High frequency trading - page 7

High-frequency trading (HFT) – wherein computers transact thousands of times per second with incomprehensible speed – now accounts for over 60% of all trades on American exchanges. How does this sweeping market change affect retail investors?

There are two very different answers to that question. Supporters claim that high-frequency traders (HFTs) are a net-positive market force because they provide liquidity and tighten bid-ask spreads. They say that high-frequency trading is rarely if ever used for nefarious purposes, and regulators make sure of it.

On the other side, detractors claim that HFTs regularly manipulate unaware investors and otherwise destabilize markets. They say that HFTs are a net-negative force on the market and should be reined in.

The answer surely lies somewhere in between. But which is closer to the truth? To find out, we talked to Garrett, an expert on market systems and high-frequency trading. Having experienced first-hand the problems HFTs can cause, he fits firmly in the “detractor” camp, for reasons you’ll read below. Garrett gave us excellent insight into how HFTs profit, along with tips on how to make sure they don’t profit at your expense.

I found this interview highly educational, and I hope you do too. It contains the kind of inside intelligence that separates the informed from the uninformed and allows us as individual investors to understand and adapt to our changing markets.

It is currently estimated that 60% of all U.S. stock trades are executed via high frequency trading. UBS estimates that about 30%of Japanese equity trading is high-frequency. That compares with up to 10% in all of Asia, up to 10% in Brazil, about 20% in Canada, and up to 40% in Europe, according to a report by New York-based agency broker Rosenblatt Securities. High-frequency trading accounts for up to 40% of trading volume in U.S. futures, up to 20% in U.S. options, and 10% in foreign exchange, the report said.1 The current market in the U.S., while still a very viable opportunity for high frequency trading in a number of asset classes, has some serious barriers to entry in terms of its cost basis. Thus, the idea of looking at new markets to penetrate are starting to take shape – particularly in Asia. Since the U.S. and European markets have matured, companies are think-ing about their strategic growth into new markets. The Asian markets are beginning to implement changes for these strategies to exist on certain platforms: Tokyo Stock Exchange’s Arrowhead; Singapore’s SGX; Malay-sia’s BURSA and others – primarily Hong Kong and Australia – are looking to beef up their regulatory environment and computer hardware infrastruc-ture to enable high frequency trading strategies within their markets. Algo-rithmic trading and high-frequency trading in the FX market are expected to grow rapidly. “If you’re in the region, the buzz is there and the growth is there,’’ said Steven Silberstein, CIO of Chi-X Global, speaking at the Global High-Frequency Trading Outlook in 2010. In 2012, that growth is now in high-gear.

Since the introduction of electronic exchanges in the late 20th century, a number of sophisticated algorithms have been developed for discovering reoccurring patterns in financial time series. However, the most accurate techniques available produce opaque models, from which it is impossible to discern the rationale behind trading decisions. It is therefore desirable to sacrifice some degree of accuracy for transparency. One fairly recent evolutionary computational technology that creates transparent models, using a user-specified grammar, is grammatical evolution

When developing trading models, it is necessary to consider both entry- and exit strategies. The entry strategy decides when to enter a bid or offer into the market (or when to remain idle), whereas the exit strategy decides when to cut losses short or when to take profit by closing open positions in the market. Furthermore, a highly desirable property of any trading model is a high return-to-risk ratio with a low trading cost. Therefore, a trading model needs to consistently produce positive returns whilst minimizing risk and trading costs.

The most lucrative form of trading is high-frequency trading, i.e. when trading decisions are made intra-day, usually using a minute-, second- or millisecond time resolution. In a high-frequency setting, the availability of timely, fundamental economic and financial information is scarce, hence technical indicators are employed in this study.

In financial economics, a long-standing issue is how information finds its way into market prices and whether market prices are informationally efficient. Grossman and Stiglitz (1980) argue that markets must possess inefficiencies to compensate informed investors for the costs of gathering and trading on that information. According to their model informed investors earn excess returns as compensation for information acquisition and distribution. Campbell, Lo and Mackinlay (1997) assert that, “in a large and liquid market, informational costs are likely to justify only small abnormal returns, but it is difficult to say how small, even if such costs could be measured precisely.”

In this paper, we study market efficiency by focusing on a particular type of investor, High Frequency Traders (HFTs). HFTs depend on speed, which is closely related to information – it is the ability to react to and incorporate information into market prices. Whether it is using expedited information to make an investment, reduce risk, or mitigate the costs of adverse selection, those quickest to react can capture informational rents.

We document that HFTs generate consistent and large excess returns in one of the most liquid and competitive financial markets - the E-mini S&P 500 futures market. These excess returns likely arise as a result of exploiting fleeting informational advantages at short time scales. We find that while HFTs bear some risk, they generate an unusually high average Sharpe ratio of 9.2. The concentration of profits among these few rapidly trading market participants - 31 out of over 31,403 traders – reveals deviations from market efficiency at very short intervals. Within these 31 firms, a small group of aggressive (liquidity-taking) HFTs are the most profitable.

We examine whether high-frequency traders (HFT) increase the transaction costs of slower institutional and retail traders (non-HFT). Using a differences-in-differences test around the introduction of a new data feed that decreases HFT latencies, we find that non-HFT limit order trading costs increase relative to the costs for HFT. We attribute the increase in non-HFT execution costs to more predatory HFT. After the reduction in trading latencies, we show that HFT are more successful at trading ahead of non-HFT limit orders. The execution probability of non-HFT limit orders falls, thereby increasing the costs of their limit order strategies.

Financial markets have always been high-speed, high-return activities. The ability to execute a transaction before your competitors determines who gets the profit from a deal. But we are a long ways from the days of ticker tape and phone calls were fast enough. With billions of dollars on the line, markets and traders have invested heavily in technologies to help them rapidly access and analyze data and conclude a sale.

For high frequency trading (HFT) response times have gone from milliseconds to microseconds. In such an environment, even fiber optic connections can be too slow. Microwave connections, with latency measured in nanoseconds, can provide the necessary speed.

The New Market Maker HFT grew out of the SEC’s 1998 decision to allow electronic exchanges to compete with the NYSE and other marketplaces. By 2010, HFT was accounting for more than 70% of the trades in U.S. equity markets, and a growing percentage of trades in other countries.

Little known species you should be aware of

•Predatory algorithms are a special kind of informed traders. Rather than possessing exogenous information yet to be incorporated in the market price, they know that their endogenous actions are likely to trigger a microstructure mechanism, with foreseeable outcome.Examples include:

–Quote stuffing: Overwhelming an exchange with messages, with the sole intention of slowing down competing algorithms.

–Quote dangling: Sending quotes that force a squeezed trader to chase a price against her interests.

–Pack hunting: Predators hunting independently become aware of each others activities, and form a pack in order to maximize the chances of triggering a cascading effect.

This paper develops a continuous-time model of the optimal strategies of high-frequency traders (HFTs) to rationalize their pinging activities. Pinging, or the most aggressive fleeting orders, is defined as limit orders submitted inside the bid-ask spread that are cancelled shortly thereafter. The current worry is that HFTs utilize their speed advantage to ping inside the spread to manipulate the market. In contrast, the HFT in my model uses pinging to control inventory or to chase short-term price momentum without any learning or manipulative motives. I use historical message data to reconstruct limit order books, and characterize the HFT’s optimal strategies under the viscosity solution to my model. Implications on pinging activities from the model are then gauged against data. The result confirms that pinging is not necessarily manipulative and is rationalizable as part of the dynamic trading strategies of HFTs.

High-frequency trading (HFT) practices in the global financial markets involve the use of information and communication technologies (ICT), especially the capabilities of high-speed networks, rapid computation, and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds. HFT practices exist because a variety of new technologies have made them possible, and because financial market infrastructure capabilities have also been changing so rapidly. The U.S. markets, such

as the National Association for Securities Dealers Automated Quote (NASDAQ) market and the New York Stock Exchange (NYSE), have maintained relevance and centrality in financial intermediation in financial markets settings that have changed so much in the past 20 years that they are hardly recognizable. In this article, we explore the technological, institutional and market developments in leading financial markets around the world that have embraced HFT trading. From these examples, we will distill a number of common characteristics that seem to be in operation, and then assess the

extent to which HFT practices have begun to be observed in Asian regional financial markets, and what will be their likely impacts. We also discuss a number of theoretical and empirical research directions of interest.

This paper develops a model of the optimal strategies of high-frequency traders (HFTs) to rationalize their pinging activities. Pinging is defined as limit orders submitted inside the bid-ask spread that are cancelled shortly. The HFT in my model uses pinging to control his inventory or chase the short-term price momentum without learning or manipulative motives. I show that the model can match over 70% of the pinging activities observed in the data, and demonstrate how the HFT's pinging behaviors vary across stocks. The result confirms that pinging is rationalizable as part of the dynamic trading strategies of HFTs.

Number of Pages in PDF File: 50