High Frequency Trading

Gomber, Peter; Haferkorn, Martin

doi:10.4018/978-1-4666-5888-2.ch001

Cited by 14 publications

(8 citation statements)

References 19 publications

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“…Moreover, it provides the avenues for future research for SMIFs, such as using SMIFs as an experimental ground to test behavioral models, examine innovative trends in investing (ESG investing and security valuation) and providing insight into better teaching methodologies. For example, an innovation that has become commonplace in the investment industry is high-frequency trading knowledge, a concept that is critical for students entering the investment industry (Gomber and Haferkorn, 2015). Further, we find that sustainable finance and ESG-based investment, although important topics in practice, have not received sufficient attention.…”

Section: Gaps In the Literaturementioning

confidence: 90%

Student-managed investment funds: a review and research agenda

Abukari

Oldford

Willcott

2021

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PurposeIn recent years, student-managed investment funds (SMIFs), experiential learning programs at an increasing number of universities, have attracted significant scholarly interest. In this article, we review the academic literature on this pedagogy.Design/methodology/approachWe use the systematic review method to assess a sample of 85 articles published in 30 journals during the period 1975 to 2020.FindingsOur literature review reveals four streams of research: best practices and challenges, investment management, innovation and trends and SMIFs in a research setting. We also propose future research directions, including specific gaps in the literature, a focus on innovations to traditional programs, systematic investment performance and expansion into behavioral finance issues.Originality/valueWe contribute a comprehensive view of the body of scholarship on SMIFs, identifying existing streams of research and future research directions that will help guide the development of SMIF research into a cohesive and productive space.

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Section: Gaps In the Literaturementioning

confidence: 90%

Student-managed investment funds: a review and research agenda

Abukari

Oldford

Willcott

2021

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“…It continuously scans the markets and places orders when certain criteria, such as volume, price, resistance, or support, or any other element that the trader or other market participant is at ease with, are fulfilled. Any computer application and its features are built on algorithms [44]. Since the rules of algorithmic trading can be quantified and tested again, it performs better than discretionary trading.…”

Section: Discussion Implications and Conclusionmentioning

confidence: 99%

Algorithm trading and its application in stock broking services

Vedapradha¹,

Hariharan

Praveenraj

et al. 2023

E3S Web of Conf.

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Purpose: Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct. The aim of the study is to examine the level of awareness among the brokers when integrated with technology for the purpose of executing the trades. Design/Methodology: A self-administered and structured 350 questionnaires were designed and circulated to collect the preliminary information from the stock brokers operating in NSE and BSE within the geographical limits of Bangalore district using the Systematic Sampling method to obtain a sample size of 235. Awareness, Automated trading, Elimination of human error, portfolio management, tracking order, order placement were the critical variables observed to validate the hypothesis using Simple Percentage Analysis & Chi-Square Analysis using Statistical Analysis Software (SAS). Findings: It was found that there is robust association between the level of awareness of the mentioned technology in its application by the stock brokers of NSE and BSE operating in Bangalore. Portfolio management and automated trading are the highly associated application of Algorithmic trading among the stock brokerage services. Originality: Algorithmic trading makes use of complex formulas, combined with mathematical models and human oversight, to make decisions to buy or sell financial securities on an exchange. It can be used in a wide variety of situations including order execution, arbitrage, and trend trading strategies. Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second.

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“…Proof of lemma 6. We can compute the error of residuals as follows: suppose that u t = Xβ − y (31) and u t = Xβ − y (32) are the residuals and estimated residuals, respectively. The error of the second regression variable u t is…”

Section: Realistic Case Analysismentioning

confidence: 99%

“…The main idea of arbitrage is to describe the comovement out of securities and portfolios and make profit from other traders' pricing error. While lots of classical algorithms for quantitative trading have been proposed [26][27][28][29], and traditional hardware techniques including infrared communication and field programmable gate array have been employed over the years [30,31], still the requirement for speed cannot be satisfied when implementing those complicated statistical methods, especially in the quicker-take-all situation of high-frequency trading (HFT) whose need of computing speed is crucial [32]. In statistical arbitrage, one needs to find a potential cointegrated pair via many linear regressions and cointegration tests involving a huge matrix of historical data.…”

Section: Introductionmentioning

confidence: 99%

Quantum computational quantitative trading: high-frequency statistical arbitrage algorithm

Zhuang

Chen²,

et al. 2022

New J. Phys.

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Quantitative trading is an integral part of financial markets with high calculation speed requirements, while no quantum algorithms have been introduced into this field yet. We propose quantum algorithms for high-frequency statistical arbitrage trading by utilizing variable time condition number estimation and quantum linear regression. The algorithm complexity has been reduced from the classical benchmark $O(N^2d)$ to $O(\sqrt{dN}\kappa_0^2\log{(1/\epsilon)}^2))$, where $N$ is the length of trading data, and $d$ is the number of stocks, $\kappa_0$ is the condition number and $\epsilon$ is the desired precision. Moreover, two tool algorithms for condition number estimation and cointegration test are developed.

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High Frequency Trading

Cited by 14 publications

References 19 publications

Student-managed investment funds: a review and research agenda

Student-managed investment funds: a review and research agenda

Algorithm trading and its application in stock broking services

Quantum computational quantitative trading: high-frequency statistical arbitrage algorithm

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