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Quantitative

We have 90 million + distinct data points in our feed.

OUTSIDE OF GIVING US THE CAPACITY TO BE DISCIPLINED, OBJECTIVE, AND FLEXIBLE.

A quantitative process offers a real and exciting way to thrive as investors in the modern era. Equity investing today is like no other time in history, we have been gifted with an absolute overabundance of data. The information currently available to investors is unprecedented. Currently, humankind is producing and capturing more data in a single day than it had over entire spans of history.

It is a complete paradigm shift, big data has forced a full stop change in the way that investments are chosen and portfolios are run. The available data for use in investment processes has long since surpassed the ability of a human mind to keep track of it all. While some still hold to more traditional methods and evaluation techniques the best managers in the world are those that leverage data science and powerful computing to their advantage.

There are more than Ninety Million distinct data points in the feeds that we source for our process, and this represents only a portion of what is commercially available.

Distilling this trove of data into a tradable signal is no small task. Here at Fifth
Horizon harnessing this vast abundance of data in a way that generates a tradable signal is precisely what we do. A key component of our edge lies in our ability to find the stream inside of the nebula … to find the signal amongst all of the noise. We run a quantitative process not only because it is an incredible way to express our core investment philosophy but also because it is

AN INCREDIBLY POWERFUL WAY TO INVEST.

So, what do we mean by “quantitative”?

“Methodical evaluation techniques expressed
inside of an algorithm”

FIRST AND FOREMOST OUR PROCESS MITIGATES HUMAN BIAS BY DESIGN. WE BUILD ALGORITHMS THAT PROCESS VAST AMOUNTS OF DATA AND OUTPUT PORTFOLIOS.

The process is built to limit subjective judgements. We recognize the incredible importance of this fact and thus actively work to be rigorous in our implementation (the system is given priority).

WE (the humans) build the algorithm — the boundaries and the constraints are set by us. The insights are ours; we draw on many years of collective experience, vast amounts of research in human psychology and financial theory in the design process. We hone and adapt as new data and tools are made available.

But ultimately it is the algorithm that generates buy/sell signals. We add a final (human driven) qualitative screen to the output before building a final portfolio, but priority is given to the signals identified by the algorithm.

It is both “Art” and “Science” it takes human insight to build the algorithm, to hone and adapt, and to check the final output but only with the help of a machine can the full scope of the dataset be harnessed and the impact of human bias truly be mitigated. We want the outcome of our process to be dictated by our insight and experience without being limited by our capacity to predict the future.

There are many benefits and advantages to running a process like this but for us the choice to run a quantitative process can be reduced to a singular motivation: we want to mitigate our biases and eliminate the need to make perfectly accurate forecasts. The signals do the work.