WHERE IS THE VALUE IN HIGH FREQUENCY TRADING? Álvaro Cartea and José Penalva. Documentos de Trabajo N.º PDF


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WHERE IS THE VALUE IN HIGH FREQUENCY TRADING? 20 Álvaro Cartea and José Penalva Documentos de Trabajo N.º WHERE IS THE VALUE IN HIGH FREQUENCY TRADING? WHERE IS THE VALUE IN HIGH FREQUENCY TRADING? (*) Álvaro Cartea UNIVERSIDAD CARLOS III DE MADRID José Penalva BANCO DE ESPAÑA (*) We would like to thank Harrison Hong for his valuable comments and discussions. We are also grateful to Andrés Almazán, Gene Amromin, Michael Brennan, Pete Kyle and Eduardo Schwartz for their comments. We also thank seminar participants at CEMFI and Universidad Carlos III. The usual caveat applies. We welcome comments, including references we have inadvertently missed. The views expressed in this paper are those of the authors and do not necessarily reflect those of the Banco de España. Documentos de Trabajo. N.º 20 The Working Paper Series seeks to disseminate original research in economics and fi nance. All papers have been anonymously refereed. By publishing these papers, the Banco de España aims to contribute to economic analysis and, in particular, to knowledge of the Spanish economy and its international environment. The opinions and analyses in the Working Paper Series are the responsibility of the authors and, therefore, do not necessarily coincide with those of the Banco de España or the Eurosystem. The Banco de España disseminates its main reports and most of its publications via the INTERNET at the following website: Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged. BANCO DE ESPAÑA, Madrid, 20 ISSN: (print) ISSN: (on line) Depósito legal: M Unidad de Publicaciones, Banco de España Abstract We analyze the impact of high frequency trading in fi nancial markets based on a model with three types of traders: liquidity traders, market makers, and high frequency traders. Our four main fi ndings are: i) The price impact of the liquidity trades is higher in the presence of the high frequency trader and is increasing with the size of the trade. In particular, we show that the high frequency trader reduces (increases) the prices that liquidity traders receive when selling (buying) their equity holdings. ii) Although market makers also lose revenue to the high frequency trader in every trade, they are compensated for these losses by a higher liquidity discount. iii) High frequency trading increases the volatility of prices. iv) The volume of trades doubles as the high frequency trader intermediates all trades between the liquidity traders and market makers. 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, high frequency trading and traditional market making coexist as competition drives down the profi ts for new high frequency traders while the presence of high frequency traders does not drive out traditional market makers. Keywords: High frequency traders, high frequency trading, fl ash trading, liquidity traders, institutional investors, market microstructure. JEL classification: G2, G3, G4, G28. Resumen Analizamos el impacto de las operaciones que se realizan en mercados fi nancieros a gran velocidad utilizando un modelo con tres tipos de operadores: consumidores de liquidez, creadores de mercado y operadores de alta frecuencia. Nuestros cuatro resultados principales son: i) el impacto de las operaciones de liquidez sobre los precios es mayor cuando hay operadores de alta velocidad y este efecto adicional aumenta con el tamaño de la operacion. En concreto, demostramos que la presencia de operadores de alta frecuencia reducen (aumentan) los precios que los consumidores de liquidez reciben cuando venden (adquieren) sus participaciones en Bolsa. ii) Aunque los creadores de mercado tambien pierden ingresos en sus operaciones con operadores de alta frecuencia, estas perdidas se ven compensadas por un mayor descuento de liquidez. iii) Las operaciones a gran velocidad aumentan la volatilidad en los precios. iv) El volumen de negocio se dobla ya que el operador de alta frecuencia intermedia en todas las transacciones entre consumidores de liquidez y creadores de mercado. Este volumen de negocio adicional proviene de transacciones especialmente diseñadas para extraer parte de las ganancias de intercambio entre consumidores de liquidez y creadores de mercado, y no proviene ni de operaciones fundamentales ni de operaciones tontas (noise trading). En equilibrio, operadores de alta frecuencia coexisten con creadores de mercado tradicionales: Mientras que la competencia entre operadores de alta frecuencia reduce los benecios de los nuevos operadores, la existencia de operadores de alta frecuencia no expulsa a los creadores de mercado de la Bolsa. Palabras claves: Operadores de alta frecuencia, trading con alta frecuencia (a gran velocidad), fl ash trading, consumidores de liquidez, inversores institucionales, microestructura de mercados. Códigos JEL: G2, G3, G4, G28. Introduction Around 970, Carver Mead coined the term Moore s law in reference to Moore s statement that transistor counts would double every year. There is some debate over whether this law is empirically valid but there is no discussion that the last forty years have seen an explosive growth in the power and performance of computers. Financial markets have not been immune to this technological advance, it may even be one of the places where the limits of computing power are tested every day. This computing power is harnessed to spot trends and exploit profit opportunities in and across financial markets. Its influence is so large that it has given rise to a new class of trading strategies sometimes called algorithmic trading and others high frequency trading. We prefer to use algorithmic trading (AT) as the generic term that refers to strategies that use computers to automate trading decisions, and restrict the term high frequency (HF) trading to refer to the subset of AT trading strategies that are characterized by their reliance on speed differences relative to other traders to make profits and also by the objective to hold essentially no asset inventories for more than a very short period of time. The advent of AT has changed the trading landscape and the impact of their activities is at the core of many regulatory and financial discussions. The explosion in volume of transactions we have witnessed in the last decade, and the speed at which trades are taking place, is highly suggestive that AT is very much in use and that these strategies are not being driven out of the market as a result of losses in their trading activities. Indeed, different sources estimate that annual profits from AT trading are between $3 and $2 billion (Brogaard [200] and Kearns et al. [200]). These strategies have supporters and detractors: on one side we find trading houses and hedge funds who vigorously defend their great social value, whilst being elusive about the profits they make from their use; and on the other hand there are trading This definition is consistent with the one used in Kyle [Kirilenko et al. [200] p3]: We find that on May 6, the 6 trading accounts that we classify as HFTs traded over,455,000 contracts, accounting for almost a third of total trading volume on that day. Yet, net holdings of HFTs fluctuated around zero so rapidly that they rarely held more than 3,000 contracts long or short on that day. BANCO DE ESPAÑA 9 DOCUMENTO DE TRABAJO N.º houses that denounce high frequency traders (HFTs) as a threat to the financial system (and their bottom line). Although AT in general and HF trading in particular have been in the market supervisors spotlight for quite some time and efforts to understand the consequences of HF trading have stepped up since the Flash Crash in May (SEC [200], Commission et al. [200], Kirilenko et al. [200], and Easley et al. [20]) there is little academic work that addresses the role of these trading strategies. The objective of this paper is to provide a framework with which to analyze the issues surrounding HF trading, their widespread use, and their value to different market participants. To analyze the impact of HF trading in financial markets we develop a model with three types of traders: liquidity traders (LTs), market makers (MMs), and HFTs. In this model LTs experience a liquidity shock and come to the market to unwind their positions which are temporarily held by the MMs in exchange for a liquidity discount. HFTs mediate between LTs and MMs. HFT mediation instantaneous, buying from one and selling to the other while holding no inventory over time. HFTs, because of their information processing and execution speed, make profits from this intermediation by extracting trading surplus. The same model without HFTs, which corresponds to that of Grossman and Miller [988], serves as benchmark to analyze the impact of HFTs. Naturally, we find that HFT s additional intermediation increases the volume of trade substantially (it doubles). The additional volume is neither driven by fundamentals (only the original trades, without the HFT, are driven by fundamentals) nor is it noise trading. Far from it, the extra volume is a consequence of trades which are carefully tailored for surplus extraction. Moreover, HF trading strategies introduce microstructure noise : in order to profit from intermediation HFTs buy shares from one trader at a cheap price and sell it more dearly to another trader, generating price dispersion where before there was only a single price. These properties, which are built into the model, closely correspond to observed behavior (e.g. Kirilenko et al. [200]). Our main findings are: (i) the presence of HFTs exacerbates the price impact of the initial liquidity trades that generate a temporary order imbalance, imposing a double burden on liquidity demanders: the direct cost from the trading surplus extracted by the HFT, and the BANCO DE ESPAÑA 0 DOCUMENTO DE TRABAJO N.º indirect cost of a greater price impact; (ii) furthermore, this effect is increasing in the size of the liquidity need, consistent with the results in Zhang [200]; (iii) the higher initial price impact arises as traders anticipate the future additional trading costs from the presence of HFTs, which generates an increase in the liquidity discount. Thus, MMs suffer countervailing effects from HFTs: increased trading costs from HFT surplus extraction versus increased expected returns from higher liquidity discounts. In our model these two effects cancel each other, leaving expected profits for MMs unchanged. (iv) Standard measures of market liquidity may lead to erroneous conclusions: in our model, HFTs do not increase liquidity and yet we observe increased trading volumes. In fact, liquidity traders face overall lower sales revenue and higher costs of purchase, suggesting that liquidity is better measured through total cost of trade execution. (v) Finally, we consider competition between HFTs and the decision for MMs to become HFTs. We find that profits from HF trading attract entrants who are willing to invest in acquiring the skills necessary to compete for these profits, but that competition is limited. As the number of HFTs increases, the expected profits of HF trading falls until the expected skills of an entrant (relative to those of existing HFTs) are insufficient to generate enough profits to cover the initial investments required to become an HFT. Thus, in equilibrium traditional MMs with low expected skills as HFTs will continue in their traditional role, coexisting with others acting as skilled and profitable HFTs. Our analysis focuses on the effect of HFT s surplus extraction on trades initiated by liquidity needs that generate temporary trading imbalances. Nevertheless, our analysis of the role and effect of HFTs also applies to a broader set of circumstances. In particular it applies to trading by mutual fund managers, hedge funds, insurance companies and other large investors, and trading motivated not only by immediate liquidity needs, but also trading to build up or unwind an asset position, for hedging, etc. Two contemporaneous empirical papers lend strong support to the stylized features that our theoretical model captures as well as the implications concerning the impact that HF trading has on financial markets. The recent work of Zhang [200] firmly concludes that HF trading increases stock price volatility and that this positive correlation between volatility and HF trading is stronger for stocks with high institutional holdings, a result consistent with the view BANCO DE ESPAÑA DOCUMENTO DE TRABAJO N.º that high-frequency traders often take advantage of large trades by institutional investors. Kirilenko et al. [200] study the impact of HF trading during the Flash Crash on May Their findings about the activities of HFTs also provide strong support for the theoretical description we use to include HFTs as pure surplus extractors in our theoretical model. They find that HFTs have among all types of traders the highest price impact and that HFTs are able to buy right as the prices are about to increase. HFTs then turn around and begin selling 0 to 20 seconds after a price increase. Moreover, they find that The Intermediaries sell when the immediate prices are rising, and buy if the prices 3-9 seconds before were rising. These regression results suggest that, possibly due to their slower speed or inability to anticipate possible changes in prices, Intermediaries buy when the prices are already falling and sell when the prices are already rising. These findings strongly support our assumption that HFTs (due to their speed advantage) can for the most part effectively anticipate and react to price changes as a key part in their strategies for surplus extraction. Before delving into our analysis of HF trading, in Section 2 we provide a brief overview of HF trading and HFTs, what HFTs could be doing, and what is it about trading speed that is so profitable for some and damaging for others. After this quick overview, in Sections 3 and 4 we develop our framework and analysis with a single HFT, and use the model to discuss the main issues raised by the presence of HFTs, respectively. In Section 5 we introduce competition amongst HFTs and the decision of an MM who considers setting up an HF trading desk. In Section 6 we conclude and discuss some key features about HFTs that require further research (and quality data). 2 Trading Algorithms, High Frequency Traders, and Financial Markets 2. Financial Market Developments Over the last years all major exchanges have revamped their systems to give way to the new era of computerized trading. Speed of trading and volume figures speak for themselves. In BANCO DE ESPAÑA 2 DOCUMENTO DE TRABAJO N.º the SEC s report on Findings regarding the market events of may 6, 200 (SEC [200]) we read that NYSE s average speed of execution for small, immediately executable orders was 0. seconds in January 2005, compared to 0.7 seconds in October Also, consolidated average daily share volume in NYSE-listed stocks was 2. billion shares in 2005, compared to 5.9 billion shares in January through October Consolidated average daily trades in NYSE-listed stocks was 2.9 million trades in 2005, compared to 22. million trades in January through October Consolidated average trade size in NYSE-listed stocks was 724 shares in 2005, compared to 268 shares in January through October Other important metrics that intend to capture market efficiency and information transmission have also undergone considerable changes as a result of modifications of market rules and the prominent role that computing has taken in financial markets. For example, Chordia et al. [200] focus on comparisons of pre- and post-decimal trading in NYSE-listed stocks (subperiods from and ). Some of their findings are that average effective spreads decreased significantly (from $0.022 to $ cents for small trades ( $0,000) and from $0.069 to $ for large trades ( $0,000)), while average depth available at the inside bid and offer declined significantly (from,30 shares to 2,797 shares). From the mean trade size is $82,900 and from $36,400 while the mean number of transactions is,36 and 4,779 respectively. 2.2 What characterizes Algorithmic and HF Trading We adopt Hendershott and Riordan [2009] and Hendershott et al. [200] s definition of AT: the use of computer algorithms to automatically make certain trading decisions, submit orders, and manage those orders after submission. We distinguish HF trading as a subset of AT. An HF trading strategy is an AT strategy that is based on exploiting greater processing and execution speed to obtain trading profits while holding essentially no asset inventory over a very short time span usually measured in seconds, mostly less than a few minutes, and certainly less BANCO DE ESPAÑA 3 DOCUMENTO DE TRABAJO N.º than a day. 2 One sometimes finds these strategies described as latency arbitrage. 3 HFTs are proprietary firms and proprietary trading desks in investment banks, hedge funds, etc, that based on these strategies have the ability to generate large amounts of trades over short periods of time, Cvitanić and Kirilenko [200]. There are other AT strategies that are in use for other purposes. For example, there are AT liquidity strategies which strategically post and cancel orders in the order book to exploit widening spreads, or AT strategies designed to execute large orders with the smallest price impact. Our analysis focuses exclusively on HF trading strategies, which we believe are the ones most critics of AT have in mind. 4 Making the distinction between HF trading and AT is important because it highlights the substantial difficulty one encounters when measuring the impact that HFTs have on markets according to metrics such as volume, spreads, and liquidity. For example, estimates of HF trading volume in equity markets vary widely depending on the year or how they are calculated, but they are typically between 50% and 77% of total volume, see SEC [200] and Brogaard [200] although how much is actual HF trading versus generic AT is unclear. Also, Hendershott et al. [200] find that for large-cap stocks AT improves liquidity and narrows effective spreads. They also find that AT increases realized spreads which indicates that revenue to liquidity suppliers has increased with AT, but it is difficult to infer how much of these effects are due to AT that is not HF trading. Similar identification problems are present in another recent study, Brogaard [200], which finds that HFTs contribute to price discovery and reduce volatility. Thus, this identification problem, as well as possible collateral effects on other AT strategies, have to be taken into account in any regulatory implications one may draw from our analysis, as we focus exclusively on HF trading. Finally, Hasbrouck and Saar [20] use high frequency data (order submissions, cancelations and executions) to propose a methodology to identify runs of trades 2 In their study of the Flash Crash, Kirilenko et al. [200] find that, holding prices constant, HFTs reduce half their net holdings in 5 seconds. 3 For a general discussion of latency in electronic markets see the theoretical models of Cespa and Foucault [2008] and Moallemi and Saglam [200]. 4 Hasbrouck and Saar [20] distinguish between two types of algorithmic trading: agency algorithms and proprietary algorithms. Agency algorithms are used by buy-side institutions to minimize the cost of executing trades in the process of implementing changes in their investment portfolios. Proprietary algorithms are used by electronic market makers, hedge funds, proprietary trading desks of large financial firms, and independent statistical arbitrage firms, and are meant to profit from the trading environment itself (as opposed to investing in stocks). In this p
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