It’s no secret that big data processing technology is one of the most disruptive forces in the financial market today. Some firms have honed their AI-based stock picking subroutines down to a science and are reaping the benefits as a result. Others have only just gotten on board the AI bandwagon, but even they are enjoying increased gains as a result.

Just a few years ago, some people were suggesting that investors would never be able to trust digital advice because there’s always the risk that someone might be pulling strings behind the scenes when it comes to certain channels. Fast forward to today and you have a market that’s experiencing an explosion of day-trading activities while others put money on meme stocks and joke cryptocurrencies. In spite of the somewhat anarchic look of the current situation, there’s actually a lot of solid data processing behind it and a market environment that’s allowing people to separate the noise from good advice that’s helping them make better decisions about their money.

The Driving Forces Behind a Huge Market Shift

While artificial intelligence code forms the backbone of areas as diverse as weather and healthcare, investment picks are often made using the same decision-making techniques seasoned analysts have relied on since the very first markets opened up. In spite of the fact that the financial and investment spaces are among the most dynamic and progressive of all industry segments, there’s been at least some distrust of automated solutions. This makes sense, as experts have been beating the street for as long as there has been a Wall Street to throw money at.

When the concept of cloud computing was first proposed back in the 1970s, it was difficult for people outside of the ARPANET circle to see any real way to translate this technology into a positive cash flow. While we’ve made countless developments since that time, it seems that many financial services operators are still having some difficulties when it comes time to integrate big data processing routines with their investment programs. Considering how much many individuals stand to make by simply leveraging their current streams of information, countless firms are doing their best to change the way that they look at questions regarding data and investments.

At least one study claims that the entire cloud industry will be looking at 175 zettabytes of data by the year 2025, and it’s quite possible that there will be even more information floating out in the ether by that point. That alone should suggest that even the most conservative of investors will want to think about changing their current habits.

On the other hand, this also speaks volumes about how much virtual noise there must be in this sort of environment. Anyone with even the most basic experience operating a low-code programming platform can spin up some kind of financial tool that may or may not be effective. Those who are interested in using any given solution have been encouraged to consider the source of data behind it.

Taking a Deep Dive into Financial Data Streams

Evidence suggests that AI tools are very useful when it comes to portfolio management, but they need a heavy amount of human supervision to make sure that they’re working right. Since these tools work without the burden of emotion, however, some hedge funds and ETF managers have built statistical models that operate only with a modicum of regular intervention. They’re able to do so only because they rely on solid sources of information to perform their calculations. Streams of data that come directly from major exchanges, such as the NASDAQ and even OTC Markets, are usually considered trustworthy because it’s unlikely that anyone would have the ability to manipulate them before they reach inputs exposed by an API for use with these virtual investment management tools.

Cerulli Associates conducted an examination of any assets under the management of several AI-enabled funds and found that the aggregate return of those funds were nearly 34 percent compared to just over 12 percent for those that didn’t make use of AI technology. Another study by the Turning Institute found that just under 10 percent of hedge funds are currently using machine learning to build statistical models. While this doesn’t sound like much, it’s certainly a statistically relevant number and does suggest that the Cerulli numbers hold water.

Classical portfolio management techniques certainly have some shortcomings, most notably those surrounding the lack of foresight that many investors end up with. It’s easy to want to sell a security that’s performed very poorly in recent periods, and funds that are purely human-managed will often dump these. One that’s run by an AI will normally do so as well, if the information fed into its inputs isn’t of a high enough caliber. The results could be more interesting if it were restricted purely to sources of information put out directly by the various exchanges. As people continue to look for new sources of income, there’s a high probability that many will turn to these solutions. Those who pick ones that use the right kind of inputs can potentially increase their chances of financial success dramatically.

Admittedly, things took a turn for the worst last year and some of the best-performing hedge funds lost quite a bit of money. To some degree, this is true of both hedge funds that leverage digital data as well as those that were managed in the most conventional ways. A few have therefore said that these criticisms shouldn’t be levied against AI-based tools specifically. Regardless of how anyone feels personally about the issue, professional-grade stock screeners were still able to do fairly well even as the AI hedge fund index performed slightly poorer than the market as a whole. Interestingly enough, however, the overall direction was still positive if you only consider those screeners that took information exclusively from existing exchange feeds.

Analysts continue to be bullish about the future prospects of big data-based trading solutions, though they also caution that people should exercise the same due diligence about selecting tools that they would when finding a security to invest in.

Creating a Culture of Responsibility in the Market

Specialists have been paying a lot of lip service to the issue of responsibility in regards to AI-based investment advice for some time, but the truth of the matter is that big data tools don’t pose any new problems that investors didn’t already have to deal with. They simply make the current issues more pressing. Potential investors have always had to be careful about which research websites and news services they use. Big data-based solutions are much the same in many ways, because it all comes down to the original source of information being worked with.

Some of the better trading bots can automatically identify the right time, size and specific venue for placing a particular trade. This has become equally true of those who manage traditional securities like stocks as well as those that manage forex accounts and other less typical investment products. Perhaps most visibly, the sheer amount of data being collected and processed by most cryptocurrency networks couldn’t be parsed any other way than by use of automatic bots.

Validating and back-testing risk models is of great importance regardless of what kind of investment product is being considered. This makes it possible for responsible AI-based solutions to cut out dramatic sales events that are caused by sudden drops in equity prices, such as those that were seen in March 2020. On the other hand, those who are less scrupulous could potentially tamper with these bots. Thus it’s been important for potential users to form ad hoc groups that force those developing these solutions to be responsible and look for the best sources of data.

It’s likely that market pressure, as opposed to the heavy hand of regulators, will have the most impact when it comes to creating a culture of genuine responsibility in the field of big data-driven trades. There’s an increased potential for using AI and automation tools in a back office environment as well, which has further driven the need to encourage people to cut down on the chaff and only use the best tools possible. The news, however, isn’t all negative.

In fact, some specialists feel that AI-based tools can level the proverbial playing field as far as trading goes, because smaller individual investors who wouldn’t otherwise be able to get solid advice can now automate their workflows and get up-to-the-minute information about every investment product they’re dealing with. Even a small hobbyist could theoretically configure a desktop PC that’s hooked up to around-the-clock Ethernet connection and enjoy a second income that’s got far more generous profit margins than any cryptocurrency mining operation ever could at this point.

As with all things, however, those who want to buy into a big data-based solution today should be skeptical because any deal that seems too good to be true probably is.