Stock Market Data Streams

Decoding the Stock Market: What "Stylized Facts" Reveal About Modern Trading

Lena Kashyap in Business & Economy August 2025 • 4 min read.

"Are long-held beliefs about stock market behavior still relevant in today's high-speed, algorithm-driven trading world? A new study re-examines classic market 'stylized facts' to find out."

The stock market can often feel like a chaotic and unpredictable beast. However, for decades, financial experts have identified common patterns across different markets and assets. These patterns, known as "stylized facts," act as fundamental benchmarks for understanding market behavior and evaluating financial models.

One of the most influential compilations of these stylized facts came from Rama Cont in 2001. Cont's work synthesized a wide range of research, identifying 11 statistical properties that seemed to consistently appear in financial price changes (or "returns"). These properties were viewed as essential constraints – any model aiming to accurately represent market returns should be able to reproduce them.

But the financial world has changed dramatically since 2001. The rise of automated trading, evolving regulations, and technological advancements have reshaped market dynamics. This raises a critical question: Do the stylized facts Cont identified still hold true in today's modern stock markets? And, should we expect every stock to behave according to all these facts all the time?

The Core Question: Do Old Rules Still Apply?

A recent study has tackled these questions head-on, revisiting Cont's stylized facts in the context of today's U.S. stock market. The researchers analyzed high-frequency trading data from October 2018 to March 2019, focusing on the individual stocks within the Dow Jones Industrial Average (Dow 30).

The goal was to see whether the intraday returns of these stocks exhibited each of Cont's 11 stylized facts. The findings offer valuable insights into which of these time-tested properties remain relevant and how we should interpret them in the modern market environment.

Here's a quick rundown of Cont's 11 stylized facts, which the study sought to validate:

Absence of Autocorrelations: Returns aren't easily predictable, except for very short intraday periods.
Heavy Tails: Returns distributions have more extreme values than a normal distribution would predict.
Gain/Loss Asymmetry: Stock prices tend to experience larger drops than rises.
Aggregational Gaussianity: As the time scale increases, returns distributions look more like a normal distribution.
Intermittency: Returns show irregular bursts of volatility.
Volatility Clustering: High volatility events tend to cluster together in time.
Conditional Heavy Tails: Returns still exhibit heavy tails even after correcting for volatility clustering.
Slow Decay of Autocorrelation in Absolute Returns: The autocorrelation of absolute returns decays slowly over time.
Leverage Effect: Volatility is negatively correlated with an asset's returns.
Volume/Volatility Correlation: Trading volume is correlated with volatility.
Asymmetry in Timescales: Measures of volatility can predict volatility at finer timescales better than the reverse.

The study employed a range of statistical methods, including correlation analysis, moment calculations, and distribution analysis, to test each of the stylized facts. They also compared their results to those obtained from randomly generated "white noise" returns to establish a benchmark for consistent, nontrivial market behavior.

The Verdict: Which Stylized Facts Still Ring True?

The research found strong evidence supporting eight of Cont's original stylized facts in today's market. These included the absence of autocorrelations, heavy tails, aggregational Gaussianity, intermittency, volatility clustering, conditional heavy tails, and the slow decay of autocorrelation in absolute returns. However, they found no support for gain/loss asymmetry, the leverage effect, or asymmetry in timescales when looking at individual stocks.

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This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2311.07738,

Title: Revisiting Cont'S Stylized Facts For Modern Stock Markets

Subject: q-fin.st q-fin.cp

Authors: Ethan Ratliff-Crain, Colin M. Van Oort, James Bagrow, Matthew T. K. Koehler, Brian F. Tivnan

Published: 13-11-2023

Everything You Need To Know

What are 'stylized facts' and why are they important for understanding the stock market?

'Stylized facts' are common patterns observed in financial markets across different assets. They serve as fundamental benchmarks for understanding market behavior and evaluating financial models. These patterns help experts to explain and predict the market behavior. Some important examples of the 'stylized facts' include absence of autocorrelations, heavy tails, aggregational Gaussianity, intermittency, volatility clustering, and slow decay of autocorrelation in absolute returns. These help in creating more realistic models of the market and trading strategies.

What is the significance of Rama Cont's work on 'stylized facts' in 2001?

Rama Cont's 2001 work was a significant compilation of 'stylized facts'. Cont synthesized a wide range of research to identify 11 statistical properties that consistently appeared in financial price changes. His work provided a foundation for understanding and modeling market behavior, acting as constraints that financial models should be able to reproduce to accurately represent market returns. These properties were viewed as essential constraints – any model aiming to accurately represent market returns should be able to reproduce them.

How has the modern stock market changed since 2001, and what impact has this had on the relevance of 'stylized facts'?

Since 2001, the stock market has undergone significant changes due to automated trading, evolving regulations, and technological advancements. These changes have reshaped market dynamics, raising questions about the continued relevance of 'stylized facts'. A recent study revisited Cont's stylized facts to assess their validity in today's U.S. stock market, specifically looking at stocks within the Dow Jones Industrial Average. The findings revealed which time-tested properties remain relevant and how they should be interpreted in the modern market environment. The study found that some 'stylized facts' still hold strong while others are not observed anymore.

Which of Cont's 'stylized facts' were found to still be relevant in the modern stock market according to the study?

The study found that eight of Cont's original 'stylized facts' remained relevant in today's market. These include the absence of autocorrelations, heavy tails, aggregational Gaussianity, intermittency, volatility clustering, conditional heavy tails, and the slow decay of autocorrelation in absolute returns. This suggests that despite the advancements in technology and trading practices, these core properties of the stock market continue to hold true.

Can you explain some of the 'stylized facts' that the study validated, such as 'Heavy Tails' and 'Volatility Clustering'?

The study validated several of Cont's 'stylized facts'. 'Heavy Tails' means that the returns distributions have more extreme values than a normal distribution would predict, indicating that extreme price movements occur more frequently than expected. 'Volatility Clustering' implies that high volatility events tend to cluster together in time. The study confirmed these two properties, along with others like 'Absence of Autocorrelations' (returns are not easily predictable), 'Intermittency' (returns show irregular bursts of volatility), and 'Slow Decay of Autocorrelation in Absolute Returns' (the autocorrelation of absolute returns decays slowly over time).