The Future of the Stock Market: Machine Learning-based Predictions
Since the arrival of automated investment and artificial intelligence in the stock markets, the search for the Holy Grail of stock market investment has been to develop and refine the algorithm that would allow for predicting the behavior of the stock market and the actions of the companies in the future listed.
Needless to say, knowing how to predict the future trends of stocks translates into cash and sound money, and it is also necessary to act based on those predictions ahead of other investors, before the scenario is discounted by all in the market. And now there is a new generation of Machine Learning (ML) that yields a success rate in the future that cannot be the result of mere chance: yes, ML already hits a very high percentage, and also with a success rate much larger than the vast majority of human stock advisors, 79% and even 90% in certain cases.
In the stock market, first having always meant earning more money or losing less. Being the first to negotiate literally translates into money. It is taking it for granted that “information is power”, and operating with an anticipatory vision where others are disoriented and giving “blind sticks” in the markets. It goes without saying that in the second it is usually them who end up losing the sticks of losses because there is nothing worse in the bags than having no more strategy than a few misleading hunches. In this, an automated investment may be contributing a lot to the markets, since it establishes clear and synthetic investment rules, and avoids the scenario of breaking them down by human passions that are very dangerous for your pockets, such as euphoria or panic.
But even with an AI that will obviously be marketed, the more massively the better, it is highly likely that those predictions in the future will be available to many investors -human or synthetic. And under this scenario, when many in the market have that prediction with a high probability of being fulfilled, again it must be said that being the first to negotiate will result in money again, with the addition that now the speed will be absolutely decisive to shed profits or losses on each operation.
Only technical analysis is used as a tool for stock predictions because it is considered to be easy-going for the algorithm to learn and the human to interpret, giving predictions where there is only one attribute i.e., historic prices of stock. The current algorithm gives predictions of one single stock that is given as input to find future predictions.
Here are a few companies that use Machine Learning in Technical analysis for stock prediction:
● Trading Technologies.
● GreenKey Technologies.
Recently an Israel based stock forecast company named ‘I Know First’ using predictive Artificial Intelligence demonstrated an accuracy of up to 97% in its predictions for S&P 500 and Nasdaq indices, as well as their respective ETFs. So there’s a lot that can be achieved or explored with the use of Machine Learning in stock prediction.AI is just a new twist to what has already been the virtualization of markets since the arrival of automated investment.
As we said before, this profitable telecommunications-operational symbiosis is not exactly something new as it has been that way since the dawn of automated investment some five years ago. It is true that it began by taking no significant benefits from the small (even imperceptible) market fluctuations, in which the agility of the operation was fundamental since these spikes in share prices can last even for a fraction of a second. If one was able to operate in the same order of temporal magnitude, there was a possible benefit to be taken out of the market. But what is really news now is that, as we will analyze it, this ultra-rapid factor in the operation acquires double relevance under the scenario of the existence of a successful AI algorithm.
We must emphasize that these algorithms may be contributing to improve price formation and to make the market work better, but the negative side is to delegate human decision-making capacity to algorithms that know how they will react to black swan events. Indeed, we said before that human error is being carried away by euphoria or panic, but we said this assuming regular conditions. In scenarios of volatility not suitable for cardiac and black swans, although many investors can continue to fall prey to those unprofitable passions, there is the moment when the value of a mature, professional, and experienced manager is literally worth in gold, being the moment when he should take the helm.
It is necessary to consider as a requirement of the software architecture of the investment programs that something like the automatic pilot of an airplane is implemented: in regular conditions, the aircraft is perfectly piloted by the automated system, but when things look rough, the pilot can regain control of the ship and get passengers out of vital trouble. The automated investment must take these same precautions because today, the human mind is still infinitely more intuitive and analytical than an algorithm that after all is based on historical data which may sometimes not serve as a seed to iterate the learning of artificial intelligence. This can be especially so when we must weigh factors of subjective perception, which can also have a strong influence on the market, and whose subjective complexity is a great degree of added difficulty for an objective robot, not to mention the global cost of continually training with recurring iterations a multitude of investment robots across the planet.
Bid farewell to the simple real-time investment that was new in the 90s and welcome the era of the real-time market investments. We will operate based on an ephemeral ever-changing market scenario, which will cease to exist as soon as we do it together with a certain critical
mass of investors. Never before have your investments had more aggregate capacity to cause disruptions in the market. Welcome to the new reality.
This article is co-authored by Dr. Raul Villamarin Rodriguez and Rajat Toshniwal, Woxsen School of Business