Artificial Intelligence is transforming equity investments for both new-age and traditional investors, bridging gaps in knowledge, time, and expertise. In an exclusive interview with The News Strike, Bruce Keith, CEO of InvestorAI, highlights that AI's potential lies in providing personalized insights, mitigating emotional biases, and enhancing decision-making. However, ethical considerations such as transparency, regulatory oversight, and accurate data sourcing are crucial for fostering trust and ensuring sustainable growth in the AI-driven investment ecosystem.
1. How can AI tools simplify the process of analyzing financial data for Gen Z and millennial investors with limited time or expertise?
There is a vast array of freeware tools, socially created investment advice and GenAI answers to all sorts of investment questions, but their output is often questionable. AI should be providing learning and education, screening tools to help find the right solutions and explanations on its results. Ultimately the user should be in control, but not being able to access all of these things will produce a disappointing experience in the long run.
2. What specific AI-driven platforms or tools are most effective in providing real-time stock market insights for traditional investors?
Real time and AI for retail investors is difficult – the sheer level of compute required really puts this in the hands of the big institutions and hedge funds. Individual investors should pick where they use AI so it complements their existing skills. Aa good example is intra-day trading where only 10% of individual investors make money, AI is able to deal with vast quantities of data and provide that edge – we see that 10% number rising to over 70% as a result.
3. Can AI recommend personalized equity investment portfolios based on the unique risk appetites and financial goals of new-age and traditional investors?
There is a tricky regulatory conundrum here – AI is great at dealing with vast amounts of data and producing recommendations and it will personalise it based on what it knows. Unfortunately when we answer questionnaires we don’t always present the full facts and it often takes a human to understand the nuances of the response. It is therefore difficult to allow AI to fully personalise and it tends to be restricted to declared risk tolerances and known time horizons
4. How does AI use predictive analytics to forecast stock market trends and assist investors in timing their trades effectively?
The secret sauce! There are two schools of thought – quant and neural nets. Quant strategies tend to be momentum based and do very well in bull markets, but less well in challenging conditions. Neural nets (similar to what ChatGPT can do) try and mimic a human brain and look for patterns. There impact is relatively recent, but from our own experience they can do well in both rising and falling markets.
5. In what ways can AI-powered tools minimize emotional biases in equity investment decisions for younger and more seasoned investors?
We all have biases – it’s part of what makes us human. AI (if trained correctly) can avoid some of the impacts of bias (both good and bad) and will take a very unemotional look at all the facts and make decisions. This is especially true in short term decision making with vast quantities of data, but for a longer term view I would turn to human advice.
6. How can AI-based education platforms help Gen Z and millennial investors quickly grasp complex financial concepts and market dynamics?
One of the problems with first time investors is that they are lacking in confidence. For me the trick is consistency – generate a little more every time and let compounding do the rest. AI can absolutely address this both in terms of education and guidance. Helping the next generation understand how to make their money work for them will have a huge impact on their lives.
7. What are the potential benefits of using AI chatbots or virtual financial advisors for traditional investors less familiar with digital platforms?
AI chatbots have to be well controlled. A journey into the land of ChatGPT or any other LLM is just as likely to deliver a hallucination as a good piece of advice. The regulator has a role to play here so that AI chatbots cite the sources of their advice and that the businesses that operate them are responsible for the input data.
8. How can AI assist investors in assessing and mitigating risks associated with equity investments, especially during market volatility?
Human biases often result in us selling at the wrong time and buying too late. Rule 1 – stay invested, rule 2 – follow the strategy (or chose a different strategy), rule 3 (optional) – don’t try and second guess the machine!
9. Can AI algorithms help both demographics achieve better portfolio diversification by identifying hidden opportunities across sectors or markets?
AI model portfolios or recommendations have a place for any equity investor, be that long term, intra-day, MTF or F&O. AI’s strength is in dealing with the short term and allowing an investor to act like a trader without the stress of figuring out what to do between 9.15am and 3.15pm.
10. What are the ethical considerations and limitations of relying on AI for equity investment decisions, and how can both new-age and traditional investors address these challenges?
Understanding the data sources that any AI uses and its track record (either the win rate – how often its predictions made money, or the beat rate – how often they did better than the index) is key. AI providers have an obligation to provide this transparency to investors otherwise it is just black box investing.
