Thinking Outside the Black Box

One of the biggest criticisms, and rightly so, of quantitative trading is that any given black box strategy, much like a set mathematical function, is rigid and mechanical.  It isn’t a perfect fit and it can’t account for all scenarios and market movements.  Artificial Intelligence, involving deep learning and neural networks, can address this shortcoming.  Where traditional data mining fails, AI succeeds.

You may have heard (or read) about big data, machine learning and neural networks at some point.  Let’s keep it simple - big data is just that, a collection of data. How do you capture, curate, search, store, update and analyze all of it?  We look at equity market data and financial performance metrics for over seventy-five years for all companies listed on North American exchanges.  That’s a lot of information, with more being recorded every day. So how do we process that information?

We could have hired an army of analysts to sift through the data, but that would be disorganized and inefficient at best.  Instead, we apply a smarter, more efficient and more effective method. We use our artificial neural networks, which are algorithms inspired by the human brain, designed to learn the complex, non-linear relationships between sets of inputs and the desired output (prediction).  It is a form of machine learning, where a machine (or computers) process the information, analyze it and develop an opinion on what they see. No human team, however large or talented, can compete with an AI-based process.

Here at Greyfeather, we don’t like to think of ourselves as quant traders.  When we talk to others in the industry, the term doesn’t really explain what we do. Sure, our strategy is quantitative, but our technology and research approach is what makes us different. Traditional quant traders tend to computerize trading execution of a strategy that is typically either trend-following or mean-reverting and based on technical factors (price/volume) or fundamentals.  We are an AI-based trading strategy in which we seek to have all relevant factors assimilated and optimize predictions.

Quant trading was an evolutionary advancement from discretionary trading that brought computerized discipline to strategy execution. AI represents a further advancement from quant trading that brings supercomputer learning to understanding the markets - an advantage no quant or discretionary trader enjoys.  This technology renders the "black box" obsolete.  We are replacing the outdated "black box”, by bringing the latest developments in AI to equity long/short investing strategies on Wall Street.