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AI is a predictive model that looks at more than technical patterns of trading; it has the ability to identify financial features of companies that will make money in the long run

Technology can be used either to make our lives better or make money. With the former, you set up a technology company such as Grab to make it easier for consumers to get a ride. The latter is normally used in trading which is commonly done in a financial hub like Singapore.

Technology is constantly evolving and for this article, we will be focusing on the latter which is to use technology to make money. The general public would have already heard of high-frequency trading (HFT) which uses algorithms to decide their buy and sell trades based on preset criteria. HFT firms like Tradeworx make their money by being faster than the average trader by taking microseconds to place a trade.

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HFT trading firms have been active since 1999 when the SEC authorised electronic exchanges in 1998. Today, the markets have realised that just being fast does not necessarily mean that you will make money. You will also have to be smarter than the rest of the market.

The New Edge

If HFT firms represent the old-age of algorithm trading, firms like Aditya and Sentiment Technologies are the new edge of algorithm trading infused with Artificial Intelligence (AI). HFT algorithms are static in searching for pre-determined trading patterns that is beyond human comprehension in both scale and speed. It will work for a week, a month or a year before the rest of the market catches up with it.

Then the quants of HFT firms will have to develop new algorithms to find another set of pre-determined trading pattern. AI algorithms have the ability to learn from the changing patterns of the market. So when the old profitable trading patterns stopped making money, the AI can switch effortlessly into a new trading pattern.

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In the one month or more that HFT firms used to identify new patterns and code their algorithms manually, AI firms have the advantage of trading in a relatively uncrowded market.

Artificial Intelligence – Cross Disciplinary Approach

The challenge for AI firms would be to create the versatile and adaptable algorithm in the first place. Aditya’s Chief Scientist, Dr. Benjamin Goertzel, is a mathematics professor who combined his expertise in machine learning, computational linguistics, natural language processing, computational finance, bioinformatics, complex systems and cognitive science with a team of talented scientists to create the AI algorithm.

Sentiment’s Chief Scientist Babak Hodjat was behind Apple’s Siri platform and a prominent developer. Sentiment used models based on biological evolution and deep learning to create their proprietary software named ‘Evolutionary Intelligence’.

AI is a predictive model that looks at more than technical patterns of trading. It has the ability to identify financial features of companies (e.g. price to earnings ratio, long term business loans) that will make money in the long run. This requires capabilities from different areas of study and massive computational power which is why it is only prevalent in recent years.

Aditya was started in 2011 in Hong Kong and started its first fund after three years’s of effort. Sentiment started their AI project in 2007 and only emerged in late 2014 with their product, Evolutionary Intelligence.

Conclusion

HFT is designed for short-term trades which lead to the Flash Crash of 2010. This is also a system which incurs a high transaction cost to make money. AI trading allows for long term trades that would make money over the next year.

For instance, AI can search through countless analysis of the yen and predict if Toyota will make or lose money before they publish their results three months later. The market would gain more stability and this appears to be the new way forward.

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