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Understanding equity algorithms is key in today's investment environment.

by Bret Rosenthal | @bretrosenthal

Equity algorithms are everywhere in today’s investing world. In fact, algorithmic investing actually began to mass-develop across asset classes in the 1980s in the form of execution algorithms. Trading volumes expanded, electronic markets proliferated and investors desired better execution experiences that controlled cost and market risk. The business of execution algorithms surged in the 1990s.


As execution algorithms began to dominate overall trading activity institutions such as banks and funds recognized a profit opportunity: using algorithmic investing programs not as a service but as an alpha-generation tool. Competition in the sector spurred the development of complex algorithms designed for speed and smarter execution. This led to “High Frequency Trading” (HFT). In the HFT world large volumes of shares are automatically bought and sold at high speeds.

For many years institutions profited greatly from algorithmic investing while the individual investor was not allowed access to this valuable information.

Today, the balance begins to shift.


I’m recently returned from an investment research trip where many professionals came together and discussed investing strategies. As usual, the fundamental analysts continued to espouse their ability to uncover value. The primary focus of the conference, however, lay in a more newly relevant idea:

Everyone (even the fundamental guys) is trying to understand and assess the importance of the algorithm in today’s investing environment.

The key reason for such interest in equity algorithms is the universal agreement that 2008 forever changed market structure. Gone are the days of easily identifiable economic cycles from which to draw valuation expectations. Instead, we exist in the age of central bank intervention on a massive global scale.

Simplified to an alarming degree: Investment valuations now rely on the whims of a group of bankers from disparate backgrounds chasing different goals. In a broad sense I am referring to the use of quantitative easing. Now, analysts must attempt to guess the level of liquidity generated by central banks and the effect it will have on various markets.


It is no coincidence that correlations have converged to a dangerous level. In the past, the rule of diversification was a sure-fire way to reduce risk in a portfolio. However, the theory of spreading risk among many different asset classes no longer works in a world where liquidity is the driving force of investment. Now when liquidity is reduced, or appears to be reducing, formerly uncorrelated assets drop in value together.

Enter the equity algorithm.

There are many different interpretations of algorithmic work. Some traders build artificial intelligence computer learning algorithms while others attempt to set up neural networks.

In our case, we believe that one of the best ways to reduce risk but still capture the upside is to employ a computer-based platform that incorporates mathematical models and statistical analysis.

Probabilities and statistics don’t lie and can’t be corrupted. The more complex the algorithm becomes, the more moving parts the greater potential for future breakdown. We like to keep it simple and focus on consistent optimization of the algorithms.


Rosenthal Capital Management’s partners hold over one hundred years of investing experience. To build our equity algorithm we have:

  • collected this investing and trading knowledge
  • focused on the last sixteen years
  • prioritized post-2008
  • developed a comprehensive approach to risk management
  • produced statistically significant entry and exit logic

The rcmAlgorithmic Platform crunches reams of data on a specific asset (for teaching purposes SPY is our target) and gives us specific times when it is best to:

  1. place capital at risk
  2. protect capital

We call these times the best “risk vs. reward execution times.”

When applying the algorithm to the SPY we see seven out of ten entries reaching our 1st targeted exit over the last eight years.  (See here for more statistics on our back-tested equity algorithm study.)


One of the most compelling aspects of a correctly applied, quality algorithmic platform lies in how it can effectively counteract the biggest hurdle to investment success: emotion. In their reliance on mathematical models and statistical analysis equity algorithms help neutralize the Fear/Greed response that so often derails solid investment decision making.

Even in the presence of reduced emotion it’s important to remember: Any solid equity algorithmic process must be married to a strong risk management protocol. Equity algorithms (including ours) are not guarantees of success. Instead they are mathematical models of probability. In the way that they are designed (by humans) to crunch reams of data equity algorithms are not magic and are certainly fallible. They are continually evolving organisms, which means our work is never done refining and testing for accuracy.



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