New Information Edge: Wealthy investors rush to outer space in pursuit of higher returns

A joint statement came after certain reports in the media raised issues relating to the Online Dispute Resolution mechanism rolled out recently in the securities market.

Post-Covid has turned out to be a golden age for small investors dabbling in stocks. Thanks to the proliferation of fintech apps and stricter disclosure rules mandated by the Securities and Exchange Board of India (Sebi) for companies, much of the relevant data and information is now just a click or swipe away.

‘Democratisation of data’ as the term goes, now allows small investors to compete on an equal footing with high networth individuals, or even institutional investors for that matter, say market observers.

But does it really?

Small investors are happy that they can now easily view key financial metrics and ratios such as price to earnings, price to book value, cash flows, return on equity, return on capital of the companies they own shares in or want to invest in, and even compare those with corresponding figures of competitors, all on a single webpage. But unknown to them, the game has moved a few notches higher.

With ample data and information freely available, the race is now for the not-so-easily accessible stuff, which wealthy individual investors are in a better position to pay for. Alternative datasets, as they are called in market parlance is becoming popular with this group of investors, who have much larger sums at stake, and are eager to have an edge that can help them either improve their returns or, if not, at least protect their portfolio against any nasty surprises. Moneycontrol learned this by speaking some of the wealthy investors who use such datasets.

The charges for these various services range anywhere between Rs 1.5 lakh and Rs 2 lakh per annum and, in some cases, Rs 1.5 lakh per month.

What exactly are these alternative datasets?

Thanks to advancement in data analytics, it is now possible to get a host of data and information other than from traditional sources like financial statements and official reports. These are collected from diverse sources and give insights into various aspects of businesses, industries, and economic trends. At the basic level, web scraping and social media data provide sentiment analysis, brand mentions, customer reviews, and social media trends.

One level above, alternative economic indicators are created from non-traditional sources like shipping (export) and transportation data. Higher up the chain, you have satellite imagery and geospatial data offer that offer insights into economic activity, traffic patterns, agricultural productivity, and infrastructure changes.

Firms with the such capabilities then add a layer of machine learning to it, which helps produce insights based on which the investors using them can take decisions.

How does all this work?

An auto company, for instance, is reporting strong monthly sales. But those are vehicles being delivered to the dealer. The question is how much of it is resulting in actual retail sales. The Vahan portal gives the number based on the data compiled from Regional Transport Offices (RTOs) across the country. But not all RTOs upload the data to Vahan at the same time. So, there could be a lag of a few days. An alternative dataset provider can compile the data as the individual RTOs upload it on their own site first. And if there is lag there too, the algorithms are trained to extrapolate past data trends and give estimates.

A quick service restaurant claims that its newly opened outlet is doing well. Satellite imagery can give an idea about how many people are frequenting that outlet. And if you have data about every single outlet, you can have a rough picture about the earnings.

Similarly, the numbers of cars heading into the parking lot of a mall can give an idea about the footfalls in that mall.

Construction companies often store building materials outdoors. Satellite images can provide insights into the volume of materials which can indicate the level of construction activity in a particular project.

In the case of a mining company, larger stockpiles outdoors could reflect higher production levels, while smaller stockpiles could indicate a slowdown in mining activity. Similarly, vehicular movement at a mine would also provide clues about the activity levels.

It is possible to track the earnings of stock exchanges almost on a daily basis with the help of software that will quickly analyse traded volumes and the number of trades in each segment, as these numbers are published by the exchanges every day.

Specialised software can trawl the websites of e-commerce sites, snoop on shopping carts and see what brands people are buying the most.

The big picture

Investors using these datasets admit that analysis of a company’s fundamentals is at the core of any good investment. In other words, based on how much additional data points an investor may have, it is the basic research and understanding of the company’s business model that is most important.

And yet, alternative datasets can help add a layer of extra returns in some situations.

“It can work at two levels,” a wealthy investor who uses some of services told Moneycontrol. “One, it can help you estimate a company’s financial performance up to a certain extent. Two, it can help cross check what the management is saying, because often there is a gap between the management claims and ground reality,” the investor said.

“If you have a portfolio worth Rs 50 crore or more, it pays to spend Rs 10-20 lakh annually if that allows you a good picture of how the business is doing at any point in time.”

And yet, there can be limitations of the effectiveness of alternative data sets.

“Quant funds regularly use such datasets all the time because their very model is based on analysing every possible data that is available. In that sense, HNIs have only got into this game recently,” the investor said.

This investor requested that one of the datasets he uses not be named in the story because not many were aware of it right now, even though the service provider has a website with details of its offerings spanning multiple sectors.

“The upshot of all this is that stocks are reflecting all the information at a much faster pace than before,” the investor said.

Piecing it together

The founder of one of the alternate dataset service providers has said in a presentation to students from his alma mater that the best way to get the most of out of such data was not to use more of the same kind of data, but to use data points from related sectors and then connect the dots.

He cited an example where he was able to capitalise on an opportunity in the tractor segment some years back though the rest of the auto sector was faring poorly. He was able to do that by studying the activity in the agriculture sector, looking at fertiliser demand and the water level in reservoirs.