Stock Market Prediction: AI And Chaos Theory

I Know First Research Team LogoThis article was written by the I Know First Research Team.

Rationality and Chaos

There have been many attempts to model the inner workings that make markets tick the way they do, starting from those as fundamental as the Smithsonian unseen hand correcting all the wrongs. However, when it comes to things less abstract and academic, one of the main questions on everybody’s minds is whether market, and, more specifically, stock market predictions are a possibility or not.

stock market predictions

In extreme cases, both a simple “Yes” and “No” seem to equally contradict the reality on the ground. On the one hand, the famous Efficient Market Hypothesis postulates that stock prices fully reflect all information available to the investors. This, by extension, means that stocks are neither under- or overvalued, and no amount of analysis can produce a strategy that would generate consistent profits. However, investors capable of staying ahead of the market for years, like Warren Buffet, would not exist in an environment like this, since it all but rules any persistency out. If stock prices are on a random walk, you might indeed as well blindfold a monkey, give it a bunch of darts to throw at a list of stocks and pick up whatever financial instruments it happens to hit, as suggested by Burt Malkiel.

On the other hand, if the stock market had been completely predictable, investment would have been a carefree endeavor, with no risks to speak of. We would not see hedge funds losing billions on low gas prices due to mild winter weather, for example, and many other cases of bets going wrong. While this would have been a nice world to live in, it is, unfortunately, not really the case for us.

Thus, at the end of the day, as it often happens in life, the truth is somewhere in between – while the stock prices do tend to fluctuate randomly at times, stock market predictions are in fact very much a thing. This means that your investment decisions are no monkey business, and there may be better ways to model the market’s behavior.

Chaos theory can be the answer here, as it is pretty good at dealing with complex dynamic systems in which a minor change in one of the features can produce a massive fallout. In other words, they can be thrown off balance by a relatively small event that may even look unrelated at first sight. Stock markets are easy to imagine as such systems: most price fluctuations result from the decisions made by the investors, sometimes fully rational and calculated, and sometimes taken in the heat of the moment, when chaos strikes and emotions run high. Here, there’s a causal relationship not unlike that between an earthquake and tectonic activity.

Needless to say, there are other factors at play as well, and at times, drastic change can indeed come all out of the blue, caused by major events like the 9/11 or even faulty computer code at exchanges. Stock prices fluctuate around their actual value, overshooting it and slumping down under. And yet, there is also a system to this madness, a chaotic system that can be explored and even modelled via advanced mathematical computation methods.

These systems can indeed be hard to predict, and yet, with enough data at hand and some advanced mathematic formulas, we can try to model and forecast their behavior. This is exactly where AI comes into play.

Stock Market Predictions In The Age of AIs

While the AI industry as such has began to blossom only quite recently, a major share of the maths behind it has been on the radar for quite a while. The problem was in the computational power, which was not really there back in the late 20th century. But as chips packed more and more punch, the computers became capable of processing tremendous amounts of data, looking for patterns and establishing meaningful connections between variables, whole clusters of those, and specific outcomes.

stock market predictions

This, in a simplified way, is what the AI industry as we know it is all about: what we are talking about in most cases is some very advanced statistical programming and modelling, mathematical functions rather than an actual consciiousness that might one day become self-aware and try to reenact the Terminator franchise. Self-learning is a whole different matter, however: when fed a new trove of data, algorithms are capable of validating their existing formulas and coefficients against it and changing those to increase the accuracy of their models.

Applying this to the stock markets, we can immediately note the potential for use of AI in price dynamics prediction: in essence, what it comes down to is filtering out the systematic components through the random noise in the flow of the market data and calculating the right model to account for those. Preferably, this should be done in a way that would leave the door open to self-learning; in other words, rather than telling the machine that predictors A, B and C define the response D, and this is etched in stone, we leave it up to the computer to figure out the relationships within the dataset and re-calculate them as new data comes in.

Now, a certain degree of irreducible error is inherent to most prediction algorithms, since some of the factors defining the variance of the distribution that we are looking at are probably left unaccounted by the formulas that the machine has built. In some cases, this could perhaps even come down to something along the lines of the proverbial butterfly in Brazil creating a tornado in Texas by flapping its wings. Nevertheless, given enough time and computational power, the algorithm can learn to filter this random noise out and use it to estimate the reliability of its own predictions.

Also, since, as we noted earlier, stock prices tend to overshoot or underestimate the real value of the company, what we are dealing with is not a normal bell-curve distribution, but rather a fat-tailed one, where the data is skewed off center. Thus, extreme scenarios are way more likely than we would assume them to be under the normal distribution, where they belong on the thin ends. Cases like the 1987 Black Monday and the 2008 financial crisis work as somber reminders of the fact that the world does not necessarily always behave the way mathematicians expect it to. However, there are solutions to the problem, such as using fractal time-series analysis.

Another point to be made is that the algorithm would only look at the price dynamics and cash flows across markets rather than reading the news and reports that shape the decisions of the investors and strategists. Traders do, however, and this, curiously enough, allows the algorithm to gauge their sentiment indirectly by looking at the market data without having to go through a bunch of respected financial publications itself. 

In terms of stock trading, all this ultimately shows that AIs and algo-trading do offer a solid promise to investors looking for big returns, especially if they play it smart and only act on the strongest and most predictable signals. Other risk-management techniques, such as keeping your portfolio diversified, also apply, obviously enough.

We Know First – And We’ll Keep You Posted

stock market predictions
Fractal, a complex geometrical figure that shares the feature of self-similarity with chaotic systems. (Source:

If you are willing to dabble in algo-trading, consider I Know First – an Israel-based AI stock market predictions company that has been in the industry for almost 10 years. What this means is that our proprietary algorithm, built on a massive database with application of artificial neural network and genetic coding techniques, has had plenty of time to adjust itself to the dynamics of the stock market and polish off its models and formulas. The philosophy behind its design partially draws from the chaos theory, viewing the stock markets as complex systems where minor events can have major repercussions.

Developed and trained by Dr. Lipa Roitman, seasoned expert with years of experience in machine learning, the I Know First AI models the behavior of thousands of markets, working with over 10,000 financial assets, including stocks, ETFs and national currencies. In doing so, it relies on historical data covering 15 years of trading as well as the latest market data. The latter goes through a running cycle that includes deep learning and self-learning components. First, the new daily stock market data is compared against the historic database covering 15 years of trading; then, the algoritm validates the existing models against the new data, utilizing its self-learning capacity, which allows it to learn from its past successes and failures. This improves the accuracy of its predictions with every new iteration.

stock market predictions

All this makes for a solid prediction accuracy rate that allows to make profit even on fully automated trading, with a bot that simply buys and sells the stocks with the highest expected returns and predictability. In fact, last year’s evaluation remonstrated that a portfolio built on I Know First predictions beats that based on the S&P 500 index.

stock market predictions

stock market predictions

The output of the algorithm is delivered as a heatmap, with signal and predictability indicators; the former shows how well a stock is supposed to do compared to others on the forecast, and predictability shows how sure the algorithm is in its own predictions. The time horizons vary in a range from 3 to 365 days, covering short-term, mid-term and long-term outlooks.

The precision of the algorithm allows it to work as an important tool for seasoned traders, both private and institutional. The recommendations for its use are quite simple, almost to the point of being self-explanatory: look for a strong signal with high predictability, and when the predictability goes low, brace for a storm.

Besides AI-driven stock market predictions, the company also publishes its own financial analysis and does on-demand portfolio assessment.