Machine Learning in Finance by Dixon, Halperin, Bilokon – A Critique

May 16, 2022

Check out the video version of this post on YouTube:

 

In this post, I’m going to write about one of my all-time favorite subjects: the wrong way to predict stock and cryptocurrency prices.

Despite the fact that I’ve discussed this many times before, I’m very excited about this one.

It’s not everyday I get to critique a published book by a big name like Springer.

The book I’m referring to is called “Machine Learning in Finance: From Theory to Practice”, by Matthew Dixon, Igor Halperin, and Paul Bilokon.

Now you might think I’m beating a dead horse with this video, which is kind of true.

I’ve already spoken at length about the many mistakes people make when trying to predict stock prices.

But there are a few key differences with this video.

Firstly, in past videos, I’ve mentioned that it is typically bloggers and marketers who put out this bad content.

This time, it’s not a blogger or marketer, but an Assistant Professor of Applied Math at the Illinois Institute of Technology.

Secondly, while I’ve spoken about what the mistakes are, I’ve never done a case study where I’ve broken down actual code that makes these mistakes.

This is the first.

Thirdly, in my opinion, this is the most important topic to cover for beginners to finance, because it’s always the first thing people try to do. They want to predict future prices so they know what to invest in today.

If you take my course on Financial Engineering, you’ll learn that this is completely untrue. Price prediction barely scratches the surface of true finance.

 

In order to get the code I’ve used in this video, please use this link: https://bit.ly/3yCER6S

Note that it’s a copy of the code provided with the textbook, with added code for my own experiments (computing the naive forecast and the corresponding train / test MSE).

I also removed code for a different type of RNN called the “alpha RNN”, which uses an old version of Keras. Removing this code doesn’t make a difference in our results because this model didn’t perform well.

 

The mistakes I’ll cover in this post are as follows.

1) They only standardize the price time series, which does nothing about the problem of extrapolation.

2) They never check whether their model can beat the naive forecast. Spoiler alert. I checked, and it doesn’t. The models they built are worse than useless.

3) Misleading train-test split.

 

So let’s talk about mistake #1, which is why standardizing a price time series does not work.

The problem with prices is that they are ever increasing. This wasn’t the case for the time period used in the textbook, but it is the case in general.

Why is this an issue?

The train set is always in the past, and the test set is always in the future.

Therefore, the values in the test set in general will be higher than the values in the train set.

If you build an autoregressive model based on this data, your model will have to extrapolate to a domain never seen before in the train set.

This is not good, because machine learning models suck at extrapolation.

How they extrapolate has more to do with the model itself, than it has to do with the data.

We analyzed this phenomena in my course on time series analysis.

For instance, decision trees tend to extrapolate by going horizontally outward.

Neural networks, Gaussian Processes, and other models all behave differently, and none of these behaviors are related to the data.

 

Mistake #2, which is the worst mistake, is that the authors never check against the naive forecast.

As you recall, the naive forecast is when your prediction is simply the past known value.

In their notebook, the authors predict 4 time steps ahead.

So effectively, our naive prediction is the price from 4 time steps in the past.

Even this very dumb prediction beats their fancy RNN models. Surprisingly, this happens not just for the test set, but the train set as well.

 

Mistake #3 is the misleading train-test split.

In the notebook, the authors make a plot of their models’ predictions against the true price.

Of course, the error looks very small and very close to the true price in all cases.

But remember that this is misleading. It doesn’t tell you that these models actually suck.

In time series analysis, when we think of a test set, we normally think of it as the forecast horizon.

Instead, the forecast horizon is actually 4 time steps, and the plot actually just shows the incremental predictions at each time step using true past data.

To be clear, although this is not a forecast, it’s also not technically wrong, but it’s still misleading and totally useless for evaluating the efficacy of these models.

As we saw from mistake #2, even just the naive forecast beats these models, which you wouldn’t know from these seemingly good plots.

 

So I hope this post serves as a good lesson that you always have to be careful about how you apply machine learning in finance.

Even big name publishers like Springer, and reputable authors who might even be college professors, are not immune to these mistakes.

Don’t trust everything you see, and always experiment and stress test any claims.






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