In the last four decades, technological progress led to an electrification of stock trading systems. Traders were enabled to place their orders, which were later processed via electronic networks, with the help of computers. Soon they realized that the profitability of trading strategies could be increased by employing computer algorithms to trade autonomously, reducing time needed to analyze information, publish quotes as well as trigger and process trades. This led to the implementation of Algorithmic Trading (AT). High Frequency Trading (HFT) is a subset of AT, at which financial instruments are traded by algorithms at very high speed.
The past has shown that negative developments on capital markets are intensified by HFT. Andrei Kirilenko explains in his work “The Flash Crash: The Impact of High Frequency Trading on an Electronic Market” that HFT did not trigger the Flash Crash but intensified the volatility that resulted from the event. Also on the 19th of October 1987, “Black Monday”, the increasing computerization of stock trading processes led to a significant price drop. As a consequence, the high and still growing market share of HFT leads to an increase in risk that a simple correction turns into a serious drop in prices causing market instability. Theoretically HFT should increase efficiency in financial markets. However, due to the empirical observation mentioned above, it seems that HFT takes effect the other way round. It seems that, at least under certain circumstances, HFT enlarges volatility. This cannot be explained by the economic neoclassical theory. This problem is discussed in a lot of literature in which several different approaches have been made to explain it.
The aim of this paper is to discuss why HFT cannot be fully explained by the neoclassical theory of economics. Therefore, the controversial positions in literature will be presented and discussed. Primarily, its negative influence on volatility seems to contravene the modern finance. Furthermore, in the course of this work it will be illustrated that, by employing strict regulation of financial markets, this negative impact cannot be reduced to a sufficient extent in order for HFT to be characterized as market optimizing, according to the neoclassical theory of economics.
Table of Contents
1 Introduction
1.1 Problem
1.2 Aim
1.3 Research Questions
1.4 Scientific Method
1.5 Structure of the paper
2 Fundamentals of HFT
2.1 History of HFT and its presence in current markets
2.1.1 Algorithmic Trading (AT)
2.1.2 Definition and characteristics of HFT
2.1.3 HFT’s fraction of market activity in the US and Europe
2.2. Technology used for HFT
2.2.1 Software for HFT
2.2.2 Hardware for HFT
2.3 Users of HFT
2.4 Strategies of HFTs
2.4.1 Liquidity Provision (Market Making)
2.4.2 Liquidity Detection
2.4.3 Arbitrage
2.4.3.1 Market Neutral Arbitrage (“Pairs Trading”)
2.4.3.2 Cross Asset-, Cross Market- & ETF-Arbitrage
3 The effect of HFT on capital markets and the Economic Neoclassical Theory
3.1 The Economic Neoclassical Theory and its relation to HFT
3.2 The effect of HFT on liquidity
3.3 The effect of HFT on price discovery
3.4 The effect of HFT on volatility
4 The regulation and supervision of HFT
4.1 Manipulation and the importance of regulation and supervision of HFT
4.2 The Flash Crash
4.3 General regulatory measures
4.3.1 Unfiltered/Naked Sponsored Access
4.3.2 Flash Orders
4.4 The regulatory response to the Flash Crash
4.4.1 Circuit breakers
4.4.2 Stub quotes and erroneous trades
4.5 Other possible regulatory measures and their effect on volatility
4.5.1 Tobin tax
4.5.2 Short sale constraints
5 Conclusion
Objective and Thematic Focus
The primary objective of this thesis is to critically analyze why High Frequency Trading (HFT) cannot be fully explained by the neoclassical theory of economics. By examining the impact of HFT on market quality metrics—specifically liquidity, price discovery, and volatility—the paper investigates whether strict regulatory interventions are capable of aligning HFT activities with the market-optimizing assumptions inherent in neoclassical theory.
- Evolution and technical foundations of HFT infrastructure.
- Mechanisms and strategies of liquidity provision, detection, and arbitrage.
- Assessment of HFT's impact on liquidity depth and spread narrowing.
- Analysis of HFT's role in price discovery and long-term price efficiency.
- Evaluation of regulatory measures, including circuit breakers and tax policies, in mitigating market instability.
Excerpt from the Book
2.4.2 Liquidity Detection
HFTs employing liquidity detection strategies use ultra-high speed technology in order to analyze the market activity of other traders for the purpose of profiting from information they have (see Gomber et al. 2011, p. 28). HFTs “sniff out” the market in order to find out if a large order is placed. That would be an indicator that a market participant has valuable information based on which he wants to trade. This can be detected by simply posting an immediate-or-cancel order, priced at the bid-ask midpoint, in the market. If the order is executed, this might indicate that a large order is posted (see Reynolds 2011, p. 24). The ability of trading much faster than other market participants based on this data, gives HFTs an enormous advantage (see Gomber et al. 2011, p. 28). For this reason, such strategies can be profitable for HFTs, but they often are a matter of concern for institutional investors and other non-HFTs. Jones (2013) describes this problem with the help of an example: If an institutional investor is buying shares, HFTs might be able to deduce that from information detected on the market. As a consequence, they can drive the price of these shares up and sell them at a higher price, probably even to the institutional investor who initially posted the buy-order (see Jones 2013, p. 9). This means that the profit of HFTs employing such strategies can be a loss for other investors. According to Brogaard (2010), HFTs as a whole do not engage in such “anticipatory trading”, but he also states that, due to the complexity and large variety of HFT-strategies as well as their influence on his approach to detect anticipatory trading, it cannot be concluded that there is no anticipatory trading (Brogaard 2010, p. 22).
Summary of Chapters
1 Introduction: This chapter defines the research problem, noting the technological shift to automated trading and its potential to increase volatility, and outlines the thesis's goal of evaluating HFT through the lens of neoclassical economic theory.
2 Fundamentals of HFT: This section details the historical progression of electronic trading, the technical requirements for HFT infrastructure, and specific operational strategies such as market making and arbitrage.
3 The effect of HFT on capital markets and the Economic Neoclassical Theory: This chapter analyzes how HFT affects liquidity, price discovery, and volatility, challenging the assumption that HFT aligns with neoclassical market efficiency.
4 The regulation and supervision of HFT: This section investigates whether regulatory interventions, such as circuit breakers and transaction taxes, can mitigate negative impacts of HFT and restore market stability.
5 Conclusion: The concluding chapter synthesizes the findings, arguing that due to its impact on market volatility and departure from rational agent assumptions, HFT cannot be fully explained by neoclassical theory.
Keywords
High Frequency Trading, HFT, Algorithmic Trading, Market Quality, Liquidity, Price Discovery, Market Volatility, Flash Crash, Neoclassical Economics, Market Regulation, Arbitrage, Financial Markets, Circuit Breakers, Market Manipulation, Trading Technology.
Frequently Asked Questions
What is the core subject of this thesis?
The work examines High Frequency Trading (HFT) and assesses whether it fits within the traditional neoclassical theory of economics, particularly regarding its impact on market stability and efficiency.
What are the central thematic fields covered?
The thesis covers the evolution of trading technology, the specific strategies employed by high-frequency traders, the empirical effects of HFT on capital markets, and the effectiveness of current and proposed regulatory frameworks.
What is the primary research question?
The research asks if the negative impacts of HFT on market quality can be sufficiently reduced by regulation, and if HFT can be ultimately justified or explained by neoclassical economic theory.
Which scientific methods are utilized?
The paper employs a comprehensive literature review, synthesizing findings from academic working papers, professional articles, and financial studies to provide a realistic assessment of HFT.
What does the main body discuss?
It covers the technical infrastructure behind HFT, specific trading strategies like liquidity detection and arbitrage, the impact on liquidity and price discovery, and the regulatory responses triggered by events like the "Flash Crash."
How would you characterize this work with keywords?
Key terms include High Frequency Trading, Market Volatility, Price Discovery, Neoclassical Economics, Liquidity Provision, and Financial Regulation.
How does the author define HFT in the context of the Flash Crash?
The author discusses evidence suggesting that while HFT did not directly trigger the 2010 Flash Crash, its activity significantly intensified the resulting market volatility.
What conclusion does the author reach regarding the neoclassical theory?
The author concludes that HFT cannot be fully explained by neoclassical economics because it contradicts the fundamental assumptions of individual rational behavior and optimal price reflection.
What is the significance of the "Limit Up-Limit Down" mechanism?
The author notes that this mechanism is perceived as having a more positive impact on market quality than previous systems, as it prevents the execution of trades at irrational prices before they occur.
- Arbeit zitieren
- Stefan Höppel (Autor:in), 2013, High Frequency Trading. Economic Necessity or Threat to the Economy?, München, GRIN Verlag, https://www.hausarbeiten.de/document/267885
