By Jordan French

Algorithms are no longer purely mechanical: by understanding and integrating behavioral biases, traders can turn human emotions into measurable signals for market advantage. A recent study published by Cornell University analyzed over 2 million Reddit comments using a custom Sentiment Volume Change metric, which tracked both sentiment and discussion spikes. The research found that SVC-based strategies outperformed buy-and-hold by up to 84% and reduced losses in down markets. The findings highlight how shifts in online crowd sentiment can serve as effective signals for short-term trading. With the growing complexity of financial markets and the increasing dominance of algorithmic and blockchain-enabled trading systems, one fundamental question is gaining traction among researchers and practitioners alike: Can machines understand market emotions?

Maksim Baradziuk, an internationally recognized quantitative strategist and expert in algorithmic trading, is convinced the answer is yes. His methodologies, adopted by hedge funds and proprietary trading firms across the U.S. and Europe, have been acknowledged for improving portfolio resilience during periods of extreme volatility. Evolving within institutional trading, he became known not only for building scalable systems but also for pioneering behavioral-aware risk protocols, a contribution that leading academics describe as one of the most innovative applications of behavioral economics in quantitative finance over the last decade.

Known for building institutional-grade trading systems and leading cross-asset quant research, Maksim has contributed to several cutting-edge hedge funds and proprietary trading desks. His latest research explores how trading algorithms can be enhanced by incorporating behavioral biases, a field traditionally seen as incompatible with cold, logic-driven code. This research led him to the academic work that was published in The American Journal of Management and Economics Innovations.

In this interview, Maksim Baradziuk explains the feasibility and necessity of teaching algorithms to account for fear, greed, overconfidence, and other human instincts — and why doing so might unlock new levels of risk mitigation in modern markets.

Maksim, your research focuses on leveraging cognitive and behavioral patterns to build high-performing algorithmic strategies. Which behavioral biases do you see as most impactful in trading?

Overconfidence bias, herding behavior, and loss aversion are among the most impactful. These effects don’t just distort individual decisions — they amplify market-wide conditions. During high-volatility periods, traders tend to make more impulsive moves, use higher leverage, and react more emotionally when trades go wrong. You can often see this at market tops, where a kind of trading frenzy sets in. It’s the final stage of many bubbles, a collective emotional overshoot that algorithms can potentially identify.

As a developer of a new class of algorithmic trading strategies, delivering consistent alpha across volatile market conditions, do you believe it’s truly possible to train an algorithm to recognize and account for the market’s emotional state?

Absolutely. Most non-HFT algorithms are essentially pattern recognizers, and those patterns are often byproducts of human or human and machine behavior. So, in a way, we’re already training algorithms to quantify behavioral responses, whether consciously or not. Sometimes, strategy design starts with spotting inefficiencies left behind by other algorithms, which themselves reflect their creators’ cognitive biases.

From your experience of an implementation of advanced risk-management protocols, how do behavioral biases, such as anchoring or overconfidence, affect the effectiveness of trading strategies?

Anchoring often affects microstructure inefficiencies, like how the market reacts to news. The price might not move as much as you’d expect, because people are mentally anchored to a prior belief or level. Overconfidence, on the other hand, tends to stem from poor understanding of how markets truly work. Traders convince themselves they’ll “get it right next time,” even when they’re trading without a clear edge. That hindsight bias is extremely dangerous.

What methods are used to integrate behavioral factors into algorithmic models?

One useful approach is layering: building behavioral correction layers on top of existing models. These layers can detect and respond to extreme behaviors without disturbing the core strategy logic. In some cases, you may even profit from these extreme emotional market states rather than fight them. Jim Simons and Renaissance Technologies have reportedly used a kind of market state theory, which is a great example of quantifying human behaviour.

How do you evaluate the performance of algorithms that incorporate behavioral biases compared to traditional models?

I tend to see most algorithms, whether labeled “behavioral” or not, as descriptions of human behavior to some extent. So I wouldn’t draw a hard line. But if you’re actively targeting behavioral inefficiencies, you’re likely improving robustness in volatile or extreme regimes where traditional models break down.

Maksim, you’ve developed custom risk metrics and analytics tools that have improved transparency and decision-making across both algorithmic and discretionary teams. Can you share any examples of successful applications of behavioral factors in algorithmic trading?

Volatility decay models behave very differently once specific emotional or cognitive thresholds are crossed. Traders’ reactions become less rational, and you can monetize those patterns — for instance, by shorting volatility via options after identifying overreactions. These situations often fall outside of standard volatility distributions and can break traditional assumptions about mean reversion or implied volatility curves. Baradziuk’s volatility models, deployed during the COVID-19 market shock, outperformed benchmarks by maintaining positive Sharpe ratios while many quant funds posted double-digit losses.

Maksim, you managed $15 million on the trading desk and contributed to the oversight of $200 million AUM within the broader investment department. According to your experience, what’s the future of integrating behavioral economics into algorithmic trading?

The most promising area is modeling post-extreme volatility behavior. People consistently misjudge what could happen next, both overreacting and underreacting. As retail access to markets grows, with higher leverage and more data available in real time, we’re going to see more such inefficiencies, not fewer. Algorithms that can model and adapt to these behavioral dynamics will have a real edge.

This industry announcement article is for informational and educational purposes only and does not constitute financial or investment advice.