What do we want from AI stock analysis?
Our users come to us using keywords such as AI stock analysis or AI stock analyzer. They intend to use AI to find the stock that will subsequently grow the most in price. Everyone would like to buy a stock today that will endlessly grow at a dizzying pace. But today’s AI won’t help you find that stock.
What can we get from AI stock picking?
Computers and machine learning (that’s the famous AI) can be used for AI stock analysis in two ways.
- Computers allow you to analyze large amounts of data faster and make more informed decisions. You can count large numbers of financial indicators faster, for example for Technical analysis or Fundamental analysis. We call these indicators Factors. The computer will also help you to determine whether these factors are statistically significant or not.
- Machine learning then helps you to combine the factors for stock valuation appropriately. In other words, create models that include only statistically significant and uncorrelated factors.
With the help of machine learning, you can create a model that will value stocks and tell you which should grow faster and which should grow slower. The trick is that this approach only works if you repeat it regularly.
It makes sense, values are changing for companies, and market conditions are changing too. So the fact that firms are well valued today says nothing at all about their valuations a year from now. Our tests also show that at a one-year rebalancing horizon, the performance of a portfolio composed in this way is already broadly similar to the entire underlying universe.
What to beware of?
Therefore, the use of AI for stock analysis is based on the necessity of regular (and as fast as possible) portfolio adjustments. The problem is that every portfolio adjustment comes with fees and in the case of institutional investors also with reporting obligations. If you are serious about using AI for stock valuation, please be aware of the above facts.
The backbone of AI-powered investing is longevity and gaining a statistical advantage. It works the same in investing as it does everywhere else. A beautiful example is tennis, where one of the best players of all time, Roger Federer, won only 54% of the balls! In other words, even the best player in the world can win barely half the balls.
Similarly, you will be targeting stocks that are rising faster than the market. But with the help of machine learning, you’re more likely to succeed than without it. That way, you can be one of the best ‘players’ too.
For better investment decision-making process and the creation of AI-powered investment models, there are applications such as our Analytical Platform where you can create Smart-Beta investment strategies and apply them in the long term. We cover the topic of AI in investing in more detail in our article Stock Analysis with the Power of AI. The study of Stock Screener vs Factor Analysis is also discussed in detail by my colleague Vacula in his article.