Transaction cost reduction

Transaction cost reduction

Most strategies referred to as Algorithmic Trading (as well as algorithmic liquidity seeking) fall into the cost-reduction category. Large orders are broken down into several smaller orders and entered into the market over time. This basic strategy is called "iceberging". The success of this strategy may be measured by the average purchase price against the VWAP for the market over that time period. One algorithm designed to find hidden orders or icebergs is called "Stealth". Most of these strategies were first documented in 'Optimal Trading Strategies' by Robert Kissell.

Strategies that only pertain to dark pools

Recently, HFT, which comprises a broad set of buy-side as well as market making sell side traders, has become more prominent and controversial.These algorithms or techniques are commonly given names such as "Stealth" (developed by the Deutsche Bank), "Iceberg", "Dagger", "Guerrilla", "Sniper", "BASOR" (developed by Quod Financial) and "Sniffer".[26] Yet are at their core quite simple mathematical constructs.[27] Dark pools are alternative electronic stock exchanges where trading takes place anonymously, with most orders hidden or "iceberged."[28] Gamers or "sharks" sniff out large orders by "pinging" small market orders to buy and sell. When several small orders are filled the sharks may have discovered the presence of a large iceberged order.

“Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”

One of the unintended adverse effects of algorithmic trading, has been the dramatic increase in the volume of trade allocations and settlements, as well as the transaction settlement costs associated with them. Since 2004, there have been a number of technological advances and service providers [30] by individuals like Scott Kurland, who have built solutions for aggregating trades executed across algorithms, in order to counter these rising settlement costs.

High-frequency trading
Main article: High-frequency trading

In the U.S., high-frequency trading (HFT) firms represent 2% of the approximately 20,000 firms operating today, but account for 73% of all equity trading volume.[31] As of the first quarter in 2009, total assets under management for hedge funds with HFT strategies were US$141 billion, down about 21% from their high.[32] The HFT strategy was first made successful by Renaissance Technologies.[33] High-frequency funds started to become especially popular in 2007 and 2008.Many HFT firms are market makers and provide liquidity to the market which has lowered volatility and helped narrow Bid-offer spreads making trading and investing cheaper for other market participants.HFT has been a subject of intense public focus since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission implicated both algorithmic and HFT in the May 6, 2010 Flash Crash.

High-frequency trading is quantitative trading that is characterized by short portfolio holding periods (see Wilmott (2008), Aldridge (2009)). There are four key categories of HFT strategies: market-making based on order flow, market-making based on tick data information, event arbitrage and statistical arbitrage. All portfolio-allocation decisions are made by computerized quantitative models. The success of HFT strategies is largely driven by their ability to simultaneously process volumes of information, something ordinary human traders cannot do.

Market making

Market making is a set of HFT strategies that involves placing a limit order to sell (or offer) above the current market price or a buy limit order (or bid) below the current price in order to benefit from the bid-ask spread. Automated Trading Desk, which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both NASDAQ and the New York Stock Exchange.[36]

Statistical Arbitrage

Another set of HFT strategies is classical arbitrage strategy might involve several securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency. If the market prices are sufficiently different from those implied in the model to cover transaction cost then four transactions can be made to guarantee a risk-free profit. HFT allows similar arbitrages using models of greater complexity involving many more than 4 securities. The TABB Group estimates that annual aggregate profits of low latency arbitrage strategies currently exceed US$21 billion.[5]

A wide range of statistical arbitrage strategies have been developed whereby trading decisions are made on the basis of deviations from statistically significant relationships. Like market-making strategies, statistical arbitrage can be applied in all asset classes.

Event Arbitrage

A subset of risk, merger, convertible, or distressed securities arbitrage that counts on a specific event, such as a contract signing, regulatory approval, judicial decision, etc., to change the price or rate relationship of two or more financial instruments and permit the arbitrageur to earn a profit.

Merger arbitrage also called risk arbitrage would be an example of this. Merger arbitrage generally consists of buying the stock of a company that is the target of a takeover while shorting the stock of the acquiring company.

Usually the market price of the target company is less than the price offered by the acquiring company. The spread between these two prices depends mainly on the probability and the timing of the takeover being completed as well as the prevailing level of interest rates.

The bet in a merger arbitrage is that such a spread will eventually be zero, if and when the takeover is completed. The risk is that the deal "breaks" and the spread massively widens