Artificial Intelligence Investing

Advancement is achieved not through a competition of human versus machine, but a collaboration of human plus machine.

Our friends at PrimeAlpha have asked us at Signum Investments, a San Francisco-based Core US Equity long/short fund manager launched in 2015 that applies quantitative and machine learning techniques to traditional fundamental active management, to write a thought piece for the PrimeAlpha community. We thought it would be interesting to expand upon a section, “Rise of the Machines: Should the Computer run the Portfolio?” of a longer thought piece that we recently previewed with PrimeAlpha.

My name is Mike, and I run a fund that uses Artificial Intelligence to buy Cannabis assets using Cryptocurrencies on the Blockchain.

Really??

No.

While a few of you readers may have started salivating at the prospect of such a fund, most readers saw this for what it was: a series of silly marketing buzzwords that are currently hot in the press. What is not silly, however, is the fact that such marketing verbiage is rampant across the modern investment management landscape, and is used to describe the quantitative techniques the so-called Market Titans employ— leaving those responsible for diligencing these funds understandably at a bit at a loss. (Hint: A.I. is not a product.) At Signum we feel the time is overdue for a responsible manager to cut through the buzzwords and help sort out the current state of machine learning and artificial intelligence in investing.

HUMAN VS MACHINE

I’ll go on the record with a prediction: machines will not cause the extinction of the human investment professional, but will rather extend the influence of the best ones.

The line between human and machine investing is blurring as fundamental investors adopt tools and approaches long employed by “quants,” and as quants broaden their risk/return framework to rely more on fundamental or thematic judgment to compensate for the limitations of their quantitative models.

We're witnessing this phenomenon because investors are embracing the factor-based and quantitative approaches that have delivered excess returns. Research shows that, over the past 20 years, broad market factors—such as value, growth, quality and momentum— have driven about 65% of an equity manager’s relative returns (Morningstar). Thus, investors are gravitating to quantitative managers because they believe they can thereby get more precise exposure to these “winning” factors and potentially even to as-yet undiscovered factors that correlate to excess returns.

quant /kwänt/ definition: shorthand for a person who specializes in the application of mathematical and statistical methods to financial and risk management problems; i.e. uses computers to tell them what to buy and sell; a.k.a. the Rocket Scientists of Wall Street

The word “quantitative” with respect to investing, however, can be divisive for a myriad of reasons that fall into three categories.

1. First, transparency: black box managers refuse to show their investing system for fear of copycats, leaving outsiders nervous that a blow-up awaits under the hood.
2. Second, the scale, speed, and complexity of quantitative processes: some are rightly concerned that computers at the helm will lead to bigger and more sudden accidents.
3. And third, skepticism: quants cannot replicate that unique value that human judgment brings to the investment process.

Positive Attributes of the Quantitative Approach

In our view, it is not a competition of human versus machine, but a collaboration of human plus machine. In the hands of a skilled manager, quantitative techniques can be used with surgical precision to assess the current market state, the dominant factor/style regime, and even to cluster companies by operating model for more accurate relative attractiveness comparisons. This targeted application of sophisticated techniques reveals information that the human brain is ill-equipped to uncover. As the lines continue to blur and techniques are “borrowed” from opposing camps, a new investment approach has emerged—Quantamental.

FUNDAMENTAL VS QUANTITATIVE

Quantitative and fundamental techniques are not competitors, but rather are two different languages trying to tell the same story.

Quantitative investing is usually considered an opposing style to fundamental investing since it relies on disciplined computer models rather than research and intuition. This is a misconception. Quantitative and fundamental techniques should not be viewed as competitors, but rather as two different and complementary lenses through which to determine the investability of an equity.

Fundamental research is a deep analysis with intuition allowing experts to recognize familiar elements in a new situation and to act in a manner that is appropriate to it-- to identify what unique data are relevant from a nearly infinite sea of information. Intuition in this sense is not the same thing as gut instinct and doesn’t start with “I feel.”

Quantitative research is also a processed, deep analysis, with its algorithms able to select which of the numerous data items are truly relevant and how they are related—even if the equations describing a system are not known.

Generalized, the quant process is about finding relationships in the data, modeling those relationships, making predictions from the models, and gaining experience from those predictions.