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High Frequency Trading (HFT)

Marketing Professional and Master of Science by Research in Management Science

High-Frequency Trading

HFT is an electronic trading platform employs by large investment banks, hedge funds, and investment firms. It uses powerful systems to analyze multiple orders at very high speeds. It is a trading technique used to execute a large number of orders in portions of a second using strong computer programs. It uses complicated algorithms for analysis and order based on business conditions in different markets. Traders are typically more efficient with the quickest running speed than slower running speed traders. High-frequency trading is often distinguished by high sell rates and order to trade ratios, along with the high speed of orders. Tower Research, Citadel LLC, and Virtu Financial are among the best-known high-frequency trading companies.

High-Frequency Trading Comprehension

When exchanges began to give companies incentives to add liquidity to the market, high-frequency trading became common. For example, the NYSE has a group of liquidity providers called the SLPs which aim to add competitiveness and Liquidity to current exchange quotes. The SLPs are often called the SLPs. SLPs. The NYSE pays a premium or a discount for this liquidity as an opportunity for businesses. For NYSE and NYSE MKT, the average SLP reimbursement in July 2016 was $0.0019. This creates a great deal of profit with millions of transactions every day. The SLP began in 2008 when liquidity was a major concern to investors after the fall of Lehman Brothers.

HFT Advantages

The key advantage of HFT is the enhancement of market liquidity and the removal of bid expansion, previously too weak. This was checked with the addition of fees on HFT, which increased the bid spread. One research analyzed how the spread of the Canadian offer challenge shifted when the Government implemented HFT charges and found that the spread of the offering process grew by 9 percent.

HFT Criticism

HFT has been controversial and criticized severely. It has substituted a variety of broker-dealers and uses math and algorithms to evaluate, evaluate, and communicate humanely. Decisions take place in milliseconds, and this may lead without justification for major market movements. As an example, the Dow Jones Industrial Average (DJIA), which dropped 1000 points and fell 10 percent in 20 minutes before it rose again, experienced its biggest drop in the intraday world on 6 May 2010. A government inquiry blamed a huge order that caused the crash to be redeemed.

Another criticism of the HFT is that it helps big business to take advantage of "little kids" or institutional and retail investors. The liquidity given by HFT is also the "ghost liquidity," which means that it offers liquidity that is usable for market access one second and then the next, which actually prevents traders from trading this liquidity.



This paper tests whether relative latency can explain cross-sectional differences in High-Frequency Trading (HFT) performance. As a result, small differences in trading speed are associated with large differences in trading revenues across firms, with trading concentrated among the fastest HFTs. Motivated by the view that competition based on relative latency differs from the competition of traditional market intermediaries.

While the E-mini is totally associated with one trading location and has a relatively high relative tick size, Swedish equities trading is split among multiple venues, and structures smaller relative tick sizes and lower trading volumes.

The disparity proposes that the fastest HFTs are no more accurate than other HFTs at processing and examining information on a given individual trade, but their latency advantage allows them to grab more trading opportunities without taking on higher risk.

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The objective of Research:

The main objective of the research paper is to find out that the latency, particularly relative latency in relation to High-frequency trading (HFT); i-e, speed when trading passively is catering the risk management. The study is organized on the analysis of transactional-level data with a trading firm identifiers provided by the Swedish financial supervisory authority, Finansinspektionen. Furthermore, the 5-years length of data is significant in allowing the researcher to hint at the “long-term” evolution of the HFT industry, at-least comparative to the fast pace of innovation in the industry.


This paper is first to contribute that small differences in the trading speed are associated with the large differences in trading revenues.

Consistent with the predictions regarding competition on relative latency, researchers find that:

1) firm-level and industry-wide HFT performance is persistent;

2) HFT concentration of trading revenues and trading volumes is high and non-declining over the 5-year sample, despite new HFT firm entry and a decline in overall HFT latency; and

3) New HFT entrants are typically slower, earn lower trading revenues, and are more likely to exit, which likely reinforces concentration in the HFT industry.


This is cross-sectional empirical research over the time period of 5 years, in which the researcher tends to find out that the relative latency has an impact on risk and return in high-frequency trading (HFT). The Transaction Reporting System (TRS) is our primary database. Finansinspektionen, the Swedish financial supervisory agency, provides us with proprietary knowledge.

The test is conducted between HFT latency and trading performance. The major trading performance measure is Revenue, captured daily for each HFT firm as the net of purchases and sale, marking end-of-day position to the market. We do have success metrics tailored to the threats, including returns, alphas of the factor model, and Sharpe ratios. We find that the HFTs show large, persistent cross-sectional performance differences, with trade revenues accumulating in a few companies disproportionately. The findings are stable for expected exchange rates and liquidity rebates, which alter the results in a marginal way.


Future Implications

This research opens doors for HFT to go ultra-fast by low latency and adopting the latest technologies and hiring highly experienced professionals who will adopt low latency in high-frequency trading. As previously sophisticated strategies were adopted by HFT which lack the speed but now firms are also adopting low latency for ultra-speed as well as sophistication in trading.


The study shows the part of latency in the presentation of HFT firms. We archive a number of measurements steady with prevalent venture execution by HFTs. There are enormous cross-sectional differences in execution in the HFT business, with trading incomes disproportionally aggregating to a couple of firms. The quickest firms will in general win the biggest trading incomes. While latency diminishes considerably over the 5-year test, we show that it is relative latency, not nominal latency that clarifies contrasts in execution across HFTs. Besides, we look at how speed might be utilized in explicit HFT methodologies. We find proof that relative latency is significant for achievement in trading on fleeting data, for risk management in liquidity arrangement, and for cross-market exchange.

In the end, we investigate the ramifications of rivalry on relative latency with respect to HFT fixation. In the event that little contrasts in latency are significant, at that point the HFT business ought to be portrayed by industriousness in execution, high and non-declining fixation, and difficulty of new entry. We discover proof that is reliable with these expectations. Firm trading execution is determined, trading incomes are high and non-declining, as is HFT focus, also, new HFT contestants will in general be slower, fail to meet expectations, and bound to exit. In spite of worries in the theoretical literature about relative latency rivalry, we find that the cost of the HFT business brought about to other investors isn't higher than normal exchange taker fees, what's more, a request to the extent lower than the powerful offer ask spread.

This article is accurate and true to the best of the author’s knowledge. Content is for informational or entertainment purposes only and does not substitute for personal counsel or professional advice in business, financial, legal, or technical matters.

© 2020 Tamoor Danish

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