AI and finance are useful for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye. Financial firms have invested heavily in AI in the past, and more are starting to tap into the financial applications of machine learning (ML) and deep learning.

We’re now joined by CEO and Co-founder of OptionMag, whose company offers AI trading applications for enterprises and individuals. Lu gives us examples of ways Kavout is using artificial intelligence in stock trading to build better products and services.

Is meshing with ML convenient for traders?

Alex Lu: “The big boys from Wall Street, when they looked at AI models, they found that by using machine learning they can do things almost impossible before. They are trying to recruit people from Google, from Microsoft and from Apple to help them build huge AI clusters, to leverage this technology for trading and investing today.”

Will it bring advantages to all trading firms?

AL: “Since we started, only some of the very large hedge funds and financial institutions were able to gather enough resources to invest in this field, so it’s stll not fully known.

Considering some of the data we see in 2017/18, the AI-based trading firms are doing pretty well, while the rest of industry is not doing that good… I believe that in 2020/2025 this space is going to get very crowded, but it’s not something everybody can do.”

It sounds like deep learning and machine learning are on the way to grab an edge. Is that true?

AL: “ML has been evolving in the last 15 years. It’s definitely a new technology, capable of helping people to manage lots of data sources, to estimate trading, ideas, and make better investing decisions. I think that’s also why you see so many big firms investing in this area.

You see, Apple just acquired a ML company in Seattle, Turi. Not only on Wall Street, but also the traditional big tech companies are moving into this space.

You at Kavout are also working on allowing traders to leverage AI tools. What are some of the applications that are now available to consumers?

AL: “We’re facing thousand of stocks to pick every day, it’s a very daunting task; today by using AI, we can actually do all the number crunching, look at all the news media, the social media, blogs, and also the real-time codes: we can basically scan thousands of stocks in real time and give you the best idea, so that’s where the technology is very good today.”

Talk us through what the differences are that ML or AI has made, what’s happening differently in the trading world.

AL: “…Nowadays all the traders have so many real-time streaming news. To mine information from these unstructured data sets becomes very important: we need new technology to handle this, which is new even to Wall Street.

ML and deep learning can now mine lots of trading insights in a way we could not imagine before: by natural language processing. We can have a computer understand the semantics and meaning of how people say something. This is something we call “sentiment analysis”. We are building a “sentiment score”, which means we are leveraging all the sentiment we collect from traders, news or blogs.”

What other bits of utility are now available to consumers that maybe didn’t’ exist 5 years ago?

AL: “The chart pattern recognition. On Wall Street we used to have people to look at charts every day and recognize some patterns, but now we have the technology to actually scan every single stock and find all the tradable classical chart patterns. That will save you lots of time and help you capture more trading opportunities.”

You talked about a more calibrated approach per person, would this be taking into account their goals? How?

AL: “Well, all the robotics advisory and financial planning done today is assuming you stick to the strategy for 30 or 35 years, but a study shows most people change their strategy every 3 to 5 years. We have to build new technology to take people’s behavior into consideration.”

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