Brendan Sanford 
Imagine that one company’s shares are worth $10 and another trader wants to purchase them; should their respective interests coincide, each share would trade for its true market value of $10 per share.
High-frequency traders go to great lengths to outwit their competition. They rely on special high-speed data feeds and co-location services in order to minimize communication times.
Liquidity
The stock market provides investors with an invaluable asset – purchasing and selling shares for returns that far surpass any available from bank savings accounts. Unfortunately, however, the market can be subject to manipulation by high-frequency traders to gain an unfair edge against retail investors – prompting calls for additional regulations but with few specific proposals that don’t compromise trading freedom.
Joshua Mollner and Markus Baldauf conducted a study analyzing the impact of HFT trading on market liquidity. Their researchers discovered that HFT dramatically accelerated trade execution; however, this did not translate to lower transaction costs for retail investors due to less informative prices, thus decreasing overall economic efficiency. Furthermore, adding short processing delays such as no cancellation orders could mitigate some negative effects caused by HFT arbitrage.
Speed
High-frequency traders can complete trades in just 64 millionths of a second with the aid of automated systems that scan markets and respond faster than humans ever could if manually entering trade orders into computers. Their speed helps them earn profits while at the same time lessening market efficiency.
Organizations may gain an edge by renting fast data links from exchanges and renting space near an exchange’s market data servers; however, these costs can quickly add up if trading volumes increase significantly.
Sniping techniques are also employed by high-frequency traders to front run orders from liquidity providers, while remaining legal as long as the provider sends cancellations before snipers can trade against her remaining quotes. Unfortunately, this process can result in significant losses for larger investors such as mutual funds and pension funds, although high-frequency traders assert their activity increases price precision.
Efficiency
High-frequency trading involves a race between market-makers and high-speed speculators to execute orders as fast as possible, leading to reduced liquidity for investors and higher transaction costs. Furthermore, HFT traders employ practices like ping orders and quote stuffing in order to manipulate markets and take profits, with regulators often being unaware of these practices due to HFT’s lightning fast speed.
Traders rely on their knowledge of the market to use algorithm programming aligned with their strategies and leverage orders in the order book such as size and age properties to identify opportunities for profit.
Competitors then engage in an arms race where traders spend money to become faster, further decreasing investor welfare and economic efficiency in the process. If this race goes on unchecked, market quality could diminish significantly while investors’ costs grow dramatically; therefore it is critical that trading algorithms are thoroughly tested prior to being introduced into financial markets.
Costs
As well as market speed and price volatility, transaction costs also play a part. When trading, higher tick sizes mean increased fees paid per trade made. Bid-ask spreads also raise transaction costs significantly.
HFT industries are marked by a race for faster trading technologies that minimize latency – the time it takes a trade to reach its exchange. High-speed traders frequently utilize microwave networks over fiber optic cables for long distance communication; microwaves can reach exchanges fractions faster.
However, due to the hazy line between what counts as HFT and traditional automated trading platforms has made it hard for regulators to target reckless behavior without creating overbearing frameworks that limit trading freedoms. One approach proposed by Kellogg assistant professor of managerial economics Joshua Mollner may reduce high-frequency trading’s impact by adding a small processing delay for order types other than cancellation orders – something Kellogg professor Joshua Mollner himself has suggested doing.
