Become a Millionaire by Taking my Financial Engineering Course

May 17, 2022

I just got an excellent question today about my Financial Engineering course, which allowed me to put into words many thoughts and ideas I’d been pondering recently.

Through this post, I hope to get all these ideas into one place for future reference.

 

The question was: “How practical is this course? I’ve skimmed through several top ratings on Udemy but have yet seen one boasting how much money the student made after taking it

Will you become a millionaire after taking my financial engineering course?

 

Let’s answer this question by starting with my own definition of “practical”, and then subsequently addressing the student’s definition of practical which appears to mean “making money”.

In my view, “practical” simply means you’re applying knowledge to a real-world dataset.

For example, my Recommender Systems course is practical because you apply the algorithms we learn to real-world ratings datasets.

My Bayesian Machine Learning: A/B Testing course is practical because you can apply the algorithms to any business scenario where you have to decide between multiple choices based on some numerical objective (e.g. clicks, page view time, etc.)

In the same way, the Financial Engineering course is extremely practical, because the whole course is about applying algorithms to real-world financial datasets. The application is a real-world problem.

This is unlike, say, reading Pattern Recognition and Machine Learning by Bishop, which is all about the algorithms and not the fields of application. The implication is that, you know what you’re doing and can take those algorithms and apply them to your own data.

On one hand, that’s powerful – because you can apply these algorithms to any field (like biology, astronomy, chemistry, robotics, control systems, and yes, finance), but at the same time, you have to be pretty smart to do it. The average Udemy student would struggle.

In that sense, this is the most practical you can get. Everything you learn in this course is being directly applied to real-world data in a specific field (finance).

You can grab one of the algorithms taught in the course and start using it today on your own investing account. There’s a lecture about that in the Summary section called “Applying This Course” for those who need extra help.

Importantly, do keep in mind that while I can teach you what to do, I can’t actually make you do it.

In A/B Testing, I can show you the code, but the rest is up to the student to make it practical, by actually getting a job where they get to do that in a production system, or by inserting the code into their own production website so they can feed it to live users.

Funny enough, A/B Testing isn’t even about finance nor money. But will you make money with those techniques? YES. Amazon, Facebook, Netflix, etc. are already using the same techniques with great success.

The only reason some students might say it’s not practical is because they are too lazy/incompetent to get off their butts and actually do it!

Same here. I can teach the algorithms, but I can’t go into your brokerage account and run them for you.

 

Now let’s consider the definition of “practical” in the sense of being guaranteed to “make money”.

This is a common concern among students who are new to finance and don’t really know yet what to expect.

Let’s suppose I could guarantee that by taking this course, you could make money.

Consider some obvious questions:

  • If this were true, anyone (including myself) would just scale it up and become extremely wealthy without doing any work. Clearly, no such thing exists (that is public and that we know of).
  • If this were true, why would anyone work? Financial engineering graduates wouldn’t bother to apply for jobs, they would just run algorithms all day. They would teach their friends / family to do the same. No one would ever bother to get a job.
  • If this were true, why would hedge funds bother to hire employees? After inventing an algorithm, they could just run it forever. What’s the point of wasting money to hire humans? What would they even do?
  • If this were true, why would hedge funds bother to hire PhDs and why would people bother to get PhDs? Imagine you could increase your investments infinitely from a 20 hour online course. What kind of insane person would work for 4-7 years just to get a pittance and a paper that says “PhD”?

On the contrary, the reality is this.

The financial sector does hire very smart people and it is well-known that they have poor work-life balance.

They must be working hard. What are they doing?

Why can’t they just learn an algorithm and sit back and relax?

 

Instead, let’s expand the definition of “practical”.

Originally, this question was asked in a comment on a video I made about predicting stock prices with LSTMs. Is this video practical? YES. If you didn’t know this, you could have spent weeks / months / maybe even your whole life trying to “predict stock prices with LSTMs”, with zero clue that it didn’t actually work. That would be sad.

Spending weeks or months doing something that doesn’t even make sense is what I would consider to be very impractical. And hence, learning how to avoid it would be very practical.

A lot of the course is about how to properly model and analyze. How to stay away from stupidity.

One of the major themes of the course is that “Santa Claus doesn’t exist”.

A naive person might think “there must be some way to predict the stock price, you are just not telling me about the most advanced algos!”

But the “Santa Claus doesn’t exist” moment is when we prove mathematically why certain predictions are impossible.

This is practical because it saves you from attempting something which doesn’t make any logical sense.

Obviously, it doesn’t fulfill the childhood dream of meeting Santa (predicting an unpredictable time series), but I would posit that trying to meet Santa is what is really impractical.

What is actually practical is learning how to determine whether you can or cannot predict a time series (at which point, you can then make your predictions as normal).

I’ll give you another example lesson.

If you used the simplest trading strategy from this course, you could have beat the market from 2000 – 2018.

Using the same algorithm, you would have underperformed the market from 2018 to now.

The practical lesson there is that “past performance doesn’t indicate future performance”.

This is how you can have a “practical” lesson, which doesn’t automatically imply “guaranteed rate of return” (which is impossible).

Addendum: actually, it is possible to guarantee a rate of return. Just purchase a fixed-income security like a CD (certificate of deposit) at your bank. The downside is that the rate of return is very low. This is yet another practical lesson from the course – the tradeoff between risk and reward and how real-world entities automatically adjusts themselves to match present conditions. In other words, you’ll never find a zero-risk asset that guarantees 1000x returns. Why is this practical? Again, you want to avoid wasting time searching for that which does not exist.






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