Finance is a complex subject requiring specialised knowledge. Financial planning is a world riddled with jargon and technical terms that even those with years of experience and exposure may not be able to easily grasp, let alone articulate.

In today’s world of AI and machine learning, one would imagine there would be tools to help investors make informed choices and also stay updated on their investments. And with the explosion in financial news, which closely tracks developments in various industries, one would imagine the information gap between big institutional investors and retail investors would shrink substantially.

From tips on Reddit to social forums where many people swap stock predictions, today’s retail investor is armed with just enough to take on financial gurus of the equity markets. In fact, these retail mobs have actually contributed to several large hedge funds going belly up in recent years.

In India, we have yet to see this kind of mobilisation. Most financial news articles listed on Google’s discover feed are actually created by bots. These are essentially programmed to repurpose standard data available from market data providers such as Refinitiv and the exchanges.

Consequently, the financial updates most people end up consuming are largely clickbait articles that offer barely enough information for retail investors to make informed investment decisions.

Therefore, retail investors are compelled to scour through financial news Web sites to scavenge for information about prospective companies they would like to invest in or have already invested in. They try to interpret financial documents and technical information about these firms to gather insight into their future prospects. But, to a large extent, most seek information that would help reinforce their convictions rather than make investment decisions.

The problem is also that a large section of the financial media is littered with AI bots that barely go beyond offering basic information. This lacks analysis and is rarely backed by projections. Most people struggle to interpret this information and fail to understand that a simple trend analysis based on stock market terms on Google trends might actually provide better investment signals compared to rehashed news.

Considering this gap in information, at HT Media Labs, we conceptualised MintGenie because we saw an opportunity to define the future of financial news from an investors’ perspective. We realised news can be tailored from a collective point of view to offer signals to investors and empower them with insight on where the market is headed and how specific stocks are moving.

It is understandable that many small cap companies may not have access to a lot of information, but that’s definitely not the case with large cap companies. Hence, we use AI techniques to read market sentiments. We try and gauge three different aspects when running our analysis:

• Fear factor: The fear factor tries to identify the amount of fear in the marketplace. This is derived by specific terms that people search for when looking at the market. This can be used as an overall indicator for when and if an investor would like to enter the market.
• Buzz: The buzz factor is based on a company’s mentions in the news. It may not accurately reveal whether a stock is aggressively being bought or sold but will offer indicators on movements in the price of the stock based on the kind of buzz generated.
• Sentiment: The sentiment factor is a tell for whether the market is bearish or bullish toward a stock or the overall market in general. This can also sometimes be used as a contrarian indicator. For example, if the sentiment is too grim about the economy or a company, the reality might be that it is not so bad after all and is a buying opportunity.

Although these signals are not always completely accurate given the global nature of world markets, it does allow users to quantify what is otherwise basic news to make more nuanced decisions. In the long run, these guide points can be refined to help improve the investment process for retail investors.

In the past, investment decisions were made based on primary data (that is, data we can see from a company’s balance sheet, historical stock prices, dividends, institutional holdings, traded volume, and company specific information). Large fund houses with extensive research budgets went a step further to do competitive analysis and channel checks on their own accord.

For most retail investors, this was both not practical or affordable. But with the explosion of data mining techniques, we can now add additional checks just based on search trends and news flow in specific sectors and companies. This kind of information might be a game changer in bridging the gap between large institutions and small investors. This will democratise information access and bring about a more level playing field in the financial sector.

Our goal should be eventually to eliminate the information arbitrage that exists today between big money players and small investors. For this churn to actually become a reality, it is imperative that financial media houses do not act as mouthpieces of larger players and instead become beacons of information serving the entire investment world.

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