An old Chinese game known as Go was once the most difficult game ever invented, and Ke Jie was the best player in the world at it.Stock market, in 2017, Ke, who spent all of his life mastering that particular game, was easily defeated by a computer program.
With its victory over the human, robotics marked a milestone for artificial intelligence (AI) and machine learning (ML) technology through an aspect of AI in which computers learn to perform a task without being specifically programmed to do so, in spite of learning and improving as they attain experience.
Artificial intelligence and machine learning are now being applied to a wide range of industries, including transportation and health care. Using AI algorithms, driverless cars are taught how to navigate city streets and even diagnose patients. In finance, too, these technologies are becoming more and more important for deciding what stocks to buy and sell.
Artificial Intelligence and Machine Learning are changing the dynamics of the stock market
Many people have been pleasantly surprised by how successful machine learning has become in a variety of industries. It wasn’t long ago that we started witnessing all these incredible breakthroughs, such as machines defeating the best players at [the board game] Go.
Having computers that can solve these problems has revolutionized our society. As well as having a lot of data previously unavailable, we have sorted out ways to use these data in conjunction with algorithms.
As a result, ML algorithms are experts in areas where only humans were deemed experts in the past.
Challenges of artificial intelligence in stock trading
This industry is unique because it does not produce very large data sets for things like recognizing faces or driving cars, which are data sets that are used for training these algorithms.
The stock market evolves as well, you will see some stocks moving in after market trading based on sentiments originated from a reddit or discord group. A market learns to prevent you from taking advantage of its profits next year when you withdraw money from it.
Machine Learning to Invest is often doomed to failure because machine learning is treated as a black box. A black box prediction using these techniques is not feasible at this point.
The applicability of the model cannot be understood unless understood the reason it works in the first place.
Investing through Technology
First of the key point here is research should be conducted using machine learning rather than forecasting. Once a new theory is identified, the machine role ends, because you no longer need it.
Taking this approach to finance is what I believe is the right one.
Machine learning should be used to develop theories and test theories, but once a theory has been identified, it should not be run by the machine, but by the investor itself.
Algorithms are used to perform the bulk of transactions today. Humans are involved in a very small number of transactions. In other word we are no longer needed. The execution of this plan should be considered by anyone who doubts its efficacy.
Prospects for an AI-based trading system
Artificial Intelligence, at some point, will be able to solve many tasks that humans are currently incapable of. It is only a matter of when, not if.
Automated investing will eventually replace human investment. When that might happen is anyone’s guess. Everything comes down to analyzing data. It is possible to achieve success when you have a machine that can objectively and efficiently process information.
Impact of technology on Financial markets
In the long run, it will have a very positive impact on society. A more efficient allocation of assets ensures that the companies that have the greatest chance of being successful get the assets, and the companies that aren’t very likely to be successful aren’t granted assets.
Nevertheless, if these technologies are correctly deployed, they will make stock market more efficient, and investors will be more inclined to invest. Science-based investment approaches will help society in the sense that financial and investment decisions should be made based on scientific evidence rather than on pure speculation.