Retail investors: how investment habits have changed and how AI can help
Gone are the days where investors prioritize sector, country and financial performance distinction over themes, trends and values. Unlike institutional investors, retail investors decisions are driven by thematic and trends classification, not by sector or countries distinction.
It is no longer just a matter of the highest possible profit, but how to generate real value through money, according to values and beliefs.
Investors, especially the younger generations, are keen to invest in brands that have an impact on the world and avoid companies with ESG risks exposure.
Environmental, Social, and Governance, and thematic investing are two concepts that have become increasingly important in recent years. ESG investing focuses on companies that are environmentally conscious, socially responsible, and have good governance practices. Thematic investing, on the other hand, involves investing in companies that are focused on specific themes or trends, such as technology or healthcare. Artificial Intelligence has played a crucial role in both ESG and thematic investing, and has helped investors make more informed decisions.
As the role of AI continues to grow in investing, we are investing in more and more sophisticated techniques and applications that will help investors achieve their investment goals while aligning their values and interests.
FinScience leverages users beliefs and values, and this way it enhances experience in different steps of the investment process funnel.
ESG investing: how AI can help retail investors in aligning their portfolio to values
ESG investing has gained popularity in recent years, as more investors are looking for ways to align their investments with their values. AI has played a critical role in ESG investing by providing data analysis and modeling techniques to help investors make better decisions. For example, AI can be used to analyze a company’s environmental impact by looking at factors such as carbon emissions, water usage, and waste management. This can help investors identify companies that are more environmentally conscious and make informed decisions about where to invest.
Advanced Machine learning techniques such as natural language processing (NLP) enables investors to extract powerful ESG insights from unstructured data online, collecting and combining news, blogs, forums and social media, in real time. Thanks to this kind of alternative data, you can have a more accurate, timely and complete perspective on ESG controversies.
Thematic investing: how AI can help retail investors in aligning their portfolio to beliefs
Thematic investing has also gained popularity in recent years, as investors look for ways to capitalize on emerging trends and technologies.
AI has played a critical role in thematic investing by providing data analysis and predictive modeling techniques to help investors make better decisions. For example, AI can be used to analyze data on emerging technologies such as artificial intelligence, renewable energy, or biotechnology. This can help investors identify companies that are likely to benefit from these trends and make informed decisions about where to invest.
AI can also be used to analyze consumer behavior and market trends, which can help investors identify emerging topics and invest in companies that are likely to benefit from these trends. For example, AI can be used to analyze data on consumer preferences and buying habits, which can help investors identify emerging consumer trends and invest in companies that are likely to benefit from these trends.