High Frequency Trading and Its Impact on the Performance of Other Investors

Transcription

1 Arhus University Business and Social Sciences High Frequency Trading and Its Impact on the Performance of Other Investors Evidence from the Copenhagen Stock Exchange Master Thesis Authors: Karolis Liaudinskas MSc in Finance Mantas Malkevicius MSc in Finance Supervisor: Carsten Tanggaard Professor of Finance Department of Economics and Business August 2012

2 Abstract This paper investigates the activity of high frequency traders in the Copenhagen Stock Exchange (CSE), and focuses on the impact that HFT has on other market participants. The authors provide a thorough review of both the institutional background and the relevant academic literature. Moreover, they perform an empirical analysis examining the HFT s impact on the stock price efficiency, and the HFTs involvement in such predatory trading practices as front running and price trend creation. A unique data set, provided by the CSE, enables the authors to distinguish between the trades executed by HFTs. The methodologies applied in the research are consistent with Litzenberger et al. (2010), Brogaard (2010) and Aldrige (2011). The results of this paper suggest the following. Firstly, HFT activities tend to improve the stock price efficiency. Secondly, HFTs do not tend to get involved in price trend creation strategies. Thirdly, HFTs tend to systematically pursue front running strategies at the expense of other market participants.! ii!

3 Table of Contents! 1. Introduction... 1! 1.1. Controversy and motivation for the study!...!1! 1.2. Research question!...!3! 1.3. Empirical research and main findings!...!4! 1.4. Outline of the paper!...!4! 2. Background Information... 5! 2.1. Market microstructure!...!5! 2.2. Evolution of markets and market fragmentation!...!7! Evolution of markets!...!7! Market fragmentation!...!9! 2.3. High frequency trading and related concepts!...!11! Definitions!...!12! AT Characteristics!...!12! HFT Characteristics!...!13! A glance at HFT strategies!...!15! 2.4. Danish Stock Market!...!16! General Characteristics!...!16! Trading Practices!...!17! 2.5. Regulations in US and Europe!...!19! Differences between the U.S. and the European Market System!...!19! HFT regulations in U.S.!...!20! HFT regulations in Europe!...!22! 3. HFT Strategies... 26! 4. Literature Review... 36! 4.1. HFT effects on market quality!...!36! Theoretical models!...!36! Empirical studies!...!38! 4.2. Empirical research on HFT strategies!...!43! Initiation of price trends!...!43! Front running!...!46! 5. Data... 49! 5.1. Why Danish stock market?!...!49!! iii!

4 5.2. Dataset!...!50! 6. Methodology... 53! 6.1. Testing price efficiency!...!53! 6.2. Testing predatory trading strategies!...!60! Price trend creation!...!61! Front running strategies!...!64! 7. Results... 67! 7.1. Price efficiency!...!67! Historical CSE price efficiency analysis!...!67! CSE Price efficiency analysis. Shorter time spans!...!68! Random Walk analysis on a micro level. Relation with HFT!...!70! 7.2. Price trend creation!...!72! Main results!...!72! Weekly results!...!75! Robustness check!...!77! 7.3. Front running!...!79! Replicating the Brogaard s test!...!79! Taking the whole HFT activity into account!...!81! 8. Discussion... 83! 8.1. Implications of findings!...!83! 8.2. Answering the research questions!...!85! 8.3. External validity!...!85! 8.4. Regulatory discussions!...!85! 8.5. Data limitations and suggestions for future research!...!87! 9. Conclusion... 89! 10. Reference List... 91!! iv!

5 1. Introduction Financial markets have changed dramatically over the last few decades. Ever since computers were introduced in the industry in the 1970 s, the extent of human interaction in the trading process has been constantly decreasing (Cliff et al. 2010). Not only did the traditional trading venues become automated but also new players called electronic communication networks (ECNs) entered the market. Computerization and competition among exchanges established better conditions for traders, who were able to trade increasingly faster. In turn, traders developed their systems in order to automate not only the execution process but also decision making. All these developments created a niche in the industry and facilitated the entrance of new market participants called High Frequency Traders (HFTs). Their general trading strategy is based on the speed of execution and extremely short-term price movements, rather than fundamentals (Papagiannis, 2010) Controversy and motivation for the study High frequency trading (HFT) is a relatively new topic as it was first brought to the public s attention in 2009, when The New York Times published one of the first articles regarding the issue (Duhigg 2009). Today, however, high frequency trading is one of the most controversial and actively discussed topics in the financial world. There are a number of reasons why HFT attracts so much attention and why it needs to be studied. First of all, HFTs constitute a significant part of the total trading activity in stock, derivatives and foreign exchange markets. For instance, in the U.S. stock market high frequency traders generated around 55% of trading volume in 2011, and some sources report even higher numbers (Patterson & Eaglesham 2012). In Europe this number reached 42%, and it has been growing rapidly (Sybase 2012). Secondly, the event, known as the Flash Crash, triggered a wave of accusations towards high frequency traders. On May 6, 2010 US stock market experienced a sudden swing in prices, when the Dow Jones Industrial Average suddenly plummeted by 9% and recovered within a minute. HFTs were blamed to have used abusive trading strategies, which exacerbated price volatility and created price trends (Nanex 2010).! 1!

6 Thirdly, very little is known about how exactly HFTs operate, what strategies they employ, and what impact they have on the overall market quality. The opaqueness of the issue exists due to the fact that complicated trading algorithms, together with the speed of execution, are the key sources of competitive advantage for HFTs. Firms specializing in this type of trading tend to avoid revealing any inside information by all possible means (Friederich & Payne 2011). Fourthly, as the issue is relatively new, there has been little academic research made on HFT, although the amount of relevant literature is rapidly growing (Gomber et al. 2011). Furthermore, academics fail to arrive at a common conclusion regarding the HFT s impact on price efficiency, volatility and liquidity (Brogaard 2010) (Zhang 2010). These market characteristics significantly affect other market participants when making investment decisions (Brogaard 2010). Fifthly, the literature and empirical evidence concerning particular HFT strategies and predatory trading practices is particularly scarce. Even strategies that are generally considered to improve market quality (e.g. market making improves liquidity and statistical arbitrage improves price efficiency) are questioned by opponents of HFT (Arnuk & Saluzzi 2008). On top of that, institutional investors accuse HFTs of applying predatory strategies that directly exploit them either through price trend creation or liquidity detection and front running. A recent survey by Liquidnet (2011) revealed that 66%, 60% and 50% of institutional investors in the U.S., Europe and Asia Pacific, respectively, are concerned about HFTs. Institutional investors consist of pension funds, investment funds etc., thus the ultimate victims of predatory trading would be not only rich corporations and individuals but also ordinary people (Friederich & Payne 2011). However, there is little or no direct evidence of predatory practices being employed by HFTs (McGowan 2010). Sixthly, the extent of HFT activities and the investments made by these firms into hardware, software, co-location and human resources, suggests that they achieve very high profitability. Brogaard (2010) estimated aggregate yearly profits of HFTs operating in the U.S. to be $3 billion, while Kearns et al. (2010) stated that $3.4 billion is the upper bound of HFT profitability. According to Aite Group (2009), there are around 400 HFT firms in the U.S., which means that on average each of them earns approximately $8 million per year. The question remains whether these profits are not! 2!

7 generated unfairly at the expense of other traders and investors (Friederich & Payne 2011). Finally, authorities in both USA and Europe have procrastinated the introduction of laws that would regulate HFT activities. Although institutional investors and other traders are pressuring the SEC and the European Commission to restrain HFTs, the opaqueness and the lack of empirical evidence concerning their activities make it difficult to formulate optimum regulations (Patterson & Eaglesham 2012), (Liquidnet 2011). Some evidence suggests that HFT tend to enhance the overall market quality, therefore, some hasty restrictions on HFTs may eventually harm all market participants (Gomber et al. 2011) Research question Due to all the mentioned reasons the topic of high frequency trading is highly controversial and further research is essential. The purpose of our paper is threefold: (1) to provide a thorough background of the HFT industry, (2) to summarize the relevant up-to-date academic literature, and (3) to contribute to the existing literature by empirically investigating the effect of high frequency trading on other market participants. In particular, due to either lacking or contradicting empirical evidence, we focus our research on HFT s involvement in predatory trading and its overall impact on the stock price efficiency. Consequently, we formulate our set of research questions as follows. (1) What effect does high frequency trading have on the stock price efficiency? (2) Do high frequency traders employ predatory trading strategies that harm other market participants? These two questions are closely related. According to Hendershott and Riodan (2011), high frequency traders can be compared to other types of intermediaries and speculators. Generally speculators tend to enhance price efficiency as they trade against mispricings and thus add more information into prices. Predatory trading and manipulative strategies, on the other hand, can worsen price efficiency (Hendershott & Riodan 2011). Therefore, the evidence of HFT having a negative impact on the stock price efficiency would suggest that HFTs tend to engage in predatory trading relatively more than in strategies improving efficiency (e.g. statistical arbitrage).! 3!

8 1.3. Empirical research and main findings In order to investigate our research questions, we apply the methodologies developed by Litzenberger et al. (2010), Brogaard (2010) and Aldrige (2011). We follow Litzenberger et al. (2010) to study the relationship between HFT and the stock price efficiency. Aldrige s (2011) methodology is employed to investigate the feasibility of pump-anddump arbitrage, and thus, the incentives of HFTs to initiate price trends. Brogaard s (2010) research is used to study the tendency of HFTs to exploit other traders by front running. One of the major advantages of our research is the superiority of our dataset. As we use the trade data of large cap companies listed on Copenhagen Stock Exchange (CSE), we explicitly observe details of each trade that occurred on the exchange in January and February, Furthermore, we are able to identify trades, which HFTs participated in, using the same approach as Nasdaq OMX Nordic. The results of our research suggest that high frequency trading generally tends to improve price efficiency. We also find that HFTs are not able to exploit other traders by creating price trends, as we do not find evidence of pump-and-dump arbitrage feasibility. However, our evidence suggests that HFTs tend to front run other traders Outline of the paper The remainder of the paper is organized as follows. Section 2 provides a thorough background of high frequency trading. Section 3 discuses the most common HFT strategies. Section 4 reviews the relevant academic literature on HFT. Section 5 presents the data used for the empirical research. Section 6 describes the methodologies employed to study both of our research questions. Section 7 presents the results. Section 8 discusses the main findings and gives suggestions for practical implications. Section 9 concludes. As our empirical research focuses on two research questions, the sections of Literature review, Methodology and Results are each divided into two broad parts, namely price efficiency and predatory trading. Furthermore, we investigate two types of predatory trading: price trend creation and front running, thus the predatory trading parts throughout the paper are divided accordingly as well.! 4!

9 2. Background Information 2.1. Market microstructure In order to understand how the majority of modern stock markets work, we provide a simple description of the market mechanism. This background is necessary to fully understand high frequency trading strategies discussed in subsequent sections of this paper. According to Kearns et al. (2010), the most widely used market mechanism is called the open limit order book. It is used to implement a type of continuous double auction and works in the following manner. Suppose a trader decides to buy 500 shares of some stock. He can submit a limit order that specifies the volume (in this case, 500) and the maximum price he is willing to pay for the stock (e.g. $10.02). Assume that currently there is no seller willing to sell for $10.02 or less. The order gets registered in the buy order book. This book consists of all buy limit orders, sorted by price. If there are orders submitted at the same price, they are sorted by time of submission, the oldest ones being the first to be executed. The highest price is called the bid and is placed at the top of the order book. The exchange also keeps a sell order book, which lists submitted orders to sell the stock. The lowest price is placed at the top of the book and is called the ask (Biais et al. 1995). The difference between the bid and ask prices is called the bid-ask spread, and these two most competitive prices are together called the inside market. All these orders lying in the limit order book are called passive orders as they supply liquidity to other market participants (Kearns et al. 2010). If a trader wants to purchase shares immediately at the best prices available, he can submit a marketable order, or equivalently, a buy limit order at the price higher than the current ask. Similarly, for an immediate sell, one should submit a sell limit order at the price lower than the current bid. Such orders are called aggressive orders as they consume liquidity. Table 1 shows an example of a limit order book. In the example, if an investor now decided to submit a buy limit order of 500 shares at $10.10, the trade would be executed immediately by filling the three sell limit orders at the top of the sell order book. As a result, our investor would purchase 100 shares at $10.08, 200 shares at $10.09 and 100 shares at $ The remaining 100 shares would be placed in the top! 5!

10 of the buy order book and the new bid would be $ The new ask would be $ Every submitted order can be canceled before it is executed (Kearns et al. 2010). Table 1. The example of the limit order book Buy orders Sell order Shares Price Shares Price 200 $ $ $ $ $ $ $ $10.11 After orders get matched, clearing houses have to make sure that securities are transferred and payments are made. Only then a trade is considered finished. It takes three days to clear and settle the trade (Menkveld 2012). Most of exchanges use this simple market mechanism, however, they often have some additional more complicated order types (Kearns et al. 2010). For instance, Nasdaq OMX Nordic, which we study in our research, offers order types such as pegged orders, hidden orders and reserve orders (Nasdaq OMX 2012d). According to Nasdaq OMX (2012d), pegged orders enable traders to price their orders relative to the current market price. The trading system adjusts the prices of such orders automatically as the Best Bid Offer (BBO) changes. Hidden orders are limit orders that are not displayed in the order book. They are executed only when there are no visible orders left at that price. Despite the priority for visible orders, hidden orders among themselves are executed like ordinary limit orders in price/time priority. Hidden orders lying in the order book are often referred to as hidden liquidity. A reserve order, sometimes also called iceberg order, allows traders to specify what portion of the total order they want to appear in the order book and what part they want to submit as a hidden order (Nasdaq OMX 2012d).! 6!

11 While the bid-ask spread is a considerable part of investors transaction costs, it is also seen as a compensation for market makers (traders who provide liquidity) for the costs incurred by holding the position (Menkveld 2012). There are three types of costs, namely (1) order-handling cost (e.g. a fee paid to exchanges for executing orders), (2) the cost of being picked off by better informed traders, and (3) a risk of a sudden price change (Madhavan 2000). Recently, however, due to intense competition, in order to attract market makers, many exchanges started paying liquidity rebates for liquidity providers, and charging liquidity takers with extra fees (Gomber et al. 2011). The other two components of the cost incurred by market makers have been shrinking as well. Introduction of computers to trading venues resulted in lower latency (faster response time), which enabled market makers to quickly update quotes in case of arrival of new public information. As a result, they are less likely to be adversely selected by better informed traders. All these changes explain the dramatic decrease in bid-ask spreads across markets over the last decade (Menkveld 2012) Evolution of markets and market fragmentation Evolution of markets According to Cliff et al. (2010), the whole history of financial markets is characterized by gradual increase in the speed of communication and data processing. Before computers were invented, people who possessed extraordinary arithmetic skills gained a significant advantage and outperformed other market participants. Means of communication have also changed dramatically. In the 19 th century, important financial information was transferred by horse-riding messengers, who were later replaced by faster carrier pigeons. The invention of telegraph reshaped the way of communication, which was further fastened by telephone (Cliff et al. 2010). Up until the last quarter of the 20 th century, the only way to trade stock was gathering into floor-based exchanges and meeting dealers physically. Only licensed brokers could access stock markets and trade on behalf of individual investors (McGowan 2010). Eventually, in the mid 1970 s, financial markets started adopting computers. In 1976, the New York Stock Exchange introduced the designated order turnaround (DOT) system, which was upgraded to Super-DOT in This system made it possible to submit buy and sell orders to specialists electronically (Gyurko 2011). In 2000, around! 7!

12 90% of trades at NYSE were implemented through SuperDot, and only the largest trades were still submitted and executed physically by humans (Markham et al. 2008). Once trading venues started establishing computerized communication systems, order execution became much faster and traders could choose to be connected to trading platforms instead of meeting physically (McGowan 2010). In addition to NYSE developments, NASDAQ became the first electronic market in Instead of having one specialist providing liquidity for each stock, it offered the opportunity for dealers to compete in market making activity (Markham et al. 2008). According to McGowan (2010), the introduction of computers in financial markets resulted in the development of a new trading strategy called program trading, widely used in 1980 s. The strategy consisted of buying (or selling) stock index futures (e.g. S&P 500) and simultaneously selling (or buying) corresponding equity. This double order could be programmed to be executed whenever prices of stocks and corresponding futures moved apart too much (McGowan 2010). This type of automated trading, called index arbitrage, has been considered to cause the Black Monday crash in October 1987 (Cliff et al. 2010). The Black Monday lead to an increased skepticism towards computer based trading. Nevertheless, consistently with the Moore s Law, the costs of computers dropped by half every two years (Cliff 2011). This process continuously created a soil for more intelligent programs. By the end of the century, computers became an integral part of the investment funds management. Sophisticated programs were used to identify and trade on securities, which would help to diminish or even eliminate the portfolio risk. This hedging practice gave birth to the concept of hedge funds (Cliff 2011). In the beginning of the 21 st century, automated trading systems still focused on order execution rather than decision-making (Cliff 2011). When a human made a decision to purchase or sell a particular asset, an execution was implemented by automated execution system (AES). It considered the optimal timing and portions of the order to be submitted. Eventually, financial institutions started experimenting with AES and created different sophisticated algorithms in order to find ways to further reduce the market impact. Consequently, the concept of algorithmic trading was introduced (Cliff 2011). The boom of algorithmic trading was stimulated by decimalization of stock! 8!

13 prices in US in Instead of 1/16 of a dollar per share, 1 penny became the minimum tick size, which increased liquidity in markets, and attracted more algorithmic traders (Moyer & Lambert 2009). Simultaneously with the development of AES, various models searching for arbitrage opportunities evolved (Cliff et al. 2010). The new programs were able to scan and identify profitable statistical arbitrage based on not only two but rather on thousands of assets. This breakthrough was enabled by both powerful machines used by traders to analyze markets, and the computerized trading infrastructure. Straight Through Processing (STP) was introduced in trading venues, which significantly reduced the latency of order execution. The whole process from the submission of an order to clearing and settlement became fully computerized. Furthermore, Direct Market Access (DMA) provided an opportunity for traders and investors to interact with exchanges directly without the intermediation of investment banks or brokers. Both of these factors increased the speed of execution, which is vital for exploitation of short-lasting arbitrage opportunities (Cliff et al. 2010) Market fragmentation Meanwhile, financial industry became increasingly fragmented. Market fragmentation has been characterized by both the introduction of new entities involved in securities trading, and the establishments of new electronic exchanges such as Chi-X or BATS (Better Alternative Trading System) (Financial Times Lexicon 2012). Traders were enabled to trade the same securities at different venues simultaneously, which provided more incentives to seek for pricing inconsistencies and arbitrage opportunities. Moreover, a tightened competition among market venues drove bid-ask spreads down and facilitated the establishment of new entities involved in trading (Cliff 2011). The fragmentation of the financial industry started in 1990s with the establishments of automated trading systems that disseminated orders in trading venues for third parties and dealers and could execute such orders within the networks themselves (McAndrews & Stefandis 2000, pp. 1). These systems, named electronic communication networks (ECNs), provided new opportunities to traders and facilitated the development of new trading strategies and algorithms (McGowan 2010).! 9!

14 ECNs could execute orders sent by registered individuals (without the intermediation of brokers) internally in their networks. However, it could also forward orders to primary exchanges (e.g. NYSE or NASDAQ) if they offered better deals for ECNs clients. Therefore, at first, authorities perceived ECNs as broker-dealers and did not require them to be registered as security exchanges (McGowan 2010). However, a few ECNs gained a considerable market share and were willing to register as exchanges. In 1998 the U.S. Securities and Exchange commission (SEC) issued Regulation Alternative Trading Systems (Reg. ATS), which officially acknowledged ECNs as separate trading venues. In this way they could engage into direct competition with traditional financial markets (Markham et al. 2008). However, in 2005 the SEC released a Regulation National Market System (Reg. NMS), which partly restricted the flexibility of actions available to ECNs in the U.S. For example, a Trade Through Rule (Rule 611) said that a marketable order always has to be forwarded to and executed at a trading venue, which offers the best price nationally (McGowan 2010). Nevertheless, it did not discourage ECNs from the competition. For instance, BATS achieved 10% market share in US equities in the first couple of years of operation, as it became a licensed exchange in 2007 (Gyurko 2011). A successful example of an ECN in Europe is Chi-X that was launched on April 16, 2007 and has been successfully competing with other exchanges. It is considered to be one of the fastest trading platforms in the investment industry with the latency of only two milliseconds. During the first year of operations in Europe the venue traded stocks in six Western European countries and captured 4.7% of all trades. In 2011, Federation of European Exchanges acknowledged Chi-X as the largest equity market in Europe (Menkveld 2012). A typical ECN provides automatized fast order execution at low cost (Stoll 2006). However, today there is a wide variety of ECNs that differ from each other with respect to (1) targeted clientele, (2) order routing strategies (e.g. some ECNs simply route orders to other networks), (3) speed, (4) quality and certainty of execution, and (5) accessibility to limit-order books (Gyurko 2011). Another type of venue, which evolved as a solution to reduce the market impact, is a dark pool. Institutional investors sometimes wish to sell or buy a large block of a financial instrument, and doing so on typical exchanges or ECNs would result in a! 10!

15 strong impact on the asset price. In order to reduce the market impact, investors can divide a large order into many small orders and submit them for execution one by one. Alternatively, they can execute trades in dark pools, as they offer anonymity of traders and prevent information leakage (Gyurko 2011). Furthermore, limit orders in dark pools are not quoted in the order books, thus execution is uncertain and unpredictable. Finally, in dark pools trades are executed at prices that are determined at primary exchanges rather than within dark pools (Gyurko 2011). All these features of dark pools contribute to the reduction of market impact when selling or purchasing large blocks of stocks. All in all, the combination of (1) cheap computer power, (2) rapid development of sophisticated trading programs, (3) direct access to trading venues, (4) favorable regulations and (5) fragmentation of the financial industry created new trading opportunities for market participants. It lead to the development of new trading strategies, which were based on expected short-term asset values. As a result, these rapid changes in the financial industry initiated a race among traders, where the key competitive advantage is the high speed of data analysis and order execution. In order to completely eliminate the intervention of humans and further increase the speed, trading algorithms were programmed not only to execute orders but also to make trading decisions based on observed order flows. These traders held positions for a matter of seconds or milliseconds and became known as High Frequency Traders. (Cliff 2011) 2.3. High frequency trading and related concepts Today high frequency trading is dominating in financial markets in terms of trading volume both in the U.S. and Europe (McGowan 2010). According to the TABB Group, high frequency trading generated 61% of U.S. stock-trading volume in 2009 and dropped marginally to 56% in 2010 and 55% in 2011 (Patterson & Eaglesham 2012). These numbers are impressive, considering that HFT firms 1 constitute only 2% of the overall 20,000 companies involved in trading in the U.S. (Aite Group 2009). Meanwhile in Europe in 2010, high frequency traders were involved in roughly 33% of trades, which made up around 32% of the total trading volume. In 2011, the share of HFT reached 42% of trading volume, while it is projected to increase up to 45% in 2012 (Sybase 2012).!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1!E.g.!Getco LLC, Knight Capital Group, Citadel LLC, Jump Trading etc.! 2 In this paper HFT(s) can also refer to high frequency trader(s).! 11!

16 Although the involvement of HFT has been growing the opaqueness of HFT strategies makes it difficult not only to discuss their overall impact on financial markets but also to define the HFT itself (Friederich & Payne 2011). The purpose of this section is to define high frequency trading and introduce its key characteristics Definitions Due to the fact that high frequency trading (HFT) 2 is a rather new, rapidly changing and relative term, there is no general agreement on its single definition. As a result, academics often refer to each other and use similar definitions in order to remain consistent while defining HFT (Gomber et al. 2011). In order to be consistent with the existing literature, in our paper we define HFT and algorithmic trading (AT) in line with Brogaard (2010) and Hendershott & Riordan (2011). According to Brogaard (2010), high frequency trading is a type of investment strategy that is engaged in buying and selling financial assets very rapidly by using computer algorithms, and holding those assets for a very short period (a matter of seconds and microseconds). HFT firms are defined as firms engaged in proprietary high frequency trading, who tend to hold neutral positions in assets overnight. Algorithmic trading is defined as the use of computer algorithms to automatically make trading decisions, submit orders, and manage those orders after submission (Hendershott & Riordan 2011, pp. 2). HFT and AT are similar in a sense that both use automated decision making technology, however AT investment horizon is not specified and could be any period of time from seconds to years. Meanwhile HFT tend to hold positions only for extremely short periods. Therefore, HFT is a subset of algorithmic trading (Brogaard 2010) AT Characteristics Most non-hft algorithmic trading strategies are employed by institutional investors, who attempt to reduce the market impact of large orders. For instance, if an institutional investor decides to sell a large block of stock and submit the whole block for execution, it should significantly decrease the price in order to encourage other market participants!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 2 In this paper HFT(s) can also refer to high frequency trader(s).! 12!

17 to absorb the increased supply (Angel et al. 2010). Alternatively investors can use algorithms that divide large trade orders into a number of smaller orders and submit them one by one into the market for execution. The most common non-hft algorithms are classified into four generations by Almgren (2009). Order execution with first generation algorithms is based on benchmarks that depend on the existing situation in a market. They do not depend on the actual order characteristics or the situation in the order book. For instance, Participation Rate Algorithms could be programmed to participate in a market by constantly trading 3% of the total market volume until the desired position is liquidated or built. Another example is Time Weighted Average Price (TWAP) algorithms, which can split an order and submit smaller orders for execution in equal time intervals. The length of time intervals and the order size are usually predefined by humans. A third example is Volume Weighted Average Price (VWAP) algorithms. They attempt to execute orders at a price, which would be the same or better than a volume weighted average price observed in the market. (Almgren 2009) The second generation algorithms are more intelligent as they attempt to tailor a specific benchmark for every individual order and take the timing risk (potential negative price movements during the execution process) into account. The third generation algorithms constantly re-evaluate their order execution schedule and adapt to new market conditions. The fourth generation is programmed to automatically react to the news concerning particular instruments. These algorithms can employ text-mining techniques or simply subscribe to low latency electronically processable news feeds provided by news agencies and exchanges. (Gomber et al. 2011) These non-hft algorithmic trading strategies are relatively easy to predict and exploit, and high frequency traders are often blamed to be engaged in such activity. The next two sub-sections present HFT characteristics, which distinguish it from other types of trading, and shortly introduce to the main strategies employed by HF traders HFT Characteristics Generally speaking, high frequency trading specializes in capturing small gains on short-term fluctuations of asset prices. Most HFT firms attempt to find temporary mispricings and other inefficiencies in financial markets and exploit them before they! 13!

18 disappear. Other HF traders are engaged in market making at high frequency. As a result, one of the main characteristics of HFT is high capital turnover. Other characteristics include a dependence on low latency, a relatively short shelf life of algorithms, and participation on multiple trading venues (Iati 2009). Latency is a speed factor, which describes the delay experienced in a system. The lower latency in trading systems results in faster execution of trades. Ultra-low latency, which provides the ability to execute trades in less than 1 microsecond, is a vital component of all high frequency trading strategies. In order to earn profits, a high frequency trader has to process information through his algorithms microseconds faster than his competitors. Therefore, HF traders constantly upgrade their hardware in order to remain competitive. (Mackenzie 2009) Furthermore, in order to further reduce latency, various trading venues started providing facilities for HFT firms to co-locate their servers next to the exchanges. In this way, real-time market information can reach a high frequency trading platform instantaneously. The platform can process the information, create orders and submit them back to the exchange s server for execution. All this procedure can be done in less than a millisecond (Iati 2009). Profits earned from a single trade usually amounts to pennies or less. Therefore, HFT firms execute thousands of trades everyday in order to earn significant profits (McGowan 2010). HFT firms also purchase real estate in different cities next to the buildings of different exchanges and set up their offices. Consequently, real estate prices around exchanges skyrocketed; however, HFT firms are still willing to pay that money. This fact testifies the significance of benefits provided by co-location (Mackenzie 2009). Although low latency and speed of execution is a vital source of competitive advantage in HFT business, firms also compete in recruiting specialists, who would be able to create and update competitive trading algorithms. Traders that graduate from the best schools with mathematics and computer science degrees are particularly demanded (Wahba & Chasan 2009). It is important to be in possession of intelligent specialists, since the shelf life of trading algorithms is very limited. In order to retain a competitive advantage, HFT firms have to update their codes regularly - sometimes a few times per week (McGowan 2010). There are two reasons why the usefulness of algorithms dilutes over time. Firstly, high frequency trading strategies rely on correlations among markets! 14!

19 and individual securities. In today s extremely volatile markets, various market and stock characteristics change rapidly, thus financial engineers have to react and adjust codes accordingly. Secondly, trading algorithms have to be altered regularly because of the threat of reverse engineering by competitors. If a rival trading firm learns a trading pattern of its competitor, it can exploit this knowledge to its advantage. The most profitable strategies can become the riskiest in one day, if rivals manage to predict algorithm s behavior. As a result, in order not to become a victim of reverse engineering and to retain the competitive advantage, a firm has to constantly alter its codes. (McGowan 2010) As mentioned earlier, another major characteristic of HFT is participation on multiple trading venues. HFT firms trade in different asset classes including stocks, options, futures, currencies etc. Moreover, they trade the same assets on different exchanges simultaneously (McGowan 2010) A glance at HFT strategies The strategies employed by HF traders are too opaque and diverse to discuss them all (Gomber et al. 2011). However, some of them are well known and not new to the market. In fact, most of the known HFT strategies are old strategies implemented with better technology and at higher speed (Friederich & Payne. 2011). Gomber et al. (2011) suggests the following classification of strategies pursued by HF traders. We enrich this list with strategies identified in other sources (McGowan 2010; LSEG 2010; Menkveld 2012; Brogaard 2010; Angel et al. 2010; Egginton et al. 2012; Arnuk & Saluzzi, 2009). 1. Electronic liquidity provision a. Market making b. Liquidity rebate trading 2. Market Arbitrage a. Market neutral arbitrage b. Cross asset, cross market & ETF arbitrage 3. Liquidity detection a. Pinging/Sniffing/Sniping b. Quote Matching! 15!

20 4. Other a. Latency arbitrage b. Short Term Momentum c. Predatory algorithms d. Quote stuffing e. Spoofing and Layering Market making and market arbitrage strategies are considered to be beneficial to the market as a whole, since these strategies either provide liquidity and narrow bid-ask spreads (MacGowan 2010) or increase price efficiency (Hendershott & Riodan 2011). However, other practices such as liquidity rebate trading, pinging, quote matching, latency arbitrage, predatory algorithms, quote stuffing, spoofing and layering are very questionable as they are considered to help HF traders unfairly exploit their speed advantage at the expense of slower institutional investors and other traders. Some of those strategies, namely, quote stuffing, spoofing and layering, are already scrutinized and planned to be banned by authorities in US and Europe (Gomber et al. 2011). However, even market making and market arbitrage strategies can gain a negative perception concerning fairness, if they are combined with liquidity detection techniques, such as pinging (Arnuk & Saluzzi 2009). Our paper provides a thorough description of HFT strategies. It is enriched with particular examples and explanations of how they work. This discussion, however, is rather extensive; therefore, we provide it separately in the next section named HFT Strategies Danish Stock Market General Characteristics The Danish stock market is quite fragmented with 62.89% of all trades being executed in lit stock exchanges, 2.72% attributable to dark pool and the rest 34.39% traded over the counter. What concerns exchange-traded stocks, in January, 2012, 48.49% of all Danish stocks were traded on Copenhagen Stock Exchange (CSE) and the rest 14.42% were spread among 5 other alternative market providers with Chi-X gaining the largest share of 8.59% (Nasdaq OMX 2012a).! 16!

21 The exchange controlling the highest share of Danish stocks traded, Copenhagen Stock Exchange (CSE), is a part of NASDAQ OMX Group, which is the largest single cash equities securities market in the world in terms of share value traded (Nasdaq OMX 2012b). In the exchange shares, bonds, treasury bills, financial futures and options are traded. Currently there are 175 companies listed in the CSE with an average equity turnover of around 2.6 billion Danish kroner per day (Nasdaq OMX 2012c). The exchange maintains the European Best Bid and Offer, the European version of the National Best Bid Offer in the U.S. In the CSE companies are categorized into three segments, namely Large Cap, Mid Cap and Small Cap firms, according to their market capitalization level. Large Cap segment includes companies with market capitalization of 1 billion Euros or more, while Mid Cap firms have capitalization level between 0.15 and 1 billion Euros, and the Small Cap segment comprises of companies with market capitalization of less than 0.15 billion Euros Trading Practices During regular trading day, there are four main trading sessions in the CSE, namely Opening, Continuous Trading, Closing and After Market. The trades are executed using one of the most advanced trading platforms called INET. It was introduced on the 8 th of February, 2010 both in Nordic and Baltic branches of Nasdaq OMX, CSE included. Since then all equity markets operated by Nasdaq OMX around the world have been using the same trading platform that enables investors to access distant markets easier. The INET system is able to handle around one million messages per second at extremely low latency level (Nasdaq OMX 2010). Currently the number of trades in Nasdaq OMX Copenhagen is around 50,000 per day (Nasdaq OMX 2012c). The majority of trades, around 95%, comprises of trades in large capitalization companies. The Figure 1 summarizes the evolution of average daily trades executed and average trade size in the CSE during past few years.! 17!

22 Figure 1. The average trade size and average dauly number of trades executed in CSE. Average trade size expresed in DKK. Data source: Nasdaq OMX.! Average'Trade'Size'in'CSE' ,000 50,000 40,000 30,000 20,000 10,000 Average'Daily'number'of' Trades'in'CSE' As it can be seen from the Figure 1, the daily trade number has experienced a large growth, accounting to 139% from the year On the other hand, average daily trade size has diminished considerably by 74%. Such changes can be attributed mainly to the increased market fragmentation and lower displayed liquidity due to the tick-size change in 2010 (increased up to 4 decimals) (Nasdaq OMX, 2012a). What concerns order size, electronic trading systems often slice the order into smaller parts. Quite similarly, smart order routing, also used in the CSE, splits an order into smaller bits and then searches for the best execution options (Hatrick & Deliya 2008). Thus, the evolution of electronic trading has reduced the average size of the order considerably. In the CSE, the order flow comes from personal broker accounts, routing accounts or algorithmic accounts. Volume traded from algorithmic accounts increased rapidly during the last couple of years in line with the explosion in the number of trades executed daily in the exchange (Nasdaq OMX 2012a). From the legal perspective, in order to deploy such AT practices in the CSE, investors have to obtain automated trading rights. It entitles investors to trade through automated trading facilities using software that automatically generates orders in response to specific pre-programmed factors. A special form of an automated trading account, called AUTD, can also be set up, which entitles investors to a specific discount on a current stock price. However, the AUTD account has to be used purely for automatic trading and other execution practices are not allowed (Nasdaq OMX 2012d).! 18!

23 Co-location services that enable exchange customers to reduce the response latency to the minimum, is of high importance to Algorithmic and HF traders. Nasdaq OMX Nordic, including Copenhagen OMX, offers its customers to acquire space for their servers in the same datacenter where the exchange s central matching machine operates as well as other support services (Nasdaq OMX 2012e). The extremely low-latency execution system INET, co-location services and the market fragmentation offering profitable trading opportunities, make the CSE attractive to HF traders. As a result, an increasing trend of HFT activity has been observed lately. According to Nasdaq OMX data, the percentage of HFT shares in total equity turnover almost tripled during last year and amounted to 7.5% (Nasdaq OMX 2012a) Regulations in US and Europe In this section we provide an overview of the main regulatory practices implemented and the initiatives proposed in both the U.S. and Europe in order to control the activities of HFTs. Closely following the paper of Gomber et al. (2011), the overall differences between the two market systems are discussed. Then the overview of proposals on how to manage risk and ensure fairness in both markets is presented Differences between the U.S. and the European Market System One key difference between the two market systems is the approach to the best execution regime. While the U.S. practices a rules-based approach, Europe favors a principle-based method. There are two main features enforced by the U.S. Regulation Market System that guarantee fair execution of trades at the best prices prevalent among exchanges. The first one is the National Best Bid Offer (NBBO), which is the aggregated best bid/ask spread for any stock traded around U.S. Every marketplace has to distribute their best quote for every security traded which then is aggregated nationwide to arrive at the NBBO. To ensure that trades are always executed at the NBBO, the regulation 611 REG NMS, also known as the order protection rule, was implemented (SEC 2005). It forbids the exchanges to trade at the prices worse than those quoted in the NBBO. In case the marketplace is not able to match the NBBO, it has to route the order to another exchange where such a quote is available. Once implemented, this rule has increased the competition among marketplaces that resulted in better quotes for investors (Gomber et al. 2011).! 19!

24 In Europe, Markets in Financial Instruments Directive (MiFID) requires investment firms to ensure that trade is executed at the most favorable terms for their customers (European Commission 2004). It differs from the U.S. not only by the fact that it is not a strict rule, but also that the party responsible for the fair execution is an investment firm. In contrast, in the U.S. the responsible party is the exchange. Nevertheless, both regimes have made it easier for new marketplaces to compete with the old established ones and thus, have improved the prices offered to customers HFT regulations in U.S. In the following section the most debatable areas concerning HFT regulations in the U.S. are presented and the attitude regulatory bodies have towards them is described. Flash orders There is an exception to the order protection rule stating that a trade can be executed at the least aggressive quote 3 present over the previous 1 second at the exchange where the trade is being executed (McInish & Upson 2012). It means that for one second the order does not have to be routed to other exchanges in case there is not enough liquidity at the NBBO locally. Due to this exception, some exchanges within the U.S. have started to use so called flash orders (Gomber et al. 2011). They help exchanges increase the probability that orders are executed locally and not routed to any other exchanges. A flash order is a marketable order, which can be converted into a limit order for a few milliseconds by the exchange (Gomber et al. 2011). Consider the following example. If an investor chooses to place a marketable order, which cannot be executed instantly in the exchange due to a lack of liquidity at the prevailing NBBO, the order is not immediately routed to the other marketplace. It stays in the original exchange for several milliseconds more. During this short period of time, the exchange changes the marketable order into a limit order at the prevailing NBBO and expects that some traders would consume the supplied liquidity. If the counterparty is found, the order is executed inside the exchange where the order was initially placed, even though in the beginning there were no offers that would comply with the order protection rule (Gomber et al. 2011). Since such a trading opportunity lasts just for several!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 3 It is called the Flicker price! 20!

25 milliseconds, only HFTs can react and participate in it. Thus, such trading orders are generally perceived to be unfair to regular investors. Even though the actual impact of these orders is still unclear, it is argued that they may impair the price discovery process. Furthermore, such a practice can worsen limit-order generated liquidity provision. The reason is that limit orders become less likely to be executed against orders being routed from other marketplaces that are using flash orders. Therefore, investors might have less incentive to place limit orders and, consequently, liquidity may decline (Gomber et al. 2011). As a result, the SEC proposed to ban flash orders (SEC 2009). However, no actual measures have been implemented yet. Co-location Services In June 2010, the Commodity Futures Trading Commision (CFTC) proposed the regulation which should ensure fair access to co-location services for market participants (CFTC 2010). The commission argues that the possibility to locate a trader s server to the exchange s servers as close as possible in order to reduce latency should be accessible to everyone. However, in practice such places are very expensive to rent and mostly occupied by HF traders that are willing to invest highly in it to gain the crucial competitive advantage (McGowan 2010). The CFTC proposes that uniform fees for co-location and related services should be established to assure equitable pricing to all market participants. The commission also suggests that exchanges should report information about the latency of different classes of investors and update it regularly. Circuit Breaker A circuit breaker is defined as a procedure that halts temporarily, or, under extreme circumstances, closes a market before the normal close of the trading session, if a stock experiences a severe and unanticipated price decline (SEC 2012). The thresholds to trigger a circuit breaker are set to 10%, 20% or 30% decline of stock market value within five minutes. Depending on the magnitude of a drop, trading can be halted for a time period between one hour and one day. Stub quotes Sometimes market makers could quote prices of securities that are unreasonable and far! 21!