How Big Data is Poised to Disrupt Personal Investment

Last year, in a piece about big data’s impact on finance, we touched on the notion that location data sets can simplify the practice of investing. The idea is that accurate data regarding consumer movement can provide insights on consumer trends — and, thus, potentially, corresponding market movements. In this article, we’re going to expand on that general idea with a closer examination of big data’s potential influence on personal investment.

 

Big Data & Investment Today

First and foremost, it’s necessary to briefly discuss the current state of big data in the investment world. In the piece, as mentioned above, we mainly covered an idea of how data, and specifically location data, can be applied to market management. However, the truth of the matter is that some significant investment funds and financial firms are already using massive troves of data of all kinds to inform their investing decisions.

At this level, AI and big data are transforming the investment process in several ways (and through companies as big as JP Morgan, BlackRock, and others). In some cases, AI labs are being used to analyze investor performance and recommend changes that yield a quick, significant result. In others, advanced AI applications are using a deep-learning approach to sift through astonishing amounts of data with the straightforward goal of predicting near-future stock prices. There are mixed results with approaches like this one, but the potential for genuinely predictive analytics in large-scale investing is significant.

There aren’t full AI operations of this nature explicitly focused on making use of location data. However, deep-learning approaches are reasonably comprehensive in theory. They can certainly make use of this specific type of data and share prices, company data, macro-economic indicators, asset histories, and so forth. Location data can primarily be used as a piece of a sprawling analytical effort.

 

Automated Investing Tools

While significant investment firms may be well situated to take advantage of big data and act on it quickly, most ordinary people can’t do the same. A true day trader with access to adequate data (on locations and otherwise) may be able to make quick decisions to react to fresh information. But for most people who are simply managing stock portfolios, it’s more realistic to attempt to leverage data relating to broader movements. That’s where some of today’s automated investing tools come into play.

When you hear about automated tools, you might first think of mobile apps like Acorns and Stash that can, to some extent, manage your investments for you. Generally, these apps allow you to create a portfolio — or at least a type of collection — based on your risk aversion and/or category preferences. They then conduct trades according to your preferences, such that your money goes to work for you in an automated fashion.

Automation is also involved in more traditional trading methods, though, and it introduces an interesting way of potentially leveraging big data. On some platforms, when trading contracts for difference or stock futures, for instance, investors can place profit and loss limits on their positions. This means that while investors still determine entry points on individual stocks, they can set up automated safeguards that will pull money when a certain profit has been attained, or a certain loss has occurred. Through this sort of feature, investors can still set up their investments and contracts, but trust automation to manage them after that.

This all relates to big data because it can be more comfortable for an investor to make a long-term play based on big data than a short-term trade. To give an easy contrast, consider consumer location data based on a product release versus that based on a more significant trend. A surprise hit film might lead to data suggesting a short-term surge of moviegoers, such that a day trader or larger fund could stand to profit by investing in major cinemas. But the average investor likely can’t recognize and act on that data quickly enough. On the other hand, if consumer location data indicated a widespread, gradual return to shopping malls in 2021 (when they’ve been largely abandoned in 2020), an investor could make a play to buy stock in department stores, with automated limits set in place. In short, automated options allow for more responsible long-term positions, such that investors can feel more comfortable attempting to leverage certain types of data.

 

Location & Digital Payment

As you’ve likely noted in the sections above, one of the problems with big data in personal investment is that the most information and the best analysis tend to belong to the giant funds and finance companies. While there are ways for individuals to access more advanced analysis of investment-related data, it isn’t easy to compete on a day-to-day level. Sometimes, the significant funds will interpret data and move on a trend quickly that there’s not much opportunity left for everyone else.

This is why location data may prove to be particularly interesting for individual investors, however. When we talk about comprehensive data analysis done by leading financial firms, we’re referring to all sorts of in-depth material that is difficult for an individual to manage, or in some cases, even get a hold of. By contrast, location data may be getting more convenient in the near future.

This assertion is based on the simple fact that electronic payments are up; by 2019, some 2.1 billion consumers were estimated to have used a digital wallet of one kind or another, and moving forward, even more people are likely to be embracing digital payments. These types of payments are meant to be more secure and convenient for consumers and businesses alike, but they’re also generally more traceable. In theory, it may soon be the case that information about where consumers are spending digitally will be reasonably easy to access (whether through public ledgers, payment processing company reports, etc.). That would turn consumer location data into something individual investors could access and use with relative ease compared to some other kinds of relevant data.

 

In Conclusion

The use of big data in investing is still an evolving concept with less to do with individual investors than larger funds. However, certain methods of investing and certain types of data can be of use to independent traders, and will likely only become more useful in moving forward.

James is the marketing manager at Tamoco.