Month: July 2026

Algorithmic Trading, HFT, and Market Stability

Advances in computing power, declining hardware costs, and the rapid rise of machine learning and algorithmic trading have fundamentally transformed modern financial markets. While these technologies have improved market efficiency and execution, they have also introduced new challenges and risks.

In this post, we examine research on the impact of algorithmic trading, from its influence on corporate behavior and stock price crash risk to the role of high-frequency trading in liquidity, volatility, and overall market quality.

How Algorithmic Trading Impacts the Markets

Algorithmic trading is a method of executing trades using algorithms, or sets of predetermined rules, to make trading decisions. These algorithms are designed to take into account a variety of market conditions, such as price, volume, and timing. Algorithmic trading is often used by large institutional investors, such as hedge funds and investment banks, to execute trades quickly and efficiently. Algorithmic trading is also becoming increasingly popular with individual investors who have access to sophisticated trading software.

Algorithmic trading has a number of advantages over traditional methods of trading. First, algorithms can take into account a wider range of market data and make better-informed decisions. Second, algorithms can execute trades faster than humans, which can be especially important in fast-moving markets. Third, algorithmic trading can help to reduce costs by eliminating the need for human traders.

Algorithmic trading has grown enormously in the last two decades to become the dominant type of trading in the capital markets. Reference [1] studies the impact that algorithmic trading has on the markets.

Findings

-The study examines whether algorithmic trading (AT) increases firm-specific stock price crash risk.

-The authors argue that the short-term focus of algorithmic traders encourages managers to prioritize short-term earnings and delay the disclosure of bad news.

-The empirical results show that higher levels of algorithmic trading are associated with greater future stock price crash risk.

-The study finds that firms with more algorithmic trading are more likely to exhibit opportunistic financial reporting and disclosure practices.

-The relationship between algorithmic trading and crash risk is stronger when managers have greater incentives or the ability to withhold bad news.

-The findings suggest that algorithmic trading may reduce monitoring by fundamental investors, allowing bad news to accumulate over time.

-The results are supported by both instrumental-variable analysis and evidence from the SEC’s 2016 Tick Size Pilot Program.

In short, the authors conclude that increased algorithmic trading can contribute to higher firm-specific crash risk, with potentially adverse consequences for shareholders.

Reference

[1] Ahmed, Anwer S. and Li, Yiwen and McMartin, Andrew Stephen and Xu, Nina, The Rise of Machines: Algorithmic Trading and Stock Price Crash Risk, SSRN 4203738

The Role of HFT in Modern Financial Markets

High-frequency trading (HFT) is a type of algorithmic trading that uses computer programs to place orders at very fast speeds. High-frequency traders use sophisticated algorithms to analyze market data and make trades based on their predictions. These traders typically trade in large volumes of shares and use very short-term strategies.

While the previous article examined the broader impact of algorithmic trading on market behavior and stock price crash risk, Reference [2] focuses specifically on high-frequency trading. Rather than analyzing managerial incentives, it investigates how HFT affects market quality, providing direct evidence on its role in liquidity provision, volatility, and overall market efficiency.

Findings

-The study investigates the impact of high-frequency trading (HFT) by examining a major exchange infrastructure failure that temporarily prevented low-latency trading.

-The outage provides a natural experiment for assessing the role of HFT in modern financial markets.

-The authors find that the disruption has only a modest effect on trading volume and the number of trades.

-However, liquidity deteriorates significantly when high-frequency traders lose low-latency access.

-Market volatility also increases during the outage, although the effect is less pronounced than the decline in liquidity.

-The results suggest that investments in HFT infrastructure generate positive spillover benefits for all market participants by improving overall market quality.

-The findings support earlier research showing that HFT enhances market liquidity and, to a lesser extent, reduces volatility.

The authors conclude that markets remain functional without HFT, but trading becomes more expensive, and market quality deteriorates when high-frequency traders cannot operate at low latency.

Reference

[2] Benjamin Clapham, Martin Haferkorn and Kai Zimmermann, The Impact of High-Frequency Trading on Modern Securities Markets, Bus Inf Syst Eng, 2022

Closing Thoughts

Taken together, these two papers illustrate that algorithmic trading is neither inherently beneficial nor harmful; its impact depends on the aspect of the market being examined. While algorithmic trading may encourage short-term corporate behavior and increase stock price crash risk, high-frequency trading appears to enhance market quality by improving liquidity and reducing transaction costs. As algorithmic trading continues to evolve, understanding its diverse effects on market efficiency, stability, and price formation remains an important area of research.

The Market Impact of Retail Options Trading

Retail trading, especially in the options market, which has traditionally been the domain of institutional traders, has received relatively little attention. However, with the rapid growth of educational content, AI, social media, and commission-free trading platforms, this is no longer the case.

Today, retail investors account for a significant share of options market volume and are changing market dynamics. In this post, we examine the characteristics of retail options trading and how it is reshaping the options market.

How Retail Investors Trade Options

Options trading is often thought of as a professional’s domain. However, with the advent of online trading platforms, retail traders now have access to the same tools and information as professional traders. This has changed the dynamics of the options market, as retail traders can now trade options on a level playing field with professionals.

However, a question remains to be answered: do retail options traders have the same knowledge, experience, and discipline as the professionals? Reference [1] examined this question

Findings

-The paper documents a rapid increase in retail participation in the U.S. options market in recent years.

-It also finds a sharp rise in payment for order flow (PFOF) paid by wholesalers to retail brokerages for executing customer option orders.

-The authors develop a novel measure of retail options trading using transaction-level data and new regulatory reporting requirements.

-The measure closely tracks other proxies for retail trading activity and declines significantly during brokerage outages and trading restrictions.

-The study finds that retail investors strongly prefer inexpensive weekly options.

-These options have very wide quoted bid-ask spreads, averaging approximately 12%, making them costly to trade.

-The paper finds that retail investors frequently fail to exercise call options optimally before ex-dividend dates.

-Market makers and arbitrageurs profit from these mistakes through nearly risk-free “dividend play” arbitrage strategies.

-The study reports that retail trading now accounts for over 60% of total U.S. options trading volume, with nearly 90% of PFOF originating from three major wholesalers.

The findings are very interesting. In the next paper, we’ll look at how retail traders have changed the volatility term structure and dynamics of the option market.

Reference

[1] S. Bryzgalova, A. Pavlova, T. Sikorskaya, Retail Trading in Options and the Rise of the Big Three Wholesalers, SSRN 4065019

The Impact of Retail Options Trading on the Implied Volatility Surface

Retail options trading is rising rapidly, driven by factors such as the growth of retail brokers, the popularity of social media, and more flexible working hours. Alongside this trend, there has been an increased interest in research on retail options trading behavior.

Reference [2] examines how retail trading reshapes the implied volatility (IV) surface dynamics. The authors utilize OPRA and Nasdaq data for this study. To isolate the effect of retail options trading, they apply a difference-in-differences approach around retail broker outages, 82 events from 2019 to 2021, comparing implied volatility between high-retail and low-retail stocks, during versus pre-outage periods.

Findings

-The paper documents a sharp increase in option trading activity driven by retail investors.

-It finds that retail trading is concentrated in call options, short-dated options, and out-of-the-money call options.

-The authors use brokerage outages as exogenous shocks to identify the impact of retail trading on option markets.

-Retail buying volume falls significantly during outages for the option contracts most favored by retail investors.

-In contrast, buying volume for long-dated options increases during outages, consistent with retail investors typically being net sellers of these contracts.

-The study finds that retail demand has a significant impact on option implied volatility.

-Implied volatility declines during brokerage outages, particularly for call, short-dated, and out-of-the-money options.

-Implied volatility increases for long-dated options during outages, reflecting reduced retail option-writing activity.

-The findings suggest that retail demand influences not only the level of implied volatility but also the term structure, moneyness curve, and call-put spread of the implied volatility surface.

-Robustness tests confirm that these effects are specific to brokerage outages and are not driven by a small subset of actively traded options or by the choice of trading dataset.

In short, retail investors systematically buy short-dated, out-of-the-money (especially calls) and sell long-dated options, creating predictable pressure across the surface. When retail trading activity is reduced during brokerage outages, IV falls for short-dated and OTM options but rises for long-dated options.

This paper contributes to a better understanding of retail options trading and shows how retail traders can materially affect the implied volatility surface.

Reference

[2] Eaton, Gregory W., T. Clifton Green, Brian S. Roseman, and Yanbin Wu (2025). Retail Option Traders and the Implied Volatility Surface. SSRN 4104788

Closing Thoughts

Taken together, these two papers show that retail investors have become a major force in the options market, influencing not only trading volume but also option pricing. Their preference for short-dated, out-of-the-money call options has measurable effects on implied volatility, while payment for order flow and trading behavior have reshaped market microstructure. For practitioners, understanding retail option flows is becoming increasingly important, as they now represent a significant driver of option prices and volatility dynamics.