Artificial intelligence has been helping businesses in several ways, from automation to interpreting huge data sets. Similarly, AI can offer several benefits to the fintech industry. Here, Stephen Mathai-Davis, founder and CEO, Q.ai, discusses a few benefits AI can offer in investing.
Applications of artificial intelligence (AI) are enabling individuals and businesses to more easily interpret large data sets, make more accurate predictions, and build new sources of information. The availability of AI resources, many of which are open-source, allows for increased democratization in investment management.
AI is enabling investment advisors and individuals to use tools that were previously the exclusive domain of hedge funds and high net worth individuals.
Following are the three major benefits of the use of AI in investing:
Automation of Investment Research
Historically, investment research has been a costly endeavor. Large institutions employ large numbers of research analysts, many of whom have narrow specialties in particular industries and geographies. This wide breadth of internal knowledge and human capital offers advisors and clients unique insights, especially for timely investment themes.
Artificial intelligence is enabling individuals, smaller firms, and robo-advisors to build dynamic investment strategies at a much faster pace with minimal incremental costs. If an advisor wanted to offer a new thematic investment strategy (perhaps to capture the upside of surging oil and gas prices for the Energy sector), this might traditionally require specialized industry expertise and significant research time.Â
Instead, AI can deliver insights and predictions to new areas rapidly and with few additional costs. Coupled with strong investment knowledge and disciplined risk management, small firms, including robo-advisors, can produce new strategies much more easily than before.Â
Additionally, there is comfort in knowing that the model can’t retire or leave for another firm, unlike an expensive portfolio manager.
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Improved Forecasting and Risk Management
AI has the added benefit of improving the probability of positive outcomes across a variety of asset classes, especially for short-term windows. Investors can enable advanced deep learning reinforcement learning algorithms to â€œlearnâ€ from Big Data sets, sometimes even from disparate sources and alternative Big Data sets.Â
These AI models develop insights and predictions that can be more easily tested than what is more often used in investment management: human intuition. Artificial intelligence is already helping advance the investment management industry beyond â€œgut feel.â€Â
With better quantitative forecasts, investors and advisors can more effectively protect holdings from expected negative forces through hedging strategies. Hedging is the use of specialized investments to offset specific risk factors for an asset or portfolio of assets.Â
For example, nearly all U.S. equities have some positive correlation with the overall U.S. stock market. This sensitivity, varying by security, creates â€œmarket risk.â€ An investor who sees a negative turn in the market can use cash or uncorrelated assets to temper this risk factor or add hedging assets, like inverse ETFs, to offset this risk further.
Without accurate forecasts, investment managers might take protective actions too late. In this case, negative conditions have already depressed investment values and made hedging assets more expensive. An alternative, using hedges constantly, mutes the appreciation of investments when risk factors abate and conditions turn positive.Â
Dynamic hedging, powered by probabilistic forecasts from AI, can help protect holdings while capturing upside in favorable environments. Also, these forecasts and related protective trades can be automated for varying sets of holdings. This can empower a robo-advisor to tailor specialized hedging strategies for each of its millions of investors that desire this risk management tool.
Incorporation of Alternative Data
Pairing financial data with more qualitative insights is a classic element of investment research. But reading and interpreting numerous articles on thousands of investment options is an insurmountable task for smaller firms and individual investors.
Natural language processing (NLP), the application of artificial intelligence to human language and unstructured data, provides ways to level the playing field. After the appropriate application and modifications by data scientists, NLP engines can rapidly interpret millions of news stories and other texts to identify companies with major changes in sentiment and/or public interest.Â
While even the best AI still lags behind the human interpretation of the nuances of natural language, NLP can act as a signal to conduct more targeted research. With AI-powered estimates as a starting point, investors and advisors can target their attention to places with the most research value.
In addition to financial data, neural networks and other AI models can incorporate these alternative data sets. These data sets can include sentiment analysis of stocks and other securities in press releases, news stories, analyst reports, and retail investors’ opinions on public forums.
Cleaning up this data is a challenging upfront task for data scientists. But the payoff is the ongoing creation of additional, specialized sources of information that other AI models can use. Some of the AI applications mentioned before can be improved with the incorporation of unique and proprietary data sets, which could only be built and refreshed with artificial intelligence.Â
Tasking thousands of individuals with reading news stories and creating numerical scores for otherwise qualitative properties is not just prohibitively expensive; it lacks the consistency and relative evaluations that artificial intelligence can provide to this unstructured data.
The true power of artificial intelligence harnesses the power of predictive analytics to accomplish tasks quickly and more efficiently while improving the probabilities of positive outcomes. As a result, AI-powered technology is expanding access to financial services that were previously reserved for established firms and high net worth clients.
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