High Frequency Trading Research Articles

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.

What (If Anything) is Wrong with High-Frequency Trading?

This essay examines three potential arguments against high-frequency trading and offers a qualified critique of the practice. In concrete terms, it examines a variant of high-frequency trading that is all about speed-low-latency trading-in light of moral issues surrounding arbitrage, information asymmetries, and systemic risk. The essay focuses on low-latency trading and the role of speed because it also aims to show that the commonly made assumption that speed in financial markets is morally neutral is wrong. For instance, speed is a necessary condition for low-latency trading's potential to cause harm in "flash crashes." On the other hand, it also plays a crucial role in a Lockean defense against low-latency trading being wasteful developed in this essay. Finally, this essay discusses the implications of these findings for related high-frequency trading techniques like futures arbitrage or latency arbitrage-as well as for an argument as to why quote stuffing is wrong. Overall, the qualifications offered in this essay act as a counterbalance to overblown claims about trading at high speeds being wrong.

Machine learning and speed in high-frequency trading

The creative destruction wrought by high-frequency algorithmic trading has raised increasing concerns about the effect of machine learning behaviors and ultra high-frequency trading on financial markets. By employing a genetic algorithm with a classifier system as an adaptive learning tool, we address some of these concerns by studying a dynamic limit order market model with asymmetric information and varying speeds of high-frequency trading (HFT). We show that HFT benefits uninformed traders, improves information efficiency but reduces market liquidity. We find that there is a trade-off where a competition effect erodes the information and speed advantages of high-frequency traders, increasing trading speeds of HF traders reduces market liquidity but generates a hump-shaped relationship to the profitability of high-frequency traders and information efficiency. This research finds there may be potential benefits to throttling the trading speed arms race to improve market efficiency. We also find that strategic algorithmic trading compensates for diminishments in speed advantages, providing an insight on machine behavior in the FinTech age.

Review of Statistical Approaches for Modeling High-Frequency Trading Data

Review of Statistical Approaches for Modeling High-Frequency Trading Data

Regulation of Algorithmic Trading: Frameworks or Human Supervision and Direct Market Interventions

This paper identifies the regulatory gaps that currently exist in algorithmic trading and provides a framework for machine learning regulation in finance. It compares the regulation of algorithmic trading in the capital markets by both human supervision and direct market intervention in the UK, the EU and the US to identify techniques they have in common, as well as local differences. Section II sets out what algorithmic trading is, how it is defined, which of its functions have a positive effect and which are negative for risk and impact. Section III examines how trading risks can be managed by human supervision. Section IV looks at how direct market intervention can mitigate the risks of algorithmic trading, focusing on the circuit breaker requirement. Finally, the liability of the parties involved (traders, firms, and trading venues) are examined and the possible enforcement actions that regulators may take are set out. Algorithms, high frequency trading, machine learning, financial regulation, MIFIDII, ESMA, FCA, SEC, circuit breaker, systemic risk

Order Protection Through Delayed Messaging

Several financial exchanges (e.g., IEX and NYSE American) recently introduced messaging delays to protect ordinary investors from high-frequency traders who exploit stale orders. To capture the impact of such delays, we propose a simple parametric model of the continuous double auction market format. The model examines the dynamics of midpoint pegged order queues and finds their steady states. It shows how messaging delays can protect pegged orders and improve investor welfare, but typically increase queuing costs. Recently available field data show that the empirical distribution of queued pegged orders is highly leptokurtotic and resembles the discrete Laplace distribution predicted by the model. This paper was accepted by Haoxiang Zhu, finance. Funding: Financial support from the European Research Council under the European Union’s Horizon 2020 research and innovation programme [Grant 741409] and the Center for Analytical Finance at the University of California Santa Cruz [seed funding of the Experimental High-Frequency Trading project, of which this paper is a part] is gratefully acknowledged. Supplemental Material: The online supplement is available at https://doi.org/10.1287/mnsc.2022.4370 .

High-frequency trading, stock volatility, and intraday crashes

We examine the effect of high-frequency trading (HFT) on the price volatility of Euronext-listed stocks. Under stable market conditions, greater HFT intensity is associated with decreased stock price volatility. However, during periods of intraday crashes, rapid interactions between HFT algorithms lead to high rates of order cancellations and simultaneous withdrawals of high-frequency traders from the limit order book. High-frequency traders submit aggressive orders during these periods and consume more liquidity than they provide, resulting in increased stock price volatility.

Heterogeneous Criticality in High Frequency Finance: A Phase Transition in Flash Crashes.

Flash crashes in financial markets have become increasingly important, attracting attention from financial regulators, market makers as well as from the media and the broader audience. Systemic risk and the propagation of shocks in financial markets is also a topic of great relevance that has attracted increasing attention in recent years. In the present work, we bridge the gap between these two topics with an in-depth investigation of the systemic risk structure of co-crashes in high frequency trading. We find that large co-crashes are systemic in their nature and differ from small ones. We demonstrate that there is a phase transition between co-crashes of small and large sizes, where the former involves mostly illiquid stocks, while large and liquid stocks are the most represented and central in the latter. This suggests that systemic effects and shock propagation might be triggered by simultaneous withdrawals or movement of liquidity by HFTs, arbitrageurs and market makers with cross-asset exposures.

Analysis of Individual High-Frequency Traders' Buy-Sell Order Strategy Based on Multivariate Hawkes Process.

Traders who instantly react to changes in the financial market and place orders in milliseconds are called high-frequency traders (HFTs). HFTs have recently become more prevalent and attracting attention in the study of market microstructures. In this study, we used data to track the order history of individual HFTs in the USD/JPY forex market to reveal how individual HFTs interact with the order book and what strategies they use to place their limit orders. Specifically, we introduced an 8-dimensional multivariate Hawkes process that included the excitations due to the occurrence of limit orders, cancel orders, and executions in the order book change, and performed maximum likelihood estimations of the limit order processes for 134 HFTs. As a result, we found that the limit order generation processes of 104 of the 134 HFTs were modeled by a multivariate Hawkes process. In this analysis of the EBS market, the HFTs whose strategies were modeled by the Hawkes process were categorized into three groups according to their excitation mechanisms: (1) those excited by executions; (2) those that were excited by the occurrences or cancellations of limit orders; and (3) those that were excited by their own orders.

High-Frequency Trading (HFT) and Market Quality Research: An Evaluation of the Alternative HFT Proxies

We examine the soundness of high-frequency trading (HFT) proxies that are widely defined on the limit order book (LOB) information. We use a unique TRTH (Thomson Reuters Tick History) millisecond time-stamped intraday trades and quotes dataset enriched with 10 levels of LOB depth messages for 149 highly fragmented LSE listed stocks for the period 2005 to 2016. We explore a sharp uptrend in HFT activities and accompanying improvement in market liquidity in the European market. We show that alternative HFT proxies built on LOB are not equally powerful. The HFT proxy defined on the five best LOB prices (the mid point of a typical limit order book) provides a better HFT identification than the one popularly defined on the first best prices (BBO). We suggest that picking the LOB information beyond a certain level (e.g., the best five prices) of market depth in developing HFT proxy is counterintuitive. Evidence indicates that high-frequency traders (HFTs) participate in both competitive (narrow) and passive (wider) quoting as a market making strategy; however, they do not participate in passive quoting excessively.

High frequency correlation dynamics and day-of-the-week effect: A score-driven approach in an emerging market stock exchange

Intraday correlation dynamics poses challenges to financial econometricians, especially in recently popular high frequency domain, due to non-synchronous trading and market microstructure noise. Traditional models fail to address the issues inherent to the nature of the data, which is riddled with noisy signals and missing values. We employ a recently developed method based on Generalized Autoregressive Score (GAS) framework and State-Space Modeling to remedy these characteristics of high frequency data and estimate intraday correlations in Turkish equity market Borsa Istanbul. Our findings reveal that average intraday conditional correlation rises as trading commences and lingers around certain altitude for some time, with the eigenvalues associated with market factor becoming progressively more dominant. An upward trend closes out the trading day on Mondays, which we attribute to the US market opening, whereas the rest of the week does not show a generalizable closing-time effect. Assessment of the findings across different market conditions and days of the week reveals elevated correlation levels in volatile markets as well as a distinguishable path for the beginning of the week. Beyond the scholarly contribution, the methodology can be used as a nowcasting tool and the findings are of interest to various parties like high-frequency traders, risk and portfolio managers and regulatory agencies in formulating their high frequency trading practices, hedging, portfolio construction schemes and margin requirements, respectively.

Are High Frequency Traders Really Market-Makers?

Are High Frequency Traders Really Market-Makers?

The interaction between volatility and high frequency trading

In this study, we explore the linkage between high frequency trading activity and market volatility. Though the findings of existing studies for this topic until now are rather inconclusive, they agree with the impact HFT exert to volatility. In our design, we take advantage of the trading activity to predict real time trading. We use the VIXY which is an ETF of VIX and focuses on short term performance, finding that the ask side of high frequency trading activities has far more significant predictability for both the level of VIXY and return of VIXY, while the bid side seems to be trivial.

Cadeias de Markov em estratégia de negociação de ações em alta frequência

The price prediction problem for any given stock has been an object of study, deepening and evolution within the past few decades, to achieve the basic goal of positive financial realizations with the smaller risk possible. A known risk minimization strategy is a high frequency trading regime, taking benefit from minor price variations, and achieving small profits multiple times a day. By the hypothesis and the interpretation that prices variations behavior as a Random Walk, it is possible to characterize a Markov (stochastic) process with states and transition probabilities known, allowing to describe price variations through time. Therefore, this study aims to estimate the financial outcome using Markov chains for automated decision making in a high frequency trading strategy, and then comparing the computed results with stock’s valuation (or devaluation) within the same analyzed period.

High-Frequency Traders and Liquidity Supply During the Pandemic

High-Frequency Traders and Liquidity Supply During the Pandemic

Pricing Information: Evidence from Transaction-Level Data on High-Frequency Traders

Pricing Information: Evidence from Transaction-Level Data on High-Frequency Traders

International High-Frequency Arbitrage for Cross-Listed Stocks

We explore latency arbitrage activities with a new arbitrage strategy that we test with high-frequency data during the first six months of 2019. We study the profitability of mean-reverting arbitrage activities of 74 cross-listed stocks involving three exchanges in Canada and the United States. Our arbitrage strategy is a hybrid between triangular arbitrage and pairs trading. We synchronize the high-frequency data feeds from the three exchange venues considering explicitly the latency that comes from the transportation of information between the exchanges and its treatment time. Other trading costs and arbitrage risks are also considered. The annual net profit of an HFT firm that uses limit orders is around CAD $8 million (USD $6 million), a result that we consider reasonable when compared with the previous literature. International latency arbitrage with market orders is never profitable.

High-Frequency Trading with Machine Learning Algorithms and Limit Order Book Data

<abstract><p>In this paper, we examine the usefulness of machine learning methods such as support vector machines, random forests and bagging for the extraction of information from the limit order book that can be used for intraday trading. For our empirical analysis, we first get 50 raw features from the LOBSTER message file and order book file of the iShares Core S & P 500 ETF for the time period from 27.06.2007 to 30.04.2019 and then construct 18 higher-level features (aggregated to 5 minutes frequency) which serve as predictors. Using straightforward specifications for the machine learning procedures and thereby avoiding excessive data snooping, we find that these procedures are unable to find high dimensional patterns in the order book that could be used for trading purposes. The observed significant predictability is mainly due to the inclusion of only one variable, namely the last price change, and is probably too small to ensure profitability once transaction costs are taken into account.</p></abstract>

High-Frequency Trading and Market Quality: Evidence from Account-Level Futures Data

High-Frequency Trading and Market Quality: Evidence from Account-Level Futures Data