Effect Of High Frequency Trading Research Articles

Information Efficiency and the Effect of High Frequency Trading in the Us Futures Markets

Information Efficiency and the Effect of High Frequency Trading in the Us Futures Markets

Modelling Intervalling Effect of High Frequency Trading on Portfolio Volatility

This paper theoretically examines whether reducing the minimum trading interval could affect portfolio volatility. Modelling the underlying de-trended asset price with Ornstein Uhlenbeck process, the paper investigates the volatility of portfolios that employ buy and hold strategy and momentum strategy. The paper presents theoretical evidence that the fast trading could increase portfolio price fluctuation and hence potentially suggests another cost of high frequency trading, besides the well-known damages including herding, aggressive trading strategies dark pools, immediate-or-cancel type orders.

High-Frequency Trading and Conflict in the Financial Markets

The last few decades has seen an ever-increasing growth in the way activities are productized and associated with a financial cost. This phenomenon, termed financialization, spans all areas including government, finance, health and manufacturing. Recent developments within finance over that past decade have radically altered the way trading occurs. This paper analyses high-frequency trading (HFT) as a necessary component of the infrastructure that makes financialization possible. Through interviews with HFT firms, a software vendor, regulators and banks, the effects of HFT on market efficiency, and its impact on costs to long-term investors are explored. This paper contributes to the literature by exploring the conflict that exists between HFT and traditional market makers in today's fragmented markets. This paper argues that society should be unconcerned with this conflict and should instead focus on the effects these participants have on the long-term investors, for whom the markets ultimately exist. In order to facilitate the best outcomes, regulation should be simple, aimed at keeping participants’ behavior stable, and the interactions among them transparent and straightforward. Financialization and HFT are inextricably linked, and society is best served by ensuring that the creative energy of these market participants is directed on providing liquidity and removing inefficiencies.

High-Frequency Trading Strategies

We examine the effect of high frequency trading on market quality from the perspective of a limit order trader. By competing with slower limit order traders, high frequency traders (HFT) impose a welfare externality by selectively crowding out the most profitable limit orders. The order book imbalance immediately before each order submission, cancelation and trade suggests that high frequency traders strategically use limit order book information to supply liquidity on the thick side of the order book and demand liquidity from the thin side. This strategic behavior is more pronounced during volatile periods and when trading speeds increase.

How to Predict the Consequences of a Tick Value Change? Evidence from the Tokyo Stock Exchange Pilot Program

The tick value is a crucial component of market design and is often considered the most suitable tool to mitigate the effects of high frequency trading. The goal of this paper is to demonstrate that the approach introduced in [Dayri, K., and M. Rosenbaum, 2015, Large Tick Assets: Implicit Spread and Optimal Tick Size, Market Microstructure and Liquidity, 1, 1550003.] allows for an ex ante assessment of the consequences of a tick value change on the microstructure of an asset. To that purpose, we analyze the pilot program on tick value modifications started in 2014 by the Tokyo Stock Exchange in light of this methodology. We focus on forecasting the future cost of market and limit orders after a tick value change and show that our predictions are very accurate. Furthermore, for each asset involved in the pilot program, we are able to define (ex ante) an optimal tick value. This enables us to classify the stocks according to the relevance of their tick value, before and after its modification.

High frequency traders in a simulated market

Purpose An agent-based market simulation is utilized to examine the impact of high frequency trading (HFT) on various aspects of the stock market. This study aims to provide a baseline understanding of the effect of HFT on markets by using a paradigm of zero-intelligence traders and examining the resulting structural changes. Design/methodology/approach A continuous double auction setting with zero-intelligence traders is used by adapting the model of Gode and Sunder (1993) to include algorithmic high frequency (HF) traders who retrade by marking up their shares by a fixed percentage. The simulation examines the effects of two independent factors, the number of HF traders and their markup percentage, on several dependent variables, principally volume, market efficiency, trader surplus and volatility. Results of the simulations are tested with two-way ANOVA and Tukey’s post hoc tests. Findings In the simulation results, trading volume, efficiency and total surplus vary directly with the number of traders employing HFT. Results also reveal that market volatility increased with the number of HF traders. Research limitations/implications Increases in volume, efficiency and total surplus represent market improvements due to the trading activities of HF traders. However, the increase in volatility is worrisome, and some of the surplus increase appears to come at the expense of long-term-oriented investors. However, the relatively recent development of HFT and dearth of appropriate data make direct calibration of any model difficult. Originality/value The simulation study focuses on the structural impact of HF traders on several aspects of the simulated market, with the effects isolated from other noise and problems with empirical data. A baseline for comparison and suggestions for future research are established.

High Frequency Trading and Learning

This paper introduces a limit order market model of fast and slow traders with learning to examine the effect of high frequency trading (HFT) and learning on limit order markets. We demonstrate that informed HFT makes significant profit from trading with other traders and, more importantly, it is the learning and information advantage that plays more important role than the trading speed in generating HFT profit. Overall HFT increases market liquidity consumption and supply, trading volumes, bid-ask spread, volatility and order cancellations, reduces order book depth, improves information dissemination efficiency, and generates significant event clustering effect in order flows. Interestingly, the speed of HFT is positively related to trading volume and spread and negatively related to market depth; however it has an U-shaped relation to liquidity supply and market efficiency, but an invert U-shaped relation to HFT profit and liquidity consumption. The findings provide some insight on the profitability of HFT and the current debates and puzzles about HFT.

The Impact of High-Frequency Trading on Market Volatility

Several studies have investigated the impact of High-Frequency Trading (HFT) on financial stability and volatility in particular. Albeit opinions differ, most findings lean into the direction of a beneficial HFT impact, leading to lower volatility. However, if the effect of HFT seems to be generally positive in normal times, it is far less clear how it affects volatility under stress, with some authors claiming a negative impact altogether. This paper presents the results of an Agent-Based Model simulation under two different cases: a quiet situation and a market following a trend. Whereas the quiet situation does not identify any abnormal behaviour, when the market is under stress the participation of High-Frequency (HF) traders leads to a statistically significant increase of volatility. The result can be explained with the delay suffered by market orders posted by low-frequency traders during a trend. Quite often they are executed at a price which, because of its rapid movements, is worse than intended when it was posted a few milliseconds earlier, with the effect of increasing volatility. As the number of HF traders increases, volatility starts to diminish again. This can be explained with the more homogeneous situation when most trading is executed by players experiencing similar latencies.

How to Predict the Consequences of a Tick Value Change? Evidence from the Tokyo Stock Exchange Pilot Program

The tick value is a crucial component of market design and is often considered the most suitable tool to mitigate the effects of high frequency trading. The goal of this paper is to demonstrate that the approach introduced in Dayri and Rosenbaum (2015) allows for an ex ante assessment of the consequences of a tick value change on the microstructure of an asset. To that purpose, we analyze the pilot program on tick value modifications started in 2014 by the Tokyo Stock Exchange in light of this methodology. We focus on forecasting the future cost of market and limit orders after a tick size change and show that our predictions are very accurate. Furthermore, for each asset involved in the pilot program, we are able to define (ex ante) an optimal tick value. This enables us to classify the stocks according to the relevance of their tick value, before and after its modification.

High frequency trading and end-of-day price dislocation

Abstract We show that the presence of high frequency trading (HFT) has significantly mitigated the frequency and severity of end-of-day price dislocation. The effect of HFT is more pronounced on days when end of day price dislocation is more likely to be the result of market manipulation. Moreover, the effect of HFT is more pronounced than the role of trading rules, surveillance, enforcement and legal conditions in curtailing the frequency and severity of end-of-day price dislocation. We show our findings are robust to different proxies of the start of HFT by trade size, cancellation of orders, and co-location.

Effects of High-Frequency Trading in the Multi-Dealer Spot Foreign Exchange

Contributions of various trading styles into liquidity of the multi-dealer FX spot market are described. Market impact of the intervention of the Bank of Japan in 2011 is discussed. Particular attention is given to the effects of high-frequency trading (HFT). It is argued that the HFT orders occupy top of the limit order book most of the time and, as a result, HFT determines the market volatility. Possible solutions to maintaining market liquidity in the presence of HFT are addressed.

High Speed Equities Trading: 1993–2012

AbstractHigh speed trading has drawn the attention of regulators who fear that such trading harms markets and leads to excessive speculation. The Flash Crash of May 6, 2010 is taken as evidence of the potential harmful effects of high frequency trading. On the other hand, some view high frequency trading as a manifestation of technological advances that have reduced the optimal trade size and improved order routing. From that perspective, high speed trading is a continuation of a long‐standing trend to more rapid and more efficient trading. This study shows that the speed of trading has changed dramatically. The average number of trades per day for large cap New York Stock Exchange stocks has risen from about 500 to more than 40 000 in the period 1993–2011. At the same time, the average trade size has fallen from 1600 shares to 200 shares. The ultimate sources of these changes are the technology that has automated almost all aspects of trading and the regulatory developments that have helped reduce bid‐ask spreads and made markets more accessible. The result of these developments is that markets are considerably more liquid and less costly. High frequency traders draw on this liquidity and also contribute to it.

High Frequency Trading and Long-Term Investors: A View from the Buy-Side

With the proliferation of high-frequency trading (HFT), understanding the effects of HFT on market quality and the opportunities that HFT creates for long-term (LT) investors is important in building an efficient regulatory framework. This paper demonstrates an approach that allows us to estimate the effects of HFT on market quality using information on daily aggregate volumes of HFT and of LT investors provided by a bulge-bracket broker in two trading environments that are different in terms of electronic liquidity: the evolving Tokyo equity market and the mature London equity market. Our results suggest that in both markets HFT volumes are highly concentrated in mega-cap names with the highest floating shares. High-frequency trading is mostly involved in opportunistic liquidity provisioning rather than engaging in predatory strategies. However, HFT does not aim to maximize liquidity provision to LT. It is mostly active within a “comfort zone” of moderate volatility regimes, while largely avoiding news-driven open sessions and winding down open positions prior to the closing bell. We confirm that HFT activity can be linked to an increase in short-term volatility; this adverse effect on the transaction costs is offset by significant bid–ask spread compression. For our universe of trades the net result is cost savings. Other investment strategies may fare differently in the presence of HFT; the framework we present here can be used to analyze the net HFT impact.

High Frequency Trading and End-of-Day Manipulation

We show that the presence of high frequency trading (HFT) has significantly mitigated the frequency and severity of end-of-day price dislocation, counter to recent concerns expressed in the media. The effect of HFT is more pronounced on days when end of day price dislocation is more likely to be the result of market manipulation on days of option expiry dates and end of month. Moreover, the effect of HFT is more pronounced than the role of trading rules, surveillance, enforcement and legal conditions in curtailing the frequency and severity of end-of-day price dislocation. We show our findings are robust to different proxies of the start of HFT by trade size, cancellation of orders, and co-location.