Optimal Algorithmic Trading Strategy with the Price Appreciation Cost

2014 ◽  
Vol 631-632 ◽  
pp. 62-65
Author(s):  
Ru Zhen Yan ◽  
Ping Li ◽  
Yong Zeng
Keyword(s):  
Financial Markets ◽  
Transaction Cost ◽  
High Frequency ◽  
Opportunity Cost ◽  
Trading Strategy ◽  
Price Impact ◽  
Algorithmic Trading ◽  
Optimal Trading ◽  
Price Appreciation ◽  
The Cost

Financial markets has witnessed an explosion of algorithmic trading strategy which can help traders especially involved in high-frequency trading efficiently reduce invisible transaction cost. The VWAP strategy usually used by traders can only decrease the cost of price impact by breaking block order into small pieces. However, the behavior of such order splitting may result in inevitable opportunity cost as well as price appreciation. This paper establishes a new algorithmic trading strategy to minimize total transaction costs including price impact, opportunity cost and price appreciation. The results show that the total transaction cost of this optimal trading strategy is lower than VWAP strategy.

The Regulatory, Technology and Market ‘Dark Arts Trilogy’ of High Frequency Trading: A Research Agenda

2017 ◽  
Vol 32 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Wendy L. Currie ◽  
Jonathan J. M. Seddon
Keyword(s):  
Information Systems ◽  
Financial Markets ◽  
Empirical Data ◽  
High Frequency ◽  
Research Agenda ◽  
High Frequency Trading ◽  
Algorithmic Trading ◽  
Flash Crash ◽  
Financial Trading ◽  
Dow Jones Industrial Average

Computerization has transformed financial markets with high frequency trading displacing human activity with proprietary algorithms to lower latency, reduce intermediary costs, enhance liquidity and increase transaction speed. Following the “Flash Crash” of 2010 which saw the Dow Jones Industrial Average plunge 1000 points within minutes, high frequency trading has come under the radar of multi-jurisdictional regulators. Combining a review of the extant literature on high frequency trading with empirical data from interviews with financial traders, computer experts and regulators, we develop concepts of regulatory adaptation, technology asymmetry and market ambiguity to illustrate the ‘dark art’ of high frequency trading. Findings show high frequency trading is a multi-faceted, complex and secretive practice. It is implicated in market events, but correlation does not imply causation, as isolating causal mechanisms from interconnected automated financial trading is highly challenging for regulators who seek to monitor algorithmic trading across multiple jurisdictions. This article provides information systems researchers with a set of conceptual tools for analysing high frequency trading.

scholarly journals High‑Frequency Trading in the Modern Market Microstructure: Opportunities and Threats

2019 ◽  
Vol 7 (3) ◽  
pp. 25-36
Author(s):  
M. Zharikov
Keyword(s):  
Financial Markets ◽  
Financial Market ◽  
Market Microstructure ◽  
High Frequency ◽  
Financial Engineering ◽  
Market Design ◽  
High Frequency Trading ◽  
Optimal Trading ◽  
Trading Market ◽  
Long Run

The article covers some ideas about the research on high-frequency trading and financial market design. The topic is time-relevant because today there exists a need to convince traders that there is a simple structural floor in the way that the financial markets are designed. The article reveals the significance of trading on the floor that the foremost fundamental constraint is limited time. The author proves that time on the financial market feels, to some extent, infinite when someone counts it in millions of seconds, but time is nevertheless finite. The author then gets into the actual research on high-frequency trading in the financial market design. The motivation for this project is to analyse activity among high-frequency trading firms by which investments of substantial sums of money are understood as economically trivial speed improvements. The theoretical significance of the research’s outcomes lies in outlaying the systemic approach to dealing with stochastic control problems in the context of financial engineering. The practical relevance of the paper lies in the mechanism that allows solving problems surrounding optimal trading, market microstructure, high-frequency trading, etc. The article concludes by talking about the issues in the modern electronic markets and by giving lessons to dealing with them in the long run.

FinTech and Stock Market Behaviors

2019 ◽  
pp. 170-205
Author(s):  
Taner Sekmen ◽  
Mercan Hatipoglu
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
High Frequency ◽  
Stock Return ◽  
Garch Models ◽  
Algorithmic Trading ◽  
Risk Return ◽  
The Past ◽  
Volatility Asymmetry ◽  
Technological Developments

This chapter examines the effects of high-frequency trading (HFT) and algorithmic trading (AT) activities, which represent important technological developments in financial markets in the past two decades, on Borsa Istanbul in terms of volatility. To clarify stock market behaviors in terms of volatility, asymmetry, and risk return after the BISTECH transition, the GJR-GARCH-in-Mean and I-GARCH models were used. The dataset consists of the daily stock return series of the main and sub-sector indexes of Borsa Istanbul, covering the period from October 24, 2012 to June 1, 2018. Although there are mixed results for the sub-indexes, it is observed that in the post-BISTECH period, volatility increases significantly in the BIST 100 and BIST 30 indexes, where AT and HFT activities are used more frequently. In particular, the duration of volatility returns to average after shock increases about seven times for BIST 100 and about eight times for the BIST 30 in the post-BISTECH period. Overall, the results indicate that AC and HFT activities may have disruptive effects on financial markets.

scholarly journals Prudential supervisory disclosure (PSD) with supervisory technology (SupTech): lessons from a FinTech crisis

2021 ◽  
Author(s):  
Stefan Zeranski ◽  
Ibrahim E. Sancak
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
Market Value ◽  
Financial Supervision ◽  
Algorithmic Trading ◽  
Rapid Decline ◽  
Flash Crash ◽  
Market Participants ◽  
History Of ◽  
Financial Supervisors

AbstractThe U.S. financial markets faced an unprecedented rapid decline and recovery on May 6, 2010, known as the May 6 flash crash. Roughly one trillion $ market value in less than thirty minutes vanished with the biggest one-day point decline in the history of the DJIA at the time. Since the market events took place in electronic markets, and algorithmic trading and high-frequency trading, parts of FinTech, played significant roles, we handle the May 6 flash crash from the FinTech, SupTech, and financial supervision perspectives. With the flashback method, we analyzed the reactions of market participants, media, and two financial supervisors, the SEC, and the CFTC, to the market crash. We find that the technological imbalance between financial markets or institutions and their supervisors drove the markets in uncertainty, hence in a fear and panic environment. Since the imbalance has not diminished yet, the same risks still exist. As a remedy, we introduce a new concept and model with a well-functioning SupTech system to cope with the May 6 type FinTech crises.

scholarly journals Quantifying the High-Frequency Trading “Arms Race”

2021 ◽  
Author(s):  
Matteo Aquilina ◽  
Eric Budish ◽  
Peter O’Neill
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Stock Exchange ◽  
Limit Order Book ◽  
Price Impact ◽  
High Frequency Trading ◽  
Order Book ◽  
Limit Order ◽  
The Cost ◽  
Latency Arbitrage

Abstract We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The key difference between message data and widely familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency arbitrage races are very frequent (about one per minute per symbol for FTSE 100 stocks), extremely fast (the modal race lasts 5–10 millionths of a second), and account for a remarkably large portion of overall trading volume (about 20%). Race participation is concentrated, with the top six firms accounting for over 80% of all race wins and losses. The average race is worth just a small amount (about half a price tick), but because of the large volumes the stakes add up. Our main estimates suggest that races constitute roughly one-third of price impact and the effective spread (key microstructure measures of the cost of liquidity), that latency arbitrage imposes a roughly 0.5 basis point tax on trading, that market designs that eliminate latency arbitrage would reduce the market’s cost of liquidity by 17%, and that the total sums at stake are on the order of $5 billion per year in global equity markets alone.

scholarly journals Where is the Value in High Frequency Trading?

2012 ◽  
Vol 02 (03) ◽  
pp. 1250014 ◽  
Author(s):  
Álvaro Cartea ◽  
José Penalva
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
Market Price ◽  
Price Impact ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Noise Trading ◽  
Surplus Extraction ◽  
The Impact ◽  
Professional Traders

We analyze the impact of high frequency (HF) trading in financial markets based on a model with three types of traders: liquidity traders (LTs), professional traders (PTs), and high frequency traders (HFTs). Our four main findings are: (i) The price impact of liquidity trades is higher in the presence of the HFTs and is increasing with the size of the trade. In particular, we show that HFTs reduce (increase) the prices that LTs receive when selling (buying) their equity holdings. (ii) Although PTs lose revenue in every trade intermediated by HFTs, they are compensated with a higher liquidity discount in the market price. (iii) HF trading increases the microstructure noise of prices. (iv) The volume of trades increases as the HFTs intermediate trades between the LTs and PTs. This additional volume is a consequence of trades which are carefully tailored for surplus extraction and are neither driven by fundamentals nor is it noise trading. In equilibrium, HF trading and PTs coexist as competition drives down the profits for new HFTs while the presence of HFTs does not drive out traditional PTs.

The profitability of algorithmic trading systems based on football sentiment

2020 ◽  
Vol 42 (1) ◽  
pp. 33-46
Author(s):  
Raúl Gómez-Martínez ◽  
Camila Marqués-Bogliani ◽  
Jessica Paule-Vianez
Keyword(s):  
Financial Markets ◽  
Investment Decisions ◽  
Futures Market ◽  
Algorithmic Trading ◽  
Investment Strategies ◽  
Measurable Effect ◽  
Behavioural Finance ◽  
Trading Systems ◽  
European Football

Behavioural finance has shown that investment decisions are the result of not just rational but also emotional brain processes. On the assumption that emotions affect financial markets, it would seem likely that football results might have a measurable effect on financial markets. To test this, this study describes three algorithmic trading systems based exclusively on the results of three top European football teams (Juventus, Bayern München and Paris St Germain) opening long or short positions in the next market season of the futures market of the index of each country (MIB (Milano Italia Borsa), DAX (Deutscher Aktien Index) and CAC (Cotation Assistée en Continu). Depending on the outcome of the last game played a long position was taken after a victory and a short position after a draw or defeat. The results showed that the algorithmic systems were profitable in the case of Juventus and Bayern whereas in the case of PSG, the system was profitable, but in an inverse way. This study shows that investment strategies that take account of sports sentiment could have a profitable outcome.

Design and Construction of a Small Reverberation Chamber Applied to Absorption and Scattering Acoustic Measurements

2014 ◽  
Vol 1077 ◽  
pp. 197-202
Author(s):  
D. Hernandez ◽  
E.J. Liu ◽  
J.H. Huang ◽  
Y.C. Liu
Keyword(s):  
High Frequency ◽  
Integration Method ◽  
Sound Field ◽  
Acoustic Measurements ◽  
Acoustic Characteristics ◽  
Reverberation Chamber ◽  
Scattering Coefficients ◽  
Reverberation Chambers ◽  
The Cost ◽  
Design And Construction

Reverberation chambers are used to create a diffuse incidence sound field, where multiple types of acoustic measurements can be performed. The chambers tend to have a large volume in order to extent the reverberation time. However, this requirement may be conditioned by the cost and the infrastructure limitations. This paper presents the design and construction of a small-scaled reverberation chamber of 3 m3 for middle-high frequency acoustic measurements. On the design, the acoustic characteristics of chamber are confirmed via finite element computer simulation. As case studies, absorption and scattering coefficients of several materials and diffusors are measured. The reverberation times needed for the measurements were obtained by the impulse response integration method. The small reverberation chamber demonstrated to be a reliable tool for middle and high frequency acoustic measurements.

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