The Shortcomings of Neural Networks for Trading Predictions

As someone who is devoting a large-portion of their senior year (and very likely time beyond that) to researching potential applications of deep learning in trading, I wasn’t thrilled to learn about the recent shortcomings of quantitative traders. Let’s begin with Marcos López de Prado, a frequently cited algorithmic trader who recently  published Advances in Financial Machine Learning. One thing that De Prado talks about is the idea of ‘red-herring patterns’ that are extrapolated by machine learning algorithms. These types of algorithms are, by design, created to analyze large bodies of data and identify patterns within this data. In fact, this idea of noticing patterns is one of the main assumptions I am basing my work on (using Word2Vec embeddings to identify past financial patterns and apply them to real-time data for more accurate predictions). But, what happens when these algorithms identify patterns that aren’t real? An aggressive neural network (In my case: One which adjusts vector weights heavily while learning from data) is prone to make these types of mistakes. Think of this example: A stock happens to go up a couple percent points every Thursday for three weeks in a row. A (poorly written) neural network would deduce that every Thursday in the future, this stock would go up by at least a percent point or two. Now, this is easily avoidable by training a trading algorithm on larger sets of data, but even large data sets are prone to these types of red-herrings. Once a trading algorithm clings on to a pattern, it could backfire horribly when that pattern eventually breaks.
This brings the idea of Black Swans into light. The theory of Black Swans was popularized by Nasim Taleb in his accurately-titled book The Black Swan: The Impact of the Highly Improbable. The general gist of this theory is that the most profoundly impactful events oftentimes are the ones we least expect, due to our fallacious tendencies in analyzing statistics (I will go into more detail on these topics and more in a future blog post, once I am done reading the whole book). Taleb argues that one of our biggest shortcomings in analyzing data is creating ‘false narratives’, which are more convenient and easier to sell to clients. These false narratives oftentimes omit crucial data (silent data), which backfires once the narrative breaks.
But, on the other end, a more passive neural network (one which more slightly adjusts vector weights) can sometimes come to no meaningful conclusions, which means wasted time and computational energy. I want to create a Word2Vec model which can detect patterns, but I also don’t want it to actively follow patterns with no longevity.
So, what does one do? How aggressive/passive should I make my Word2Vec neural network? 

Another theory which I encountered over the weekend is the idea of survivorship bias. In training neural networks, how do we treat data from companies which have failed? If we are analyzing the stock price data for various important stocks over time, what do we with data from once-important stocks which are now defunct, such as Lehman Brothers? I initially thought it would be best to throw this data out, since it is no longer applicable, but it turns out this strategy can have negative consequences. If we only train our network on stocks which have survived, then we will miss out on crucial data about when stocks go bankrupt. So, how do we properly treat this type of data?


All of these seemingly insignificant flaws in trading algorithms can evoke catastrophic mistakes. This concept is synthesized by quantitative investment officer Nigol Koulajian, saying: “You can have one little pindrop that can basically make you lose over 20 years of returns.” This ‘little pindrop’ which Koulajian mentions is the eventual divergence from the false patterns identified by neural networks. I personally think it would take more than a little pindrop to erase 20 years of returns, but the idea still stands. So, this warrants the question, how do we avoid the little pindrop? My (far-fetched?) theory is that you can use neural networks to estimate worst-case scenarios int the same way they are designed to estimate best-case scenarios, and then work to avoid this.
In broader terms, Bloomberg reports that the Eureka Hedge Fund Index, which tracks the returns of hedge funds which are known for using machine learning, has under performed yearly compared to the S&P 500. The harsh truth (right now) is that simply investing in the S&P500 will return ~13% yearly, while machine-learning based hedge funds return ~9% yearly.
Eureka Hedge Fund Index
(The keen observer will notice that despite all the noise, the index has been steadily going up over the past 7 years)
These are some of the questions I ask those few who read what I am writing, and are the types of questions I will ask through my personal research interviews (Good News! I have my first interview scheduled this upcoming Tuesday, and, interviewee permitting, I will post a summary of our talk later in the week).
In my personal opinion, the recent under performance of trading algorithms in general is not a bad sign. This is still a relatively new field, meaning that more research needs to be done and new discoveries need to be made. I think of it this way: If trading algorithms are working perfectly, then what’s the point of a newcomer (like me) coming in and doing research on them? If it ain’t broke, don’t fix it.

Economic Advice From A 14 Year Old

A message to my fellow American Citizens:

As the worldwide bubble we have been inflating for the past seven years slowly begins to deflate and history begins to repeat itself, the many who have been living seemingly perfect, credit-fueled lives will have to face harsh realizations. Homeowners whose properties have seen prices increase like never before will soon discover the consequences of near-negative interest rates imposed by the Federal Reserve, and those who purchased cars worth more than suburban neighborhoods with artificial money will have to go back to living with their Honda Civics.
The fact of the matter is that this has happened before. This is not the first time when stock price inflation, low interest rates, and rising house prices created an unsustainable bubble which encouraged nonsensical spending. It’s not that difficult to understand. Bubbles don’t last, they have to pop in the end and bring everyone down to earth.
We can not continue ignoring previous incidents and telling ourselves that such things can’t possibly happen again. For growth to occur, we must learn from our mistakes and change our actions accordingly to benefit ourselves in the future. So, today, I will be supplying you all with some helpful and possibly life-saving advice.
Credit– It’s like financial morphine; Extremely useful when you need it for something important, but when you use it when it’s not imperative, it becomes a substantial problem.
For example, if you have ambitions to create a startup or business of some sort but do not have the funds to do so, taking credit would be considered to be an understandable solution. However, if you want a $350,000 Ferrari or a $2 million house to show off to your friends but you are short on cash and want to take credit, then ask yourself this:
Do you really need that car? Or that house?
Yeah, most likely not.
Another common scenario involves people whose properties see massive price jumps. In bubbles, the mean house price generally goes up, meaning that this happens to almost everybody at some period in time. When this occurs, the proprietor thinks to himself:
“Hey, this is kinda cool. Since my house costs $500,000 and the price keeps rising, I guess that means I can go get a loan for $300,000 and I’ll be able to repay it easily.”
The problem is that these people forget to take into account the possibility of a sudden devaluation, in which case they would still have to repay the principal borrowed plus interest but with a property that is worth less than before.
When the world around you is buying and spending like never before, it becomes tempting to join the crowd, especially when interest rates are practically at 0% (or negative in some countries). And spending can be good, but just remember what happened in 2008. And 2000. And 1987. And 1929.

Everything is perfect, until it isn’t.