Algorithmic trading (AT) has transformed global financial markets — but does it stabilize markets or make them more volatile?
A new peer-reviewed study from Nature Scientific Reports (2025) provides a definitive answer: algorithmic trading significantly reduces market volatility, especially in emerging markets like China.
Let’s unpack what the researchers found — and what it means for traders, regulators, and algorithmic strategy developers.
Key Takeaway: Algorithms Calm the Market, Not Agitate It
The study, conducted by Dan Yang et al., analyzed Level-2 order book data from the Shenzhen Stock Exchange (SZSE) — one of the world’s busiest equity markets.
Unlike prior Western studies with mixed conclusions, this paper shows that algorithmic trading reduces stock price volatility by replacing emotional, sentiment-driven decisions with data-driven execution.
For every one-unit increase in algorithmic trading activity, the standard deviation of intraday returns dropped by 0.817 points — a substantial reduction in volatility.
The Data Behind the Findings
Dataset: 2117 SZSE stocks, 204 trading days across 2018–2019
Scope: A down-market period (June–Oct 2018) vs. a stable period (June–Oct 2019)
Method: Two-stage least squares (2SLS) with Shenzhen–Hong Kong Stock Connect participation and order-to-trade ratio (OTR) as instrumental variables.
How Algorithmic Trading Reduces Volatility
The research team built a serial multiple mediation model (SMM) to explain how AT affects market volatility through two behavioral channels:
1. Investor Sentiment – The Big One (≈25% of total effect)
Algorithms:
Replace emotional human trading with code-based decision logic.
Improve market efficiency and reduce price misalignment.
Split large orders to prevent panic or momentum reactions.
As a result, investor sentiment decreases, and with it, irrational trading activity and price swings.
2. Herd Behavior – Smaller but Real (≈4% of effect)
By fragmenting large trades and removing visible “whales,” algorithmic trading reduces imitation behavior among retail and institutional investors alike.
This weakens the herding effect that often amplifies volatility during market stress.
3. Direct Effect – The Dominant Force (≈70%)
The remaining effect comes directly from order splitting — algorithms executing large trades more smoothly, preventing sudden market shocks and large price gaps.
Sector Insights: AT Is Most Valuable for Growth Stocks
The stabilizing power of algorithmic trading isn’t uniform across the market.
Market Board
Effect Strength
Key Driver
Main Board (large-cap)
Strongest volatility reduction
Direct AT execution
GEM Board (growth/tech)
Highest behavioral impact
Sentiment & herd reduction
SME Board
Moderate
Mixed behavior–liquidity effects
In simpler terms:
AT brings the most “behavioral stability” to high-volatility growth sectors, while large-cap markets benefit more from efficient execution.
When the Market Falls, Algorithms Step Back
Interestingly, the paper also shows that during downward markets, AT’s stabilizing effect weakens.
When sentiment is overwhelmingly negative, algorithms often exit or switch to defensive strategies, reducing liquidity and leaving markets more prone to sharp moves.
In short — AT is stabilizing in normal conditions but less effective in crashes.
Implications for Traders, Regulators, and SaaS Platforms
For Traders:
Incorporate AT indicators (like OTR) into volatility forecasting models.
Recognize that automated execution can protect portfolios from sentiment-driven noise.
For Regulators:
Encouraging AT adoption — especially in emerging or retail-heavy markets — can improve stability and market quality.
Oversight is still crucial in downturns when AT liquidity may dry up.
For Fintech and SaaS Founders:
There’s a growing opportunity in algorithmic risk mitigation tools — platforms that help smaller investors access AT-driven execution without introducing systemic fragility.
Behavioral modeling (sentiment & herding analytics) can be productized for volatility prediction SaaS.
The Bigger Picture
This research adds quantitative weight to what many systematic traders already suspect:
Emotion, not automation, drives most volatility.
By reducing investor sentiment and herd behavior, algorithmic trading — when properly designed and regulated — creates calmer, more efficient markets.
In the long run, that stability benefits everyone — from institutional funds to retail investors and SaaS developers building the next generation of fintech tools.
📚 Reference
Yang, D., Yang, Y., Luo, J., Wang, Z., & Sha, H. (2025). Research on the impact of algorithmic trading on market volatility. Scientific Reports, 15, Article 30073. https://www.nature.com/articles/s41598-025-15020-w
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