Imagine you are following a stock on the Saudi financial market and notice that its price has fallen to an attractive level for buying.

You open your trading app, check the price, choose the quantity, and press the buy button.

This process may take several seconds.

But at that very moment, a computer system is monitoring price movements, analyzing buy and sell orders, and deciding whether to execute a trade or modify an existing order—all without waiting for human intervention at every step.

In some advanced trading systems, these processes can take place in tiny fractions of a second.

This illustrates one of the most important shifts in modern financial markets: not all market participants make trading decisions in the same way or at the same speed.

While traditional investors rely on analysis and make decisions manually, financial institutions use systems that can process information and submit orders automatically.

This practice is known as algorithmic trading (Algorithmic Trading).

It has become part of the operational infrastructure of global financial markets, led by the U.S. market, before expanding into other markets, including Saudi Arabia.

But the spread of this technology raises important economic and regulatory questions: Does it make trading more efficient? Does it give large institutions an advantage over individual investors? Can it affect stock prices and liquidity?

These questions help explain the importance of the project proposed by the Saudi Capital Market Authority in September 2026 to regulate algorithmic trading.

First: What Is Algorithmic Trading?

Algorithmic trading is the use of a computer program to submit and manage securities buy or sell orders automatically, according to predefined rules and criteria.

To understand this definition, we should start with the meaning of the word “algorithm.”

An algorithm is a set of logical steps that a computer follows to accomplish a particular task.

For example, a trading system could be programmed with the following instructions:

If a particular stock’s price falls to a specified level, and the quantity offered for sale is suitable, submit a buy order for the required quantity.

If the price rises to another level, submit a sell order.

This is a simplified example of a trading algorithm.

But the systems used by financial institutions can be more complex. They can analyze price data, trading volumes, and bid-ask spreads, and execute strategies based on a large number of variables.

The fundamental difference between manual and algorithmic trading is that, in the first case, a person makes and executes trading decisions, while in the second, a program carries out predefined instructions.

That does not mean a computer always makes independent investment decisions. An investment manager may set the goal of a trade and leave the system to choose when and how to execute it.

Is Algorithmic Trading the Same as High-Frequency Trading?

Although the terms are related, they do not mean the same thing.

Algorithmic trading is a broad concept that encompasses various trading strategies based on automated orders.

By contrast, high-frequency trading (High-Frequency Trading – HFT) is a specialized type of algorithmic trading that typically relies on extremely fast execution and the submission, modification, or cancellation of large numbers of orders over short periods.

For example, an investment fund could use an algorithm to spread a large purchase over several hours, without seeking to profit from price movements over fractions of a second.

By contrast, a high-frequency trading firm might use an algorithm to take advantage of very small price differences, holding its positions for short periods.

In other words, all high-frequency trading is algorithmic trading, but not all algorithmic trading is high-frequency trading.

This distinction is also reflected in studies published by the U.S. Securities and Exchange Commission, which note that algorithms are also used to execute large orders for institutional investors, not just for rapid speculation.

Second: Why Do Financial Institutions Use Algorithms?

To understand the economic value of algorithmic trading, suppose an investment fund wants to buy 100,000 shares of a listed company.

If the fund places a buy order for the entire quantity at once, there may not be enough shares available at the current price.

It may have to buy at several price levels, raising its average purchase cost.

This problem is related to an economic concept called market impact (Market Impact).

This refers to the change in a security’s price resulting from the execution of a trade, especially when the order is large relative to available liquidity.

An algorithm can address this problem by splitting a large order into smaller ones and spreading them over different periods.

Instead of buying 100,000 shares at once, the system might spread the purchase across dozens or hundreds of smaller orders, depending on market conditions.

The goal is not necessarily to make quick speculative profits, but to reduce the cost of executing the trade and limit its impact on the price.

This is one reason investment institutions use algorithms.

Other uses include continuously monitoring prices, responding to changes in supply and demand, managing trading risks, and taking advantage of price differences between related financial instruments.

How Do Algorithms Make Profits?

Some algorithmic trading strategies take advantage of small price differences.

Suppose the best bid for a particular stock is SAR 19.98, while the best ask is SAR 20.02.

The difference between the two prices is four halalas.

This difference is called the bid-ask spread (Bid-Ask Spread).

Some market-making strategies may place buy and sell orders and earn this spread when both sides are executed, while taking on the risks of price movements, inventory, and commissions.

When trading is repeated at high volumes, small price differences can become economically significant.

But these trades are not guaranteed to be profitable. The price may move unexpectedly, the system may fail to execute one side of the trade, or trading costs may exceed the expected return.

That is why speed alone is not enough to make a profit.

Third: How Much Algorithmic Trading Takes Place in the Saudi Market?

Automated trading is no longer limited to U.S. and European markets.

Its use has expanded in the Saudi financial market alongside improvements to trading infrastructure and increased participation by local and international financial institutions.

The annual reports of Saudi Tadawul Group offer some indicators of this shift.

According to the group’s 2024 annual report, high-frequency trading clients accounted for about 25% of average daily trading, with the share rising on some particularly active days.

This figure is significant because it shows that high-frequency trading has become a tangible part of activity in the Saudi market.

However, two points should be kept in mind.

First, the data is from 2024 and should not be treated as a direct measure of the volume of algorithmic trading in October 2026.

Second, the figure relates to high-frequency trading clients, not all forms of algorithmic trading, since automated trading includes other strategies that do not necessarily depend on ultra-high speed.

It would therefore be an inaccurate oversimplification to say that “robots control a quarter of the Saudi market.”

It is more accurate to say that algorithm-based trading has become an important part of market activity, and measuring its scale requires distinguishing between the types of strategies used.

Who Uses These Systems?

Algorithms are used by a range of institutions, including investment and brokerage firms, asset managers, market makers, and specialist trading firms.

In October 2026, during an interview with Al Arabiya Business, Capital Market Authority Chairman Mazen Al-Sudairi said that foreign financial institutions were the most frequent users of algorithms in the Saudi market.

Al-Sudairi linked the monitoring of this trading to the importance of maintaining fairness among investor groups.

This does not mean that foreign institutions are violating rules simply by using these technologies. Algorithmic trading is a legitimate financial practice; the regulatory question is how the technology is used and how it affects the market.

Fourth: Why Is the Capital Market Authority Regulating Algorithmic Trading?

On September 24, 2026, the Saudi Capital Market Authority announced a consultation on a draft set of rules relating to restrictions on algorithmic trading.

The Authority opened the proposal for public comment for 30 days, until October 24, 2026.

According to the Authority, the proposal aims to develop controls governing algorithmic trading and enhance its safety and efficiency, while limiting potential negative effects on the market.

To understand why this step matters, it is important to first recognize the difference between an order sent to the market and a trade actually executed.

When an investor submits a buy order, it does not necessarily mean the purchase has taken place.

The order may remain pending, its price or quantity may be changed, or it may be canceled before execution.

The same applies to sell orders.

In modern markets, algorithmic systems can enter, modify, and cancel large numbers of orders over short periods.

The number of trades actually executed may be far lower than the number of messages and orders received by market systems.

This raises a regulatory question: Does a large number of orders reflect genuine interest in buying and selling, or is it simply intensive automated activity that does not necessarily result in actual trading?

The answer cannot be determined from the number of orders alone, but tracking the relationship between orders and trades helps regulators assess trading activity.

What Does the 20-Order Limit Mean?

The proposed Saudi rules would cap the ratio of algorithmic orders to executed trades at 20 orders for each executed trade, calculated according to the method set out in the draft proposal.

This does not mean a system is prohibited from submitting more than 20 orders in a day, or that it must not submit 20 orders at once.

Rather, it concerns measuring the volume of orders relative to the trades that are actually executed.

To illustrate, if a trading system submits 1,000 orders for a particular stock during a trading day and 40 trades are executed, a simplified calculation gives us:

1,000 orders ÷ 40 trades = 25 orders per trade.

This is above the proposed limit.

If the system submits 1,000 orders and 100 trades are executed, the simplified ratio becomes:

1,000 orders ÷ 100 trades = 10 orders per trade.

This is below the proposed limit.

It should be noted, however, that these examples are for illustrative purposes only.

The formula in the Capital Market Authority’s draft is more detailed: it takes into account orders entered, modified, and canceled, and gives modified orders double weight in the calculation.

Therefore, simply dividing the number of new orders by the number of trades is not enough to determine actual compliance with the rules.

The proposed restrictions apply to securities listed on the Main Market, except for securities classified as “very high liquidity.”

The ratio is also calculated for each trader registered with the market and responsible for the trading activities of a market member and its clients.

The draft also includes requirements for testing and monitoring algorithms, ensuring they can be shut down when necessary, and retaining relevant records.

According to the official announcement, the target date for the final rules to take effect is November 1, 2026, following consideration of the feedback and approval of the final version.

Fifth: How Is Algorithmic Trading Related to Stock Liquidity?

Liquidity is one of the most important economic concepts associated with trading.

A stock’s liquidity refers to how easily it can be bought or sold within a reasonable time and at a price close to the prevailing market price, without the trade causing a significant change in price.

To illustrate, let’s imagine two stocks in the market.

The first sees continuous buying and selling, with large quantities available at closely spaced price levels.

The second has only limited quantities available, and there may be a large gap between the buy and sell prices.

Liquidity is generally higher in the first case.

In the second case, liquidity is lower.

So what is the connection with algorithms?

Some algorithmic systems can increase liquidity by continuously placing buy and sell orders, which may help narrow the gap between the best bid and the best ask.

But some strategies may quickly withdraw their orders when volatility rises or market conditions change.

When this happens, available liquidity may decline, especially in stocks that already trade infrequently.

That is why algorithmic trading cannot be said to always increase liquidity or always reduce it.

The effect depends on the type of algorithm, the trading strategy, the stock’s liquidity, and market conditions.

This conclusion is supported by a research study published by the Bank for International Settlements in November 2024, examining how liquidity in financial markets has evolved over 25 years.

The study found that algorithmic trading, along with other factors such as market fragmentation, is associated with narrower average bid-ask spreads, but may also be accompanied by increased liquidity fragility in some conditions.

Liquidity fragility means that a market may appear able to absorb trades smoothly under normal conditions, but becomes less able to do so when pressure intensifies.

Sixth: Can Algorithms Affect Stock Prices?

Yes. Algorithmic trading can affect price movements, just like other forms of trading.

But the degree of impact varies according to order size, the stock’s liquidity, and the type of strategy used.

To understand this, suppose a sudden increase in buy orders occurs for an illiquid stock.

These orders may push the price up if there are not enough shares for sale at current prices.

If the orders are large or submitted rapidly in succession, the price movement may become more pronounced.

But that does not mean every rapid rise or sudden drop is caused by algorithmic trading.

Stock prices are also affected by corporate earnings, economic news, interest rates, future expectations, and changes in investors’ risk appetite.

Can Algorithms Be Used to Manipulate the Market?

Automated systems can be used for unlawful practices if they are designed to carry out manipulative behavior.

One well-known practice around the world is spoofing (Spoofing).

This occurs when a trader places buy or sell orders they do not intend to execute, in order to create a misleading impression of significant demand or supply.

This behavior may influence other traders’ decisions before the orders are canceled.

In previous cases, the U.S. Securities and Exchange Commission has announced enforcement actions against entities that used fictitious orders to influence prices and send misleading signals to market participants.

But it is important to distinguish between two things.

Canceling a trading order is not, in itself, evidence of manipulation.

An investor may cancel an order because the price has changed or they have revised their decision.

Manipulation, by contrast, depends on the nature and intent of the behavior, the surrounding circumstances, and whether it violates the rules governing the market.

That is why monitoring order volumes alone is not enough to prove manipulation, although it may be one of the tools used for oversight.

Seventh: Do Global Markets Face the Same Problem?

Interest in regulating algorithmic trading is not unique to Saudi Arabia.

Automated systems have become an essential part of global financial markets, prompting regulators to develop rules for managing the risks they create.

The U.S. Experience

U.S. markets saw an early expansion in the use of automated and high-frequency trading systems.

For years, the U.S. Securities and Exchange Commission has examined the potential effects of these systems on liquidity and fair access to the market.

The issues considered by the Commission include some firms’ ability to access data and execute orders faster than others, and the possibility that certain strategies could increase volatility or harm investors.

But the Commission has also noted that algorithmic systems offer benefits, including improved order execution and lower trading costs in some cases.

This shows that the issue is not a choice between human and automated trading, but rather establishing rules that ensure technology is used effectively and responsibly.

The European Experience

In the European Union, the financial market rules known as MiFID II include specific requirements for firms that use algorithmic trading.

Key requirements include effective risk-management systems, testing trading programs, preventing erroneous orders, and monitoring systems to reduce the risk of market disruption.

European frameworks also include the concept of an order-to-trade ratio, which helps monitor the intensity of trading activity.

This does not mean that European rules necessarily apply a uniform limit similar to the one proposed in Saudi Arabia; calculation and implementation methods differ across regulatory frameworks and markets.

The U.S. and European experiences show that regulation evolves alongside trading technology, and that the central challenge is preserving the benefits of innovation while limiting its risks.

Eighth: How Could Saudi Regulation Affect Investors?

The potential effects of the Saudi proposal can be analyzed through three interconnected economic dimensions.

1. Improving Order Quality

A limit on order volumes may encourage institutions using algorithms to improve how they manage their orders and reduce messages that do not provide a clear benefit to trade execution.

This could help make trading activity more efficient.

But a lower number of orders alone does not prove that market quality has improved. It is necessary to determine whether canceled orders were truly unhelpful or were a normal part of providing liquidity.

2. The Effect on Liquidity

If regulation reduces excessive orders without affecting genuine buy and sell quotes, trading efficiency may improve.

But if it leads some participants to reduce their activity in certain stocks, available liquidity may decline.

That is why assessing the proposal’s success requires monitoring results after implementation, rather than simply measuring whether the number of orders has fallen.

3. Strengthening Confidence and Market Fairness

Trust is an important element in the functioning of financial markets.

Investors need confidence that prices are formed according to clear rules and that regulators monitor practices that could affect the integrity of trading.

Regulation may help strengthen this confidence by improving oversight, testing, and compliance systems.

But a fair market does not necessarily mean that all investors have access to the same technological tools.

Differences in resources, expertise, and speed of access to information exist in most markets.

The role of regulation is to curb harmful practices, ensure compliance with the rules, and promote competition and integrity.

Ninth: How Can the Success of Algorithmic Trading Regulation Be Measured?

From a research perspective, it is not enough to say that regulation has succeeded simply because it has reduced the number of orders submitted to the market.

Its effect on trading quality should also be studied.

One important indicator is the bid-ask spread. If the gap between the best bid and best ask narrows, this may indicate lower trading costs.

Another is market depth, which refers to the volume of shares available to buy or sell at different price levels.

The more shares that can be traded without causing a significant price change, the better the market is generally able to absorb trades.

Meanwhile, price volatility measures how much prices change over a given period. It must be interpreted carefully, because an increase may result from economic news or genuine changes in expectations—not just algorithms.

The cost of executing orders, the speed of trade execution, and the number of modified and canceled orders should also be monitored.

The best approach is to compare these indicators before and after regulation takes effect, while taking changes in market conditions into account.

The exemption for stocks classified as very high liquidity could be used in designing a comparative study, but that comparison alone is not enough to establish a causal effect; the included and exempted stocks may differ in other important ways.

This would make it possible to assess whether the regulation has struck a balance between reducing the risks of automated trading and preserving liquidity and market efficiency.

Tenth: Can Individual Investors Compete with Algorithms?

This brings us to a question that matters to a broad group of investors.

If some institutions have systems that can analyze information and execute orders in tiny fractions of a second, how can individual investors compete?

The answer depends on the nature of the competition.

If individual investors try to beat a high-frequency system at reading price movements and executing orders, they may face a clear technological disadvantage.

Specialized systems may have ultra-fast technology and communications infrastructure that individual investors find difficult to match.

But investing does not depend on speed alone.

An investor seeking to own shares in a company for several years has a different goal from a trader trying to profit from a small price movement over a fraction of a second.

The first may focus on the company’s earnings growth, management quality, financial position, and ability to pay dividends in the future.

The second may focus on price differences and the timing of trade execution.

They are therefore not necessarily competing in the same arena.

Why Does Time Matter?

This highlights the importance of the investment horizon (Investment Horizon).

This is the period for which an investor plans to hold an investment before selling it or using the invested funds.

An investment horizon may be short—ranging from minutes to days—or long, extending over years.

As investment horizons differ, so do the criteria investors use to assess opportunities and risks.

Small price movements targeted by some high-frequency trading systems may be far less significant to an investor evaluating a company’s ability to grow its earnings over five years.

But this does not mean long-term investing guarantees a profit, or that holding any stock for a long time necessarily produces a positive return.

Waiting does not create economic value for a company by itself.

The value of a long-term horizon lies in allowing investors to make decisions based on company fundamentals and their financial goals, rather than trying to keep up with every short-term price movement.

This allows us to restate the idea of competition between humans and machines more accurately:

Investors do not need to be faster than algorithms; they need an investment strategy that suits their goals and risk tolerance.

How Can We Balance Technological Speed and Market Fairness?

Algorithmic trading highlights an important shift in the nature of financial markets.

Technology is no longer just a way to submit buy and sell orders; it now influences how trades are executed, liquidity is provided, and prices are formed.

Algorithms have helped develop trading methods and reduce some execution costs, but they have also created challenges related to order volumes, speed of response, and oversight of automated systems.

In this context, the Saudi Capital Market Authority’s proposal to regulate algorithmic trading is an effort to update the rules in line with technological progress in the market.

It is not possible to say in advance that the proposed limit will improve liquidity or reduce volatility, since these outcomes depend on participants’ behavior, trading conditions, and how the rules are implemented.

What is clear, however, is that regulating financial markets involves more than setting trading limits. It also means building an environment where investors can operate under clear rules and institutions can use modern technologies without undermining market efficiency or integrity.

For economics students, this issue offers a practical example of the interaction between three key elements: technological progress, market efficiency, and economic regulation.

For individual investors, the most important lesson is that successful investing does not necessarily require winning a speed race against machines.

An algorithm may be able to execute thousands of instructions in a short time, but making a good investment decision still depends on understanding an asset’s value, assessing its risks, and choosing an appropriate time horizon.

An algorithm may be better at executing a decision quickly, but it does not make the quality of the decision itself any less important.