How Technology Is Changing the Way Investors Make Decisions

1. How has technology changed the way retail investors track and respond to movements in the stock market compared with a few years ago?

If I compare today with five years ago, access itself is not new. Retail investors already had smartphones, trading apps, live prices, and online research. What has changed is the depth of technology around that access. Today, almost everything is at your fingertips. Within seconds, you can see a price movement, find the news behind it, pull up a chart, look at different market indicators, see what people are saying, and increasingly use AI to make sense of what has happened.

That is a very positive change. The retail investor today has access to information and tools that would have been far more difficult to put together even five years ago.

But there is another side to this speed, which is about how retail investors are responding to this change in technology. Our attention spans have become shorter in almost every part of life, and investing is not immune to that. When information reaches you in seconds, and execution also takes seconds, there is a tendency to believe that the decision needs to be made just as quickly.

A stock falls sharply, and you immediately have ten explanations for why it happened. Before you have had time to understand whether anything has actually changed in the business, you are already thinking about whether to buy more, sell, or exit.

That is where I think we have to separate access to information from depth of understanding.

Technology can make me aware of something faster. AI can help me analyse it faster. Both are valuable. But the stock market still demands patience. Understanding a business still takes depth, and understanding risk still takes thought.

So technology has made retail investors much faster. The challenge now is to use that speed without allowing it to make retail investors more impatient.

2. With access to real-time market data, analytics and AI-powered tools becoming increasingly mainstream, are investors becoming more data-driven in their decision-making?

I would make a distinction between being data-informed and data-dependent.

We have more data than we have ever had. Prices, valuations, technical indicators, sector movements, institutional flows, options data, and company information are all easily accessible. AI has made it possible to process some of this information much faster as well.

But more data does not automatically create a better decision. I think this becomes clearer when you look at how sophisticated investors have traditionally operated. Their advantage was never simply that they had more numbers on their screens. The advantage was having a structured process for deciding which numbers mattered for a particular decision, and that distinction is important.

A retail investor today can have fifteen indicators on a chart and still not know which three are relevant to the decision they are trying to make. In fact, more data can sometimes make us more confident about a decision without necessarily making the decision better.

This is where I find AI particularly interesting. Its real value may not be in producing even more information. It can be in synthesis.

Can you take hundreds of inputs, remove what is irrelevant, and help an investor understand the few things that actually matter right now?

That begins to bring something to retail investors that was historically much harder to access: a structured way of processing information. The institutional edge was never raw data alone. It was the ability to turn data into a decision. Technology now has an opportunity to narrow that gap.

3. How can AI and machine learning help investors identify market trends, risks, and opportunities, and where should investors be cautious about relying too heavily on technology?

We actually work with this problem at Hedged.in. One of the tools we have built is the Hedgeometer, or Nifty Crash Meter, where we use machine learning together with human expertise to assess market risk and alert investors when conditions appear to be deteriorating. For me, this is where AI becomes genuinely interesting. It can process multiple signals continuously and identify patterns that would be extremely difficult for an individual investor to keep tracking manually.

That ability has applications far beyond one tool. AI can help with pattern recognition, changes in market behavior, portfolio risk, company data, and identifying relationships across very large datasets.

But there is one thing investors need to understand very clearly: a model is still a model of the market. It is not the market itself.

Every model learns from data and assumptions. The real test comes when the environment changes. A model that has performed well in one market regime can behave very differently when volatility, liquidity, or correlations suddenly change. Markets are particularly difficult because they keep evolving. Something can work repeatedly until the conditions supporting it disappear.

That is why I would be cautious about treating any AI output as certainty.

The way I see it, AI should make an investor better prepared for uncertainty rather than creating the impression that uncertainty has been eliminated. It can tell me something I may have missed. It can challenge an assumption. It can identify risk earlier. But I still need to understand what the tool is designed to do, where it can fail, and what happens to my money if it is wrong.

4. There is an increasing amount of market information available to investors today. Is technology helping investors make better decisions, or is information overload becoming a new challenge?

Technology is absolutely helping. But I think we are solving the wrong problem if we keep measuring progress by how much information we can give an investor. The problem today is not really information. It is synthesis.

Before the Indian market even opens, I can know what happened in the US, what Asian markets are doing, where GIFT Nifty is trading, what happened to crude and currencies, what institutional investors did, which companies made announcements, and what hundreds of people online think all of this means.

Then the market opens, and another stream of information begins. More charts do not automatically create better decisions because somebody still has to connect all those pieces.

This is where institutional investing offers an interesting comparison. A portfolio manager is generally not expected to personally process every raw piece of information in the market. There are systems, research processes, analysts, and risk frameworks that help turn a very large amount of information into something usable.

Retail investors increasingly need the technology equivalent of that process. I would rather have one well-processed signal than a hundred raw feeds.

That is where I think investment technology has to move next. Don’t just tell me everything that happened today. Help me understand which part of it matters to the portfolio I actually own. The next generation of tools will probably create value not by showing investors more, but by helping them ignore what does not matter.

5. How are tools such as sentiment analysis, market indicators, and personalised analytics changing the way retail investors approach portfolio construction and risk management?

Sentiment analysis has become a useful additional input because markets are ultimately driven by people as well as numbers. Options data, market positioning, news sentiment, and other indicators can give an investor a better understanding of how the broader market is behaving.

But there is an important distinction here: knowing what the market is doing and knowing what you should do are two different things.

A sentiment indicator might tell me that the market has become extremely fearful. That still doesn’t tell me whether my particular portfolio is too risky, whether my investment horizon has changed or whether I actually need to take any action. That is where personalised analytics becomes much more interesting.

Today, many investment journeys begin with some form of risk assessment or investor profiling. But genuine personalisation should go much further than placing somebody into a category at the beginning of their investment journey.

Your life changes. Your income changes. Your liabilities change. Your portfolio changes, and, importantly, the risk inside your portfolio can change even when you haven’t bought or sold anything.

Suppose one part of your portfolio rises dramatically over two years. Without doing anything, you may now be far more concentrated in that asset or sector than you originally intended.

A genuinely personalised system should be able to identify that drift.

So for me, the next step is not simply better sentiment indicators. It is technology that continuously understands the relationship between market risk, portfolio risk, and the investor’s own risk. That would make risk management far more useful for retail investors.

6. Looking ahead, what role do you see technology playing in democratising access to sophisticated market intelligence, and how could this change investor behaviour in India?

India crossed 13 crore unique registered investors on NSE this year. What is even more interesting is the speed of that growth. NSE says its registered investor base grew at a compound annual rate of more than 26% between FY21 and FY26. So I think the first phase of democratisation has clearly happened: participation has become easier.

Technology made onboarding easier, information more accessible, and execution almost instantaneous. An investor does not have to be sitting in Mumbai or working in finance to participate in the market anymore.

The next phase, in my view, is the democratisation of investment intelligence.

And there is still a gap here. SEBI’s Investor Survey 2025 found that complexity and lack of accessible information remain significant barriers for non-investors, alongside concerns around risk and returns.

This is where AI can potentially change things. Sophisticated analysis that historically required considerable infrastructure, expertise and time can increasingly be packaged into tools that an individual investor can actually understand and use.

But the behavioural outcome matters more to me than the technology itself.

Can we move from a reactive investor to a more structured investor?

Someone who doesn’t immediately panic because the market fell today. Someone who understands when their portfolio risk has genuinely changed. Someone who can distinguish a meaningful signal from ordinary market noise.

That is the opportunity.

We already know technology can make millions of people participate in markets. Now we have to see whether it can help them participate with greater structure and discipline.