Clearly defined quantitative decision models

We believe that the best way to generate steady, above-average positive returns with low volatility and downside exposure, is to employ an investment methodology that has the potential to recognize and measure consistent and repeating behavioral patterns in the financial markets. 

With that goal in mind, we have developed clearly defined quantitative decision models that strive to minimize subjectivity in the decision making process. 

Foundational Principles

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Recognition that investor psychology plays a major role in shaping the character of a market in determining how price action unfolds.

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Model components are purposely designed with either a buy or sell side bias.

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Signals are based upon a modular convergence of multiple combinations of set ups and triggers.

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Emphasis is placed on the need to avoid reliance on the standard and popular indicators in the lexicon of technical analysis in order to gain advantage over the universe of technically driven investment methods.

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Isolation of price events that repeat with sufficient frequency to warrant actionable responses, but are typically not obvious to the larger universe of investors.

Investor Psychology

It’s important to recognize the impact of human nature on investment/trading decisions. We make the underlying assumption that investor decisions are not always based on a rational and thoroughly reasoned premise. Even when that is the case, basing expectations of future price behavior on present observations and conclusions, no matter how accurate, introduces a high level of uncertainty in the outcome. Thus, the stage is set for three human emotions to play an over-riding role in investor behavior – fear, greed and complacency:

  • Fear unfolds in a different way when compared to greed. Investor psychology is quicker to react to losing money than making money. Fear is much more contagious than greed and often builds rapidly to a crescendo of selling.
  • Greed builds slowly and gradually, rarely evolving to bubble proportions.
  • Complacency lulls investors into a false sense of confidence and interferes with their ability to recognize changing market dynamics.

The declining phase of the price cycle tends to be shorter in duration than the advancing phase. That’s because of the forceful emotional intensity of fear. It requires much less time for the collective psyche to reach an exhaustion point of selling than it does to reach an exhaustion point of buying. We believe a key ingredient for the formulation of optimum entry and exit criteria is to first identify when buying and selling pressures are at or near an exhaustion point. This is a precursor condition before a major reversal in price trend is likely.

Entry and Exit Parameters are Differentiated

The influence of investor psychology on the behavior of markets explains why entry and exit criteria must usually be different from one another. As a consequence, indicators with a given set of parameters that can be used to identify an oversold market will probably require modification in formulation and require a different set of parameters to identify an overbought market.

A Consensus of Non-Correlated Indicators

The concept of overbought or oversold is an example of a precondition for a possible entry or exit point. Additional indicators are necessary to trigger and confirm the actual entry or exit signal. We strive to attain a balance that requires a sufficient number of indicators to reliably achieve this goal, without introducing too much complexity. The indicators must attack the problem from different perspectives, while being careful to minimize redundancy. The following is a sample of indicators that exhibit a few of the key analytical techniques that are incorporated into the models:

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Reliance on proprietary smoothing techniques to help clarify the trend and further reduce the probability of false signals.

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The identification of a minimum percent swing in price from a low or high to indicate a statistical probability of a tradable change in trend.

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Momentum divergence. The momentum of price movement tends to lead actual price. The failure of momentum to confirm a new high or new low in price is usually a precursor to a change in trend.

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A crossing of price below a proprietary trailing stop calculation to validate a sell signal. The reliability of this indicator is enhanced by incorporating a volatility component, thereby helping to allow room for random and “noisy” price movement in order to minimize false signals and overtrading.

None of the above indicators can stand-alone as a definitive trigger for entry or exit. There must be an adequate degree of consensus by different indicators before a tradable signal is rendered. This is not intended to present a complete analysis and may therefore oversimplify the actual construction of the indicators in order to protect their proprietary nature. One should keep in mind that a key principal is to construct indicators using non-standard methods or with a proprietary interpretation of the mathematical formulation of standard indicators. The objective is to gain market advantage over competing model-driven methods.

Modular Design

A distinctive attribute of the model is reliance upon the concept of a consensus by several non-correlated indicator readings in conjunction with one or more necessary pre-conditional setups to trigger entry and exit signals. This process results in the construction of a number of preliminary self-contained models (or modules) that in turn can be reinforced by other “first stage” modules to generate a consensus that identifies the final trade signal. Although this adds a level of complexity to the decision-making process, it serves to inject a high level of “intelligence” into the models. We believe this methodology is unique in facilitating the challenge of trying to navigate and recognize market conditions that, on the surface and to most observers, appear to exhibit a great degree of chaotic variability from one time frame to another, but are in reality repetitions of similar market dynamics and therefore recognizable as conditions to which model criteria can be applied with recurring confidence.

Kensington Analytics

Offerings

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Managed Income Strategy

Flagship Strategy With 28-Year Track Record Of Providing Compelling Risk-Adjusted Returns In Varying Cycles

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Dynamic Growth Strategy

Tactical Allocation Strategy Designed To Alternate Between a “Risk-On” and “Risk-Off” State During Times of Market Strength and Weakness.

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Diversified Income Strategy

Diversified Exposure across a wide range of income producing securities while utilizing a two-fold approach to risk management

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Kensington Managed Income Fund

Flagship Strategy With Track Record Of Providing Compelling Risk-Adjusted Returns In Varying Cycles - Now Accessible Via 1940 Act Fund

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Kensington Dynamic Growth Fund

Five Year Track Record Delivering Compelling Risk-Adjusted Returns Throughout Various Market Cycles. Now Accessible Via 1940 Act Fund