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.
Black-Swan-900
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?

lehman

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.

A Teenager’s View Of Smartphones

Recently, rumors have emerged mentioning possible specs and release dates for the new iPhone, likely to be named “The iPhone 6s.” This news comes a few months after the Samsung Galaxy 6 and 6 Edge were officially put out for sale. Amid this sea of “new’ mobile phones deemed smart were models from Microsoft, Nokia, Amazon, and Oppo. Today, I am not writing to give my opinion of my latest phone or provide you with likely false assumptions on what the new iPhone will look like, but to present a teenager’s honest view of smartphones, since large companies always seem to be interested in discovering what teens think of their products.
To begin, I would like to explain why I put the word “new” in quotations in my third sentence. I do this because it annoys me very much when a company like Apple comes along with a shiny phone with a screen that is slightly larger than its predecessor’s and says that it is “A new, innovative piece of technological art that will change the world with its 8-megapixel camera.” We understand that it was constructed this year, but that doesn’t mean you can tell everyone that it is completely new when the most significant change took place when you increased the price. If you want to receive our respect and consequently receive our money, you should consider investing some money in actual research&development. It’s not like you don’t have the funds to do so, anyway…
AAPL MARKET CAP
The next point I would like to address is the fact that us teenagers are gradually beginning to decrease the time we spend using our smartphones. In fact, there are only around five uses for these overpriced touchscreens that you can talk to. These include:

  • Checking our Instagram or Twitter feeds (No, none of us use Facebook).
  • Using Google to find the definition of words we don’t understand and to locate the nearest Chipotle.
  • Showing our friends Youtube videos we think are funny.
  • Documenting all of our actions on Snapchat.
  • Downloading apps that seem fun until you actually open them.

Sorry to break it to you Google, but we don’t use Google+ or Gmail anymore.
On behalf of all teenagers, I am going to send a message to phone makers:
The smartphone craze is nearing an end. We are not interested in buying any more phones or tablets alike, no matter how large their screens are or how quickly their processors operate. All we want is a practical device that allows us to take pictures, play games, and text. The only time when buying a new phone seems reasonable anymore is when the screen on our old one breaks or when it stops working properly. Based on the attitudes and thoughts of my peers, which closely resemble mine, I am going to make the assumption that iPhone, Galaxy, and all other smartphone sales are going to begin to decline soon. One day, the masses are going to realize that a phone with a touchscreen and a decent camera shouldn’t cost over five hundred dollars.
Conclusion
I strongly advise tech companies and phone makers alike to start exploring and manufacturing new technology–And no, Apple, by new I don’t mean ios9 or iPhone 7– that is practical, interesting, and simple. It’s really that easy. The time is ripe for a completely new product that differs from what we currently have in a good way.