scholarly journals Does Speed Matter? The Role of High-Frequency Trading for Order Book Resiliency

2015 ◽  
Author(s):  
Benjamin Clapham ◽  
Martin Haferkorn ◽  
Kai Zimmermann
Keyword(s):  
High Frequency ◽  
High Frequency Trading ◽  
Order Book

scholarly journals DOES SPEED MATTER? THE ROLE OF HIGH‐FREQUENCY TRADING FOR ORDER BOOK RESILIENCY

2020 ◽  
Vol 43 (4) ◽  
pp. 933-964
Author(s):  
Benjamin Clapham ◽  
Martin Haferkorn ◽  
Kai Zimmermann
Keyword(s):  
High Frequency ◽  
High Frequency Trading ◽  
Order Book

scholarly journals Large Tick Assets: Implicit Spread and Optimal Tick Size

2015 ◽  
Vol 01 (01) ◽  
pp. 1550003 ◽  
Author(s):  
Khalil Dayri ◽  
Mathieu Rosenbaum
Keyword(s):  
High Frequency ◽  
High Frequency Trading ◽  
Microstructural Properties ◽  
Relevant Market ◽  
Tick Size ◽  
Effective Spread

In this work, we provide a framework linking microstructural properties of an asset to the tick value of the exchange. In particular, we bring to light a quantity, referred to as implicit spread, playing the role of spread for large tick assets, for which the effective spread is almost always equal to one tick. The relevance of this new parameter is shown both empirically and theoretically. This implicit spread allows us to quantify the tick sizes of large tick assets and to define a notion of optimal tick size. Moreover, our results open the possibility of forecasting the behavior of relevant market quantities after a change in the tick value and to give a way to modify it in order to reach an optimal tick size. Thus, we provide a crucial tool for regulators and trading platforms in the context of high frequency trading.

A DYSFUNCTIONAL ROLE OF HIGH FREQUENCY TRADING IN ELECTRONIC MARKETS

2012 ◽  
Vol 15 (03) ◽  
pp. 1250022 ◽  
Author(s):  
ROBERT A. JARROW ◽  
PHILIP PROTTER
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
Electronic Markets ◽  
High Frequency Trading ◽  
Common Signal

This paper shows that high frequency trading may play a dysfunctional role in financial markets. Contrary to arbitrageurs who make financial markets more efficient by taking advantage of and thereby eliminating mispricings, high frequency traders can create a mispricing that they unknowingly exploit to the disadvantage of ordinary investors. This mispricing is generated by the collective and independent actions of high frequency traders, coordinated via the observation of a common signal.

scholarly journals A material political economy: Automated Trading Desk and price prediction in high-frequency trading

2016 ◽  
Vol 47 (2) ◽  
pp. 172-194 ◽  
Author(s):  
Donald MacKenzie
Keyword(s):  
Political Economy ◽  
Financial Markets ◽  
High Frequency ◽  
Share Price ◽  
High Frequency Trading ◽  
Historical Study ◽  
Price Changes ◽  
Price Prediction ◽  
Political Economic

This article contains the first detailed historical study of one of the new high-frequency trading (HFT) firms that have transformed many of the world’s financial markets. The study, of Automated Trading Desk (ATD), one of the earliest and most important such firms, focuses on how ATD’s algorithms predicted share price changes. The article argues that political-economic struggles are integral to the existence of some of the ‘pockets’ of predictable structure in the otherwise random movements of prices, to the availability of the data that allow algorithms to identify these pockets, and to the capacity of algorithms to use these predictions to trade profitably. The article also examines the role of HFT algorithms such as ATD’s in the epochal, fiercely contested shift in US share trading from ‘fixed-role’ markets towards ‘all-to-all’ markets.

scholarly journals The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response *

2015 ◽  
Vol 130 (4) ◽  
pp. 1547-1621 ◽  
Author(s):  
Eric Budish ◽  
Peter Cramton ◽  
John Shim
Keyword(s):  
High Frequency ◽  
Market Design ◽  
Limit Order Book ◽  
Theory Model ◽  
Arms Race ◽  
High Frequency Trading ◽  
Continuous Limit ◽  
Order Book ◽  
Limit Order ◽  
Arbitrage Opportunities

Abstract The high-frequency trading arms race is a symptom of flawed market design. Instead of the continuous limit order book market design that is currently predominant, we argue that financial exchanges should use frequent batch auctions: uniform price double auctions conducted, for example, every tenth of a second. That is, time should be treated as discrete instead of continuous, and orders should be processed in a batch auction instead of serially. Our argument has three parts. First, we use millisecond-level direct-feed data from exchanges to document a series of stylized facts about how the continuous market works at high-frequency time horizons: (i) correlations completely break down; which (ii) leads to obvious mechanical arbitrage opportunities; and (iii) competition has not affected the size or frequency of the arbitrage opportunities, it has only raised the bar for how fast one has to be to capture them. Second, we introduce a simple theory model which is motivated by and helps explain the empirical facts. The key insight is that obvious mechanical arbitrage opportunities, like those observed in the data, are built into the market design—continuous-time serial-processing implies that even symmetrically observed public information creates arbitrage rents. These rents harm liquidity provision and induce a never-ending socially wasteful arms race for speed. Last, we show that frequent batch auctions directly address the flaws of the continuous limit order book. Discrete time reduces the value of tiny speed advantages, and the auction transforms competition on speed into competition on price. Consequently, frequent batch auctions eliminate the mechanical arbitrage rents, enhance liquidity for investors, and stop the high-frequency trading arms race.

scholarly journals High-frequency trading in a limit order book

2008 ◽  
Vol 8 (3) ◽  
pp. 217-224 ◽  
Author(s):  
Marco Avellaneda ◽  
Sasha Stoikov
Keyword(s):  
High Frequency ◽  
Limit Order Book ◽  
High Frequency Trading ◽  
Order Book ◽  
Limit Order

Autonomous self-evolving forecasting models for price movement in high frequency trading: Evidence from Taiwan

2020 ◽  
Vol 24 (5) ◽  
pp. 1175-1206
Author(s):  
Chien-Feng Huang ◽  
Hsiao-Chi Wu ◽  
Po-Chun Chen ◽  
Bao Rong Chang
Keyword(s):  
High Frequency ◽  
Prediction Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Supply And Demand ◽  
High Frequency Trading ◽  
Series Data ◽  
Order Book ◽  
Price Movement ◽  
Increasing Demand

Among FinTech research and applications, forecasting financial time series data has been a challenging task because this kind of data is typically quite noisy and non-stationary. A recent line of financial research centers around trading through financial data on the microscopic level, which is the holy grail of high-frequency trading (HFT), as the higher the data frequency, the more profitable opportunities may appear. The advancement in HFT modeling has also facilitated more understanding towards price formation because the supply and demand of a stock can be comprehended more easily from the microstructure of the order book. Instead of traditional statistical methods, there has been increasing demand for the development of more reliable prediction models due to the recent progress in Computational Intelligence (CI) technologies. In this study, we aim to develop novel CI-based methodologies for the forecasting task of price movement in HFT. Our goal is to conduct a study for autonomous genetic-based models that allow the forecasting systems to self-evolve. The results show that our proposed method can improve upon the previous ones and advance the current state of Fintech research.

Sign in / Sign up

Export Citation Format

Share Document