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High Frequency Trading, 고빈도매매의 비중이 여러가지 이유로 낮아지고 있습니다. 2005년이후 싹이 나오기 시작한 고빈도매매가 한 풀 꺾인 모습을 보여줍니다. 빈도가 높은 방식의 트레이딩이 퇴조하지만 빠른 속도를 요구하는 트레이딩은 계속입니다.
KKR의 CRO인 Attilio Meucci은 앞으로의 트레이딩을 data-driven quantitative systematic strategy라고 정의합니다. 여기서 Systematic은 High perfomance와 High Speed를 갖춘 시스템으로 이해함이 타당할 듯 하니다. 메릴리치에서 근무하시는 분이 올리신 글을 보면 크게 벗어나지 않은 정의인 듯 합니다.
Quant’s modeling theme: 파생 시장의 축소로 새로운 모델링을 할 일자리를 잃은 대형 금융기관의 top quant들은역시 regulatory area로 옮겨갔다: Counterparty risk쪽으로 옮겨 wrong way risk, DVA등을 새로운 modeling theme으로 삼고 새로운 일자리를 찾았다. 헤지펀드들의 가장 hot한 분야는 QE 효과로 인한 주식과 MBS쪽이라고 해도 과언이 아니었으며, 퀀트의 주요 관심사도 Black-Scholes류의 market modeling에서 MBS modeling, algorithmic trading strategy등의 statistical/behavioral modeling으로 옮겨가고 있다.
HFT의 비중이 줄어든다고 하지만 HFT와 관련한 논문을 계속 이어지고 있습니다. HFT가 시장에 미치는 영향에 대한 것 뿐 아니라 전략을 다루는 논문도 많습니다. 오늘 한해를 마감하면서 그동안 북마크했던 것중 몇 개를 추려서 소개하고자 합니다.
Latency Arbitrage, Market Fragmentation, and Efficiency: A Two-Market Model
We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We propose a simple model of latency arbitrage in which a single security is traded on two exchanges, with aggregate information available to regular traders only after some delay. An infinitely fast arbitrageur profits from market fragmentation by reaping the surplus when the two markets diverge due to this latency in cross-market communication. We develop a discrete-event simulation system to capture this processing and information transfer delay, and using an agent-based approach, we simulate the interactions between high-frequency and zero-intelligence trading agents at the millisecond level. We then evaluate allocative efficiency and market liquidity arising from the simulated order streams, and we find that market fragmentation and the presence of a latency arbitrageur reduces total surplus and negatively impacts liquidity. By replacing continuous-time markets with periodic call markets, we eliminate latency arbitrage opportunities and achieve further efficiency gains through the aggregation of orders over short time periods
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아래는 ssrn에 올라온 논문입니다. 하나씩 의견을 달아야 하지만 문외한인 저보다 저자들의 요약이 훨씬 의미있을 듯 합니다. Tabb Forum과 같은 곳에서 소개하였던 것들이 대부분입니다.
Algorithmic and High Frequency Trading in Dynamic Limit Order Markets
We consider a dynamic equilibrium model of algorithmic trading (AT) for limit order markets, where AT traders effectively have low-latency transmission of orders and informational superiority. We find that AT improves market quality ‘only’ under specific conditions and change trading behaviors of traditional agents. AT traders prefer to act as liquidity suppliers (demanders) when they represent the majority (minority) of investors. AT reduces waiting costs but finally damages slow traders’ profits. In some scenarios, AT decreases liquidity and global welfare. AT traders prefer volatile assets, and cancellation fees may be better policy instruments to control AT activity than latency restrictions.
This Article analyzes — through the lens of securities regulation — the contributions of Haim Bodek, an advocate of reforming the securities market structure and a whistleblower who brought attention to several questionable practices of high-frequency traders and trading venues, including their use of complex and, arguably, nontransparent order types. More specifically, the Article addresses several key issues raised and discussed by Haim Bodek, such as the order type controversy and its implications for high-frequency traders, the status of self-regulatory organizations, trading obligations and privileges of market makers, and the duty of best execution, and attempts to fit these issues into the evolving boundaries of civil liability under federal securities law and the reach of a private right of action.
Whole Distribution Statistical Process Control in High Frequency Trading
High frequency trading enables real-time control of outputs. However, sampling techniques in traditional statistical process control may be too slow for to detect rapid changes in market structure. We develop statistical tests that examine each event using the generalized lambda distribution. We demonstrate the manner in which this provides a more descriptive and quicker reacting method of process control than does traditional SPC.
Flow Toxicity and Liquidity in a High Frequency World
Order flow is toxic when it adversely selects market makers, who may be unaware they are providing liquidity at a loss. We present a new procedure to estimate flow toxicity based on volume imbalance and trade intensity (the VPIN toxicity metric). VPIN is updated in volume-time, making it applicable to the high frequency world, and it does not require the intermediate estimation of non-observable parameters or the application of numerical methods. It does require trades classified as buys or sells, and we develop a new bulk volume classification procedure that we argue is more useful in high frequency markets than standard classification procedures. We show that the VPIN metric is a useful indicator of short-term, toxicity-induced volatility.
Market Microstructure and the Risks of High-Frequency Trading
The current research assesses the risks commonly attributed to the presence of HFT in the context of different market structures deployed by the U.S. exchanges. In particular, we find that, by design, the so-called “normal” exchanges have the lowest market quality, including the highest proportion of limit orders cancelled, the lowest ability to detect spoofing market manipulation, the highest volatility and probability of market crashes, yet the highest liquidity. The so-called “inverted” exchanges have higher market quality, including a lower proportion of limit orders cancelled, higher ability to detect spoofing market manipulation, lower volatility and probability of market crashes, but lower liquidity levels. Finally, we show that “pro-rata” markets possess even higher market quality. We derive these results theoretically and then show that they hold empirically. We also derive the theoretical quality of markets with no-cancel ranges, and optimal order sizes in pro-rata markets.
Media-Driven High Frequency Trading: Evidence from News Analytics
We investigate whether providers of high frequency media analytics affect the stock market. This question is difficult to answer as the response to news analytics usually cannot be distinguished from the reaction to the news itself. We exploit a unique experiment based on differences in news event classifications between different product releases of a major provider of news analytics for algorithmic traders. Comparing the market reaction to similar news items depending on whether the news has been released to customers or not, we are able to determine the causal effect of news analytics on stock prices, irrespective of the informational content of the news. We show that coverage in news analytics speeds up the market reaction by both increasing the stock price update and the trading volume in the first few seconds after the news event. Such coverage also increases prices if the content of the news is positive. Placebo tests and econometric robustness checks, either based on difference-in-difference specifications or different samples, confirm the results. The fact that a provider of media analytics impacts the market in a separate and distinct way from the underlying information content of the news has important normative implications for the regulatory debate.
We examine information, market impact and trade sizes using a data-set of institutional trades where approximately 1/4 of the orders are labeled as having been created for cash flow purposes. We find that during the execution the functional form and scale of market impact are similar for cash flows as for other trades. After the trade is completed, the impact of cash flows reverts almost completely on average in two to five days. For trades excluding cash flows price reversion is only a fraction of total impact: for every size, the price after reversion is, on average, equal to the average execution price, leaving no immediate profits after accounting for trading costs. Observed mark-to-market profits on merged orders from multiple portfolio managers and Nasdaq-listed stocks suggest that these trades are more informed than the average. Mark-to-market losses on cash flows, trades that follow momentum and additions to a prior position seeking to take advantage of an improved price reveal the low information content of these trades. The complete price reversion for uninformed trades suggests that prices cannot be manipulated as assumed in no-quasi-arbitrage arguments for the linearity of permanent impact. There is no permanent impact, only information that causes trades.
Optimal Limit Order Execution in a Simple Model for Market Microstructure Dynamics
Market participants that have a task to acquire a certain position in a listed security at a predetermined price on behalf of a third party with no time urgency, i.e. to fill a perpetual limit order, can optimize the profitability of their trading strategy in order to accomplish this task. We study the statistical properties of the profit distribution of a particular market-making strategy: one which increments the inventory as the underlying price approaches the limit order price S0 and locks in profits by gradually liquidating the inventory as the market drifts away from S0. We do so by adopting a simple model of market microstructure in which an unobservable continuous stochastic process, the microprice, drives the dynamics of limit and market orders. In this model, the arrival of market orders and updates of the limit order book are determined by the microprice crossing a discrete set of n equidistant levels between the price ticks. Assuming normal dynamics for the microprice and adopting a standard mean-variance framework, we are able to derive a closed-form solution for the optimal inventory profile which is remarkably simple: the cumulative amount held when the market price is Si is inversely proportional to Si-S0, the distance in price terms from the limit order price. Finally, we show that n represents a sort of micro-volatility of the market that is entirely independent of the diffusive volatility of the microprice and is a measure of the intensity of the bid-ask bounce.
Recent computational advances allow investment managers to search for profitable investment strategies. In many instances, that search involves a pseudo-mathematical argument, which is spuriously validated through a simulation of its historical performance (also called backtest).
We prove that high performance is easily achievable after backtesting a relatively small number of alternative strategy configurations, a practice we denote “backtest overfitting”. The higher the number of configurations tried, the greater is the probability that the backtest is overfit. Because financial analysts rarely report the number of configurations tried for a given backtest, investors cannot evaluate the degree of overfitting in most investment proposals.
The implication is that investors can be easily misled into allocating capital to strategies that appear to be mathematically sound and empirically supported by an outstanding backtest. This practice is particularly pernicious, because due to the nature of financial time series, backtest overfitting has a detrimental effect on the future strategy’s performance.
3.
Tabb Group과 Portware의 의뢰를 받아 만든 보고서입니다.
Alpha in the Analytics: Optimizing Algorithmic Strategies
또다른 자료는 인도의 글입니다. 현재 HFT가 여전히 위력을 발휘하는 시장중 하나가 인도입니다. Lokesh Madan가 운영하는 블로그인 Algo Trading India에 올라온 글입니다.
