Costello Research Fintech

Machine learning teaches asset traders not to sweat the small stuff

Katelynn C Hipolito — Mon, 15 Jun 2026 14:40:20 +0000

Machine learning teaches asset traders not to sweat the small stuff Katelynn C Hipolito Mon, 06/15/2026 - 10:40

Financial markets are governed by a combination of rational and irrational forces, statistical probabilities, and “animal spirits.” It takes fluency in both to understand the market, let alone to beat it. Yet market actors, including asset traders, now frequently use machine-learning techniques to help generate predictions of future asset prices.

Bo Hu, assistant professor of finance at the Costello College of Business at 911��. Photo provided by Bo Hu.

Scholars such as Bo Hu, assistant professor of finance at the Costello College of Business at 911��, are researching how these machine-learning tools are changing the decision-making processes that move the market, for better or worse.

The subject of Hu’s recent paper in Management Science is a well-known machine-learning technique called LASSO (least absolute shrinkage and selection operator), which has been widely adopted by financial practitioners since its introduction in 1996 by statistician Robert Tibshirani.

“If you look at the original paper, it describes an approach created by adding a regularization penalty to the least-squares regression method,” Hu says. Translation: “The power of LASSO is that it can screen out (i.e., penalize) weak signals while capturing stronger, potentially profitable ones. A LASSO-type trading strategy involves an ‘inactive zone’ for smaller-scale activity, in which the trading strategy is to do nothing.”

The paper was co-authored by Wen Chen of Texas Tech University and Liyan Yang of the University of Toronto.

Despite LASSO’s popularity and power, the soundness of its economic rationale remains unclear. Traders are presumably seeking any edge, however small, in the pursuit of outsized returns. How could it make sense for them to adopt a system designed to relegate signals of lesser magnitude to an ignored “inactive zone”?

To resolve this question, the researchers developed a theoretical framework to model a financial market in which multiple agents (read: traders) use an asset’s price history to forecast its return and make trading decisions.

In the benchmark case, when traders know the trading environment and do not face model uncertainty, they act according to an alternative to LASSO known as MSE (mean squared error). “MSE is essentially a Bayesian learning approach grounded in economic rationality,” Hu says. “It means that rational agents use Bayesian learning to update their beliefs and design their trading strategies. That stands in stark contrast to LASSO estimation, which filters out weak signals.”

However, the researchers found that when traders faced substantial ambiguity about the distribution of asset values, the trading calculus shifted. Ambiguity-averse agents will adopt a robust, LASSO-like strategy, refraining from trading in response to weak or intermediate market signals. With linear constraints imposed on the allowable trading strategies, the equilibrium decisions exactly matched LASSO estimates.

As an equilibrium trading strategy, LASSO can improve aggregate profits relative to the edge-seeking Bayesian alternative in the benchmark case, because the more conservative positions dictated by the “inactive zone” soften competition among traders. In a large market, aggressive competition drives the aggregate profits of traders using the conventional MSE strategy toward zero. By trading less aggressively, LASSO traders may preserve positive aggregate profits—a mechanism the researchers describe as “implicit collusion,” even though the traders do not communicate or explicitly coordinate.

However, Hu underscores that LASSO’s usefulness as a hedge against ambiguity depends on how well traders’ beliefs match the market’s true distribution of risk. The profitability of a LASSO strategy therefore hangs in the balance between traders’ biases and their enhanced market power (due to LASSO’s conservatism).

This balance is especially important when traders must distinguish between temporary fads and persistent trends. “Recently, the semiconductor index had a record-breaking run of consecutive gains. That’s very strong momentum, but if you are a contrarian, you might want to bet against that trend,” Hu says. “A trader must decide whether to follow the trend or take a contrarian position. LASSO’s inactive zone can help prevent overreaction to weak evidence, but it may also delay action when an emerging trend is genuine.”

There is also the possibility that a LASSO strategy could increase market volatility when combined with the other objectives and constraints of market makers, including high-frequency traders. “These traders need to control their inventory,” Hu says. “If they follow a LASSO-type strategy with an inactive zone, they will accommodate the liquidity demands of the market until their inventory reaches a certain threshold. At that point, they may start trading like momentum traders and cause the liquidity dry-up in financial markets. This dynamic is part of what fuels incidents like the ‘flash crash’ in 2010.”

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Artificial Intelligence

Cryptocurrency’s surprising transparency advantage

Marianne Klinker — Tue, 02 Jan 2024 18:18:45 +0000

Cryptocurrency’s surprising transparency advantage Marianne Klinker Tue, 01/02/2024 - 13:18

Despite the fears of regulators and skittish investors, clear and accurate signals of cryptocurrency quality may be hidden in plain sight.

As perhaps befits a product of the post-2008 economy, the cryptocurrency space has never known normalcy. In a mere 13 years, crypto went from an untried software innovation of mysterious origin to being touted as the future of investing by major movie stars in Super Bowl commercials. Soon thereafter, of course, came the “crypto winter” of 2022, which began well before, but was surely deepened by, the downfall of FTX and disgraced wunderkind Sam Bankman-Fried.

Forthcoming in Journal of Management Information Systems, a recent paper is the first to examine whether the intensity of developer engagement with a cryptocurrency could indeed be related to its quality.

The crypto backlash appears rooted in the belief that the sector is too rife with bad actors, and too technologically complicated, to be worthy of public trust. The consensus in the crypto community, however, is that accurate clues to crypto quality are hidden in plain sight. The majority of cryptocurrencies use open source platforms such as GitHub for the development of their software. Seasoned investors will routinely inspect the publicly available development history of a cryptocurrency as a basic aspect of due diligence.

Forthcoming in Journal of Management Information Systems, a recent paper co-authored by Mariia Petryk, an assistant professor of information systems at the Donald G. Costello College of Business at 911��, is the first to examine whether the intensity of developer engagement with a cryptocurrency could indeed be related to its quality. (Her collaborators were Liangfei Qiu and Praveen Pathak of University of Florida.)

Mariia Petryk

“This paper is about how we evaluate reputation in a setting where traditional reputational instruments are not precise,” Petryk explains. “When we choose a doctor, we often rely on an endorsement from someone we trust. But in lieu of that, we look into the history of the doctor—education, training, etc.” By the same token (no pun intended), crypto investors could “judge quality based on the total effort developers made to write the code.”

Using GitHub data for 559 cryptocurrencies over the period August 2016 to December 2019, the researchers zeroed in on five open-source activities. They classed three under the heading of “quality enhancement.” Commits are code modifications submitted by developers and eventually added to the crypto source code. Before official adoption, proposed modifications exist as pull requests awaiting assessment. Issues are questions, bugs, or problems raised by developers or users for discussion within the open source platform.

The remaining two activities fall under “diffusion of software,” essentially a proxy for developer attention. Forks are mirrored copies of the original code attached to a developer’s account. Watches are a way for developers to “subscribe” in order to more closely follow the discussion within the community around a cryptocurrency.

Across the data-set, the researchers found that a one-standard-deviation increase in forks and watches would equate to a 0.56% price increase per month, or 6.7% per year.

As for the three “quality enhancement” metrics, increases in issues exerted upward pressure on token price, but pull requests and commits displayed the opposite effect. A one-standard-deviation increase in issues led to a 4.3 percent higher price over one year, while an equivalent increase in pull requests was linked to annual losses of five percent.

These contradictory results perhaps point to uncertainties about the length of time it would take for crypto admins to evaluate and implement proposed changes. It goes without saying that greater numbers of requests for revision would heighten such uncertainties and their possible implications for cryptocurrency quality.

Overall, these findings imply a “virtuous circle” whereby more promising cryptocurrencies attract more developer attention, which in turn produces quality improvements that are reflected in the token price. While the expansion of the developer community creates the potential for rising pull requests and commits to lower the token price, the price-positive impact of forks and watches was approximately six times stronger. As a general rule, then, intensity of developer attention and engagement could be viewed as a leading indicator of how the market values the tokens—with the very important caveat that broad statistical patterns may have little to no bearing on specific investment decisions.

Therefore, information from GitHub and other open-source platforms could be an important reference point for policymakers looking to evolve nuanced regulatory approaches. “One purpose of regulation is to create equal opportunity,” Petryk says. “Transparent mechanisms allow investors to learn about underlying assets and properties and make their own judgments. Open source platforms like GitHub are open to everyone, but not everyone thinks to use it. In terms of info disclosure, this could be an important factor.”

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