Is Machine Learning Worth Using in Stock Trading?

Data scientists, engineers, and individual traders have been pursuing the idea of building machine learning (ML) algorithms for stock trading for years. IT has been the major challenge for thousands of development teams across the globe involved in the stock trading industry.

If you consider the Empirical Research method, there is a chance to develop such algorithms that will take the niche of automated trading to a new level. It will be jot just about order execution as per pre-set parameters but a program that is able to learn and adopt changing market conditions depending on the situation.

In this article, we will discuss the potential symbiosis of the stock market and machine learning approach to trading different assets.

The Idea behind the ML Stock Trading Algorithms

Scientists say, ML stock market algorithms can be based on detailed historical data generated from a variety of different companies, stock futures platforms, and exchanges. This information can be used as a pre-annotated dataset that would create a basis for ML training.

The idea is to let new algorithms use historical data feed to learn and identify new patterns. Besides, they can analyze those patterns and make accurate predictions on how the stock price will move under various market conditions.

The main questions that are still to be debated are the following:

• Can machine learning be successful for trading stocks?
• How far into the future can ML algorithms make accurate predictions?

The nature of this particular argument comes out from the axiomatic debate foundation. It says that the stock market is very unpredictable. In simple words, it means that the stock market is impossible to predict even when taking into account historical data.

Additionally, we should not forget about multiplicity of variables. They also affect the stock price in one or another way. They involve not only trend moves but also stock market news (economic and political), social factors, climate changes, natural disasters, wars, and so on. What’s more, if we consider the irrational behavior of active agents, we can say that the stock price is impossible to predict as well.

When we say “hard to predict”, we generally mean long-term predictions.

For and Against Machine Learning Algorithms in Stock Trading

For obvious reasons, traders would be glad to have such technologies to trade in the stock market today. It would let them make more accurate predictions and make huge financial gains on autopilot. What’s more, scientists say, it is possible to train ML algorithms to train on historical data generated from specific companies making stock price predictions even more accurate. Taking into account extreme market volatility, this kind of tool will ensure a safer approach to trading. While humans are not able to consider all mutual dependencies right at once, ML algorithms are expected to solve this problem.

Yes, machines do not need to sleep or eat. They do not rest and can conduct analyses of an enormous amount of data. On the other hand, machines will never be able to make decisions with a specific human input. This is why the key challenge for the stock market today is to make ML algorithms learn and understand different kinds of patterns. The main problem here is that the result may include too much randomness, which interferes with the idea of accurate predictions.

Risks of Machine Learning in Stock Trading

With all the benefits ML algorithms can bring to stock traders in future, the idea can be quite risky for either individual investors or brokers. The main problems with machine learning in the stock market include the following:

• Extreme Volatility. Stock markets are extremely volatile, which makes it very uncertain. The Random Walk theory will make things a bit easier to understand. On the one hand, we can predict some factors and take them into account while making predictions. On the other hand, some variables can never be considered, for example, natural disasters.
• Lacking Accuracy. As stated earlier, ML algorithms can make sense only in the short-term timeframe. However, if we think in a longer forecast horizon, the accuracy will inevitably decrease.
• Growing Competition. We should always keep in mind that the stock market is the area of extreme competition. More and more traders enter the market to buy and sell stocks. The more agents build their own ML algorithms, the quicker they can adapt to new market conditions. In the end, we will have numerous models of the same type that simply replicate each other. Stock market news will be the only competitive thing to explore.
• Training Data Density. We can use tons of historical data available in different forms. This fact makes it easy to train ML algorithms. However, this data can turn out to be insufficient considering the decreasing information density. It can lead traders to unfounded decisions, as the stock market historical data is very flexible and movable.

The Bottom Line

Stock trading automation has already reached a high level. Market participants can benefit from advanced trading bots and other software developed to help them handle multiple trades automatically. Machine learning algorithms in stock trading are expected to be the new level of automation development.

The main challenge is to create algorithms that will match the always-changing nature of the stock market. The question is if artificial intelligence is good enough to suit such complex predictions in the fact of market uncertainty and volatility. Will scientists and engineers make it? Coming soon.

This material does not contain and should not be construed as containing investment advice, investment recommendations, an offer of or solicitation for any transactions in financial instruments. Before making any investment decisions, you should seek advice from independent financial advisors to ensure you understand the risks.