Models

Build global, proprietary, dynamic asset allocation models.

Our approach to building asset allocation models is simple: you select from the major global stock, bond and commodity markets, and build risk indexes per market which identify when markets are in “bullish” or “bearish” territory. The Covert Analytics platform benefits you in three primary areas:

1. Combine your market signals
   This allows you to combine your views on macro-economic data and technical analysis signals into one quantitative framework which allows cross asset class evaluation.
2. Build global portfolios
   As our platform is globally focused, it allows portfolio managers to systematically scour the investment landscape for the best opportunities.
3. Systematic approach
   Our “first of its kind” framework helps your asset allocation system avoid the over-pessimism of major market bottoms and the hysteria of bull markets.

Take a tour:

The guiding principles behind our system are the combination of macro and technical data into risk indexes per market.

First, select which indicator you want to add to the current market’s risk index. You can select from a variety of macro data points, first organized by category, or on the individual market series for technical analysis signals. Here we first selected “Monetary Policy and Money Supply” to see those measures, and then selected “Money Supply (Narrow)”, which is equivalent to M1.

Covert Analytics then graphically displays what the data looks like as supplied by our data sources, and allows the user to create a transformation and normalization of the data. This is a state of the art technique we pioneered to allow users to combine a myriad of data sources into one risk index.

Once you have selected a Transformation & Normalization it is ready to be added as a Risk Index Component of the current market.

Once you have selected “Add to Model” it is already incorporated into your market’s Risk Index.

Portfolios

Manage your portfolio allocations based on your models.

Covert Analytics allows you to evaluate how your asset allocation models would have performed historically when applied to actual client portfolios. This is done utilizing our proprietary “backtest” methodology and allows you to view portfolio statistics compared to the major stock market benchmark.

First, specify some parameters for the portfolio:

You setup clients by first selecting whether the client is focused more on “absolute returns” (capital growth target) or on “low volatility” (capital preservation target), as this tells our algorithm which “buckets” to fill first. Secondly, you select asset allocation ranges for the major asset classes (stocks, bonds, commodities and cash) based on the client profile, which determine the minimum and maximum asset allocations to each asset class.

Then you can backtest the model for the portfolio:

As well as run some overview statistics, comparing the portfolio / asset allocation combination to the benchmark stock index. The statistics automatically included are the annual historical return, standard deviation, and “maximum drawdown” of each:

Allocations

Monitor ongoing recommended rebalancings from your system.

The most important facet of your dynamic asset allocation system is the recommended asset allocations. Our approach is not a black box tool based on “out of sample” statistical regressions, which we eliminated from the outset. Instead Covert Analytics allows user to build portfolios based on their own custom asset allocation models, to see how the portfolios would have performed historically based on these mechanical rules, and therefore what the current recommended asset allocation is. In the end, our clients join because they agree with us and the academic research that the most important determinant of portfolio performance over the long run is the asset allocation.

Research

Utilize our one of a kind research platform to build indicators.

Our platform allows for investment advisors to perform statistical transformations and normalizations on macro-economic data and technical analysis signals. This approach is unique in that it allows you to build custom indicators which identify when a market is in an extreme reading and thus prone to a “mean reversion”. Statistically transforming data available to everyone into a “market invariant”, then normalizing and measuring the data in terms of standard deviations, will give you an edge in identifying how markets and other indicators are trending and when they are likely to reverse.

The transformations we provide include absolute changes (ie 10 yr yield – 2 yr yield), percent changes (ie rolling 12 month return on US stocks), trend deviation (% above 200 day moving average), and some key technical signals (such as the moving average convergence / divergence or MACD, and relative strength index or RSI).