Artificial intelligence and machine learning are revolutionizing the way we do business — particularly for complicated and data-heavy activities like stock trading.
Ever since Calahan’s first stock price ticker system was unveiled in the 1860s, machines have been an integral part of the global stock market. Now computers are so ingrained in the trading process that 80% of the stock market is on autopilot, largely due to the rise in passive funds and investing.
Now, advances in machine learning and artificial intelligence (AI) are opening new ways to integrate technology into the trading process and could help to democratize the stock-market and help mitigate some of the problems with automatic trading.
How Artificial Intelligence and Machine Learning Could Revolutionize the Stock Market
Good data makes for good decisions. But that can go too far. Finance companies collect literally trillions of data-points — any of which might provide important insights but together make for a lot of noise. This all needs to be sifted through and interpreted, which translates to many hours of work in order to gain a trading edge.
Recent advances in AI, particularly machine learning, have provided a solution that can help traders make sense of all that data. Machine learning algorithms are able to understand and interpret vast quantities of data. There are many different types of machine learning algorithms, but they are all capable of using insights from past data-sets in order to draw better conclusions in the future. In simple terms: machine learning algorithms improve with use.
To make a machine learning algorithm successful, a number of building blocks need to be put into place. To better understand how that works, let’s take a look at some real-world examples of machine learning in action.
Step One: Collecting the Right Data
If there is one thing you should take away from this article it is that AI and algorithms are dumb. For the moment, they are incapable of understanding whether the data you feed into it is flawed or not. This means that if you are feeding your algorithm bad data, you will get bad insights. For a solid example look no further than the 2016 presidential election.
To gather good insights, you need the right structured dataset, and most important, you need to understand what question you are asking in a fundamental way.
One company that does this very well is Gro Intelligence. It collates vast amounts of data from a variety of sources to make highly accurate predictions of agricultural commodities. The key here is that the company is accessing data-sources that are usually siloed and combining them to provide better data-sets and better insights.
Step Two: Analyzing the Data
Once you have great data-sets, the next step is to feed it into the algorithm. A machine learning algorithm is capable of emulating the work that an entire research department used to do. But it does it far faster and at a fraction of the cost. One particularly interesting example of strong data analysis is tech startup Kayrros.
The company takes advantage of anonymized data-sets, geolocation data, and satellite imagery to build unique insights. For example, Kayrros has a system that allows investors and traders to monitor oil supply from US shale basins in near real-time.
The company combines public permit and oil well metadata with optical and radar satellite imagery to detect crew activity at more than 90,000 locations in the US. This enables investors to get up to date information and make better decisions regarding investments in the oil and gas sector.
Step Three: Applying the Model
Once you’ve built a model, the next step is to make it actionable. This can be done just by applying the models to human traders. But some startups are trying to take this a step further with algorithmic trading.
This is a process for executing orders using automated and pre-programmed trading instructions. By feeding in data from machine learning models, like those produced by Gro Intelligence and Kayrros, it is possible to use these algorithms to make smarter decisions in real-time than any human trader could hope to do.
There are a lot of startups looking at ways to apply algorithmic trading. Some, such as Algotrader are working to apply this technology towards helping enterprises and hedge funds. Others, such as Streak are working to lower the barrier to algorithmic trading and remove the requirement for coding expertise.
Widened access to algorithmic trading will be key for many companies and investors in the coming years. It will likely be essential for those who want to stay competitive. And it will help ensure that capital is being used as effectively as possible, improving the economy overall.
AI Is Set to Revolutionize the Way We Trade and Invest
AI, machine learning in particular, is set to revolutionize all aspects of trading. Many startups are focusing on the importance of good data, but there are also others leveraging AI to streamline communication or make investing easier for the average person.
This has created a huge opportunity for forward-thinking startups and could be the foundation of a number of success stories in the years ahead.
Cover image via Pixabay