Investment Management – The Final Frontier for Machines
The adoption rate of new technology is dependent on several factors which include the cost of technology, the ease of adoption, available infrastructure, cost of human effort to be replaced vs cost of technology, likely benefits for customers and the personal initiative of pioneers in the particular industry. When we talk about companies as a sub-set of an industry, the senior management and profitability matrices play a bigger role. As a result, there are huge variations in how quickly the technological innovations will bring change in an industry.
In an interesting coverage by ‘The Economist’, the influence of machines on stock markets is being discussed [1]. The publication talks about how the market is now run by computers, algorithms and passive managers. It talks about how machines are taking control of financial markets including elements with a high degree of subjectivity such as monitoring the economy and allocating capital. The numbers are astonishing. For example, the funds run by computers account for 35% of America’s stock market, 60% of institutional equity assets and 60% of trading activity.
The important thing is that the new age machines are not just taking over the work which used to be done by humans in the markets. For example, the said article says that the new artificial-intelligence programs are also writing their own investing rules without human input. These are relevant changes because finance has always been an area which can influence so many other industries as well because of its control on capital. It is clear that these numbers are going to get larger. When we speculate on how other markets will adopt these changes, there hardly is a debate. Once the changes begin in one market, they quickly spread to others.
The writing is clear on the wall. There is need for more sophisticated data analytics, more advanced algorithms and use of Artificial Intelligence for generating superior investment returns. The impact of important business developments and sentiment in daily news flow is prominent factor in stock price movement. EMAlpha’s proprietary model which generates sentiment scores using our dictionary and the trades executed on the basis of those sentiment scores are beating markets. There are of course challenges in terms of what to include and what not to include when we look for the data sources and news, but there is no doubt that it works.
We have found a strong linkage between how the portfolio performance and its rebalancing on the basis of sentiment analysis. Thankfully, this linkage is not just strong but it is also positive, beating markets by a good enough margin. But does this mean that market gets just too much influenced by the news flow and stocks outperform when the sentiment is positive or underperform when the sentiment is negative. It may not be a surprise because that is how it should be but the interesting part is that market doesn’t adjust the stock prices immediately and there is enough time window available to execute on these trades.
It doesn’t just work when we carry out back tests. That was of course the background to see what we are doing. But real time scores followed by the real time trades lead to better investment decisions and better portfolio performance. And we fully agree with what ‘The Economist’ is highlighting. The Big Data and the AI wave is getting stronger and stronger in Finance and this may not reverse anytime soon.
[1] Masters of the universe: How machines are taking over Wall Street, The Economist, October 5th-11th, 2019.
Research Team
EM Alpha LLC
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About EMAlpha:
EMAlpha, a data analytics and investment management firm focused on making Emerging Markets (EMs) accessible to global investors and unlocking EM investing using machines. EMAlpha’s focus is on Unstructured Data as the EMs are particularly susceptible to swings in news flow driven investor sentiment. We use thoroughly researched machine learning tools to track evolving sentiment specifically towards EMs and EMAlpha pays special attention to the timely measurement of news sentiment for investors as these markets can be finicky and sentiment can be capricious.Our team members have deep expertise in research and trading in multiple Emerging Markets and EMAlpha’s collaborative approach to combining machine learning tools with a fundamental approach help us understand these markets better.
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