High-Frequency-Trading Technologies and Their Implications for Electronic Securities Trading

High-Frequency-Trading (HFT) has become quite prominent in public and academia after the May 6th, 2010 “Flash Crash” and in the context of the recent financial crisis. However, the public discussion is mostly based on generalizations instead of a well founded researchbased point of view, and the terminology of electronic trading is often used indiscriminately. Literature defines HFT as a subset of Algorithmic Trading. Therefore, and to foster the understanding of these terms, we first describe Algorithmic Trading. Based on this definition we will then specify HFT. Algorithmic Trading in the broadest sense is the generation and submission of buy and sell orders by an algorithm (Prix et al. 2007, p. 1). An algorithm in this context is defined as a set of instructions which processes market data in real-time and submits orders to a single or multiple market places without human intervention. Narrow definitions require the algorithm to have a direct market access, automated order management, and usage by professional market participants. While the non-HFT subset of Algorithmic Trading focuses on longterm increases or decreases of big trading positions in agent trading to prevent market impact (Gomber and Gsell 2006, p. 541), High-Frequency-Traders (HFTs) act as proprietary traders, i.e., trading for their own account utilizing corresponding trading strategies. HFT is a trading technique that is characterized by short holding periods of trading positions, high trading volume, frequent order updates, and proprietary trading. HFTs take advantage of a large amount of buy and sell orders, which are executed, modified, or deleted within a short time period. These modifications and deletions are necessary because of the fast information processing on the market, which requires to place orders close to the current market price or to delete obsolete orders from the market. Exploiting profitable market situations, e.g., arbitrage possibilities (the profitable usage of price differences across different markets), is only possible for HFTs who are able to detect these market situations based on real-time market data and who are the first to submit the corresponding buy or sell orders. Hence, HFTs require a fast reaction time of the algorithm to changing market conditions, which is achieved on the basis of an extremely low technical time-delay (latency). In this competitive environment, market participants who are physically located away from the market have a significantly higher latency and therefore a competitive disadvantage. Therefore, they strive to place their trading algorithms physically as close as possible to the trading system of the exchanges (co-location). To prevent overnight risks, accumulated positions are closed at the end of the trading day. HFTs mostly trade in liquid securities as they generate money from multiple but small transactions (with a small profit each) and as they need to close positions fast and at lowest costs. HFT is primarily conducted by specialized, technologically leading trading firms and investment banks in proprietary trading. The market share of HFT varies depending on the maturity of the respective market. Due to the lack of a uniform delineation of HFT and differing methods of quantification, the reported market shares deviate significantly. About 40 % to 70 % of total US equity trading is already HFT-based. Operators of European exchanges quantify the market share of HFT between 13 % and 40 %, HFTs quantify their market share between 30 % and >40 % (AFM 2011, p. 13).