Because of the increase in automated trading, High-Frequency Trading has increased in popularity. The basic idea is to use automated systems to anticipate and beat the market. In acting quickly with large buys, HFT takes advantage of dips in the market and then turns around to sell the assets quickly.

High-powered computers rely on complex algorithms to make quick market analyses. Once a dip in the market is found, institutions can make large buys of the shares. The small savings from buying low can mean a very profitable return.

High-frequency trading has also become a bit of a controversy because automation has become so efficient and powerful. In order for institutions to participate in HFT, they must use high powered computers to run complicated algorithms that must make nearly instant for smart, profitable acquisitions. 

Because of the magnitude of such programs, the algorithms scan multiple markets nearly simultaneously. Then, they have the power to place millions of orders. The name of the game is speed and efficiency. 

HFT buys large portions of shares, to take advantage of the price dip, and then selling once the price returns to above the market. 

There are a few challenges with HFT. The first is that it relies on the efficiency of computers, and there are many competing efficient programs running. So it becomes a competition between algorithms that analyze the markets and the speed of the computers that run them.

That means, that despite the computational powers of these competing computers, timing is everything. So if one has a few seconds of a delay then it might miss the opportunity.

Large institutional buys will cause major shifts in the market. It is at this time that small investors can also take advantage of the same things: the dips in the market, and marginal bid-ask spreads. 

Predicting Market Behavior

High-frequency trading relies heavily on computational power and effective algorithms. Programs are working to act on short-term dips in strong assets. That means that they have to analyze multiple markets, and not just focus on one in particular. 

The algorithms are designed to take into consideration all of the market prices to find an opportune short-term dip to place a large buy and then sell quickly for fast and immediate profits.

To remain competitive in high-frequency trading the program must process all of the following assets:

Suffering from Spread

One reason that HFT has become so popular for institutional traders, such as mutual funds managers, it that they can benefit greatly from such large buys with a narrow spread.

“Spread” is the difference between the highest price a buyer will pay and the lowest price a seller will accept. So the “bid-ask spread” is the amount the seller wants v. the bid placed. So the “spread” is based on how much someone wants to pay for a stock, and how much someone is willing to part with it. 

Volatile markets often have a larger spread. HFT tends to suffer less from the cost of the spread because they are executed so quickly. And as a result, there tends to be less volatility in markets. This may be because the markets do not react to negative information so strongly when using high-frequency trading techniques.

Again, smaller investors can act quickly to take advantage of the small spread as well.

Remaining Competitive

The essential features for remaining competitive in a High-Frequency Trading environment are:

  • Applying high-powered computers and sophisticated algorithms to gather mass and broad information that will affect the markets
  • Executing orders with high-efficiency and accuracy
  • Appling both co-location services and individual data feeds from exchanges and others to increase network efficiency 
  • Taking advantage of short time-frames to produce liquidity in the market  

Pros and Cons of HFT

Arguably, the most appealing thing about HFT is that it is an effective way to add value to the market quickly. As such, trade exchanges and institutions offer incentives to add liquidity to the market. 

High-frequency trading takes advantage of both the computing power to move quickly and benefit from arbitrage, as well as supply and demand. The technique is not based on the quality or change in any actual asset or company. Rather HFT acts on subtle moves and quick dips multiple markets. 

This is because HFT is taking advantage of buying many units at a small discount, which turns into more significant profits when they are immediately sold at a market price increase. 

Pros:

  • By using high powered computers, HFT is able to take advantage of small spreads and incentives. But because these are such large purchases, the accumulative trades per day become sizable profits. 
  • Large institutional HFT buys can add stability to the market, maintaining value in the market.
  • HFT potentially protects markets from too much volatility. This is because they can act so quickly and so the bid-ask spread is decreased. Increased stability can make it more accessible for individual traders to benefit from this as well because there are not the same dramatic shifts. 

Cons:

  • HFT has ebbs and flows in popularity due to varying levels of success. Average profits fell from “about a tenth of a penny per share to a twentieth of a penny,” reported Bloomberg
  • Because HFT buys based on algorithmic trading it is can mistake a genuine decline in value for a temporary dip. When that happens, a large purchase may be placed that cannot be effectively arbitraged because the stock is steadily declining.
  • HFT has become increasingly competitive, which means that the running such programs have increased in costs which reflect negatively on profits. 
  • HFT takes advantage of seconds, not minutes. So it is purely a competition of computing speed. This has caused an increase in the cost of running such computers, as well as a decrease in profitability.
  • Many believe that HFT is unethical as large investors are at a much better advantage than smaller firms. The stock market is meant to be a level playing field. However, HFT favors technology and short-term liquidity over the long-term health of assets and market savvy. 
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