The move towards electronic trading practices, coupled with the changing structure of financial markets, is driving fast growth in trading methods that are based on mathematical models. One such example is algorithmic trading.
Algorithmic trading is defined as taking a buy or a sell order of a defined quantity, and placing it into a quantitative model that automatically generates the timing and size of orders based on the specific goals of the algorithm. This approach is typically oriented around trading to a specific benchmark, such as price or time (TowerGroup, 2004). Quantitative trading (including algorithmic and arbitrage/strategy trading) has emerged as the next generation of solutions to facilitate electronic trading markets.
Algorithmic trading is generating much excitement because it empowers the buy side trader. It has the potential to change the way traders interact amongst themselves. The current electronic trading process is simply an automated execution of the way trading has historically been done. Algorithmic trading, however incorporates direct market access and gives the buy side trader ownership of the execution process. The buy side owns the costs of execution and hence expects better control of the execution process. As a result, the adoption of algorithmic trading is skewed towards buy side traders. There are, however, benefits on the sell side too. The benefit for the sell side is that algorithmic trading dramatically reduces their time to market and as a result frees up the sell side trader to concentrate on more challenging orders that require human intervention. It also allows the sell side to open their offering portfolio to consumers. For the buy side, the benefit is the ability to access a much broader range of trading options. In addition, it also provides the buy side with access to greater liquidity and to employ this liquidity more effectively.
Some big players in algorithmic trading today include Citigroup, Credit Suisse First Boston, Deutsche Bank, Goldman Sachs, Lehman Brothers, Morgan Stanley and UBS. Components of algorithmic trading include:
* Real time and historical trading data
* Algorithms to perform correlation analysis, identify trading opportunities, determine optimal timing and measure trade execution against benchmarks
* Order processing and management
* Connectivity to various entities like stock exchanges, electronic communication networks and brokers
* Integration with internal systems
Fragmentation of liquidity and intense competitive pressures are primarily driving the move towards the use of algorithms. According to the TowerGroup, the total algorithmic trading volume will double from current levels by 2006, with buy-side initiated volumes tripling in the same time frame.
Advances in algorithmic trading have in the short term, eliminated a certain number of traders within institutional brokerages, and in the long term will be characterised by fundamental change in the trader’s role. Many manually processed trade orders will be replaced by algorithmic trading. Along with direct market access, algorithmic trading not only represents a paradigm shift from the way trading is currently done, it is also here to stay.
This article was contributed by Dr Pallab Saha. He currently lectures at ISS and is a frequently invited speaker at international and local conferernces.. His current research interests include IT Governance, Enterprise Architecture and Business Process Management.
Trend following is an investment strategy that tries to take advantage of long-term moves that seem to play out in various markets. The system aims to work on the market trend mechanism and take benefit from both sides of the market enjoying the profits from the ups and downs of the stock or futures markets. Traders who use this approach can use current market price calculation, moving averages and channel breakouts to determine the general direction of the market and to generate trade signals. Traders who subscribe to a trend following strategy do not aim to forecast or predict specific price levels; they simply jump on the trend and ride it.
The pairs trade or pair trading is a market neutral trading strategy enabling traders to profit from virtually any market conditions: uptrend, downtrend, or sidewise movement. This trading strategy is categorized as a statistical arbitrage and convergence trading strategy.
Delta Neutral Strategies
In finance, delta neutral describes a portfolio of related financial securities, in which the portfolio value remains unchanged due to small changes in the value of the underlying security. Such a portfolio typically contains options and their corresponding underlying securities such that positive and negative delta components offset, resulting in the portfolio’s value being relatively insensitive to changes in the value of the underlying security.
In economics and finance, arbitrage (IPA: /ˈɑrbɨtrɑːʒ/) is the practice of taking advantage of a price difference between two or more markets: striking a combination of matching deals that capitalize upon the imbalance, the profit being the difference between the market prices. When used by academics, an arbitrage is a transaction that involves no negative cash flow at any probabilistic or temporal state and a positive cash flow in at least one state; in simple terms, it is the possibility of a risk-free profit at zero cost.
Conditions for arbitrage
Arbitrage is possible when one of three conditions is met:
1. The same asset does not trade at the same price on all markets (the “law of one price”).
2. Two assets with identical cash flows do not trade at the same price.
3. An asset with a known price in the future does not today trade at its future price discounted at the risk-free interest rate (or, the asset does not have negligible costs of storage; as such, for example, this condition holds for grain but not for securities).
Arbitrage is not simply the act of buying a product in one market and selling it in another for a higher price at some later time. The transactions must occur simultaneously to avoid exposure to market risk, or the risk that prices may change on one market before both transactions are complete. In practical terms, this is generally only possible with securities and financial products which can be traded electronically, and even then, when each leg of the trade is executed the prices in the market may have moved. Missing one of the legs of the trade (and subsequently having to trade it soon after at a worse price) is called ‘execution risk’ or more specifically ‘leg risk’.[note 1]
In the simplest example, any good sold in one market should sell for the same price in another. Traders may, for example, find that the price of wheat is lower in agricultural regions than in cities, purchase the good, and transport it to another region to sell at a higher price. This type of price arbitrage is the most common, but this simple example ignores the cost of transport, storage, risk, and other factors. “True” arbitrage requires that there be no market risk involved. Where securities are traded on more than one exchange, arbitrage occurs by simultaneously buying in one and selling on the other.
See rational pricing, particularly arbitrage mechanics, for further discussion.
Mean reversion is a mathematical methodology sometimes used for stock investing, but it can be applied to other processes. In general terms the idea is that both a stock’s high and low prices are temporary, and that a stock’s price will tend to have an average price over time.
Mean reversion involves first identifying the trading range for a stock, and then computing the average price using analytical techniques as it relates to assets, earnings, etc.
When the current market price is less than the average price, the stock is considered attractive for purchase, with the expectation that the price will rise. When the current market price is above the average price, the market price is expected to fall. In other words, deviations from the average price are expected to revert to the average.
The Standard deviation of the most recent prices (e.g., the last 20) is often used as a buy or sell indicator.
Stock reporting services (such as Yahoo! Finance, MS Investor, Morningstar, etc.), commonly offer moving averages for periods such as 50 and 100 days. While reporting services provide the averages, identifying the high and low prices for the study period is still necessary.
Mean reversion has the appearance of a more scientific method of choosing stock buy and sell points than charting, because precise numerical values are derived from historical data to identify the buy/sell values, rather than trying to interpret price movements using charts (charting, also known as technical analysis).
Scalping (trading) is a method of arbitrage of small price gaps created by the bid-ask spread. Scalpers attempt to act like traditional market makers or specialists. To make the spread means to buy at the Bid price and sell at the Ask price, to gain the bid/ask difference. This procedure allows for profit even when the bid and ask do not move at all, as long as there are traders who are willing to take market prices. It normally involves establishing and liquidating a position quickly, usually within minutes or even seconds.
The role of a scalper is actually the role of market makers or specialists who are to maintain the liquidity and order flow of a product of a market. A market maker is basically a specialized scalper. The volume a market maker trades are many times more than the average individual scalpers. A market maker has a sophisticated trading system to monitor trading activity. However, a market maker is bound by strict exchange rules while the individual trader is not. For instance, NASDAQ requires each market maker to post at least one bid and one ask at some price level, so as to maintain a two-sided market for each stock represented.
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”. Yet are at their core quite simple mathematical constructs. Dark pools are alternative electronic stock exchanges where trading takes place anonymously, with most orders hidden or “iceberged.” 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  by individuals like Scott Kurland, who have built solutions for aggregating trades executed across algorithms, in order to counter these rising settlement costs.
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. 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. The HFT strategy was first made successful by Renaissance Technologies. 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 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.
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 transactions 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.
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.
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.
HFT is often confused with low-latency trading that uses computers that execute trades within milliseconds, or “with extremely low latency” in the jargon of the trade. Low-latency trading is highly dependent on ultra-low latency networks. They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors. The revolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform in order to benefit from implementing high-frequency strategies. Strategies are constantly altered to reflect the subtle changes in the market as well as to combat the threat of the strategy being reverse engineered by competitors. There is also a very strong pressure to continuously add features or improvements to a particular algorithm, such as client specific modifications and various performance enhancing changes (regarding benchmark trading performance, cost reduction for the trading firm or a range of other implementations). This is due to the evolutionary nature of algorithmic trading strategies – they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios. As a result, a significant proportion of net revenue from firms is spent on the R&D of these autonomous trading systems.