Did you know that Walmart is a solid real estate company, Apple is one of the leading fintech providers, and Amazon competes with pharmaceutical companies?
These were the findings of AI-based research provided by Harvard scholars to learn how technological advancements blurred industry margins.
The study, provided by MarcAntonio Awada and Suraj Srinivasan, used a machine learning algorithm to follow how different companies in 25 other industry branches evolved and expanded their specialization over time. The tool took data from 1999 and onwards.
The outcomes were unexpected. With the growth of technology, the margins between industries are erased, giving new opportunities to those who embrace the change and opening new investment opportunities to those interested in profits.
Thus, during the lockdowns of 2020, Amazon stock prices went over the roof, while things weren’t so fine at Walmart. That was because Amazon had outgrown its retail niche and evolved into a solid IT player, while Walmart was still mainly a retail company suffering from lockdowns.
The AI tool simply did a job requiring tons of specialists: it checked the presence of each company researched in different niches, measured their success indicators, and returned exciting findings to be further used by investors.
Amazon, for example, shows that the company has a strong presence in the retail business but is a strong player in the cutting-edge IT niches. That makes the company attractive to investors interested in information technology.
It wasn’t the first time big data and machine learning were used to examine investment opportunities in trading. MarcAntonio Awada, previously a hedge fund trader, says trading managers are among the first to introduce machine learning in their job: “The bottom line is that we want to make money and always looking to generate alpha in our investment strategies, so if machine learning can make us profitable and improve our risk management and trading efficiency, so be it.”
According to Forbes, AI tools are widely used for investment research, as they can quickly process millions of open-source articles, which is considerably more than an average human knowledge base. AI is used for risk assessment or studying how human sentiments and likes change.
Moreover, AI halts investors from unweighted decisions. For example, news about problems with a company in the investment portfolio may push a starting investor to sell the stocks, which may be a mistake. An AI tool can quickly collect and analyze all the news related to the company and return a more realistic picture of what’s going on than superficial news release headlines.
The Google Play market now offers a solid selection of AI-based investment applications to cater to different investment needs. For example, Investting.com provides live and uninterrupted analysis of over 100,000 financial instruments from 70 global exchanges worldwide to provide users with up-to-date information.
Yet, the needs of investors are different. If you have an idea of an AI-based investment tool to build, check your opportunities with MLSDev – business analysis services. The company will help you check the viability of your idea, calculate the cost and timeline of its implementation, and give you all the advice you need to start.
Author’s bio: Anastasiia Lastovetska is a technology writer at MLSDev, a software development company that builds web & mobile app solutions from scratch. She researches the area of technology to create great content about app development, UX/UI design, tech & business consulting.
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