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High frequency Trading – decoded

, August 20, 2013, 1 Comments

Over the past few Year’s, there has been a quick shift towards algo / Quant HFT based trading,Where as Asset managers make 24% return in market & HFT traders make 300% Return. Both among long-term investors using execution algorithms to lower trading costs and short- term investors automating market making and statistical arbitrage strategies.

These short-term investors, popularly known as high-frequency traders (HFTs), account for a substantial fraction of total equity market trading volume. However, there is fairly little known about how their trading effects liquidity. To the extent that HFTs act simply as market makers, they will tend to improve liquidity. But HFTs also search trade and order data for clues about where prices will go in the future, and when they trade on this information, they may compete with long-term investors for liquidity, thereby increasing those investors’ trading costs.

In this two part series we also try to examines the extent to which HFTs profit from traditional asset managers’ trading by anticipating their future order flow & Weather HFT traders provide liquiditity real time or fake way. Traditional asset managers, such as mutual fund and pension fund managers, typically split their large trades into a series of orders executed over the course of one or more trading days.

They split their trades, because a series of small trades move prices less than a single large one.But in splitting their trades, traditional asset managers may reveal their trading intentions to other Trading Technology Experts investors who may end up trading ahead of or alongside the traditional asset manager.

To examine these issues, I examine return and trade patterns around periods of aggressive buying and selling by HFTs using trade data from the NSE Stock Market.Specifically, I focus on HFTs’ aggressive trades, that is, trades where an HFT initiates the transaction by submitting a marketable ( IOC or bidding process) buy or sell order, which are functionally equivalent to market orders, because it is a simple way to screen out liquidity providing trades ( Limit Order traders). I test whether HFTs’ aggressive share purchases predict future aggressive buying by non-HFTs, and whether HFTs’ aggressive sales predict future aggressive selling by non-HFTs.

HFT user react more fastly as compared to non HFT users.
I find evidence consistent with HFTs being able to anticipate order flow from other investors. In tests where Stock Futures are sorted by HFT net marketable buying at the some micro second horizon, the Stock Futures bought most aggressively by HFTs have cumulative standardized non-HFT net marketable buying of 0:66 over the following thirty micro seconds,) and stocks that are sold most aggressively have cumulative standardized non-HFT net marketable buying of 0:68 over the next thirty micro seconds.

Moreover, the stocks Futures HFTs buy aggressively have positive future returns, and the stocks Futures they sell aggressively have negative future returns.( By using scalping Calculation various Time series based methods – HFT traders do so).

Taken together, these two results suggest HFTs’ aggressive trades forecast price pressure from other investors.( Actually HFT trades use CEP to analysis per Micro second Market Future prediction method by using various methods).

I consider and reject the most likely alternative explanations for these results.

To rule out the possibility the results are driven by HFTs responding to news faster than other investors, I rerun the sort tests excluding periods within few second of the publication of intra-day corporate news by Bloomberg Terminal. The results excluding periods around intra-day news are nearly identical to those for the full sample.

A second explanation is that HFT and non-HFT trading are driven by the same underlying serially correlated process (i.e., same trading signals), so HFT trading predicts non-HFT order flow only because it is a proxy for lagged non-HFT trading. A final alternative is that if non-HFTs chase price trends, HFTs might actually cause future trading by non-HFTs through their trading’s effect on returns. However, the lead-lag relationship between HFT and non-HFT trading is robust to controls for lagged non-HFT trading and lagged returns, which is inconsistent with the second and third alternative explanations.

I also examine whether there are cross-sectional differences in how well different HFTs’ trades forecast future order flow. Perhaps some HFTs are more skilled or focus more on strategies that anticipate order flow, while others focus on market making or index arbitrage. My evidence indicates that there are indeed differences among HFTs. Trades from HFTs that were the most highly correlated with future order flow in a given month have trades that also exhibit stronger than average correlation with future non-HFT order flow in future months.

DARK POOL and HFT Traders.
There are two types of market’s for trading one is displayed order book type & second one is non- displayed order book type. In 2009, NASDAQ’s market share was roughly 35% in NASDAQ-listed securities, and 20% in securities listed on the NYSE. The remainder of U.S. equity trading is spread among trading venues with displayed order books, such as the NYSE and BATS, and trading venues with non-displayed order books, such as ITG’s POSIT Marketplace, Credit Suisse’s Crossfinder, and Knight Capital. Much of U.S. trading occurs on non-displayed trading venues.This part is not valid for Indian Exchanges – it also have both market type’s but large or most volumes are with displayed only order book where as some bulk deals are not visble to Order book in some cases.As you know dark pools are not allowed at India.

There was once a large difference between the market structures of different displayed markets such as the NYSE and NASDAQ. Trading on the NYSE was conducted in an open outcry trading pit, and while NASDAQ had an electronic order book with market maker quotes, brokers had to call the market maker on the phone to execute a trade. Now, displayed markets are all structured as electronic automatic execution limit- order books, and they largely compete on price. Though the NYSE still has specialists, now known as Designated Market Makers (DMM), they are for the most part the same electronic trading firms that make markets on other exchanges. For example, GETCO LLC, a large electronic market maker, became a DMM for 350 NYSE stocks in early 2010 (Kisling 2010) and is also a registered market maker on NASDAQ, BATS, NYSE ARCA, and the CBOE (McCarthy 2010).

Executions in displayed markets predominately come from professional traders. Few retail orders reach the displayed markets directly. Most retail brokerages have contracts with market making firms who pay for the right to fill retail orders. For example, in the third quarter of 2009, Charles Schwab routed more than 90% of its customers’ orders in NYSE-listed and NASDAQ-listed stocks to UBS’s market making arm for execution. Similarly, E*Trade routed nearly all its customers’ market orders and over half its customers’ limit orders to either Citadel or E*Trade’s market making arms. However, when there is a large imbalance between retail buy and sell orders in a stock, market making firms likely offload the imbalance by trading in displayed markets, so there is some interaction between retail trading demand and the displayed markets.

High-frequency traders account for a substantial fraction of equity market trading volume. High-frequency traders are proprietary trading firms using high-turnover auto-mated trading strategies. Examples of such tradering firms include Edelweiss Capital, Way2Wealth, Motilal Oswal Financial Services, Ltd. Angel Broking, India Infoline (IIFL), SMC Global Securities, Anandrathi Financial Advisor & many more. Such firms likely engage in some combination of market making and statistical arbitrage.