Finschool By 5paisa

Finschool
  • #
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • X
  • Y
  • Z

High-Frequency Trading (HFT)

High-Frequency Trading (HFT)

High-Frequency Trading (HFT)

High-frequency trading (HFT) is algorithmic trading characterized by high-speed trade execution, an extremely large number of transactions, and a very short-term investment horizon. HFT leverages special computers to achieve the highest speed of trade execution possible. It is very complex and, therefore, primarily a tool employed by large institutional investors such as investment banks and hedge funds.

Complex algorithms that are used in high-frequency trading analyse individual stocks to spot emerging trends in milliseconds. It will result in hundreds of buy orders to be sent out in a matter of seconds, given the analysis finds a trigger.

History of HFT

Interestingly, the phenomenon of ‘fast information’ delivery goes back to the 17th century.

Here, an interesting anecdote is about Nathan Mayer Rothschild who knew about the victory of the Duke of Wellington over Napoleon at Waterloo before the government of London did. How did that happen? Well, a simple answer is a combination of “Human Intelligence & Technology”! So it is said that Julius Reuter, the founder of Thomson Reuters, in the 19th century used a combination of technology including telegraph cables and a fleet of carrier pigeons to run a news delivery system. This way, the information reached Julius Reuter much before anyone else.

Many years after the 17th century, in 1983 NASDAQ introduced full-fledged electronic trading which prompted the computer-based High-Frequency Trading to develop gradually into its advanced stage. In the early 2000s high-frequency trading accounted for less than 10% of equity orders, but this has grown rapidly.

By the year 2001, High-Frequency Trading had an execution time of several seconds which kept improving further. Between 2005 and 2009, according to NYSE, high-frequency trading volume grew by 164%.

By 2010, this had shrunk to milliseconds and later in the year went to microseconds. And subsequently, each trade started getting executed within nanoseconds in 2012.

How does High-Frequency Trading work?

High-Frequency Trading is mainly a game of latency (Tick-To-Trade), which basically means how fast your strategy responds to the incoming market data. The “Bleeding Edge” firm actually talks of single-digit microsecond or even sub-microsecond level latency (Ultra-High-Frequency Trading) with newer, sophisticated and customized hardware.

Conclusively, in the past 20 years, the difference between what buyers want to pay and sellers want to be paid has fallen dramatically. One of the reasons for this is the increase in accuracy. High-Frequency Trading has also added more liquidity to the market, reducing bid-ask spreads.

Benefits from HFT
  • Increase liquidity- algorithmic trading increase liquidity. That’s because they increase the number of buy and sell orders and therefore increase the size of the order book significantly.

  • Improve pricing efficiency- high frequency trader quickly aggregate a lot of information, they improve the informational efficiency of the market. They force the price closer to the fundamental value faster than would otherwise be the case. Thus, they increase pricing efficiency.

  • Lower costs- algorithmic don’t require human traders who are typically well-paid and thus expense. Algorithms lower manual labour which in turn lowers transaction costs.

  • Tighter bud-ask spreads- because high frequency traders act as market makers, they tends to reduce

Disadvantage of HFT
  • Market manipulation- HFT may engage in market manipulation. This can be done by spoofing, quote stuffing, wash trading, or painting the tape.

  • Unfair speed advantage- one way in which high frequency traders are able to make a profit is simply by being faster than others. This may provide some traders the ability to front run other investors trades.

  • Magnification of market movements- trading algorithms may magnify fluctuations in financial markets. For example, a sell off may trigger some of these algorithmic traders to close down positions, thereby magnifying a downward trend.

  • Risk of trading errors- the high speed with which HFT execute trades also means that if things go wrong, they tend to wring quickly. Thus, the results may be disastrous if the trades are not as intended.  When this happens, it is referred to as an algorithm “going wild”.



Related Words

View All
NPV

Net Present Value (NPV)



Read More

Quantitative Trading



Read More
Real Estate Investment Trust

Real Estate Investment Trust (REIT)



Read More