Artificial Intelligence has revolutionized numerous industries, and the world of investment is no exception
In recent months, the advent of ChatGPT has brought a surge of interest in the use of AI in the investment world, which was previously much less receptive.
Many giants of the industry have already made their move: for example Euronext has launched thematic indexes using NLP algorithms.
They all realize that AI has become an invaluable tool for investors, thanks to its ability to process vast amounts of data, identify patterns, and make more and more accurate predictions.
As a matter of fact, Artificial Intelligence is reshaping the investment landscape by empowering investors with advanced tools for data analysis, risk management, automated trading, and portfolio optimization.
One of the most prominent advantages of AI in investment is its ability to analyze massive volumes of data and derive meaningful insights.
AI algorithms can efficiently process structured and unstructured data, including financial statements, news articles, social media sentiments, and economic indicators. By analyzing historical data and real-time information, AI models can identify hidden patterns and trends that the human eye might fight to detect.
The Euronext Artificial Intelligence Index
Unlike institutional investors, retail investors decisions are driven by thematic/trends classification and not by sector/countries distinction.
AI can help in automatically identifying the most relevant trends and the companies focused on those themes.
That’s the core of Euronext’s new thematic index, using NLP algorithms to analyze public data available on the web.
Thanks to SESAMm partnership, the Euronext Artificial Intelligence Index selects the 100 highest ranking Companies active in Artificial intelligence, Speech recognition, Computer vision, Computer linguistics, Machine learning, Computer audition, Robotics, Discovery, Planning, Creation.
SESAMm leverages analysis of over 20 billion articles to produce valuable insights and indicators that improve investment decision-making across various asset classes (equity, fixed-income, alternatives, crypto) and themes (thematic investing).
The next step: FinScience approach for Thematic Analysis
FinScience has dedicated its efforts to developing a robust proprietary methodology for thematic analysis.
In fact, FinScience provided the set-up and implementation of relevant thematic data analysis that will enhance the creation of a new breed of thematic products.
Over the last 5 years, FinScience has gained a specific expertise in detecting and understanding various themes (more than 70+), as well as establishing connections between equities and these themes.
This commitment to thematic analysis turned FinScience in the official Thematic Data provider for Nasdaq Data Link.
How does it work?
Thematic framework definition
Utilizing advanced artificial intelligence techniques, FinScience has run in-depth analyses to identify relevant microthemes among 1000+ sub themes. The process includes screening of trending, active and inactive themes and discovery of new themes.
Building upon the identified microthemes, FinScience links them and defines a concise list of 70-80 macro-themes, resulting from similarity algorithms and expert knowledge.
By examining the relationships and similarities between these companies, FinScience identifies similar themes and clusters them together in 20-30 groups, looking at correlation/similarity among themes based on underlying companies.
Thematic framework monitoring
By providing proprietary metrics on themes, FinScience empowers investors to make informed decisions regarding thematic investments.
These metrics include:
- Hype: This short-term momentum indicator aids in identifying performing themes by capturing market enthusiasm and trends.
- Robustness: By assessing the stability and durability of a theme over time, it helps investors identify themes with sustainable growth potential rather than ones driven by temporary momentum.
- Similarity/Correlation Algorithm: to assess similarity and correlation among themes, creating diversified investment products or building theme clusters.
This enables a comprehensive understanding of the market dynamics, identifying both short-term momentum and long-term consistency, while also facilitating portfolio diversification through a correlation analysis.
Every month FinScience utilizes proprietary metrics and entity relationship analysis to define a comprehensive list of securities associated with specific themes. This process involves excluding companies with negative exposure or residual exposure to each theme.
Two key metrics used in this analysis:
- The Theme-Company Exposure Rate metric quantifies the strength of the relationship between a specific theme or topic and a company
- The Theme-Company News Sentiment metric gauges users’ perceptions regarding both the company and the theme.