Machine Learning in Finance and Investing

Machine Learning in Finance and Investing

The first time I heard the term ‘Artificial Intelligence’ was in the 1980s, when my dad worked for Digital Equipment Corporation, in the Silicon Valley of the East (Massachusetts). And yes they made computer chips from Silicon. In the 80’s Artificial Intelligence referred to, basically, complex logical structures, with each decision leading to a tree of subsequent possibilities. It’s all compute power allowed in the 80’s and 90’s.

Fast forward to where we are today, and compute power is vastly greater, and machine learning can now avail itself of that power. Now instead of advanced decision trees, machine learning experts are trying to mimic the human brain.

AI (Artificial Intelligence) and ML (Machine Learning) tools abound today, in every industry it seems. Netflix uses it to figure out their users, as does Amazon, Google, and Apple, and Uber uses it to optimize drivers and routes for passengers. Political candidates try to use it to gain an edge on other candidates. It’s literally everywhere.

In finance, it’s being used for evaluating credit risk, make suggestions for financial planning goals, and even for investments.

Hedge funds have used machine learning for years, poring over many petabytes of data to find opportunities. They spend billions of dollars every year on data, because without great and unique data sets they can’t gain that edge that allows them to gain 30%, 50% or more every year (the best hedge funds earn over 70%).

But machine learning shouldn’t be limited to the ultra privileged at the top of the financial food chain.

We can use machine learning to make incremental improvements in our portfolios. In the same way it’s common knowledge that high investment fees erode a portfolio over time, making a dramatic difference in the end result, so too incremental improvements in a portfolio, both on the risk side and returns, can make a dramatic difference in your client being able to reach the end goal.

And even a portfolio earning the same returns with less risk means a client who isn’t constantly being bullied by the market. You can help them sleep better at night which adds to your bottom line in the end.

RIAengine will be launching a machine learning portfolio optimizer to a small beta group within a month.

Questions or comments? Love to hear them, please leave a comment below.

Tim Norton

Tim Norton is CEO of RIAengine. Formerly Head of Product Management at Machina, a firm building tools to apply machine learning in quantitative finance.

2 Comments
  • Posted at 10:14 pm, March 1, 2019

    Each step improves the result.
    Look forward to learning more.
    Bruce Bruinsma

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