What Will Drive Retail Algorithmic Trading Adoption?

When trading first evolved into a profession, it was the exclusive domain of highly trained professionals, with access to unique datasets and training at the most prestigious finance firms in the world.

With the arrival of the Internet, the idea was somewhat democratized, and online trading made it much more convenient for “normal” people to participate. But still, the technical requirements and knowledge required for becoming a successful trader were generally too high for more than a handful of retail investors to manage.

In parallel, professional trading became increasingly reliant on automated systems (“trading bots”) able to react in milliseconds and detect complex price patterns, following increasingly arcane mathematical formulas. As a result, it is estimated that algorithmic trading accounts for roughly 60% to 80% of total equity trading volume in major financial markets.

A new revolution could soon happen regarding automated trading, as it is moving beyond institutional markets. Higher accessibility, AI, and the generalization of online brokerage among the population are now driving the adoption of algorithmic trading by retail investors.

A new study by Indian researchers at the National Forensic Sciences University, the Pandit Deendayal Energy University, and the Voorhees College investigates the psychological drivers in the adoption of algorithmic trading.  They show that many factors like infrastructure quality, transparency, testing environments, risk disclosures, and investor education will determine which platforms capture retail adoption.

They published their findings in Acta Psychologica¹, under the title “What drives algo trading intentions? Insights from a stimulus organism response framework and importance performance map analysis”.

Institutional Versus Retail Investors

Typically, institutional investors (banks, insurance, hedge funds, etc.) have state-of-the-art technology platforms, technical expertise, and sophisticated systems for trading and investing.

In contrast, retail investors by and large do not have access to the low-latency execution infrastructure, colocation services, and premium real-time data feeds that institutional participants routinely deploy.

This creates a structural disadvantage for any retail trader (an individual without financial backing and generally lower total assets), even without taking into account training, time available, experience, support network & mentorship, education, etc.

In addition, institutions command superior analytical resources and market information, creating a significant information asymmetry.

But this is all changing quickly with the help of fintech and online brokerage services, which have allowed algorithmic trading tools to become increasingly available to retail investors.

“Most of the brokerage houses now have automated trading platforms, application programming interfaces (APIs), and tooling for building algorithms simple enough to engage retail investors through algorithmic trading activities.”

Importance Of Algorithmic Trading

Algorithmic trading uses computer programs to place or manage trades according to predefined instructions involving variables such as price, timing, volume, or market conditions. More advanced systems may also incorporate machine learning, artificial intelligence, natural language processing, and large datasets.

Today, technical data are supplemented by natural language processing, which allows the algorithms to extract sentiment from news and social media to enhance market analysis.

As mentioned, this type of trading now makes up the vast majority of volume in financial markets. A potential issue with such algorithms is that they take away trading from the investing public and move it into institutions.

The growing influence of algorithms can improve execution speed and market liquidity, but it also introduces concerns involving system failures, market instability, cybersecurity, and unequal access to sophisticated infrastructure. Expanding retail access may narrow part of the technological divide, although access alone does not eliminate the informational and financial advantages held by institutions.

So in theory, adoption of algorithmic trading by retail investors could be a net positive from a social standpoint. But this also means it is important to understand the factors favoring or discouraging its adoption by retail investors.

Algorithmic Trading & Retail

Key Factors For Algo Adoption

Individuals’ perceptions and attitudes toward technology are shaped by social norms and collective beliefs. So they act as a key social mechanism influencing technology perceptions, risk evaluation, and trust formation in technology adoption decisions.

The researchers analyzed the various social factors impacting adoption of algorithmic trading:

• Perceived Usefulness: how it can improve trading performance and reduce risks.
• Perceived Ease of Use: how easy it is to learn and use, thanks to intuitive interfaces, automation features, automated trading tools, and distinct strategy-building tools.
• Risk Perception: the risk of unforeseen financial losses, with the absence of humans in the decision loop raising uncertainty during volatile environments. Cybersecurity risks are another component of this factor.
• Confidence in Technology: the level of trust, reliability, and understanding of the technology, or the importance of consistent performance of algorithms across diverse market conditions.

Lastly, the technological infrastructure supporting algorithmic trading (software, APIs, online access, etc.) is also a major factor.

Each of these factors interacts with the others, and both shape and react to the social perception of algorithmic trading and the likelihood of its adoption by retail investors.

Source: Acta Psychologica

Measuring Algorithmic Trading Adoption

The researchers used a structured questionnaire with three separate sections:

• Basic demographic data.
• Factors influencing the adoption of algorithmic trading.
• The intention to use algorithmic trading.

The final population sample was 382 people, split 75% male and 25% female, with most respondents 26–35 years old (27%), 36–45 years (25%), and 46–55 years (23%). 31% were undergraduates, 54% were graduates, and 15% were postgraduates.

The first insight was that when algorithmic trading is used or endorsed by others, namely, colleagues, industry experts, or established institutions, potential users have lower perceived risk in adopting this new technology. But the researchers also noticed that this is so far the least active factor in determining adoption, showing this is a major underused vector to drive algorithmic trading adoption higher.

In contrast, the current dominant force in adoption was technological infrastructure, as it forms a prerequisite condition for adoption.

“If access, latency and reliability are bad, then investors do not really have a basis to know if the technology is useful or easy to use in the first place.”

Perceived usefulness positively influenced adoption intentions, but it was not the dominant factor. Technological infrastructure had the greatest overall importance because it also shaped whether investors viewed algorithmic trading as useful, accessible, trustworthy, and sufficiently safe.

Regarding ease of use, improved usability and a good interface are also powerful helps to boost adoption.

Risk perception was the strongest direct deterrent to adoption intentions, with a path coefficient of -0.380. It is driven by complexity, the fact that users cannot see how the system reaches its decisions, or direct cybersecurity concerns. Risk perception had a substantial influence on adoption intentions. Investors who considered algorithmic trading financially, technically, or operationally risky were considerably less willing to use it. This supports the idea that loss aversion can outweigh the perceived convenience of automation.

Confidence in technology is not a very strong predictor of adoption, but largely because investors already trust the technology to work consistently.

Combined with the result on risk perception and ease of use, it shows that adoption of algo trading by retail investors is mostly limited by their fear of personally not managing to use it, not a distrust in the technology in itself.

Investors Takeaways

While the adoption of algo trading by retail investors is likely a durable shift that will only pick up pace, it is driven mostly by technological improvement and better communications.

“The study shows that three things work together to decide whether someone even wants to pick up these tools, namely whether the infrastructure is ready, how the user thinks and feels, and what cues they take from the people around them.”

Currently, technological infrastructure around algorithms is the driving force in adoption and forms a first key competitive advantage between providers of trading tools, especially brokerage firms.

Reducing the risk of any technical issue that could cause financial loss to the trader, as close as possible to zero, is also crucial.

“Security certification from third parties, transparent performance reporting, responsive customer support, grievance resolution, and regular communication related to system updates can support users’ trust in algorithmic trading platforms and sustain them over time.”

Later on, we should expect that a durable competitive advantage could come from social endorsement of trusted institutions and individuals, as this is so far an underdeveloped vector to drive adoption of algorithmic trading by retail investors.

“Trust is built through transparent platform operations, good service performance, social reinforcement, and institutional credibility. Therefore, fintech companies should view trust development as a long-term strategic goal and not as a one-off project.”

Overall, the importance of technological platform cybersecurity and social validation should benefit brokerage platforms providing these factors, and the corresponding growing adoption of algo trading by users of these brokerage platforms should benefit their shareholders.

Investing In Algorithmic Trading

Interactive Brokers Group

IBKR Price Chart

With the “new frontier” of algorithmic trading and its adoption by retail investors, the best way to invest in the field is likely to buy stock in companies already trusted by retail investors.

One of the largest online brokerage companies is Interactive Brokers (IBKR ), a company tracing its roots to market makers in the 1970s. The company was one of the pioneers in using trading data as close to real-time as possible, and in embracing the use of computers in trading.

Today, Interactive Brokers serves 4.8 million client accounts, with its client equity worth as much as $789.4B, up 38% year-to-year.

Source: Interactive Brokers

It operates in 40 countries and 29 currencies, helping its users trade stocks, options, futures, currencies, bonds, funds, crypto, etc. The company’s growing success has translated into growing revenues, which have more than doubled in 4 years, alongside similar performance for earnings per share.

Source: Interactive Brokers

A key competitive advantage of the company has been the combination of an award-winning trading platform, designed and built in-house, with a massive breadth of global products, covering all types of financial assets, geographical regions, exchanges, and jurisdictions.

This allowed Interactive Brokers to offer its automated processes at very low costs, creating economies of scale, helping increase margins, and offering even lower prices to its users.

The structure of the company’s products also creates a natural learning curve, onboarding investors with little experience, and letting them learn until the point where they might use the company’s APIs for algorithmic trading. Experience creating user-friendly interfaces and strong cybersecurity will help build the required trust.

Source: Interactive Brokers

So Interactive Brokers’ potential success in selling algorithmic trading tools will not be based on superior results, but on the company’s ability to reach millions of active investors and be the first choice in their mind when looking for a provider of complex and potentially intimidating trading solutions.

This makes Interactive Brokers a great “picks and shovels” stock to bet on the spread of algo trading to retail investors, and overall the general theme of greater participation in increasingly complex forms of trading by the retail crowd (OTC stocks, options, futures, forex, cryptos, etc.).

Latest Interactive Brokers Group (IBKR) Stock News and Developments

Study Referenced

^{1. Heena Joshi, Narayan Baser, and Hepzibah Sinthiya. What drives algo trading intentions? Insights from a stimulus organism response framework and importance performance map analysis. Acta Psychologica. October 2026. Article: 107680. Volume 270. 10.1016/j.actpsy.2026.107680}