正反馈交易、市场效率与流动性黑洞——基于日内分笔交易数据的实证研究

金融危机爆发以来,全球资本市场流动性出现紧缩;中国市场波动幅度增加,多只股票出现流动性黑洞,市场效率显著降低。本文通过向量自回归(VAR)分析,发现投资者在不同程度上采用了正反馈交易策略。进一步地,对存在正反馈交易和不存在正反馈交易的数据分别进行多方面的比较分析,文章发现正反馈交易者显著地降低了市场的效率;通过对收益率序列和委比序列进行向量自回归分析,发现正反馈交易降低了市场的多样性,更容易产生流动性黑洞。     

市场透明度改变影响交易者行为吗？

基于中国证券市场2003年12月8日提高市场透明度这一事件，本文对市场透明度提高对交易者行为的影响进行了研究。结果发现：市场透明度提高明显改变了交易者的交易策略，主要表现为交易者整体交易指令的激进程度降低，其中交易者减少提交市价指令，增加了撤单的频率。研究还发现，市场透明度提高导致交易者提交价格增进的限价指令的比例下降，而提交小额交易指令的比例增加，同时交易者对交易环境的改变具有学习与逐步适应的能力。     

异质性交易策略与股价崩盘风险研究

基于2011—2022年中国A股交易数据，研究了异质性交易策略对股价崩盘风险的影响。研究表明，个人投资者的当期情绪以及机构投资者的前期情绪，能够显著影响风险资产特质收益率的波动：个人交易者倾向于做出骑乘泡沫行为，而机构交易者则倾向于利用市场噪声情绪推高风险资产价格并提前变现离场。同时，在对股价崩盘风险和异质性交易策略之间可能存在的系统性断点进行门限检验后发现，机构投资者的相对力量效应对风险资产价格波动的影响程度存在门限点。当机构投资者的相对力量效应超过特定的阈值点时，股价长期崩盘风险会随着相对力量效应的提高而降低；而当个体交易者在市场上占据绝对主导、机构交易者相对力量降低时，股价长期崩盘风险会提高。因此，有效引导交易者对风险资产的需求回归价值面本身，避免出现噪声蛊惑和诱导交易，是有效防范化解金融交易风险的关键。     

交易者资产构成与风险度量的研究

本文从"行为金融学"的角度出发,将交易者根据其资产构成可分成三类:第一类是只持有股票的人;第二类是只持有现金的人;第三类是同时持有股票和现金的人.作者认为股票价格是由交易者完成交易决定的,通过实验发现股票价格的变化是随着三类交易者的比例而变化的,由此提出了一新的风险计算方法,并且将风险分为上升风险和下降风险两种,用只持有现金的交易者总人数与市场内交易者的总和的比例计算市场的上升风险;用只持有股票的交易者总人数与市场内交易者的总和的比例计算市场的下降风险.此方法可以实时计算出市场的风险数值,能很方便用于现实投资的风险度量.根据新的风险定义可以上推导出:风险与市场规模成反比的规律.最后作者在自己开发的"大学生模拟交易所"做了交易行为的金融实验,对新风险定义的正确性进行了实验验证.     

乐观和悲观情绪如何驱动市场反馈交易行为？——基于噪音交易者和理性交易者共同作用的视角

本文从理论和实证层面研究乐观和悲观情绪如何驱动噪音交易者追涨杀跌和高抛低吸，以及理性交易者骑乘和套利，最终形成市场反馈交易行为。研究发现，噪音交易者主导的小盘指数中市场反馈交易行为不显著；上证A股指数和50指数中显著为正的市场反馈交易行为由理性交易者追涨骑乘所致。高波动促进了中国股市追涨骑乘强于高抛套利和美国股市高抛低吸强于追涨杀跌；融资融券的引入对中国股市负反馈交易强度的提升大于正反馈交易。     

作为投机策略的噪声交易:一个交易操纵模型

噪声交易通常被认为是外生的,噪声交易者也往往被认为是市场的当然输家.但在存在主动性的噪声交易的交易操纵模型中,通过将噪声交易作为一种策略,操纵者可以获得比普通投资者更为精确的信息,从而为后期的投资赢利占得先机.由于这种投资行为仅通过交易,而不需要其它行为及信息上的优势来实现,这是一种典型的交易操纵行为.     

高频交易与操纵市场

高频交易是资本市场上金融科技迅速发展的产物,是一种采用托管等低延时通信技术,通过高速信息处理,运用多个算法程序实施日内频繁交易的自动交易系统。尽管高频交易具有提供市场流动性、加深市场深度的积极意义,但也存在放大市场波动、为交易而交易且容易导致滥用市场交易优势的操纵行为。比较目前欧美对高频交易的监管实践,幌骗、试单、扰乱交易行为以及滥用市场成为高频交易操纵市场的主要手段。立足于我国现实,对集中于期货市场的高频交易应加强监管,完善透明、及时、公开的市场报价机制,明确涉及高频交易操纵行为的判断指标体系。     

非对称信息对资本市场高频价格变动的影响

运用信息非对称与高频价格运动模型的分析结果表明:中国证券市场的交易频率对价格变化状态具有显著的影响.知情交易者为了保持获得的信息优势,往往选择提交积极订单以迅速交易.在知情交易者比重较大的时候,市场交易频繁,此时买卖价差较大,波动剧烈;反之,当交易间隔长时,意味着未知情交易者比重大,此时买卖价差较小,波动较小.交易间隔短意味着市场存在消息驱动,可能是好消息也可能是坏消息.好消息引起价格上涨的概率和坏消息引起价格下跌的概率具有对称性.     

金融市场中的高频交易与监管

高频交易作为一种新兴的交易模式,短短几年内占据了金融市场的巨大份额,改变了传统的交易模式.近年来,我国证券市场上也有了较多的交易者进行量化交易.高频交易在为市场创造超常流动性并平滑证券价格的同时,也对市场交易秩序产生了消极影响.通过对美股“5·6闪电崩盘”过程的梳理,总结分析美国证券市场的监管措施,从技术层面提出了加强我国证券市场监管的对策建议,包括：制定高频交易技术规范,健全有效的市场冷却机制,健全跨市场联动风险预警制度及风险控制预案.     

正反馈交易与股票价格波动

在股票市场中,很多交易者不是对特定的消息作出反应,而是对过去的价格变动作出反应。如果交易者的交易策略是在股价出现上涨后买入,或在股价下跌后抛出,即"追涨杀跌",这类交易者称为正反馈交易者,其交易行为称为正反馈交易行为。本文建立了一个正反馈交易模型,分析了正反馈交易与股票价格的相关关系,最后对我国股市中的正反馈行为进行了检验,结果证明我国股市中确实存在正反馈交易行为。     

High-frequency trading and stock liquidity: An intraday analysis

This paper studies the impact of high-frequency trading (HFT) on intraday liquidity of CAC40 stocks listed on Euronext. Spreads display an intraday L-shaped pattern, while quoted depth follows an inverse pattern: low at the open and increasing towards the end of the trading day. When liquidity demand is particularly high, there is a high rate of order cancellations attributable to high-frequency traders who use frequent order cancellations to strategically manage their limit orders and close positions near the market close. Using the generalized method of moments estimator, we generate strong evidence that greater intensity of HFT is associated with lower spreads and higher depth. The positive effect of HFT on liquidity is due mainly to decreased adverse selection costs arising from asymmetric information among market participants.     

The fall of high-frequency trading: A survey of competition and profits

We investigate high-frequency trading (HFT) strategies, inventorying the strategies already studied in the literature and introducing innovative strategies detected by private institutional research. To this end, we expand the existing classification, and we offer names for new categories. In a complementary but original manner, we introduce counter reactions from professional traders in response to HFT predatory strategies. These human answers reverse the usual framework of competition between high-frequency traders (HFTs) and low frequency traders (LFTs) and also widen this cadre to HFTs algos (predators) versus execution algos.This survey notes that a continuous increase in competition, between high-speed trading algorithms themselves through predatory strategies and from professional human traders adapting and building adequate responses has made the business more difficult and has led to shrinking profits for HFT. In the end, we believe that excessive competition and a change in the current regulation (favorable to HFT) could kill the goose that laid the golden egg.     

High-frequency trading and market quality: The case of a “slightly exposed” market

Impacts of high-frequency trading (HFT) on market quality and various actors have been broadly studied. However, what happens when HFT is not a prominent figure in a market remains relatively unexplored. The paper seeks to answer this question focusing on 30 blue chip stocks in an emerging market, Borsa Istanbul, through Dec 2015 to Mar 2017. Despite a low share in the overall activity, HFT has observable effects, i.e. liquidity provision by non-HFT traders significantly reduces with HFT. Moreover, HFT generates profits on both positive and negative return days. Yet, HFT activity does not have an impact on volatility. These findings raise concerns regarding HFT and show potential externalities are not specific to the markets with HFT dominance.     

Is high-frequency trading tiering the financial markets?

Some academic research has identified the possibility of High-Frequency Trading (HFT) creating a two tier market, in which the fast traders mostly deal with each other at most favourable prices and spread, leaving the slower investors to share the least profitable deals. Yet, although intriguing, this view has been seldom quantitatively investigated − and that is the gap found in previous research. A computer simulation has been produced to mimic the behaviour of both slow and fast traders, each category showing characteristics consistent with their behaviour on the markets. In particular, High-Frequency (HF) traders show their preference for aggressive orders when the bid-ask spread is tight and are less aggressive when spread is wide. The Low-Frequency (LF) traders are then forced to live with the remaining deals, hoping to profit from longer term price movements. The purpose of this piece of research is to verify whether HF traders (HFTs) tend to deal with each other and, something not investigated by previous studies, if LF traders also mainly restrict their trading with other slow traders.The results show some tendency toward markets actually splitting into two-tiers: they confirm previous findings of HF traders' tendency to deal with their similar counterparts in case of thin bid-ask spread (which means higher probability of profits for the aggressive side), leaving mostly LF traders to deal amongst themselves when the spread is wider, so aiming at long-term gain (which makes trading inherently riskier).     

The diversity of high-frequency traders

The regulatory debate concerning high-frequency trading (HFT) emphasizes the importance of distinguishing different HFT strategies and their influence on market quality. Using data from NASDAQ-OMX Stockholm, we compare market-making HFTs to opportunistic HFTs. We find that market makers constitute the lion's share of HFT trading volume (63–72%) and limit order traffic (81–86%). Furthermore, market makers have higher order-to-trade ratios and lower latency than opportunistic HFTs. In a natural experiment based on tick size changes, we find that the activity of market-making HFTs mitigates intraday price volatility.     

Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures

The increasing volume of messages sent to the exchange by algorithmic traders stimulates a fierce debate among academics and practitioners on the impacts of high-frequency trading (HFT) on capital markets. By comparing a variety of regression models that associate various measures of market liquidity with measures of high-frequency activity on the same dataset, we find that for some models the increase in high-frequency activity improves market liquidity, but for others, we get the opposite effect. We indicate that this ambiguity does not depend only on the stock market or the data period, but also on the used HFT measure: the increase of high-frequency orders leads to lower market liquidity whereas the increase in high-frequency trades improves liquidity. We hypothesize that the observed decrease in market liquidity associated with an increasing level of high-frequency orders is caused by a rise in quote volatility.     

Front‐Running Scalping Strategies and Market Manipulation: Why Does High‐Frequency Trading Need Stricter Regulation?

Regulators continue to debate whether high‐frequency trading (HFT) is beneficial to market quality. Using Strongly Typed Genetic Programming (STGP) trading algorithm, we develop several artificial stock markets populated with HFT scalpers and strategic informed traders. We simulate real‐life trading in the millisecond time frame by applying STGP to real‐time and historical data from Apple, Exxon Mobil, and Google. We observe that HFT scalpers front‐run the order flow, resulting in damage to market quality and long‐term investors. To mitigate these negative implications, we propose batch auctions every 30 milliseconds of trading.     

The optimal bid-ask price strategies of high-frequency trading and the effect on market liquidity

We propose a model for determining the optimal bid-ask spread strategy by a high-frequency trader (HFT) who has an informational advantage and receives information about the true value of a security. We employ an information cost function that includes volatility and the volume of the asset. Subsequently, we characterize the optimal bid-ask price strategies and obtain a stable bid-ask spread. We assume that orders submitted by low-frequency traders (LFTs) and news events arrive at the market with Poisson processes. Additionally, our model supports the trading of the two-sided quote in one period. We find that more LFTs and a higher exchange latency both hurt market liquidity. The HFT prefers to choose a two-sided quote to gain more profits while cautiously chooses a one-sided quote during times of high volatility. The model generates some testable implications with supporting empirical evidence from the NASDAQ-OMX Nordic Market.     

Human vs. high-frequency traders,penny jumping,and tick size

This paper examines changes in market quality resulting from the smaller tick size of the interbank foreign exchange market. Coupled with the lower tick size, the special composition of traders and their order placement strategies created a suitable environment for high-frequency traders (HFT’s) to implement sub-penny jumping strategy to front-run human traders. We show that the spread declined following the introduction of decimal pip pricing. However, benefits of spread reduction were mostly absorbed by the HFT’s. Market depths were also significantly reduced with the occupation of the top of the order book by HFT’s. This new environment changed the market maker-market taker composition between different traders and altered price impacts of the order flows.     

Strategic trading and learning about liquidity

Many practitioners point out that the speculative profits of institutional traders are eroded by the difficulty in gauging the price impact of their trades. In this paper, we develop a model of strategic trading where speculators face such a dilemma because of incomplete information about time-varying market liquidity. Unlike the competitive market makers that they trade against, informed traders do not know the distribution of liquidity (“noise”) trades. Instead, they have to learn about liquidity from past prices and trading volume. This learning implies that strategic trades and market statistics such as informational efficiency are path-dependent on past market outcomes. Our paper also has normative implications for practitioners.