Our Technology:
Temporal Portfolio Theory TM
Temporal Portfolio Theory - Introduction
Modern Portfolio Theory (MPT)
is now over 65 years old and was developed long before computers were available for
analyzing daily market data. MPT's development was inherently
limited to long-term statistical analysis of market data and portfolios.
Without time domain (temporal) data analysis, there could be no daily trend/momentum
data analysis; limiting MPT to simplistic buy-and-hold diversification
models. It's time to extend MPT's
framework to embrace time domain data (momentum) and
the many signal processing technologies that have since been developed,
perfected and successfully deployed for Ethernet, WIFI, seismic sensors and
image processing. It has long been proven by both
academic studies and
industry studies alike that market data contains trend information that
can improve the probability of making better investment choices.
Temporal Portfolio Theory extends MPT by integrating the
cross-disciplinary sciences of
Matched Filter Theory,
Differential Signal Processing,
Fuzzy Logic, and
Holistic Risk
Management within a layered Portfolio-of-Strategies
framework to measurably improve both risk and return performance. The
primary components of Temporal Portfolio Theory are outlined below.
This is not your grand dad's portfolio theory!
True Sector Rotation®
True Sector Rotation is the method of owning the one,
and only one, best trending fund at any
time. It's quite distinct from the half-hearted partial
over- or under-weighting of multiple sectors practiced by
fund managers. The trend leader is in fact the best bet
and True Sector Rotation treats it as such. Economic cycles drive market
cycles, and individual market sectors
generally perform best during a
particular phase of the economic cycle. If market sectors
were like pistons in an investment engine,
then the smoothest and most powerful ride would be achieved when each sector
is owned only when it is delivering its power stroke.
AlphaDroid is not a market timing system that
attempts to predict what market sectors
"should" be doing next month based on
cycles, patterns, or fundamental data.
Instead, AlphaDroid measures what market
sectors "are" already doing and selects the
one best trend leader to own from a set of
up to 12 candidate funds.
It is
only by owning the trend
leader and avoiding the laggards
that one can
simultaneously improve returns and reduce
risk.
StormGuardTM Market Crash Protection
While our True Sector
Rotation
algorithms inherently avoid
poorly performing funds, our StormGuard
algorithm monitors overall market health
and advises Strategies to move to safety during
a market storm. The StormGuard
Indicator will typically range in value from
-4% to +4%. Although you can select from
among six different algorithms for
down-market protection, our
StormGuard-Armor Indicator is the industry's
best performing market sentiment indicator
by far. It incorporates three distinct data
sources with 12 different measures to
ascertain whether the return prospects
out-weigh the risk conditions of the market.
It is
designed to perform a balanced optimization
to simultaneously reduce whip-saw losses resulting
from knee-jerk reactions to market dips by
not reacting too quickly, and to minimize the
crippling losses from long duration bear markets by not reacting too slowly.
StormGuard-Armor's value is calculated daily
Integrated Bear Market Strategies
When StormGuard (a market direction indicator) signals that market conditions have become
bearish,
a Bear Market Strategy automatically takes charge and selects from a list of
trusted safe harbor investments, such as cash,
money market funds, bond funds, gold bullion,
or US treasuries. While
numerous market direction and sentiment
indicators have been developed over the years,
but
none
of them come
close to the performance provided by
StormGuard-Armor. The remarkable difference between the
yellow equity curves in the three Strategy
charts below illustrate the combined importance
of
StormGuard-Armor and Bear Market Strategies.
The yellow
equity curve, compound annual growth rate (CAGR) and
Sharpe Ratio (risk-adjusted return) in each chart above
illustrate why Bear Market Strategies matter. All three
investment Strategies rely on the True Sector Rotation
algorithm to determine which one of the eight
candidate SPDR sector ETFs to own each month during bull
markets.
However, the left-most
Strategy has no market crash protection, the
center Strategy additionally utilizes
StormGuard-Armor to determine when to exit the market to
the safety of cash, and the right-most Strategy further
utilizes StormGuard-Armor with a Bear Market Strategy.
Strategy-of-Strategies
While an
AlphaDroid Strategy selects the trend leader from among a set of candidates
funds or stocks, a Strategy-of-Strategies (SOS) selects the trend leader
from among a set of candidates Strategies. Thus, one owns the best
performing fund of the best performing Strategy. The primary benefit of an
SOS is to make sure that if the strategy you are using falters for any
reason, the SOS will automatically switch you to an alternative Strategy
that has not faltered. For example, the Strategy you own could get a "flat
tire" for a while in the future because you didn't quite have the perfect
set of candidate funds. This can easily happen if you choose not to include a
particular asset class or sector fund as one of the candidates because it had not
performed well for a few years. No one can predict when political events or new
discoveries will suddenly change the prospects for a previously poor performing fund.
A Strategy-of-Strategies is a form of insurance designed to reduce
the risk of selection
bias. This Vanguard SOS demonstrates the principle.
Portfolio-of-Strategies
A
classic Portfolio consists of a set of allocation weighted funds where the
Portfolio owns a set portion of everything on the list. By definition, the
Portfolio performance will reflect a weighted average performance of the
Portfolio's funds. To improve performance of a classic Portfolio, the funds
in the Portfolio must be selected to have better performance
characteristics. A dynamic means of selecting funds could therefore improve
Portfolio performance. For example, utilizing a set of Strategies, each of
which selects its own trend leader will produce a Portfolio composed of
trend leaders. The return performance of the Portfolio-of-Strategies will be
the average of the returns or the selected trend leaders, and the volatility
risk for the Portfolio-of-Strategies will be lower than the average risk of
the funds to the degree that the funds are uncorrelated. Note the particularly high Sharpe ratio and smooth equity curve
for this Portfolio-of-Strategies holding six Fidelity fund Strategies.
Forward-Walk Progressive Tuning
The gold-standard for backtesting
performance of a predictive algorithm (for
markets, environment, sports, etc.) is the
Forward-Walk Progressive Tuning methodology. A first set of test data
tunes the algorithm's parameters
for subsequent use waking forward in time. After each
subsequent 6-month period of time the
algorithm is re-tuned for
walking forward through the next 6-month
period, thus eliminating hindsight bias in
testing. If performance is maintained during
the forward walk periods, then tuning did
discover a reliable character. To they degree
performance declines, it is because the
better performance path is actually
un-discoverable by the algorithm because
significant events were not reasonably
predictable. Forward-Walk Progressive Tuning
is an integral part of every AlphaDroid
Strategy.
Automated Polymorphic Momentum
Both Information and Detection Theory dictate that the probability of
making an excellent investment decision is directly proportional to the
signal-to-noise
ratio of the employed momentum indicator signal. While
Matched
Filter Theory actually specifies the momentum filter shape and
duration for optimum signal-to-noise ratio,
Differential Signal
Processing further eliminates common mode noise
from the decision process. The term Polymorphic indicates that
the momentum filter is both adaptive in shape and duration to
accommodate (a) the diverse character of various equity classes
(i.e. bonds, market indexes, sectors, REITS, and commodities), (b)
the evolving character of the market, and (c) the evolving character
of Strategies as funds with shorter histories begin
to participate. See our
NAAIM Wagner Award
technical paper:
"Automated
Polymorphic Momentum."
Holistic Risk Management
Risk is not a one-dimensional
problem cured by a single act of diversification.
There are numerous sources of risk to face that relate to companies,
funds, strategies, markets, political events, natural disasters,
and even personal matters. Holistic Risk Management examines and
addresses the relevant sources of risk within an entire system.
AlphaDroid was designed to reduce risk on many levels, as described
in our white paper "Conquering
the Seven Faces of Risk".