Machine Learning In Investing

Machine learning can be used in investing in a variety of ways. This article seeks to show a few of those and describe the basics.

Hedge funds have tried for years to use machine learning (or AI) in their funds. Moving on to today, all quantitative funds use some form of machine learning. Some hedge funds even build around machine learning specifically, spurning human intervention.

Risk Management

Risk usually means standard deviation and positive correlation. However machine learning can approach risk management in a different way. Traditional investment correlation studies the way stocks move together. Machine learning today can look at correlation of risky investments with alternative data points. For example a model can look at how risk of a certain industry’s securities might rise and fall with weather data points, such as rain or temperature.

Simple methods of machine learning can be used to reduce the prediction errors in mean variance portfolio optimization. This helps manage the risk that can be reduced via diversification.

Building Portfolios

When discussing machine learning in investing, it’s natural to think about building portfolios. Since machine learning can assist with portfolio optimization, it can also be used to select investments to make up ideal portfolios.

Price Prediction

Of all the ways to use machine learning in investing, this one has gotten the most attention. This is because everyone is looking for the holy grail, also known as ‘the algorithm that accurately says when to buy and sell a security’. Many quant platforms are built around this concept.

Macro Economic Predictions

In addition to predicting prices, machine learning is also used to predict the state of the economy. Portfolio optimization depends quite a lot on return estimates, and return estimates are only as good as predicted economic conditions. Traditionally, to figure out what the economy was going to do, you would look at oil, interest rates, or similar macro data points. Now, you can throw huge amounts of alternative data at a model designed to look for similarities in past economic periods to current conditions. These models reinforce the estimates more than can be done with simple machine learning. They should result in much better estimates.

A word about Data

In order to use more complicated machine learning models, you need a lot of data. How much, that depends. It’s safe to say that the starting point to do meaningful things with deep learning, is at least a couple terabytes.