Will high-frequency trading practices transform the financial markets in the Asia Pacific Region?

High-frequency trading(HFT)practices in the global financial markets involve the use of information and communication technologies(ICT),especially the capabilities of high-speed networks,rapid computation,and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds.HFT practices exist because a variety of new technologies have made them possible,and because financial market infrastructure capabilities have also been changing so rapidly.The U.S.markets,such as the National Association for Securities Dealers Automated Quote(NASDAQ)market and the New York Stock Exchange(NYSE),have maintained relevance and centrality in financial intermediation in financial markets settings that have changed so much in the past 20 years that they are hardly recognizable.In this article,we explore the technological,institutional and market developments in leading financial markets around the world that have embraced HFT trading.From these examples,we will distill a number of common characteristics that seem to be in operation,and then assess the extent to which HFT practices have begun to be observed in Asian regional financial markets,and what will be their likely impacts.We also discuss a number of theoretical and empirical research directions of interest.

High-Frequency Trading:Deception and Consequences

This commentary is based on the work of Cooper,Davis,and Van Vliet(2016)and the commentary focuses on what problem high-frequency trading poses.It lists key literature on high-frequency trading that is missing and points out that the poker analogy to defend deception in financial markets is weak and misleading.The article elaborates on the negative impact created by spoofing and quote stuffing,the two typical deceptive practices used by high-frequency traders.The recent regulations regarding high-frequency trading,in response to the“Flash Crash”of 2010,are preventive,computerized and more effective.They reflect ethical requirements to maintain fair and stable financial markets.

A theory of very short-time price change:security price drivers in times of high-frequency trading

Academic research has identified several factors that affect price movements;however,the scenario changes abruptly in the case of very short time price changes(VSTPC).This topic is not specifically examined in the existing literature;nonetheless,the behavior of the market microstructure is quite different at the subsecond scale.Indeed,below a certain psychological time threshold,most factors typically influencing price changes cease to apply.This paper analyzes several parameters considered to affect price changes and identifies four of them as potentially influencing VSTPC.These factors are previous volatility,scarce liquidity,high quantity exchanged,and stop-loss(SL)orders(seldom mentioned in the literature).These four parameters are examined by means of a mathematical model,audit trail data analysis,Granger-causality testing,and agent-based model.The results of these four techniques converge to suggest a nonlinear combination of previous volatility,liquidity,and SL orders as the main causes of excess volatility.However,contrary to mainstream literature on trading time above a certain psychological threshold,the volumes exchanged are not integral agents for VSTPC.Currently,financial markets face many ultrafast orders,yet a coherent theory of price change at time scales incomprehensible by humans and only manageable by computers is still lacking.The theory presented in this paper attempts to fill this gap.The outcome of such a theory is important for purposes of market stability,crisis avoidance,investment planning,risk management,and high-frequency trading.

Novel modelling strategies for high‑frequency stock trading data

Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms are widely applied to mid-price stock predictions.Processing raw data as inputs for prediction models(e.g.,data thinning and feature engineering)can primarily affect the performance of the prediction methods.However,researchers rarely discuss this topic.This motivated us to propose three novel modelling strategies for processing raw data.We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks.In these experiments,our strategies often lead to statistically significant improvement in predictions.The three strategies improve the F1 scores of the SVM models by 0.056,0.087,and 0.016,respectively.

Latency arbitrage and the synchronized placement of orders

We argue that owing to traders’inability to fully express their preferences over the execution times of their orders,contemporary stock market designs are prone to latency arbitrage.In turn,we propose a new order type,which allows traders to specify the time at which their orders are executed after reaching the exchange.Using recent latency data,we demonstrate that the order type proposed here allows traders to synchronize order executions across different exchanges,such that high-frequency traders,even if they operate at the speed of light,can no-longer engage in latency arbitrage.

一种路由交换阵列节点芯片及其系统的设计与实现

介绍一种面向大数据处理数据中心应用的计算/控制/网络存储的路由交换阵列节点芯片及其所组成的安全交换阵列原型机的设计与实现;该路由交换阵列系统通过因特网远程使用软件定义网络(SDN)方式对高速安全交换网络的内部路由控制和安全等模块进行集中编程控制,满足数据中心对数据传输带宽容量的需求;同时并行计算过程中消除网络传输瓶颈,避免了数据中心网络等资源的长期占用浪费,为下一代数据中心解决方案的形成打下基础。另外还简述了其在金融交易系统领域大数据应用尝试的研究近况。

Non-Value-Added Tax to improve market fairness and quality

The promotion of both market fairness and efficiency has long been a goal of securities market regulators worldwide.Accelerated digital disruption and abusive trading behaviors,such as the GameStop mania,prompt regulatory changes.It is unclear how this“democratization”of trading power affects market fairness as economies cope with pandemic-driven shifts in basic systems.Excessive speculation and market manipulation undermine the quality of financial markets in the sense that they cause volatil-ity and increase the pain of bubble and crash events.Thereby,they weaken public confidence in financial markets to fulfill their roles in proper capital allocation to irrigate the real economy and generate value for society.While previous studies have mostly focused on market efficiency,our study proposes a tool to improve market fairness,even under periods of stress.To encourage value generation and improve market quality,we advance a graduated Non-Value-Added Tax that we implement in an agent-based model that can realistically capture the properties of real-world financial markets.A profitable transaction is taxed at a higher rate if it does not enhance the efficiency measured by deviation from fundamentals.When an agent locks in profit not supported by fundamentals but driven by trend-following strategies,the generated profit is taxed at various rates under the Non-Value-Added Tax regime.Unlike existing financial transaction taxes,the non-value-added tax is levied on profit rather than on price or volume.We show that the proposed tax encourages profitable trades that add value to the market and discourages valueless profit-making.It significantly curtails volatility and prevents the occurrence of extreme market events,such as bubbles and crashes.

Nonsynchronous Trading Model and Return Analysis

Nonsynchronous trading is one of the hot issues in financial high-frequency data processing. This paper extends the nonsynchronous trading model studied in [1] and [2] for the financial security, and considers the moment functions of the observable return series for the extended model. At last, the estimators of parameters are obtained.