Institutional Management

Signet provides investment strategies for 401k plans, advisors, endowments and foundations, pension funds and family offices.

Institutional Philosophy

We believe we can strive to achieve positive alpha by actively managing a concentrated portfolio consisting of individual US and Global equities. Imagine you drive a car. When you accelerate, the engine is very important, when you make a sharp turn - the tires, when you cruise - the aerodynamics of the car. If you are on a highway you face a nicely paved road, when in the mountains - not so much. All of the car characteristics are important but some are more critical in particular circumstances. Our goal is to try to provide you with the best car for you to ride the stock markets. We strive to have a system which would determine macro cycles in the market (environmental oversight). We work hard to attempt to correctly rank individual equities based on the best factor mix (great car characteristics), which would work across different investment regimes (on different roads/weather conditions). Our final destination - striving to outperform our composite benchmark (S&P 500 80% and Russell 2000 20%) without taking on above market risk.


Academic Base

We strive to build upon the groundbreaking works of Eugene Fama and Kenneth French, William Sharpe, Bruce Jacobs and Kenneth Levy and other recognized figures in the world of finance. We use the Return based style analysis pioneered by William Sharpe and recognize that there are distinct cycles in the market place driven by certain style axes (such as large/small, value/growth). We incorporate the Fama-French Three Factor Model and its derivatives in multiple areas of our analysis (eg. idiosyncratic risk rank). We utilize Jacobs and Levy multifactor research and construct proprietary multifactor models which contribute to our stock ranking system.

Multidimensional World

We live in a complex world where multiple investment regimes shape the dynamics of the stock market. Single factor or simple smart beta models may be easy to implement, interpret and observe (momentum model). However, we believe they tend to suffer from factor overcrowding and adverse price pressure which may lead to higher trading costs. Multifactor models, in our opinion, are designed to better fit the cross sectional analysis of the stock market, allow for proprietary mix of factors and smooth out the performance during the periods of investment regime changes. We believe a classic "cube" of size, style and sector consideration becomes more and more complex as practitioners have to cope with changing liquidity environments and safety/risk on and off periods and that "virtual cubes" with an unlimited number of coordinates are designed to incorporate an increasing number of factors and investment themes into a more viable multifactor model.

Quantitative Process To Evaluate Data On Individual Securities

We consider more than 70 fundamental, technical and behavioral factors when we construct our short and long term models with data on close to 3000 US and Global companies going back at least 10 years (in sample period is from 2004 through 2007). Not all of these factors will make it to the final models. Our goal is to come up with the models which would work well across multiple investment regimes and would allow us to segregate potential winners from losers out of sample. As you can see from the table below on an out of sample basis we achieve our goal - our top scoring quantiles have positive alpha in comparison to our low quantiles for the period from March 2010 through 2013. More importantly when we condition our Long-term Score on our Short-term score we achieve much more stable results across all investment regimes. From 2010 through 2013 we observed Large and Small, Value and Growth, Risk on and off periods.

Score Name Forecast Period Alpha Q20 Alpha Q40 Alpha Q60 Alpha Q80 Alpha Q100
Long-term Score 2 quarters -0.69% -0.01% 0.03% 0.04% 0.63%
Short-term Score 2 quarters -0.91% -0.03% 0.15% 0.67% 0.33%
Long-term Score + Short-term Score 2 quarters -0.97% -0.14% 0.28% 0.65% 0.21%
Long-term Score Conditional on Short-term Score 2 quarters -0.48% -0.65% -0.11% 0.42% 0.84%
Long-term Score 4 quarters -1.55% -0.49% -0.31% 0.46% 1.90%
Short-term Score 4 quarters -1.41% -0.04% 0.08% 1.05% 0.61%
Long-term Score + Short-term Score 4 quarters -1.54% -0.17% 0.60% 0.63% 0.53%
Long-term Score Conditional on Short-term Score 4 quarters -1.14% -0.45% -0.84% 0.74% 1.72%
Sample: (2600 Large, Mid and Small Cap companies traded on US stock exchanges) March 2010 - Dec 2013, Without Utilities and Telecom


Outperforming our composite benchmark (S&P 500 80% and Russell 2000 20%) without taking on above market risk.

Institutional Process

Process at a Glance

Our investment process combines top-down value/growth ("Value/Growth"), large/small market capitalization ("Large/Small") and sector ("Sector") allocation with bottom-up stock selection, and includes fundamental, technical, behavioral and pattern recognition elements. Large/Small and Value/Growth allocations are derived through statistical analysis designed to assess and follow ongoing cyclical evolution of currently prevailing investment preferences. This analysis is overlaid by fundamental evaluation of macro-and micro-economic backdrops that we believe may affect further evolution of market preferences. Bottom-up analysis is based on quantitative evaluation of reported financial performance by thousands of companies representing various sectors of the U.S. and global economy. Various third-party research and ratings contribute to the bottom-up selection process, allowing the investment team to follow the evolution of investors' views on different companies. We supplement the quantitative input with a qualitative overview of each position in the portfolio. The strategy, which consists of 36+ stocks, could have a substantial sector bias in comparison with our composite benchmark (S&P 500 80% and Russell 2000 20%), and is aligned with major themes in the market.

Top-Down Approach: All Market Cap, Value/Growth and Sector Strategy

Value/Growth and Large/Small Cap Cycles

We use the Return based style analysis pioneered by William Sharpe and recognize that there are distinct cycles in the market-place driven by certain style axes (such as large/small, value/growth). The team employs changing cycles in which large vs. small and value vs. growth companies outperform/underperform as two major forces among the drivers influencing investment return. We follow the cycles in our portfolio management, but do not try to predict them. We believe that size cycles are usually much longer than style cycles. From our past experience style cycles (VG) run from 12 to 24 months while size cycles could extend up to 6 years. We use the size and style cycle analysis to adjust our portfolio postures.

Sector and Industry Allocations

In order to determine our Sector weights we use a combination of Russell 1000 and Russell 2000 Value and Growth Indexes. We decompose the sector allocation of the benchmark indexes and then tilt the weights of the Sectors based on our Value/Growth and Large/Small postures. So, we get the initial Sector composition based on simple weighted sums of each Sector's weight in one of the four indexes. Then we adjust Sector weights taking into consideration economic cycle (GDP growth, contraction, etc.) and our wrap-up Sector Rating based on the underlying individual stocks' average ratings included in a particular Sector. We generally do not allow a deviation of more than 10% from the composite benchmark. The ten sectors we work with are: Consumer Discretionary, Consumer Staples, Energy, Financials, Healthcare, Industrials, Information Technology, Materials, Telecommunications and Utilities. Each Sector consists of multiple Industries. The 100 Industries we use are assigned a proprietary Industry Score, which allows us to diversify within the broader 10 Sectors, picking up individual positions that we believe to be in the most attractive industries.

Investment Regime Considerations

In our opinion, liquidity becomes a very important factor to consider especially in light of central banks around the globe relying on monetary policy to spur the economic growth and avoid stagnation. We believe that in a low interest rate environment the market participants have to adjust their assumptions to their valuations, making liquidity an important factor to consider. Safety/Risk on and off periods in the market shape up the investor preferences and those periods can be of a considerable length. We also believe that taking into account different volatility regimes can be crucial for portfolio composition. For example during the risk off period we could take a more defensive Large Cap Value posture, while during the risk on environment we could add some more Small Cap Growth flavor to the portfolio.

Bottom-Up Approach: Ranking System of Individual Stocks


Based on our multifactor models we score close to 3,000 US and Global companies in our universe and strive to identify attractive investments by comparing them to their peers by Industry and Sector.

Fundamental Factors

We observe how markets reward broader factor groups (value, growth, safety, profitability, technical and behavioral), and endeavor to understand how the market shifts its preference from one group of factors to another, in a particular economic sector and the strength of the shift. We use a combination of third-party ratings in order to reinforce our internal analysis. The third-party ratings we use belong to well established providers with several decades of history and industry wide recognition. The way we combine the ratings with the fundamental factor groups aids our understanding of the market's preferences in relation to intrinsic value of individual companies we select for our strategy.

Factors in the Multifactor Models

When we build our multifactor models we consider different time periods and investment regimes. The major types of factors are listed below.


We include price-driven factors from accounting statements. We believe that Price/Earnings in different variations is among the most fundamental measures of a stock's valuation. The more earnings for every dollar invested, the more value the security represents. Multiples of earnings and assets are discriminating measures of relative value. The same holds true for multiples of sales. We would not consider these factors on a stand-alone basis. We believe that a company with no growth prospects could have a comparatively attractive valuation but justifiably so and would not represent a good investment candidate.


Earnings, sales growth and consistency become a very important part of all our models. The dynamics of growth rate, consensus expectations and magnitude complement the discriminatory analysis of all stocks in our universe.

Profitability/Management Quality

It is important to understand how effective each company is in using its resources. Measures like Return on Assets and Return on Equity are among the factors we use to assign the probability of the companies outperforming their Industry group peers.


Banks do a very thorough analysis of borrowers financial health and we piggy back on their work when it comes to assessing the financial health of an individual company. The better the internal financial discipline, the better chances are that the company can sustain rough patches in the future. We incorporate 3rd party credit analysis and volatility statistics to assess the safety of each individual stock.


We believe that momentum factors are important indicators of future success. Relative price strength and stability along with growth dynamics support performance in short and medium time periods.


Institutional ownership and accumulation/distribution of an individual position among sophisticated investors could be a strong indicator of future performance of the stock. We believe following "smart money" can be a worthwhile strategy.

Institutional Portfolio Construction

We manage a portfolio consisting of individual equities with 36+ positions. By having 36+ stocks representing all the sizes, styles and most of the economic sectors in the market, we seek to reduce nonsystematic risk (i.e., the risk of losing due to overexposure to one company). Although, a 36+ stock portfolio would be considered concentrated, we believe that by actively managing the exposure to all the style axes and having robust analysis of each individual position we can generate excess return without taking on above market risk.

Quarterly Modeling

We run modeling quarterly because companies report financials and reveal the details about short and long term conditions they operate in on a quarterly basis. Our models are based on different time spans. Shorter-term models (half a year) are based on the combination of momentum, earning dynamics, coupled with intrinsic valuation and sentiment assessment. Longer-term models (a year+) are more fundamentally oriented with more emphasis on intrinsic valuation, long term growth, profitability and efficiency of management and safety. Combining models effective over different time spans allows us to potentially pick up positions with high probability of outperformance in the short and long run.

Attribution Analysis

Each quarter we identify the areas we need to improve on by running an extensive attribution analysis which allows us to assess if we succeeded from the macro allocation and micro selection stand points.

Qualitative Oversight

Once we have ranked the stocks and identified the areas we need to improve on we review each individual position not only by looking at the quant data but also by analyzing each company in the portfolio and candidates from the qualitative stand based on Porter five forces analysis. If a position is weak across the board it becomes a sell candidate. A buy candidate ideally would be strong from the quantitative and qualitative stand points.

Risk Management

By having 36+ stocks representing all the sizes, styles and most of the economic sectors in the market, we seek to reduce nonsystematic risk (i.e., the risk of losing due to overexposure to one company). We also employ our proprietary Idiosyncratic Risk Rating, which we believe allows us to select companies with low idiosyncratic risk characteristics that follow the major themes in the market. With our positions being fairly liquid compared to market averages, we believe we have a scalable strategy and should not have a huge negative impact on prices when trading.

Max Position Size

We try to keep an individual position below 4.5% of the portfolio.

Max Sector & Industry Exposure

We generally do not allow a deviation of more than 10% from the composite benchmark.


Small Caps are more volatile than Large Caps by nature. We aim the drawdowns to be somewhere between the S&P 500 and Russell 2000. Cash and Hedging. We usually allocate less than 1% to cash in the portfolio. We do not implement any hedging on the portfolio level. We could implement option based hedging on the individual account level.