication without express written permission of Universal Orbit © 2019 and David B. Kleinberg.

U/O Matrix - Establishing Predictive Value (113018)

Both Alpha and Beta portfolio strategies endeavored for outperformance may be readily accomplished by the development of unconstrained Alpha-Beta quantitative screens via competitive peer group business segment operations (BSOs) based on public policy, capital investment, innovation and scale.

Here's just one example how demonstrated in the recent <U/O> Matrix - Establishing Predictive Value exercise as cross-sector/industry/asset class equity exposure to BSOs of standalone Renewables, Diversified Industrials and Technology companies contrast Alternative Energy benchmark proxy ETFs and conventional performance standards.

Please note range variability of returns (message direct for model construction considerations):

         #renewableenergy  #analytics  #ETFs  #BSOs

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Forward looking statements, estimates and certain information contained herein are based upon proprietary and non-proprietary research and other sources. Information contained herein has been obtained from sources believed to be reliable but are not assured as to accuracy. Past performance is not indicative of future results. There is neither representation nor warranty as to the current accuracy of, nor liability for, decisions based on such information. This content is distributed for informational purposes only and should not be considered as investing advice or a recommendation of any particular security, strategy or investment product. The author's opinions are subject to change without notice. No part of this product, related articles, publications or web-based content may be reproduced in any form or referred to in any other publication without express written permission of Universal Orbit © 2018 and David B. Kleinberg.

The Tangential Path to Alpha

Renewables evolution within the Energy complex represents boom-to-bust then boom again scenario sequencing consistent with subindustry core emergence.  Patterned development over the past ten years is indicative of hard forged self-reliance evidenced by direct corporate ownership of wind farms and distributed solar generation located atop major retail stores, both far from government sponsorship of select alternative energy companies at above market rates.  Today, climate change skeptics face the certainty of value-based cultural movements.

Survey of US Department of Energy and International Energy Agency publications in this context detail electric generation capacity parameters (Oil&Gas, Coal, Nuclear, Renewables) and power trend growth rates effecting companies populating each Energy producing segment and classification.  Current period demand-side Energy equation power consumption dynamics converge to shape investment opportunities among supply-side Energy equation power capacity providers and requisite ecosystems across economic sectors and within industry verticals.  Corporate-sponsored Green programs place Apple, Google, Target, Walmart and similar ethos companies at the forefront of US Renewables policy alongside federal tax incentives, state mandates, municipalities and utility companies.  Here at the intersection of Renewables, Infrastructure and Environmental, Social and Governance programs lies the tangential path to Alpha.

On a Beta-relative basis, populist trends and momentum strategies in tandem long offered positive realizations yet while macro and sector bets via active management or ETF allocations are relatively straightforward, thematic- and niche-Beta strategies leave Alpha-driving issues such as constituency breadth and corporate profile maskings unreconciled.  Product development and newly crafted thematic indexes frequently compound errors by misaligning comparables, increasing susceptibility to performance drags relative to benchmark asset class indexes.  Counter-thesis positioning and comparable assignment inaccuracies (e.g. derivatives exposure against long-only benchmarks, government comps for corporate bonds) further hamper attempts for transparency and cloud opportunity costs.  Series development of independent benchmark portfolio overlays enhance performance attribution and tests the veracity of fund company marketing campaigns.

Index applications addressing limitations in allocated Beta strategies for integrated Alpha efforts necessitate a realignment of teamed analytical data sets.  Quantitative exercises to isolate Value in Growth companies (and Growth in Value) begin by deconstructing relevant Large-Cap companies into modular corporate business segment operations (BSOs), aligning revenue drivers with each other and those from Small- and Mid-Cap competitive peers.  Replacing common sector/industry/subindustry index designations with descriptive BSOs is a deliberate first step Beta-capture to an eventual Alpha-screened second cluster.  BSOs universally reflect directly the financial impacts of planned product cycle/subcycle positioning and avoid a broad array of component members recurrent in passive indexes, structured thematic portfolios and active index-plus strategies.

Benchmark proxy ETF composite reporting, typically grouped into five generic industries or nondescript third party categorizations, is often inconsistent with middle-down allocated Beta strategies on a relative basis or in standard nine-grid style/size box format.  Due diligence at this point creates an opportunity to reconstruct benchmark proxy ETF attributes from the bottom-up, realizing the value-add of BSOs placement into segment/classification industry verticals.  BSOs improve situational awareness by pairing forward-looking valuation analytics (i.e. variance of constituency portfolio weights to market capitalization per segment/classification) with competitive market information (functional peers, acquisition candidates) at defined points of inflection.

Renewables, Infrastructure and ESG benchmark proxy ETF audits reveal several useful though perhaps counterintuitive facts: 1) Renewables ETFs include Diversified Industrial and Technology companies, 2) Infrastructure ETFs include Coal and Nuclear power generating utility companies and 3) ESG ETFs include Oil&Gas companies.  These seeming contradictions are not unpredictable given ESG fund ratings criterion balancing equal considerations of corporate stewardship and a net carbon posture, after all Earth naturally provides resources for human evolution.

In transition from Energy policy whitepapers to investment strategies, classical portfolio configurations [(Alpha)+(Alpha+Beta)+(Beta+(Beta))] are advantaged by evaluation of BSOs (Alpha-Beta) portfolio position weight variances per segment/classification in efforts to isolate differentiated growth rates and, importantly, slope among representative companies (Alpha).  The Energy equation and potential at Alpha’s intersection are replicated below utilizing benchmark proxy ETFs performance results in comparison to major index ETFs, capitalization-based Growth and Value standards plus fundamentally weighted Smart Beta.  Proprietary research suggests BSOs liaise between portfolio themes and the total returns demonstrated by component members ranged performance:

In our example, Renewables BSOs populate the Energy equation’s supply-side based on an economic representation of public-private partnerships, mandated programs and corporate investment.  Forward-looking research embedded in actively managed Renewables benchmark proxies PBW/PZD incorporate projected changes in the levelized cost of energy, Energy equation weights, macro price drivers and emerging technologies among other catalysts of valuation and diminishes the usefulness of past-positive (historic) data in static benchmarks.  PBW 12-month constituency performance dispersion (PBW: +620.2%, -73.2% versus PZD: +145.9%, -16.4%) illustrates the benefit of Small- and Mid-Cap biases in Growth-orientated themes (PBW: Small-Cap 46.2%, Mid-Cap 33.1%, Large-Cap 20.7% v. PZD: Small-Cap 7.0%, Mid-Cap 31.6%, Large-Cap 61.4%; composite data as of 033118).

Contrasting persistent Large-Cap dominance of industry verticals, BSOs Negative Inversion differentials are empirical to Small- and Mid-Cap companies and serve as the building blocks of layered Alpha screens.  PBW Top 5 component member period and niche-cycle Alpha outperformers (Solar, LED) are framed by applying BSOs nomenclature (Solar—Components x 2, Solar—Systems, Solar—Wafers, LED—Components) to existing Morningstar designations (Semiconductor Equipment & Materials x 2, Solar x 2, Semiconductors).  PZD Top 5 component members included two of the Top 5 from PBW, one discretionary omission (N/A) and one of two from the same two BSOs segment/classification as carbon fiber suppliers (Wind—Components/Composites, Auto—Components/Composites) based on capitalization (Small-Cap over Large-Cap in an upcycle scenario) iteratively from Morningstar assignments (Diversified Industrials, Aerospace & Defense).

In the Energy complex, protocoled obliques focus attention on areas of profit/loss isolating Value in Growth companies (Renewables) and Growth in Value (Oil&Gas, Coal) and asserts its implications on a fundamental operating basis.  Ascribed singular and multi-dimensional BSOs ordinal tierings (primary/secondary/tertiary) distinguish between Beta trends and Alpha drivers by producing cyclical/subcyclical/countercyclical directional valuation markers, the long and short of the matter.  Perhaps inadvertently, factor tilts and Smart Beta adherents lend to the premise of BSOs in more granular form and the prospects of qualitative assessments (Alpha) outperforming big data applications (Beta).  Far from zero sum, capital market inefficiencies and mean reversions lend opportunity in research . . . as stated ‘correlation is not causation'.





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As the Earth Breathes

Three years to save Earth according to a group of United Nations scientists.  Can 150 years of planetary abuse undo over four billion years of evolution?  As the Earth breathes even climate neutralists concede probability may be at least a 51/49 percent proposition.

Global consensus reached during Paris climate accord talks demonstrates as much despite political challenges since the recent US election.  Apolitically, senior military personnel attest to the prospect of climate change remaining a significant national security risk for the foreseeable future.  Today contention persists due in large part to wariness of Western apologists seeking to redistribute wealth and reengineer cultures at perceived disadvantages.  Equally significant issues such as equity in absolute carbon emission volume measures and fairness in competition act to temper political arguments, offering economic transparency during environmental debates.

Observations in the International Energy Agency 2017 World Energy Investment whitepaper note flat levels of spending in alternative and conventional energy technology in recent years, the former driven by public spending and latter by private investment in research and development although current statuses are characterized by overcapacity and oversupply.  Given the range in governing energy equations, not surprisingly natural resource allocations (coal, natural gas, oil) coexist with renewables (wind, solar, hydropower) and science (nuclear).  Innovation in technology continuously reduces the levelized cost of energy and resets the supply/demand dynamics of conventional and renewables composition in energy equations, driving competitive marginal unit cost translations and renewables scale within the overall balance of equation weights.

Survey of government policies and corporate strategies reinforce the premise of a one energy equation not a series of separate or nascent policies.  Realistically (and importantly) fossil fuels remain a stalwart of energy generation capacity and consumption, acting as vital bridge fuel sources in evolving national policies (US, Germany, Japan, China) ceding its allocations only by renewables referendum but increasingly on a kilowatt basis.  Implications for investment strategies reveal populist and unpopular themes spanning the breadth and depth of composite energy portfolios.  In relevant macro- and sector-based momentum trend (CAPE) and thematic (Cleantech) Beta portfolio strategies, sentiment captures varying degrees of directional equity price movement.  However, with limited or indiscriminate attribution, a more deliberate approach to portfolio construction is required.

Data sets derived from publicly-sourced ETFs work well across the energy equation as benchmarks, managing the performance drivers of its investment universe.  For instance, energy policy portfolio allocations (Oil&Gas – XLE, Coal – KOL, Nuclear – NLR, Renewables – PBW) serve as a basis for industry vertical dropdowns on the equation’s two ends from Oil&Gas (Sector – XLE, Exploration & Production – XOP, Oilfield Services – OIH, Refiners – CRAK, Shale – FRAK) to Renewables (Sector – PBW and PZD, Wind – FAN, Solar – TAN, Smart Grid – GRID, Lithium – LIT, Water – FIW) and advances the utilization of sector/industry/subindustry/vertical/thematic ETF proxies as fundamental indexes for derivative applications.  Illustrating sovereign energy posture in this manner begins to support management of its tenets and assessment of data and policy trends on capital markets including corporate catalysts and balance sheet transactions.  To advance analysis, needed still is a reconciliation of specific business segment operations among representative component members apart from the general sector designations presented in standard fund reporting.

In just one example, business segments operations of widely accepted bellwether Tesla populate many ecosystems (autos, fuel cells, lithium, battery storage, solar systems...let alone space!) all with differing capital requirements and levels of profitability.  Conglomerates also display a pronounced but topical view of the business segment operations schema.  General Electric and Siemens, two competing multinational corporations, reflect distinct aspects of the one energy equation by encompassing Oil&Gas and Wind along with traditional power generation and energy efficiency business lines.  These business segment operations challenge each other as well as leading industry and independent niche players of all sizes (Large-, Mid-, Small-cap) in segments globally.  Recent divestitures of product lines in both companies, and the addition of others, highlight the importance of assessing differentiated growth rates and demands on capital structure within an organization and among competitive peers.  Intuitively, evaluation of corporate performance is readily accomplished at the operational level.

At Venn’s intersection, sets and subsets of competing interests endure.  Proprietary research suggests its dynamic principle in relation to business segment operations is three-fold: 1) multinational and Large-cap companies function as benchmark sector/industry/subindustry proxies based on scale and business segment operations, 2) Small- and Mid-cap companies compete as peers and are delineated by business segment operations and 3) among subsets of 1) and 2) are vendors provisioning multiple economic sectors, asset classes and geographies.  Successful trading strategies (systematic, momentum, thematic) isolate value in Growth by not overlooking the prospective Alpha drivers directly associated with ecosystem and supply chain verticals or profiles of Small- and Mid-cap companies functioning as competitive peers and, hence, acquisition candidates.

From a portfolio management perspective, designing strategies based on business segment operations lends the advantage of iterative index applications by exploiting the inefficiencies in third party data nomenclature assignments which inevitability skew peer group analytics and valuation.  While means and methods vary of course, a latticed framework—gleaned from competitive market information, built by segments/classifications, interpolated for integrity—exhibits the proportionality revealed by business segment operation considerations and distinguishes differentiated growth rates beyond simple revenue line aggregation.  Additionally, a developed thesis for security selection in the Energy complex incorporates: 1) a barbell to CAPE as an extrapolation, 2) a barbell of Cleantech to diversified industrials as a foundation and 3) a barbell of Value to Growth implicit in corporate anatomy.

Common portfolio position weight allocations (0.5% < x < 4.0%) may be aligned consistent with long/short peak-to-trough cyclical/counter-cyclical exposures and emerging technologies among individual and multi-listed component members.  Importantly, often discarded negative P/E companies are included to capture points of inflection for cash flow growth and forward earnings momentum.  In the end, a structure of analysis in the Energy complex is borne from the examination of business segment operations within diverse companies across economic sectors plus, on a standalone basis, competitive peers—by definition, Alpha is singular.

…fortunately for Earth, data trends reported by the IEA indicate a decoupling of worldwide carbon levels from the pace of economic activity.  So can we now then just wait for natural gas to permeate throughout energy equations, plant more trees, and lever cellphones and the Internet of Things for productivity gains to ensure posterity?  Likely not as the agreement promoted by Paris signatories provides for significantly disparate country carbon intensities over an extended time horizon.  Success of institutionalized agendas nonetheless is often best evaluated by the profitability of corporations engaged and are a direct result of policy scalability, public fortitude and company-specific operational rationality.  Green progress in black and white.




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No part of this article, related publications or web-based content may be reproduced in any form or referred to in any other publication without express written permission of Universal Orbit © 2017 and David B. Kleinberg. Forward looking statements, estimates and certain information contained herein are based upon proprietary and non-proprietary research and other sources. Information contained herein has been obtained from sources believed to be reliable but are not assured as to accuracy. Past performance is not indicative of future results. There is neither representation nor warranty as to the current accuracy of, nor liability for, decisions based on such information. This content is distributed for informational purposes only and should not be considered as investing advice or a recommendation of any particular security, strategy or investment product. The author's opinions are subject to change without notice.

Published research serves as both a quantitative reference and instructional resource in addition to a template for client services.  Metrics may be modified to accommodate the universe of sector, industry and subindustry research.  For assistance implementing the aforementioned concepts within financial/economic models or investment research process, please note three levels of consultation: Qualitative – Support, Quantitative – Directed and Quantitative – Fundamental. 

Can Smart Beta think (twice)?

Forward looking analytics embedded in recent introductions of multi-factor Smart Beta ETF products demonstrate causality among its components and an ability to replicate past future pricing.  Unclear is the extent to which Smart Beta indexes and related funds adequately discern the directional value of securities in aggregate at points of inflection to consistently outperform allocated Beta, Index-plus or Alpha portfolio strategies.

Certainly in capital market rally and recovery modes dynamic flat-weighted portfolio structures merit consideration for baseline benchmark index replacement.  In this context however, as Small-Cap Growth and Mid-Cap Value asset classes are often alternating performance leaders, distinct valuation drivers typically reveal the limitations of Smart Beta’s construct.  Performance dispersion among investment alternatives underscores the lag effects of set period-defined rebalancing, economic sector rotations and resultant fluidity within the traditional nine grid capitalization style box.

In initial review representative FTSE RAFI fundamental indexes, Smart Beta benchmarks plus corresponding Standard & Poor's and Russell indexes are detailed on a YTD, 12-month and 3/5/10 year basis:

Misleading perhaps in examination is Smart Beta's premise of interoperability in replacing benchmark indexes, sector/subsector proxies and conventional portfolio strategies as baseline comparables.  The error in assignment is demonstrated by Smart Beta's inability to consistently outperform standard benchmarks in its own zero sum environment, that Alpha is Beta in market aggregation.  Advocating alternatives to market capitalization-based industry standards (both style and size) without consistently discernable value-adds fails to significantly relieve portfolio manager and analyst responsibilities to monitor, evaluate and quantify changes in valuation over corresponding periods or alter the decision making process requisite in modifying portfolio weight allocations based on expected market conditions.

In the example below, 14 model portfolios are constructed to illustrate Smart Beta fundamental index (FTSE RAFI US 1000 and 1500), multi-factor (Small-, Mid- and Large-Cap) and single factor (Quality, Size, Value, Minimum Volatility, Momentum) performance against more traditionally constructed portfolio strategies from an investment universe of over 30 index, benchmark, sector and niche proxies (broad market, capitalization-based, finance, technology, binary-thematic, Alpha).  ETF proxies are utilized for baseline benchmark total return figures and represent performance differentials.  Portfolio Models_ 1-4, 7-14 reflect performance at date intervals and allocated weights in determined management strategies away from a market neutral posture:

Models_1-14 are ranked first (blue), second (dark green) and third (green) at period dates with representative indexes and proxy benchmarks ranked similarly and distinguished from peers by noted period performance equal to or greater than the third ranked Model portfolio (grey).  New introductions of multi-factor Smart Beta funds SMLF/EUSA/LRGF limit performance comparables (3- and 5-year) at period-end 042817 though a longer view extrapolation of 12-month performance of PowerShares FTSE RAFI US 1500 Sm-Mid ETF (22.5%), iShares Edge MSCI Multifactor USA Sm-Cap (22.7%) and iShares Edge MSCI USA Value Factor (21.1%) begins to support Smart Beta design attributes.  Single factor credence is bolstered from 3-year markers evidenced by iShares Edge MSCI USA Momentum Factor (13.8%), iShares Edge MSCI Minimum Volatility USA (12.1%) and iShares Edge MSCI USA Size Factor (10.9%) relative to peer alternatives.

Significantly, of the 13 model portfolios presented on a 3-year basis, Model_6 (composite portfolio derived equal weight single factor Smart Beta USMV/MTUM/QUAL/SIZE/VLUE) outperformed all other market capitalization weighted portfolios (11.2%; n=12).  In survey of occurrences on a 2017 YTD and 12-month standalone basis Smart Beta proxy ETFs met or exceeded third position model placement or greater on only three occasions (from 20 opportunities, 15.0%) compared to 28.6% for iShares Russell Index ETFs (n=14) and 11.1% for the iShares Morningstar J_capitalization suite (n=18).  Expanding data points to include 3- and 5-year performance results, Smart Beta bested its peers (18.8%; n=32 > 17.9%; n=28 > 13.9%; n=36, respectively) highlighted by iShares Edge MSCI USA Momentum Factor second seed placement (13.8%) over the 3-year total return period versus proxy ETF performances (XLK 16.4%).

Alpha-Beta strategies are incorporated in modeled results (JKI/JKK, XLF/XLK, XLK/LIT, PBW/LIT, IWD/IWF, IWN/IWO, TSLA) to represent common passive and combined active/passive portfolio management strategies.  Positioning in emerging technologies (XLK), nascent policies (PBW) and niche markets (LIT) serves to demonstrate Small- and select Mid-Cap company effects on a price basis more observable than conventional drivers of valuation in broad index and asset class-based strategies.  Paired themes of technological innovation and renewables exhibit policy adoption to capital investment in parallel scenarios (XLK>LIT>TSLA, PBW>LIT>TSLA) and include less granular sector exposures for economic growth (XLF), long-term trends (XLK) and subsequent catalysts for valuation changes based on fund flows (I_series, J_series, single factor Smart Beta).

From our modeled example, TLSA 12-fold rise in valuation over the past five years is captured tangentially within structured business segment operations (BSOs) industry segment verticals (Fuel Cells – Lithium, Automotive – Manufacturers) on a proportional portfolio weighted classification basis from then a Mid-Cap company (<$4BN) to its industry leading positions today (>$50BN).  Linking relevant company BSOs within an investment universe across capitalization, asset classes, economic sectors and geography in a latticed approach provides a ready matrix to advance Alpha’s generation. 

In the proceeding correlation table (thru 1Q17) measured performance period wins among the five strongest levels of correlations PRF/IWD (0.988), PRFZ/IWN (0.980), PRF/SPY (0.978), SPY/QUAL (0.978) and IWB/QUAL (0.978) predictably alternate though potential Alpha wedges (1.000 minus correlation level) are demonstrated by skewness of Small-Cap returns relative to broad market and competing asset classes.  Notably when contrasting fundamental, equal and capitalization weighted structures embedded in Smart Beta products (SMLF/EUSA/LRGF, USMV/MTUM/QUAL/SIZE/VLUE) idiosyncratic risk drives variance of Small-Cap returns from mean levels.  For companies uncorrelated to empirical Smart Beta metrics and yet to be fully captured by standard GAAP quotients, the singularity of one or several corporate profiles aligned with its functional peers (BSOs) provides for active management of Smart Beta's tenets (balance sheet attributes, technical indicators) and enables Alpha generation beyond algorithmic Alpha-Beta allocations via an exercise in peer group analytics and valuation:

Bounded by opportunity and risk, dispersions of multi-factor Small-Cap SMLF correlations range from 0.628/0.591/0.590 (market weighted Small-Cap Composite/Value/Growth) to 0.405/0.419/0.430 (thematic LIT/ tech sector proxy XLK/thematic PBW).  At 12 performance intervals for the 12 months ending 042817 thematic and sector proxies outperformed 11 times (91.7%) intimating Alpha's prospective effect in Go markets given persistent market inefficiencies in valuation of smaller capitalized companies.

Clearly sequenced over period segments, disparate performance variance from mean measured peer alternatives seemingly overshadows the feasibility of Smart Beta's past forward predictive patterning and statistically engineered ecosystem for baseline benchmark replacement.  For example, Brexit’s initial sell-off demonstrated event risk and subsequent broad-based capital market recovery underscored a soaring 40% increase in correlated effects of SMLF (price change 062316-110816: +2.9%) to PBW (-3.7%)/LIT (-5.2%)/XLK (+7.8%) pairs (0.672/0.645/0.712) and macro impact on I_series IWM (+1.8%)/IWN (+3.0%)/IWO (+0.6%) levels (to 0.822/0.827/0.810).  Sequentially, the Value trade continues to build after the US election (price change 110916-123016: IWN +13.1%, JKL +10.3%) apart and in parallel to SMLF (+10.1%, 0.869/0.840/0.876) anticipating tax and regulatory reforms in far greater effect than PBW (+2.9%)/LIT (+2.4%)/XLK (+2.1%) growth-oriented strategies (0.448/0.116/0.317) from Paris climate accords and Silicon Valley incubators.

Change is once again evident in 2017 YTD performance along flight-to-quality scenarios (JKE), reinforcement of secular trends (XLK) and continued strata of thematic investing (LIT, PBW).  Absolute ranged returns of component members from PBW (+103.6%, -46.7%), LIT (+45.5%,-22.5%) and XLK (+55.6%, -32.2%) advantages portfolio managers taking uncorrelated Alpha positions thru cycles, macro driven or tiered-specific corporate catalysts:

Borne from relative valuation, outperformance persists as the virtue of capital market endeavors and is the dominant measure in all implied institutional investment scenarios.  Proprietary research suggests outperformance is unsustainable if one forgoes fundamental company-specific financial analysis and qualitative assessments in favor of a statistically-based holistic investment approach.  To an extent pragmatic and another defeatist, Smart Beta pioneers suggest pursuit of Alpha is moot (all prospective gains lost in aggregation) though measured reallocation of resources based on Smart Beta factor rationale is supported by its stated importance of relative valuation—adjusting to current cycles and subcycles within greater cyclicalities.

Examination of Smart Beta’s premise in both single and multi-factor formats against model portfolios naturally provides a comparative dashboard to monitor capital market trends given shared variables inherent in competitive market information, strategy effectiveness and opportunity costs.  However, as data populates its empirical trendline towards forward period adjustments, Smart Beta is challenged to think again in order to capture the catalysts of change among externalities and invention.





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No part of this article, related publications or web-based content may be reproduced in any form or referred to in any other publication without express written permission of Universal Orbit © 2017 and David B. Kleinberg. Forward looking statements, estimates and certain information contained herein are based upon proprietary and non-proprietary research and other sources. Information contained herein has been obtained from sources believed to be reliable but are not assured as to accuracy. Past performance is not indicative of future results. There is neither representation nor warranty as to the current accuracy of, nor liability for, decisions based on such information. This content is distributed for informational purposes only and should not be considered as investing advice or a recommendation of any particular security, strategy or investment product. The author's opinions are subject to change without notice.

Published research serves as both a quantitative reference and instructional resource in addition to a template for client services.  Metrics may be modified to accommodate the universe of sector, industry and subindustry research.  For assistance implementing the aforementioned concepts within financial/economic models or investment research process, please note three levels of consultation: Qualitative – Support, Quantitative – Directed and Quantitative – Fundamental.