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.
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 your financial/economic models
or investment research process, please note three levels of consultation:
Qualitative – Support, Quantitative – Directed and Quantitative – Fundamental.
.png){kind=link}
No comments:
Post a Comment