1Fixed Income InvestmentsAlgorithmic Trading and High-Frequency TradingPortfolio Management: Economic Analysis, Active Management, and Trading52. Algorithmic Trading and High-Frequency Trading© Kaplan, Inc.Algorithmic Trading and High-Frequency TradingAlgorithmic Trading Algorithmic trading is a trading strategy that has been automatedthrough the use of a computer. The computer follows a set of rules or a process to reach some end result. While computers and trading algorithms make decisions and execute trades thousands of times faster than a human trader could, an algorithm generally makes the same kinds of decisions that a human trader would make.1LOS 52.a Define© Kaplan, Inc.Algorithmic Trading and High-Frequency TradingExecution vs. High-Frequency AlgorithmsExecution Algorithms Execute large orders with minimal price impact (and without other market participants taking notice). Primarily used for large buy orders. Generally executed by slicing the large order into smaller pieces.High-Frequency Trading Algorithms Analyze real-time market data in search of patterns that can be profitably traded. These algorithms identify and execute trades in milliseconds. Usually the securities are held only for a short time (generally less than a day, and sometimes for less than a second).2LOS 52.b Distinguish© Kaplan, Inc.Algorithmic Trading and High-Frequency TradingTypes of Execution AlgorithmsVolume-Weighted Average Price (VWAP) Algorithms Use historical trading patterns over a typical day, and split a large order so that larger pieces get executed during times of the day when market depth is greater.Implementation Shortfall Algorithms Balance potential market drift that may happen if an order takes a long time to execute and the negative price impact that will result when an order is executed too quickly. The aim is to reduce the difference between the execution price and the price at which the decision was made to buy or sell the security.Market Participation Algorithms Cut a large order into slices with sizes that vary proportionally with actual trading volume.3LOS 52.c Describe2© Kaplan, Inc.Algorithmic Trading and High-Frequency TradingTypes of High-Frequency AlgorithmsStatistical ArbitrageUsed to identify securities that have historically moved together but have diverged recently. The algorithm will buy one security and sell the other so as to realize a profit when they eventually converge. Examples include: Pairs trading: short an over-performing security and long an under-performing Index arbitrage: exploit temporary differences in price performance between securities and the sector to which they belong Basket Trading: apply statistical arbitrage principles to one basket of securities versus another basket4LOS 52.c Describe© Kaplan, Inc.Algorithmic Trading and High-Frequency TradingTypes of High-Frequency Algorithms Spread trading: a type of statistical arbitrage with long and short positions in two futures contracts. Examples include: Intra-market spread: long position in one futures contract month and short position in the same futures contract in another contract month Inter-market spread: long a futures contract in one market and short futures on the same commodity and delivery month in a different market Inter-exchange spread: long a commodity future on one exchange and selling a similar commodity future on a different exchange Multilegged inter-exchange spreads: Crack spread—crude oil vs petroleum products Spark spread—price of electricity from a gas-fired power plant vs fuel prices Crush spread—soybean futures vs soybean oil and meal futures5LOS 52.c Describe© Kaplan, Inc.Algorithmic Trading and High-Frequency TradingTypes of High-Frequency Algorithms Mean reversion: statistical arbitrage algorithms based on the idea that when the price of a security drifts away from its recent historical value, its price will likely move back towards that mean. Delta neutral strategies: statistical arbitrage algorithms designed to produce a small profit regardless of whether the market goes up or down. Securities (e.g., stocks and their respective options) are combined in such a way that the total delta of the portfolio is zero. The strategy aims to earn a profit from volatility changes or simply as the options approach maturity.6LOS 52.c Describe© Kaplan, Inc.Algorithmic Trading and High-Frequency TradingTypes of High-Frequency AlgorithmsReal-Time Pricing of Securities Algorithms are used to price instruments in real time by deriving instantaneous price and liquidity information from the market. Real-time pricing information can help a dealer adjust the width of his bid-ask spread to reflect what the market will bear.Trading On News High-frequency algorithms that execute in reaction to news near-instantaneously and without human intervention. The algorithms typically use high-frequency news feeds, including feeds that contain tags that identify key statistics.Genetic Tuning Self-evolving (“Darwinian trading”) system that tests the performance of many different strategies by feeding live market data to each. Seemingly profitable strategies are deployed in the markets while money-losers are killed off. Strategies that earn profits are allowed to continue to evolve and pursue emerging opportunities.7LOS 52.c Describe3© Kaplan, Inc.Algorithmic Trading and High-Frequency TradingTypes of High-Frequency AlgorithmsMarket Fragmentation Market fragmentation occurs when the same security is traded in multiple financial markets. It gives rise to the potential for price and liquidity differences across markets, and the liquidity of a security in any individual market may represent only a fraction of the total liquidity across all marketsLiquidity Aggregators and Smart Order Routing Liquidity aggregators are high-frequency algorithms that make use of a concept called a “super book,” which adds up the liquidity available for an individual instrument across multiple markets. Smart order routing is then used to direct orders to the market with the best combination of liquidity and price. In order for these algorithmic methods to be effective in addressing market fragmentation, liquidity information needs to be current. To achieve this, timely updates and low latency are necessary. Latency refers to the time lag between market data being received and a corresponding trade being