Last Updated on 11 September, 2023 by Samuelsson
With the advent of the internet and electronic trading, many traders and investors, especially prop traders and fund managers, made program trading an important part of their trading strategies. You may be wondering what program trading is about.
Program trading refers to the use of computer algorithms to generate and execute trades in a group of stocks, often trading large volumes with great frequency. Different strategies can be coded into trading algorithms, which monitor the markets and execute trades without direct human input. Since the trades are executed by computer algorithms, they are not usually influenced by human emotions and can be very profitable.
To make the post easy to digest, we are going to discuss the topic under the following subheadings:
What does program trading mean?
Program trading is a term used to describe a kind of trading where computer algorithms generate and execute trades in a basket of stocks, often trading large volumes with great frequency. With the program trading approach, thousands of trades are executed a day, unlike other methods that only execute trades every few months to rebalance long-term portfolios. This is why it is often described as high-frequency trading (HFT).
To use the program trading approach, different strategies are coded into trading algorithms, which then monitor the markets and execute trades without direct human input. The algorithms are programmed to run on their own — monitoring the markets, identifying trade setups, executing the trades, and managing them according to the coded instructions. However, these trading programs are supervised by humans who create them and can activate or deactivate them as needed.
Nonetheless, since the trades are executed by computer algorithms, they are not usually influenced by human emotions and can be very profitable. Program trading methods are implemented behind the scenes at home or trading offices, as against the trading floor. They take place quietly, oblivious to the chaos of the trading floor, but they can significantly influence the prices of the stocks in their basket through the electronic communication network (ECN).
While program trading often applies to stock, it can also apply to derivative products. So, it is not just a process of simultaneously trading a basket of stocks electronically; it can be used for portfolio restructuring and index arbitrage trading. But one thing is often common with this method: large volume and high-frequency trading.
When used for stock trading, it may mean buying or selling a basket of fifteen or more stocks simultaneously via computer programs based on predetermined conditions. It is often used by hedge funds and other institutional investors when implementing their index arbitrage or other arbitrage strategies.
It can also be used by mutual funds: for example, when they receive an influx of money, they will use that money to increase their holdings in the multiple stocks on which their funds are based. Individual traders can also use this method to arbitrage temporary price discrepancies between related financial instruments — for example, between an index and its constituent parts.
The origin of program trading
The origin of program trading can be traced to the late 1980s and 1990s when technological advances led to the growth of electronic communication networks. However, several factors help to explain the explosion in program trading in the 2000s, including easier access to electronic exchanges, better coding programs, and more funds.
As technology advanced, access to electronic exchanges, such as Instinet and Archipelago Exchange that allow thousands of buy and sell orders to be matched very rapidly without human intervention, became easier and faster. With time, program trading developed into the much broader algorithmic trading and high-frequency trading strategies employed by investment banks and hedge funds. Also, the proliferation of hedge funds, with their sophisticated trading strategies and programmers to code them into algorithms, helped drive program-trading volume.
In fact, as of 2006, program trading accounted for about 30% and as high as 46.4% of the daily trading volume on the New York Stock Exchange, according to the exchange. In 2012, Barrons showed its weekly figures for program trading, including index arbitrage and other types of program trading, and program trading made up about 25% of the volume on the NYSE, with index arbitrage constituting less than 1%.
As of 2018, program trading accounted for 50% to 60% of all stock market trades in a typical trading day, and in 2021, it is estimated to account for 70% to 80% of all U.S. stock market trades placed during a typical trading day. The number is even expected to be up to 90% during periods of extreme volatility.
While the popularity of program trading increased over the years, it has also been blamed for market crashes, especially the flash crashes of 2010 and 2014.
Understanding how program trading works
As defined by the New York Stock Exchange (NYSE), program trading is the simultaneous buying or selling of a group of 15 or more stocks that are worth a total market value of $1 million or more, as a part of a coordinated trading strategy. With this definition, this type of trading may also qualify as portfolio or basket trading.
Program trading is used by institutional investors, such as hedge fund managers or mutual fund traders, to execute large-volume trades within the shortest possible time. Since placing such a large number of orders by hand (by a human) would not be as efficient, trading algorithms are created to generate trading signals and execute orders.
These trading algorithms continuously search the market for trade setups based on the strategies they are coded with. When they spot qualifying trade setups, they place orders directly in the market and manage the trades according to a set of predetermined instructions. Trades are executed very fast and in large volumes to reduce risk and maximize returns by taking advantage of market inefficiencies. For example, a trading algo might buy a portfolio of 50 stocks over the first hour of the day to take advantage of the huge liquidity around market opens.
Institutional investors use program trading to execute large-volume trades through direct connections with the market’s computers. The trades are automated and get triggered based on the specified trade setups in the markets being monitored. Over the years, program trading has become increasingly sophisticated such that it now allows for phased buying or selling in order to reduce disruptions to the markets.
Program trading has become an important tool for hedge funds. It enables them to analyze the historical stock and index data and purchase or sell long and short futures quickly in order to manage risk.
Factors that facilitate program trading
Certain factors have helped program trading grow in popularity over the years. These are some of the factors that facilitate program trading:
- The effect of technology: Great advances in technology have made trading easier and reduced trading costs. As a result, program trading has become more efficient.
- Portfolio diversification as a way of reducing risks: The idea that a diversified portfolio of securities reduces investing risks has made investors look for easier ways to make portfolio trades.
- The emergence of more institutional investors: There are now more institutional investors and investment funds that trade equities. They use program trading to execute their diversified strategies more efficiently. For example, some investment firms may have program trading strategies that execute thousands of trades a day, while others may use program trading to only execute trades every few months.
- The need to place huge trades quickly: Indeed, the volume and frequency of program trading vary greatly with firms and the strategy the program is based on. A day trading program will be far more active than an investing program designed to only periodically rebalance a portfolio.
How program trading has affected the market
Since the rise of program trading, it has been blamed for many market events. For example, many market participants blamed it for causing extreme volatility that contributed to the market crashes of 1987 and 1990, as well as the flash crashes of 2010 in the equity market and 2014 in the bond market.
Given these effects, the NYSE introduced rules to prevent program trades from being executed when there is high volatility in the market. These rules are known as circuit breakers, and they work by halting trading in any stock that has declined by a certain percentage over a certain period.
Types of program trading
Program trading can occur under different settings, and the prominent and popular ones include principal, agency, and basis trading, as well as contra trades.
In this scenario, a brokerage firm uses program trading to purchase a portfolio of stocks, which they believe will increase in value, under their own account. Later, they may sell those stocks to their clients to receive a commission and generate additional revenue. However, while this strategy can work and make them money, its success largely depends on the ability of the brokerage firm’s analysts to select winning stocks.
Agency trading means executing trades on behalf of clients, as is done by brokerages and asset management firms. Program trading may be used by an asset management firm that trades exclusively for clients to buy stocks that are in the firm’s model portfolio and then allocate the shares to customer accounts after being purchased.
Another scenario is fund managers using program trading for rebalancing purposes. For instance, when rebalancing a portfolio to its original strategic allocations, a fund might use program trading to buy and sell baskets of stocks.
Institutional traders can use program trading to exploit the mispricing in similar securities. A trader may use the approach to purchase stocks that seem undervalued and short stocks in the same sector that seem overpriced. For example, this trader could short a group of hospital stocks that seem overvalued, while purchasing a basket of biotech stocks that appear undervalued. As the prices of the two groups of stocks converge, the trade makes profits.
These kinds of trades are used to cover one’s position in the market using an option or futures. These derivative contracts could be sold if the required security is not available at the estimated price. Later on, the trader uses the leverage in futures and options, to square off the original position with less turbulence due to the back of derivatives.
Creating a program trading strategy
While computers execute the trades, the trader has to create a strategy that would instruct the computer on what to do at any time. The strategy has to be coded into a trading algorithm, which would then execute the trades according to the instructions in the code. It can be a momentum strategy, a break-out strategy, or simply a portfolio-balancing method that maintains broad asset allocations to sector allocations, and it can be a short-term or long-term strategy.
Whatever the case, the key thing is that the trader creates the system, and the computer implements it. While the trades are almost always executed by computers, there are situations when that is not the case. For instance, when an institution splits up the sale of a large volume of trade across several different brokers.
One more thing, timing is very important. Institutions that use program trading set their system to trade at certain times of the day, which are often referred to as reversal times. These are often periods when there is adequate liquidity in the market. This is often done to reduce spikes in price swings.
Example of program trading
Let’s say a hedge fund has a portfolio of 10 stocks, with 10% of the capital allocated to each stock, and its strategy is to maintain that asset allocation. So, it sets up a program trading system that rebalances the portfolio at the end of each month to ensure that each stock maintains a 10% allocation. The system works by selling stocks that have a higher than 10% allocation and buying stocks that have a lower than 10% allocation.
Meanwhile, based on the stock selection criteria (which could be based on monthly or 3-monthly momentum), some stocks may be dropped from the portfolio, and others added. But any new stocks added will take a 10% allocation like others.
As expected, the stocks in the portfolio will rise and some will fall over time, resulting in changes in the overall portfolio value, as well as the percentage allocation of the component stocks. For instance, assuming the total value of the portfolio is $20 million, a 10% stake is $2 million. Let’s say the fund bought Microsoft at $150 per share, and it now trades at $300 per share. If we assume that all other stocks didn’t move, the Microsoft position is now worth $4 million, bringing the total portfolio to $22 million — with Microsoft having 18.2% of the portfolio ($4million/$22million). APPL represents 9.5% of the portfolio ($1 million divided by $10.5 million). Since 18.2% is more than the 10% goal for each stock in the portfolio, the fund will need to sell some Microsoft stocks to rebalance the portfolio.
In a normal market environment, all the 10 stocks would be moving every day, so at the end of the month, some may be 12% or 11.3%, and some 8% or 9% of the total worth of the portfolio. So, the portfolio will need to be rebalanced, but doing so manually would be time-consuming. In this case, the fund can have a program trading algorithm that monitors the portfolio equity at the end of the month and quickly execute all the trades at once, buying the stocks that are under-allocated and selling the ones that are over-allocated to rebalance the portfolio in a few seconds.
The pros and cons of program trading
As with any trading method, program trading comes with some pros and cons.
These are some of the pros of program trading:
- Program trading is fast: Trades could be placed in just microseconds. This fast order execution is very useful in arbitrage trading where buy and sell orders are placed at the same time, with even the slightest delay reducing the profit margin.
- It makes trading easier: The trading is automated, which is easier than manually looking for opportunities in the markets and executing them one by one. With program trading, many orders can be placed at once across different markets.
- It reduces the impact of human emotions: Since computer algorithms perform the trading, it lacks human emotions and the hurdles it creates. Automated trading reduces the effects of indiscipline, fear, greed, and other emotions in the overall trading results.
- It is more efficient: Owing to its fast-paced order placing capabilities devoid of human emotions, program trading is more efficient in exploiting the opportunities in the market.
Here are some of the drawbacks of program trading:
- Program trading increases volatility in the market, which is why the NYSE imposed certain restrictions in times of massive volatility — depending upon the price of a security, program trading is halted or subsided.
- This type of trading is quite expensive to set up because of the need for powerful computers, top programmers to code the strategies, and VPS to run the system 24/5.
- It can suffer from technical glitches and machine failures, and when that happens, losses can be too much, unless there are humans supervising things.
Risks Associated with Program Trading
Program trading is an investment strategy that involves using computer algorithms to buy and sell large amounts of securities. While it can be profitable, it also comes with its own set of risks. For example, the use of high-frequency trading algorithms can lead to market volatility and instability. A software glitch, programming error, or a sudden drop in liquidity could trigger a flash crash and cause significant losses.
In recent years, regulators around the world have become increasingly concerned about the risks posed by program trading, particularly high-frequency trading. They have introduced various regulations to limit its impact on the market and ensure fair competition. These regulations can have an impact on program trading, and a discussion of them would provide a more comprehensive understanding of the topic.
Advancements in Technology
Program trading has been evolving rapidly, and new technologies are constantly being developed to improve its efficiency and effectiveness. The use of machine learning and artificial intelligence are just some of the advancements in technology that are being used in program trading. Understanding these advancements will provide insight into how program trading is likely to change in the future.
Different Types of Program Trading Strategies
This article mentions some strategies used in program trading but does not go into detail about the different types of strategies that exist. Different strategies, such as market-making, algorithmic trading, and statistical arbitrage, have different objectives and approaches. A discussion of these strategies would provide a more complete picture of program trading.
Program trading refers to the use of computer algorithms to generate and execute trades in a group of stocks. These trading algorithms are programmed with different strategies to monitor the markets and execute trades without human input. They can be used by institutional investors, such as hedge funds or mutual funds, to execute large-volume trades efficiently. Program trading is often referred to as high-frequency trading (HFT) and can account for 70-80% of all US stock market trades in a typical day. While it has been blamed for market crashes, program trading has been popular due to its efficiency and ability to execute trades without being influenced by human emotions.