High Frequency Trading Research Articles

Exploring the Advantages of Transformers for High-Frequency Trading

Exploring the Advantages of Transformers for High-Frequency Trading

Asset flow model for a homogeneous group of investors: High-frequency trading limit

Deterministic and stochastic asset flow models have been designed to study the effects of behavioral components on market dynamics. These models have been successful in explaining the mechanisms behind price overshoots, oscillations, reversals, liquidity- and value-induced market bubbles and oscillations, and flash crashes. While these models are originally nonlinear, we demonstrate here that, in the case of a market consisting of a homogeneous group of investors with identical trading sentiments, natural modifications of the response functions yield a version that can be represented by a linear dynamical system across the entire state space of the system. We show that this linear version exhibits all types of price behavior, just like the nonlinear version, while also having the benefit of being explicitly integrable even with time-varying parameters. We present a limiting case of the model that corresponds to fast trading, which is appropriate for modern electronic markets, and provide examples illustrating the differences in the response of the price to variability in the intrinsic value of shares or liquidity.

C Design Patterns for Low-Latency Applications Including High-Frequency Trading

C Design Patterns for Low-Latency Applications Including High-Frequency Trading

Effect of high‐frequency trading on mutual fund performance

AbstractWe find that high‐frequency trading (HFT) in stocks held by mutual funds negatively affects fund performance: when sorted by HFT intensity of holdings, funds in the top quintile underperform funds in the bottom quintile by 2.64% per year. The negative relation can be at least partially explained by the illiquidity premium induced by high‐frequency traders’ preference for more liquid stocks. This reason for underperformance of mutual funds has not been previously explored or documented. In addition, we do not find evidence to support the concern that HFT raises trading costs of mutual funds.

Do algorithmic traders exploit volatility?

This study examines the impact of trading by Algorithmic Traders (ATs) and Non-Algorithmic Traders (NATs) on volatility, and conversely, the impact of volatility shocks on ATs and Non-ATs. ATs are classified as High-Frequency Traders (HFTs) and Buy-side Algorithmic Traders (BATs). Using jump robust volatility estimates, we find that excessive directional and non-directional trading by BATs and HFTs increases volatility, whereas that by NATs marginally decreases volatility. Conversely, all traders increase their non-directional trading one hour following a volatility shock. BATs carry out more directional trades during a volatility shock, whereas HFTs withdraw from such activities.

Time Dependence of CAPM Betas on the Choice of Interval Frequency and Return Timeframes: Is There an Optimum?

The traditional CAPM beta is almost exclusively calculated over a return period that spans a window length of 60-months, at one-month return frequencies. It is one of the most utilized models in the asset management industry to assess systematic risk. Yet there is limited evidence to suggest that these estimation parameters are optimal. Utilizing data between January 2000 and December 2021 for the Russell 1000 index, we test daily, weekly, and monthly beta estimations to calculate tracking errors (TE) for the use of these betas in predicting subsequent performance over daily, weekly, and monthly timeframes. We identify that daily CAPM betas are best for predicting subsequent period daily returns and that weekly CAPM betas are strongly correlated with forward weekly and monthly period returns. Leveraging the significant advances in computing resources and the increasing utilization of high frequency trading strategies, we argue that additional window length and return interval-based CAPM betas should be calculated for estimating the systematic risk embedded in diversified portfolios.

The effect of technological developments on the stock market: evidence from emerging market

ABSTRACT This study investigates the effect of High-Frequency Trading on stock liquidity and volatility in Borsa Istanbul, Turkey which is one of the largest emerging markets where high-frequency trading is newly developed. We employ a fixed effect panel estimation model with Driscoll and Kraay correction between January 2016 and December 2020. The finding shows that an increase in high-frequency trading increases the liquidity and volatility. Moreover, the results are similar before the pandemic period and rising market period, whereas the impact of high-frequency trading on liquidity disappears during the pandemic period.

The Impact of High-Frequency Trading on Modern Securities Markets

High-frequency traders account for a significant part of overall price formation and liquidity provision in modern securities markets. In order to react within microseconds, high-frequency traders depend on specialized low latency infrastructure and fast connections to exchanges, which require significant IT investments. The paper investigates a technical failure of this infrastructure at a major exchange that prevents high-frequency traders from trading at low latency. This event provides a unique opportunity to analyze the impact of high-frequency trading on securities markets. The analysis clearly shows that although the impact on trading volume and the number of trades is marginal, the effects on liquidity and to a lesser extent on price volatility are substantial when high-frequency trading is interrupted. Thus, investments in high-frequency trading technology provide positive economic spillovers to the overall market since they reduce transaction costs not only for those who invest in this technology but for all market participants by enhancing the quality of securities markets.

On-demand fast trading on decentralized exchanges

We build a model to show that decentralized exchanges (DEX) require less computing power on average than traditional exchanges to accommodate the demand for high-speed trading services. Centralized exchanges acquire excess processing capacity to accommodate activity surges: The idle capacity’s opportunity cost is an externality of low-latency trading. On DEXs, HFTs bid on gas fees in real-time to acquire time priority from a network of miners. The price of speed surges as HFTs compete during activity bursts. HFT-driven demand for speed peaks higher on DEX, but spans a shorter time interval. On aggregate, DEX infrastructure is more cost-efficient.

Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies

Artificial Intelligence (AI) has been recently recognized as an essential aid for human traders. The advantages of the AI systems over human traders are that they can analyze an extensive data set from different sources in a fraction of a second and perform actual high-frequency trading (HFT) that can take advantage of market anomalies and price differences. This paper reviews the most important papers published in recent years that use the most advanced techniques to forecast financial asset trends and answer the question of whether those techniques can be used to successfully trade the complex financial markets. All systems use deep learning (DL) and machine learning (ML) protocols to explore nonobvious correlations and phenomena that influence the probability of trading success. Their predictions are based on linear or nonlinear models often combined with social media investors’ sentiment derivations or pattern recognitions. Most of the reviewed papers have proven the successful ability of their developed system to trade the financial markets.

Critical dynamics related to a recent Bitcoin crash

The cryptocurrency market is an emerging market that is characterized by intense volatility and therefore the study of its dynamics presents increased interest. The present work investigates the issue of the critical dynamics of a financial complex system approaching a crash, by using the Method of Critical Fluctuation (MCF) which is known for its ability to uncover critical dynamics. Specifically, we study the recent crash that took place in the cryptocurrency market (starting on 12 May 2021), by analyzing the “Contracts for Difference” (CFDs) prices on Bitcoin/USD (Bitcoin to US-Dollar exchange rate) at five different high frequency trading time intervals (60, 30, 15, 5 and 1min). The results show that, for the 60-min and 30-min sampling intervals, a specific sequence of indications is identified, in agreement to the evolution towards extreme events in other complex systems, such as earthquakes. This sequence of indications isn't maintained as the sampling frequency is increased. Notably, the existence of critical dynamics during the system's evolution has been detected both in equilibrium and out-of-equilibrium by means of the same analysis method (MCF). The obtained results indicate that the MCF could provide useful information for portfolio analysis and risk management.

Liquid speed: A micro-burst fee for low-latency exchanges

A micro-burst fee on liquidity-taking orders that surges during high-frequency races reduces costs associated with latency arbitrage. Moreover, micro-burst fees provide higher revenue for exchanges versus co-location subscriptions. Unlike co-location fees, micro-burst fees scale with trading activity and allow exchanges to extract higher revenues from HFTs. To ensure long-run adoption incentives for exchanges, a regulator should impose a cap on micro-burst fees; for example, a calibration suggests that liquidity improves with a micro-burst fee as low as 7.8 bps, while a Reg NMS cap on fees of 30 bps per share would improve liquidity by 75%.

Characteristics and Forecast of High-frequency Trading

Characteristics and Forecast of High-frequency Trading

Algorithmic Trading and Challenges on Retail Investors in Emerging Markets

Mixed views on automated trading in the extant literature lead to ongoing debates on algorithmic trading (AT) and high-frequency trading (HFT). This study elaborates on the rising ethical issues and regulatory challenges of algorithmic trading and high-frequency trading in emerging markets. While developed capital markets are dominated by institutional investors, emerging markets consist of a large proportion of retail investors who may suffer from aggravated liquidity asymmetry and stock price turbulence due to HFT and AT. Furthermore, we review current regulations of HFT in the U.S. and European markets and provide a framework of regulatory enforcements on AT and HFT for investor protection in emerging markets. This study cautions policymakers in emerging markets that legal and regulatory monitoring of AT and HFT activities is especially necessary.

Targeted Human Trafficking - The Wars between Proxy and Surrogated Economy

Upon Brexit & Trade War, the research took a supply-side analysis in macroeconomic paradigm for the purpose and cause of the actions. In the geopolitical competitions on crude oil resources between the allied powers & the Russian hegemony, the latter of which has effective control over P. R. China’s multilateral behaviors, the external research induced that trade war, either by complete information in intelligence or an unintended result, was a supply chain attack in prohibiting the antisatellite weapon supplies in the Northern regions of mainland China in relation to Russia. Although no substantive change to international relations, the Trade War’s prohibitive effect on the high frequency trading in the monetary domain of CNY was observed, which had been the source of economic bubbles in the import-export control regime with centralized banking. The paper argues that by realism in economy & military strategy, trade war was ineffective in deterring the covert operations of the People’s Liberation Army by their territorial strategies and raises questions in humanitarianism in conflict situations. Moreover, with gross privacy breaches by mass surveillance in domain politics, totalitarianism with coercions, and counterfeit of drone strikes, traditional methods of threat elimination are rendered less pragmatic apart from the adversaries’ cyber security breaches. With the scientific approach, I offer an ecological paradigm with historic analysis of the Chinese military’s conducts in terms economics. The territorial methods of the PLA are contextualized into the ecological paradigm in regionalism & public administration. Electronic combats of the Chinese regime with the Great Firewall and Denial-of-Service attacks not only contribute to the diminishing natural freedoms of the population, but also transgress the fundamental right to health along with non-traditional nuclear threats to P.R.C. itself.

High frequency trading and standard asset pricing models

This paper shows how to include high frequency (HF) traders into standard continuous-time, frictionless and competitive asset pricing models. For derivative pricing models, the standard methodologies apply unaltered, except that the interpretation of the exogenous price process changes. For equilibrium models, after the inclusion, the standard models also still apply, but the equilibrium return process and risk premium change to reflect the HF traders’ trades.

The impact of algorithmic trading on market quality: Evidence from the Johannesburg Stock Exchange

ABSTRACT High-frequency trading (HFT) is a trading method that relies on sophisticated algorithms to analyse markets and execute large numbers of orders within milliseconds. In the last two decades, this new technology has gained traction globally and now accounts for the majority of the trading volume on the Johannesburg Stock Exchange (JSE). Despite the dominance of HFT, studies on the topic have been scarce outside of the United States. This study seeks to examine the effects of HFT on market quality in a South African context. First, the study makes use of a set of proxies for algorithmic trading (AT), namely average trade size, odd-lot volume ratio and trade-to-order volume ratio. Second, panel regressions are used to determine the relationship between these proxies and two measures of market quality (market liquidity and short-term volatility). The study found a strong positive relationship between market liquidity and average trade size but an inverse relationship with the other two AT proxies. Finally, the study confirmed a strong positive relationship with short-term volatility. The study concludes that, overall, AT has a positive impact on market quality, despite carrying the risk of causing instability in certain markets.

Research on Strategy Discovery and Optimisation of an Intelligent Option Trading System

'Blue Ocean' is a term that refers to an unexploited or uncontested market space. AI, machine learning and Big Data has presented a range of technologies for exploring Blue Ocean market spaces. For example, in the world of financial technology (fintech) introducing new investment strategies could have an impact on investment opportunities. Professor Chien-Feng Huang, Department of Computer Science and Information Engineering, National University of Kaohsiung, Taiwan, conducts novel research to discover and optimise an intelligent option trading system. In one line of research, he is working to solve the problem of combining different strategies to generate generalised models to tackle various financial circumstances while investing. A feasible way to do this, according to the studies of Huang and his team, is to utilise AI-based optimisation methodologies. Through their studies, the researchers are seeking to advance the research and applications of fintech and discover more efficient and effective investment models to create blue ocean strategies within finance. Huang is also working on autonomous self-evolving forecasting models for price movement in high frequency trading (HFT). He and his team have developed novel AI-based models for forecasting price movement in HFT and found that their proposed methods can increase the prediction accuracy for HFT price movement and advance the current state of HFT research. Huang has had encouraging results so far with the stock selection models, pairs-trading models and options-trading models developed and deployed in the market proving viable.

Testing Market Efficiency In Borsa Istanbul After High Frequency Trading

Yüksek frekanslı işlemlerin sermaye piyasalarında kullanımıyla birlikte yatırım ortamı değişmiş, yüksek frekanslı işlemlerin piyasa etkinliğinin tespitine ilişkin çalışmalarda araştırılması önem kazanmıştır. Yüksek frekanslı işlemlerin, kullanıldıkları piyasalarda hız ve teknoloji avantajları nedeniyle piyasa etkinliğini etkiledikleri ve kısa zaman aralıklarında zayıf formda piyasa etkinliğinin reddedilmesine yol açtıkları gözlemlenmiştir. Borsa İstanbul’un BISTECH sistemine geçmesiyle 2016 yılından itibaren yüksek frekanslı işlemler Türkiye piyasalarında kullanılmaya başlanmıştır. Bu çalışmada yüksek frekanslı işlemler dikkate alınarak, etkin piyasa hipotezinin Borsa İstanbul’daki geçerliliği sınanmıştır. Zayıf formda piyasa etkinliğinin tespiti için literatüre benzer bir şekilde varyans oranı testi kullanılarak rassal yürüyüş hipotezi test edilmiştir. Veri seti olarak Borsa İstanbul’un temel göstergesi olan BIST100 endeksinin 05/02/2019 ve 05/02/2020 dönemleri arasındaki dakikalık getirileri kullanılmıştır. Borsa İstanbul’un dakikalık zaman dilimlerinde zayıf formda etkin olmadığı gözlemlenmiştir. Yüksek frekanslı işlemlerin kullanıldığı Borsa İstanbul’da rassal yürüyüşün olmadığı, durağanlığın mevcut olduğu tespit edilmiştir. Bu sonuç Borsa İstanbul’da yüksek frekanslı işlemler kullanılarak piyasanın üzerinde getiri elde etmenin mümkün olduğunu ifade etmektedir.

Structural analysis and evolutionary exploration based on the research topic network of a field: a case in high-frequency trading

This study aims to systematically analyze the distribution dynamics of research topics and uncover the development state of the research in the specific field, which will provide a practical reference for developing professional subject knowledge services in the era of big data. The research topic network is constructed and analyzed using methods and tools of scientometrics. Basic statistics on network characteristics are performed to reveal the research status. Community detection, node ordering, and other steps are conducted to generate the evolutionary alluvial diagram. Then, relevant results are analyzed to explore the knowledge structure of the specific field and evolutionary context of research topics. Visualization analysis on the network structure of the latest period is executed to distinguish related concepts and predict the research trends. Taking high-frequency trading (HFT) as a case, this study achieves diversified scientometrics analysis of the research topic network and multi-dimensional evolution exploration of the relevant research topics in the specific field, which obtaining some knowledge insights. (1) Six major topics in HFT: liquidity & market microstructure, market efficiency, financial market, incomplete market, cointegration & price discovery, and event study. (2) The research focus about markets gradually transferred from international to emerging, meanwhile continuous attention to volatility/risk related issues. (3) The emphasis will change from theory to practice, technologies (big data, etc.) and theories (behavioral finance, etc.) will have more interaction with HFT. An effective research idea is proposed to reveal the knowledge structure of field and analyze the evolutionary context of research topics, which demonstrating the knowledge insights.