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Peer Group Analytics and Valuation, an Abstraction






Peer group analytics and valuation are essential components when assessing the optimal risk-return equation.  Historically, Modern Portfolio Theory (Markowitz; 1952, 2009) is encompassed in a broad four-step outline:  1) security valuation, 2) asset allocation decision, 3) portfolio optimization and 4) performance measurement (Barron’s 1991).  However, as opposed to an efficient frontier populated with the regressed correlated expected future returns of conventional securities or asset classes perhaps one determined by business segment operations is more advantageous.


Designed as a complement to quantitative portfolio strategies and fundamental research, efforts to support Alpha are structured initially to separate then aggregate company-specific business operating segments as an offset to prescribed Sector/Industry/Subindustry index structures.  Deviating from a conventional market neutral posture in active portfolio strategies does not create a structural bias in portfolio management any more than overweighting a particular industry based on a portfolio manager’s expectation of attributable risk adjusted excess returns.  An assimilation of general themes requisite for adjustments to benchmark presentation does not exclude a deference to statistical methods in order to temper the variability of data integrity.  Data integrity is examined within the parameters of data accuracy, impact on fair value and assessment of benchmark relevance.


In technical analysis, smoothed lines and fitted curves often mask the variability of data evident in diverse economic activities and differentiated business operating segment growth rates—the cyclicalities and subcyclicalities across sectors and within industries.  Performance attribution, portfolio construction and security selection are reflective of due diligence plus a fundamental posture relative to quantitative analytics.  Left to reconcile is the modularity of data provided by third party vendors and related product development with the goal, ultimately, a desired apples-to-apples comparison frequently obscured by institutionalized nomenclature . . . and the performance differential is measurable.


In the overlay chart below, two Alternative Energy Subindustry Benchmarks (PBW—PowerShares WilderHill Clean Energy Portfolio and PZD—PowerShares Cleantech Portfolio) are shown with the broad US equity market (SPY—SPDR S&P 500 ETF Trust) and a Small-cap standard (JKK—iShares Morningstar Small-Cap Growth ETF).  Evident are predictable combinations of outperformance, underperformance, reversion to the mean and a reacceleration of themes:

On one level, simple allocation to a particular asset class is the major driver in overall portfolio performance; on another, refined benchmark methodology for use across economic sectors and asset classes can readily identify business segment operations of component members engaged in emerging technologies with differentiated capitalization levels without exclusion due to the finite nature of indexation.  Once a relevant benchmark is established, deconstruction of its elements may begin to detail the quantitative characteristics of its component members.  Refining the structure of an assigned benchmark in accordance with a standardized process (i.e., Applied Indexation) establishes relativity and a basis of comparison for further computations.


In 2011, Beyond Alternative Energy detailed the representation of the Alternative Energy Subindustry among 18 Industry Groups based on MSCI and Standard & Poor’s Global Industry Classification Standard.  An evolution in the characterization of both the horizontal and vertical integration of segments/classifications within traditional benchmarks offers the capability to quantify both product life cycle and supply chain management along with corresponding applications associated with an identification of accelerating or deteriorating industry and company-specific fundamentals (e.g., capital expenditures, solvency, liquidity and M&A).  For example, proprietary methodology includes the Industry Segments Wind, Solar, Fuel Cells, Smart Grid, Water, LCD, Geothermal, Biofuels, Automotive and Natural Gas composed of a further 67 Classifications (verticals); its construct may be replicated to accommodate the diversity of economic behavior across sectors, asset classes and geography.


Intuitively, valuation necessitates specific business segment rationalizations and related metrics.  By managing concurrent cycles, change in a particular industry can be characterized and anticipated.  The execution of a strategy which captures the attributes of an assigned benchmark, transcends industries and fulfills requisite investment mandates efficiently creates a turnkey product adding velocity to the research process.  In the end, a baseline curve is enhanced by a ‘new frontier’ which provides relative value investors an opportunity to build more predictive financial models enabling more informed Buy/Sell/Hold decisions.

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