In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative, to begin with so that it’s hard not to find traction.
Financial firms have also invested heavily in AI in the past, and more are starting to tap into the financial applications of machine learning (ML) and deep learning. Artificial intelligence in stock trading certainly isn’t a new phenomenon, but access to its capabilities has historically been rather limited to large firms.
AI and machine learning, quantitative investing and trading
Eventually, Wall Street, when they looked at AI models, found that by using machine learning they can number crunch millions of data points in real-time and capture some of the correlations that traditional statistics models could not capture, and that is the dollar track to go after today. Especially the deep learning models, a new trend in the last two years.
This gets the attention from the big boys on Wall Street, and they are trying to recruit people from Google, from Microsoft, from Apple and IBM Watson, to help them build huge AI clusters, to leverage this technology for trading and investing today
At the very beginning of the last few years, only some of the very large hedge funds and financial institutions, like Goldman Sachs, were able to gather enough resources to invest in this field. So today it’s still not common knowledge among financial institutions, and Kavout is one of the only firms investing in this direction;
I think it’s going to be a very popular space, based on some of the data we see in 2015, in the hedge fund world, the AI-based trading firms are doing pretty well versus the rest of the hedge fund industry is not doing that good. I think in 2016 and 2017, this space is going to get very crowded, but it’s not something everybody can do.
ML has been evolving in the last 15 years, and deep learning is a breakthrough technology and helping people to manage lots of data sources and come up with new patterns to help estimate trading, ideas, and make better investing decisions.
I think that’s also why you see so many big firms investing in this area, and also you see Apple just acquired an ML company in Seattle, Turi…so not only on Wall Street but also on the traditional big tech companies are moving into this space.
We’re facing thousands of stocks to pick every day, it’s a very daunting task; today by using AI, we can do all the number crunching, look at all the news media, the social media, blogs, and also the real-time codes, we can scan thousands of stocks in real-time and give you the best idea, so that’s where the technology is very good today.
In our company, we built something see look at all the fundamentals, the technicals, and also momentum for the traders, and we come back with a score to rank every single stock.
Now all the traders have so many real-time streaming news, and to mine information from these unstructured data sets becomes very important, so we need new technology to handle this, which is new even to Wall Street, but with ML and deep learning we can now look at all these unstructured data sets and mine lots of trading insights which we could not do before
We can do all this today in natural language processing, which means we can have a computer understand the semantics and meaning of how people say something…and in news, this could be something positive or negative about certain companies, and that’s something we call sentiment analysis.
We are building something called a sentiment score, which means we are leveraging all the sentiment we collect from traders, news, blogs, and we’re collecting some of the data from transactions. For example, we collect all the insider data trading sets, so we know for which company, which CEO is buying or selling stocks; try integrating this transaction data with the trader’s sentiments, and we can come up with a better score to know how people think about a set of stocks.
The other is chart pattern recognition; on Wall Street, we call these people chartists, so firms have people to look at charts every day and recognize some patterns, but today we have technology where we can scan every single stock and find all the tradable classical chart patterns, and you don’t have to do it by using human eyes; that will save you lots of time and help you capture more trading opportunities.
There’s a very interesting study…all the robotics advisory and financial planning done today are assuming you stick to the strategy for 30 or 35 years, but the study shows most people change their strategy every 3 to 5 years, which shows the assumption for all these Robo-advisors does not work with all users…so we have to build new technology to consider people’s behavior and come up with a more adaptive asset locator.
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