The emergence of Artificial Intelligence in the stock market

The emergence of Artificial Intelligence in the stock market

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.

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.

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.


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.

Will AI take over the stock market? Robotic stockbrokers are starting to predict  changes in share prices better than humans


  • Japanese brokers are testing artificial intelligence to predict the market
  • Their algorithms has got its predictions correct 68 per cent of the time
  • Most human analysts can only get 48 per cent of their forecasts right
  • It has raised the prospect of AI computers taking over share dealing

The stock market is notoriously unpredictable – for those who can make the right calls, vast sums of money can be made, but get it wrong and the losses can be equally huge. Now some stockbrokers are hoping that artificial intelligence will tip the balance in their favour. Japanese researchers have developed a computer capable of predicting the rise and fall of the market correctly 68 per cent of the time.

It suggests learning algorithms, which assess huge volumes of data to predict stock prices and economic fortunes, could transform the markets.

While computer algorithms have been used in share trading for decades now, allowing transactions to be timed to a fraction of a second, the use of AI has been slow. Dr Junsuke Senoguchi, a senior equity strategist at Mitsubishi UFJ Morgan Stanley Securities in Tokyo, however, has being testing a new robotic stockbroker. Every month, for four years it has produced a single simple prediction – whether the Nikkei 225 Stock Average index will be higher or lower in 30 days time. Dr Senoguchi told Bloomberg Business that the robot has been getting more and more accurate. He said: ‘Artificial intelligence gives you much better results than conventional statistics. ‘Sometimes the structure of the market changes greatly.
‘The ability to change the model when this happens is a big difference from previous approaches.’

According to Dr Senoguchi, since March 2012, his robotic stockbroker has been right about the changes in the market 32 times out of 47 – giving it a hit rate of 68 per cent.
Although this is still a relatively small number of tests, it exceeds the figure that would be expected by chance. Indeed, research compiled from market forecasters since 1998 by CXO Advisory Group revealed humans were only able to accurate predict the market direction 48 per cent of the time.

With a prediction hit rate of 68 per cent, the machine is already outperforming human stockbrokers, although the number of forecasts the computer has made are still small. Some experts believe artificial intelligence could transform how trades are made. Stock image of share prices on a computer pictured

Dr Senoguchi’s algorithm combines 92 economic indicators over multiple timeframes and uses sets of rules to eliminate those that it sees as being least predictive over the past 48 months.
It then uses this data to produce model to predict what will happen in the coming month. Each month it starts afresh and takes into account any changes.

However, the AI stockbroker is still far from being perfect. According to Bloomberg it utterly failed to predict the huge fall in share prices around the world that marred the start of 2016.
It said the period ending January 10 would see the Nikkei 225 rise. Instead it fell to one of its lowest levels on record.
Regardless, artificial intelligence experts have described the approach being taken by Dr Senoguchi as ‘extremely new’ and said the computer’s level of success was ‘pretty high’.

The AI stockbroker failed to forecast plunging markets at the start of 2016, sparked by massive falls in share prices on China’s stockmarket (pictured) which reverberated around the world

How well does a “robot AI” predict the Japanese stock market?


A recent Bloomberg article reported on the work of Junsuke Senoguchi, who has developed a“robot” artificial intelligence-powered computer program that forecasts the Japanese stock market, in particular the Nikkei-225 index.
Senoguchi, who currently works for Mitsubishi UFJ Morgan Stanley Securities in Tokyo and who has previously worked for Lehman Brothers and also the Bank of Japan, has a Ph.D. in artificial intelligence (AI), and his new investment program employs AI techniques. Senoguchi is delighted when it is working well, “because I feel I can predict the future“.

While we certainly wish Mr. Senoguchi well in his efforts, we need to check his results to date. Bloomberg reports that in the four year period from March 10, 2012 through January 10, 2016, his program has predicted the next month’s movement of the Nikkei-225 index, up or down, 32 times out of 47, or 68% of the time. How does this compare with a “random” strategy?

The Halloween indicator and the January barometer

In previous columns, we discussed the Halloween indicator and the January barometer.
The January barometer is the claim that whether the S&P500 index goes up or down for the year is well predicted by whether it goes up or down in January. This has been correct 62 of the last 85 years, or 73% of the time. This may sound significantly better than a coin-flip, but we must keep in mind that the proper comparison is with a weighted coin-flip, since the S&P500 has gone up 63 out of these 85 years. If one compares the January barometer with a coin-flip weighted to turn up heads 74.12% of the time, then it is almost as good as the January barometer.

Analysis of Senoguchi’s results

So let us take a closer look at Senoguchi’s results. First of all, note that in 47 trials, a 50-50 coin flip will be correct on average 23.5 times. The standard deviation here is 3.4, so Senoguchi’s 32 successes out of 47 is a roughly two-sigma result, which corresponds to only a 5% chance that it is due merely to random chance.

But, as we noted above, it is really not appropriate to compare with a 50-50 coin flip, since in this case the Nikkei-225 index has increased 27 out of the past 47 months since March 2012. If we then consider a weighted coin with probability of heads = 27/47 = 0.574468…, then we obtain the result that a coin-toss experiment with 47 trials will be correct slightly more often —24 times.

But even this experiment, one might argue, is not quite appropriate, since beginning in March 2012 one would not know the performance of the Nikkei-225 over the next four years.

So let us consider the following additional experiment: each month (on the tenth day), beginning in March 2012, look back over the previous four years and tally the fraction of times the Nikkei-225 has gone up in those 48 months, then toss a coin with this weighted probability as the guess of the market’s behavior in the next month. We have done this with a computer program, based on historical market data. On average, our program is correct approximately 24 times on average out of 47, the same as above.

One way or the other, Senoguchi’s result is a roughly two-sigma result. While promising, in our view it is not yet time to break out the finest champagne.


As we noted in our blog on the January barometer, insomuch as models of this type rely on market indices such as the S&P500, the Nikkei-225, the FTSE100 or the DAX-30, particularly if only taken at an interval of months or years, it is very difficult to avoid backtest overfitting — there simply is not nearly enough data to make any statistically meaningful statements.

Data over a longer time horizon — 25, 50 or more years into the past — is certainly available, but it is of highly questionable relevance today. This is especially true given the rise of computerized trading in recent years, which means that any trends or patterns that may exist in data are spotted (and neutralized) by sophisticated computer programs long before human analysts see them.

In short, while we wish Mr. Senoguchi well, it is premature to say whether his system has any fundamental, persistent “skill,” particularly in today’s highly mathematical, computerized market. There are no slick tricks to wealth, and no substitute for patient, long-term, low-cost investing.

Artificial Intelligence will be crashing the Stock Market in 3,2,1…

Artificial Intelligence will be crashing the Stock Market in 3,2,1…










The Flash Crash of 2010
















Killing the Goose that Laid the Golden Eggs






The Need for Human Authority







Final Thoughts

