High frequency trading - page 5

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How Slow is the NBBO? A Comparison with Direct Exchange Feeds : how_slow_is_the_nbbo_-_a_comparison_with_direct_exchange_feeds.pdf

This paper provides evidence on the benefits of faster proprietary data feeds from stock exchanges over the regulated “public” consolidated data feeds. We measure and compare the National Best Bid and Offer (NBBO) prices in each data feed at the same data center. Price dislocations between the NBBOs occur several times a second in very active stocks and typically last one to two milliseconds. The short duration of dislocations makes their costs small for investors who trade infrequently, while the frequency of the dislocations makes them costly for frequent traders. Higher security price and days with high trading volume and volatility are associated with dislocations.
 
seekers:
Some books at this thread are explaining that too

Ok. Thanks

 
seekers:
How Slow is the NBBO? A Comparison with Direct Exchange Feeds : how_slow_is_the_nbbo_-_a_comparison_with_direct_exchange_feeds.pdf

Interesting book. Thanks

 

Algorithmic Trading and the Market for Liquidity : algorithmic_trading_and_the_market_for_liquidity.pdf

We examine the role of algorithmic traders (ATs) in liquidity supply and demand in the 30 Deutscher Aktien Index stocks on the Deutsche Boerse in Jan. 2008. ATs represent 52% of market order volume and 64% of nonmarketable limit order volume. ATs more actively monitor market liquidity than human traders. ATs consume liquidity when it is cheap (i.e., when the bid-ask quotes are narrow) and supply liquidity when it is expensive. When spreads are narrow ATs are less likely to submit new orders, less likely to cancel their orders, and more likely to initiate trades. ATs react more quickly to events and even more so when spreads are wide.
 

Does Algorithmic Trading Improve Liquidity? does_algorithmic_trading_improve_liquidity.pdf

Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change inmarket structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.

TECHNOLOGICAL CHANGE HAS REVOLUTIONIZED the way financial assets are traded. Every step of the trading process, from order entry to trading venue to back office, is now highly automated, dramatically reducing the costs incurred by intermediaries. By reducing the frictions and costs of trading, technology has the potential to enable more efficient risk sharing, facilitate hedging, improve liquidity, and make prices more efficient. This could ultimately reduce firms’ cost of capital.

Algorithmic trading (AT) is a dramatic example of this far-reaching technological change. Many market participants now employ AT, commonly defined as the use of computer algorithms to automatically make certain trading decisions, submit orders, and manage those orders after submission. From a starting point near zero in the mid-1990s, AT is thought to be responsible for as much as 73 percent of trading volume in the United States in 2009.
 

Automation, Speed, and Stock Market Quality: The NYSE’s Hybrid : the book

 

A High Frequency Trading Perspective : a_high_frequency_trading_perspective.pdf

We develop a High Frequency (HF) trading strategy where the HF trader uses her superior speed to process information and to post limit sell and buy orders. By introducing a multi-factor self-exciting process we allow for feedback e ects in market buy and sell orders and the shape of the limit order book (LOB). Our model accounts for arrival of market orders that in uence activity, trigger onesided and two-sided clustering of trades, and induce temporary changes in the shape of the LOB. We also model the impact that market orders and news have on the short-term drift of the midprice (short-term-alpha). We show that HF traders who do not include predictors of short-term-alpha in their strategies are driven out of the market because they are adversely selected by better informed traders and because they are not able to pro t from directional strategies.
 

Book Review of Econometrics of Financial High-Frequency Data, by Nikolaus Hautsch : book_review_of_econometrics_of_financial_high-frequency_data_by_nikolaus_hautsch.pdf

Nikolaus Hautsch extends and updates his earlier book on econometric models for financial trading data for scholars and practitioners. The new book is timely and highly recommended because the past decade has witnessed the radical technological transformation of stock exchanges and other financial markets through technology. Recent events highlight the importance of these market structure changes to all investors, even those who trade infrequently. On May 6, 2010 the so-called Flash Crash occurred with the stock market losing and recovering nine percent of its value in less than half an hour. This was followed by Knight Capital losing $440 million in 45 minutes due to a technology error and Nasdaq’s technological struggles with Facebook’s initial public offering. This book provides tools to help understand and model current financial market data.
 

High Frequency Trading: Evolution and the Future : high_frequency_trading_-_evolution_and_the_future.pdf

Over the last few decades, regulators globally have been promoting greater transparency and competition among the exchanges in their market. Such regulatory changes, along with the advent of highly sophisticated and fast computer technology have given rise to a new class of trading known as high frequency trading (HFT). HFT is a form of trading in which security positions are turned over

very quickly by leveraging advanced technology and the associated extremely low latency rates.

Ever since its inception at the turn of the 21st century, the popularity and usage of high frequency trading has been growing at an astonishing rate globally, bringing about significant changes in the way capital market firms carry out their trades. The floor-based style of trading is gradually being phased out, as more and more firms adapt to this new style of automated trading.

While HFT has numerous advantages, it creates its own set of unique challenges. Due to a number of market events including the infamous ‘May 6th Flash Crash’1 in 2010, HFT has come under criticism, resulting in regulators from across the world putting forth proposals aimed at curbing current HFT practices.

The recent controversies and regulatory proposals surrounding HFT have made most market participants sit up and take notice. This paper introduces the concept and origin of HFT, its impact on the markets, and what has caused regulators to pay special attention to this form of trading. The paper also talks about how both new and existing HFT firms can potentially benefit by focusing on certain investments and capabilities.

 

High Frequency Traders: Angels or Devils? : High Frequency Traders - Angels or Devils.pdf

High frequency trading (HFT) is taking world capital markets by storm, notably in the United States and the United Kingdom, where it accounted for about 50 percent of equities trading in 2012, and to a growing extent in other parts of Europe and in Canada.

Are high frequency traders angels or devils in terms of the impact on capital markets? Critics claim the latter and charge that they put retail and institutional investors at a disadvantage. Critics also blame high frequency trading for the “flash crash” on the Dow of May 6 2010 and say it has increased the likelihoodof such events happening again. A closer examination of these views is in order.

In this Commentary, I first look at what HF traders do and how HFT differs from traditional market making. I then explore the empirical evidence relating to the effect of HFT on capital markets, and canvass the policy issues that HFT raises. In the final section, I list some recommendations for policymakers with respect to HFT.

After surveying empirical studies of HFT, I conclude that it enhances market quality. For example, it lowers bid/ask spreads, reduces volatility, improves short-term price discovery, and creates competitive pressures that reduce broker commissions. Despite being at a pronounced speed disadvantage, retail traders have realized a net gain from the presence of HF traders in the world’s capital markets.

Maintain the Order Protection Rule and Contain the Spread of Dark Pools: To prevent abusive trading practices, protect client interests, and create a level playing field among different trading venues, policymakers should defend the consolidated order book by maintaining and policing the order protection rule and minimizing the leakage of trading from the “lit” markets to “dark pools.”

Do Not Interfere with Maker/Taker Pricing Models: Some observers say maker/taker pricing raises higher trading costs for retail traders, because retail trade orders are typically on the active side of the market, and associated fees are passed on to customers. However, retail traders are about as likely to be on the active as the passive side of the market. Maker/taker pricing may raise costs on the margin, but also lowers bid/ask spreads.

Focus on Circuit Breakers to Prevent “Flash Crashes”: HF traders did not cause the “flash crash,” and instead supply liquidity when markets become volatile. Canadian regulators concerned with preventing similar events should focus on circuit breakers to stop market anomalies before they turn into “flash crashes.”
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