Individuals and investment firms are increasingly interested in more than a balance sheet when making investment decisions. “Put your money where your mouth is,” is a popular ideal, whether it’s addressing where you’re purchasing a product or what company you’re investing your money in. This attitude is increasingly evident with younger generations like my own.
According to a 2017 Morgan Stanley survey, nearly 9 out of 10 millennials are interested in sustainable investing. I expect investments in companies with strong environmental, social and governance (ESG) practices will only grow in the future. Issues related to how companies handle diversity and inclusion, the gender pay gap, and climate change will soon become paramount to future investment decisions.
Not only does investing in companies with strong ESG practices appeal to individuals’ moral compasses, but in more than 2,200 individual studies, 90% show no negative impact to corporate financial performance, and a majority of studies report positive findings.
As demand grows for investment in companies with positive ESG practices, the use of artificial intelligence (AI) to gather useful data on companies with strong sustainability, diversity and inclusion, and other governance practices will be imperative to identify the strong from the weak.
Without AI, analysts or individual investors may use information like investor calls or corporate social responsibility reports to track specific company practices. But is that enough to trust how a company performs in this area? It’s easy to see how details that are critical to investment decisions could be missed. Information is vital, but deciphering the noise from relevant data is a challenge.
Whether relying on a firm or advisor to handle investments or handling it individually, AI, machine learning (ML) and, specifically, natural language processing (NLP) will be critical to reliable, useful data in informing socially responsible investment decisions. NLP simply means using technology to find text that is not in a specific, structured format and making sense of it. As AI becomes more user-friendly, it will be easier for investment firms, financial advisors and even retail investors to find and apply this information to investment decisions. Whether tracking how companies perform long-term or looking at short-term controversies, alternative data is critical to informing ESG-driven investment decisions.
Tracking companies’ financial results, annual reports and other reporting is how investors may currently follow a corporation’s ESG practices. But the use of AI to gather alternative data takes this investment practice to a new level, increasing capabilities to track more companies, finding more hidden but useful information and sharing that information with a sentiment connected to know what to do with the information that’s found.
For example, what if a company you are investing in has a complaint on a blog from a customer who says they were met with racial slurs? What if that same experience is then mentioned on social media? Separately, on a website that tracks employer reviews, an individual mentions the diversity and inclusion team keeps losing staff. Finally, a lawsuit is found alleging an employee was turned down for a promotion due to his race.
NLP can find relevant information associated with a corporation’s ESG practices, associate a sentiment with the information (positive or negative) and enable investors to access the information necessary to make informed investment decisions connected with the corporation’s ESG practices. If an investor only relied on investor calls, financial results, diversity and inclusion annual reports and the like, a volume of useful information would be missed, and investors could end up making uninformed decisions.
These NLP solutions are used today by large hedge fund companies, investment firms and others in the finance industry. But within the next five years — if not sooner — this innovation will likely be readily available to retail investors.
AI is becoming democratized so that end user investors will have access to the same information in an easily digestible way. Individuals will soon be able to access this information to make investment decisions that factor in both financial performance and ESG practices.
Both consumers and the financial world demand it, and AI-focused tech companies are eagerly working to fulfill the need. AI using NLP may become an everyday practice in making financial decisions connected to socially responsible investments. To be at the forefront of socially responsible investing, look for financial partners who are pioneering this NLP-based approach.
Socially responsible investments still require investors to consider potential returns. Here are a few tips to keep in mind when it comes to socially responsible investing and determining whether it’s right for an investor’s portfolios:
• Different types of socially responsible investing: A few of the most popular methods of ethically driven investments are SRI funds, ESG funds, impact funds and faith funds. Each fund has different drivers, but ESGs are often most attractive to investors because their primary goal is financial return.
• Risk versus reward: Any investment involves a certain degree of risk. It’s important to not only look into reports, but also read company reviews and mentions on websites, blogs, social media posts and other sources that can contain hidden information.
• Sustainable portfolios: Look for companies with sustainable competitive advantages through excellent corporate governance, as well as companies that treat their employees well, benefit their communities, etc.
Combining the traditional methods of determining whether a company has good ESG practices with AI and NLP can enable investors to track more companies, uncover important, hidden insights through unstructured text, and analyze the sentiment and relevance to make more informed investment decisions. The main ingredients for an effective NLP model to generate risk and investment insights for ESG portfolios are data, taxonomy, models, integrations and data visualization.