Bachelorarbeit, 2013
41 Seiten, Note: 1,00
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
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.
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).
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.
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.
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.
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.
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.
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.
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."
Key terms include High Frequency Trading, Market Volatility, Price Discovery, Neoclassical Economics, Liquidity Provision, and Financial Regulation.
The author discusses evidence suggesting that while HFT did not directly trigger the 2010 Flash Crash, its activity significantly intensified the resulting market volatility.
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.
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.
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